PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

e972b70d6970fd1d6b55a67f98bddf5fb7dd6960

7,884

py

Python

tools/driver/p4c_src/main.py

DTharun/p4lang

d83d078f95ae2eaf054955fb645fa4d4a7e54847

[ "Apache-2.0" ]

27

2018-06-30T13:01:40.000Z

2022-01-28T09:07:21.000Z

tools/driver/p4c_src/main.py

DTharun/p4lang

d83d078f95ae2eaf054955fb645fa4d4a7e54847

[ "Apache-2.0" ]

8

2018-06-01T11:19:02.000Z

2018-06-09T13:09:15.000Z

tools/driver/p4c_src/main.py

DTharun/p4lang

d83d078f95ae2eaf054955fb645fa4d4a7e54847

[ "Apache-2.0" ]

6

2019-02-24T11:20:22.000Z

2021-05-25T17:21:07.000Z

# Copyright 2013-present Barefoot Networks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/env python """ p4c - P4 Compiler Driver """ from __future__ import absolute_import import argparse import glob import os import sys import re import p4c_src.config as config import p4c_src # \TODO: let the backends set their versions ... p4c_version = p4c_src.__version__ def set_version(ver): global p4c_version p4c_version = ver def get_version(): return p4c_version def display_supported_targets(cfg): ret = "Supported targets in \"target-arch-vendor\" triplet:\n" for target in cfg.target: ret += str(target) + "\n" return ret def add_developer_options(parser): parser.add_argument("-T", dest="log_levels", action="append", default=[], help="[Compiler debugging] Adjust logging level per file (see below)") parser.add_argument("--top4", dest="passes", action="append", default=[], help="[Compiler debugging] Dump the P4 representation after \ passes whose name contains one of `passX' substrings. \ When '-v' is used this will include the compiler IR.") parser.add_argument("--dump", dest="dump_dir", default=None, help="[Compiler debugging] Folder where P4 programs are dumped.") parser.add_argument("--toJson", dest="json", default=None, help="Dump IR to JSON in the specified file.") parser.add_argument("--pp", dest="pretty_print", default=None, help="Pretty-print the program in the specified file.") def main(): parser = argparse.ArgumentParser(conflict_handler='resolve') parser.add_argument("-V", "--version", dest="show_version", help="show version and exit", action="store_true", default=False) parser.add_argument("-v", "--debug", dest="debug", help="verbose", action="store_true", default=False) parser.add_argument("-###", "--test-only", dest="dry_run", help="print (but do not run) the commands", action="store_true", default=False) parser.add_argument("-Xpreprocessor", dest="preprocessor_options", metavar="<arg>", help="Pass <arg> to the preprocessor", action="append", default=[]) parser.add_argument("-Xp4c", dest="compiler_options", metavar="<arg>", help="Pass <arg> to the compiler", action="append", default=[]) parser.add_argument("-Xassembler", dest="assembler_options", metavar="<arg>", help="Pass <arg> to the assembler", action="append", default=[]) parser.add_argument("-Xlinker", dest="linker_options", metavar="<arg>", help="Pass <arg> to the linker", action="append", default=[]) parser.add_argument("-b", "--target", dest="backend", help="specify target backend", action="store", default="bmv2-ss-p4org") parser.add_argument("-c", dest="run_all", help="Only run preprocess, compile, and assemble steps", action="store_true", default=True) parser.add_argument("-D", dest="preprocessor_defines", help="define a macro to be used by the preprocessor", action="append", default=[]) parser.add_argument("-E", dest="run_preprocessor_only", help="Only run the preprocessor", action="store_true", default=False) parser.add_argument("-e", dest="skip_preprocessor", help="Skip the preprocessor", action="store_true", default=False) parser.add_argument("-g", dest="debug_info", help="Generate debug information", action="store_true", default=False) parser.add_argument("-I", dest="search_path", help="Add directory to include search path", action="append", default=[]) parser.add_argument("-o", dest="output_directory", help="Write output to the provided path", action="store", metavar="PATH", default=".") parser.add_argument("--p4runtime-file", help="Write a P4Runtime control plane API description " "to the specified file.", action="store", default=None) parser.add_argument("--p4runtime-format", choices=["binary", "json", "text"], help="Choose output format for the P4Runtime API " "description (default is binary).", action="store", default="binary") parser.add_argument("--target-help", dest="show_target_help", help="Display target specific command line options.", action="store_true", default=False) parser.add_argument("-S", dest="run_till_assembler", help="Only run the preprocess and compilation steps", action="store_true", default=False) parser.add_argument("-x", dest="language", choices = ["p4-14", "p4-16"], help="Treat subsequent input files as having type language.", action="store", default="p4-16") if (os.environ['P4C_BUILD_TYPE'] == "DEVELOPER"): add_developer_options(parser) parser.add_argument("source_file", nargs='?', help="Files to compile", default=None) # load supported configuration. # We load these before we parse options, so that backends can register # proprietary options cfg_files = glob.glob("{}/*.cfg".format(os.environ['P4C_CFG_PATH'])) cfg = config.Config(config_prefix = "p4c") for cf in cfg_files: cfg.load_from_config(cf, parser) # parse the arguments opts = parser.parse_args() # deal with early exits if opts.show_version: print "p4c", get_version() sys.exit(0) if opts.show_target_help: print display_supported_targets(cfg) sys.exit(0) if not opts.source_file: parser.error('No input specified.') # check that the triplet value is correct triplet = opts.backend.split('-') if (len(triplet) != 3): parser.error("Invalid target-arch-vendor triplet: {}\n{}".\ format(triplet, display_supported_targets(cfg))) # find the backend backend = None for target in cfg.target: regex = target._backend.replace('*', '[a-zA-Z0-9*]*') pattern = re.compile(regex) if (pattern.match(opts.backend)): backend = target break if backend == None: parser.error("Unknown backend: {}".format(str(opts.backend))) # set all configuration and command line options for backend backend.process_command_line_options(opts) # run all commands backend.run()

42.616216

94

0.57864

8ad3ab9f5e46a8c67bba479a1bf16e0eaf194126

8,458

py

Python

tests/core/test_secrets.py

ShepardZhao/rancher

a747ac408ca34fb0bf465276f07557ec43bf9c89

[ "Apache-2.0" ]

1

2019-04-17T08:00:59.000Z

tests/core/test_secrets.py

ShepardZhao/rancher

a747ac408ca34fb0bf465276f07557ec43bf9c89

[ "Apache-2.0" ]

1

2021-02-23T19:12:08.000Z

tests/core/test_secrets.py

ShepardZhao/rancher

a747ac408ca34fb0bf465276f07557ec43bf9c89

[ "Apache-2.0" ]

2

2019-11-14T15:46:01.000Z

2020-05-06T15:31:37.000Z

from .common import random_str CERT = """-----BEGIN CERTIFICATE----- MIIDEDCCAfgCCQC+HwE8rpMN7jANBgkqhkiG9w0BAQUFADBKMQswCQYDVQQGEwJV UzEQMA4GA1UECBMHQXJpem9uYTEVMBMGA1UEChMMUmFuY2hlciBMYWJzMRIwEAYD VQQDEwlsb2NhbGhvc3QwHhcNMTYwNjMwMDExMzMyWhcNMjYwNjI4MDExMzMyWjBK MQswCQYDVQQGEwJVUzEQMA4GA1UECBMHQXJpem9uYTEVMBMGA1UEChMMUmFuY2hl ciBMYWJzMRIwEAYDVQQDEwlsb2NhbGhvc3QwggEiMA0GCSqGSIb3DQEBAQUAA4IB DwAwggEKAoIBAQC1PR0EiJjM0wbFQmU/yKSb7AuQdzhdW02ya+RQe+31/B+sOTMr z9b473KCKf8LiFKFOIQUhR5fPvwyrrIWKCEV9pCp/wM474fX32j0zYaH6ezZjL0r L6hTeGFScGse3dk7ej2+6nNWexpujos0djFi9Gu11iVHIJyT2Sx66kPPPZVRkJO9 5Pfetm5SLIQtJHUwy5iWv5Br+AbdXlUAjTYUqS4mhKIIbblAPbOKrYRxGXX/6oDV J5OGLle8Uvlb8poxqmy67FPyMObNHhjggKwboXhmNuuT2OGf/VeZANMYubs4JP2V ZLs3U/1tFMAOaQM+PbT9JuwMSmGYFX0Qiuh/AgMBAAEwDQYJKoZIhvcNAQEFBQAD ggEBACpkRCQpCn/zmTOwboBckkOFeqMVo9cvSu0Sez6EPED4WUv/6q5tlJeHekQm 6YVcsXeOMkpfZ7qtGmBDwR+ly7D43dCiPKplm0uApO1CkogG5ePv0agvKHEybd36 xu9pt0fnxDdrP2NrP6trHq1D+CzPZooLRfmYqbt1xmIb00GpnyiJIUNuMu7GUM3q NxWGK3eq+1cyt6xr8nLOC5zaGeSyZikw4+9vqLudNSyYdnw9mdHtrYT0GlcEP1Vc NK+yrhDCvEWH6+4+pp8Ve2P2Le5tvbA1m24AxyuC9wHS5bUmiNHweLXNpxLFTjK8 BBUi6y1Vm9jrDi/LiiHcN4sJEoU= -----END CERTIFICATE-----""" KEY = """-----BEGIN RSA PRIVATE KEY----- MIIEowIBAAKCAQEAtT0dBIiYzNMGxUJlP8ikm+wLkHc4XVtNsmvkUHvt9fwfrDkz K8/W+O9ygin/C4hShTiEFIUeXz78Mq6yFighFfaQqf8DOO+H199o9M2Gh+ns2Yy9 Ky+oU3hhUnBrHt3ZO3o9vupzVnsabo6LNHYxYvRrtdYlRyCck9kseupDzz2VUZCT veT33rZuUiyELSR1MMuYlr+Qa/gG3V5VAI02FKkuJoSiCG25QD2ziq2EcRl1/+qA 1SeThi5XvFL5W/KaMapsuuxT8jDmzR4Y4ICsG6F4Zjbrk9jhn/1XmQDTGLm7OCT9 lWS7N1P9bRTADmkDPj20/SbsDEphmBV9EIrofwIDAQABAoIBAGehHxN1i3EqhKeL 9FrJPh4NlPswwCDZUQ7hFDZU9lZ9qBqQxkqZ18CVIXN90eBlPVIBY7xb9Wbem9Pb AecbYPeu+T7KmqwWgiUUEG5RikfyoMQv7gZghK3dmkBKGWYX0dtpZR7h7bsYPp/S j5QatNhxC5l4be5CnmUHe6B4jPdUt8kRfTj0ukYGm/h3cOm/tEQeRYIIN/N6JN2Z JWYzsyqGmlOTp7suczkRIUS0AjiljT1186bQSou62iMtMqEgArusFFb9m/dXCCYo t/Q1SR4lRodDfzcF/CRbdR/ZC8gZlyCdbI4WHOw9IwwHnmrllx4MXFP/p6p+gEtl cKMzHXECgYEA27KnkDnz338qKC2cCGkMf3ARfTX6gSlqmvgM9zOa8FLWp6GR6Rvo NgVLUi63bQqv9D5qYSsweAp1QTvIxJffWMJDTWtxowOXVW5P8WJ8jp/pAXoWGRbd pnavy6Ih0XT57huwT7fGGIikXYfw/kB85PPJL3FsT/b6G4ay2+Z7OGkCgYEA0y+d bxUewYZkpNy7+kIh0x4vrJvNqSL9ZwiP2R159zu7zDwDph/fkhXej0FEtbXybt+O 4s9M3l4nNsY6AS9sIPCB5SxWguhx0z76U5cz1qFFZwIHtL8r1jHrl5iwkVyOAtVV 0BokmJG4Pn07yZo/iCmSTEfwcePvCMvOsPtcvKcCgYEAu5+SbKChfhBaz19MLv6P ttHdjcIogl/9dAU9BWxj+LO2MAjS1HKJ2ICi97d/3LbQ19TqArvgs9OymZhV+Fb/ Xgzhb1+/94icmFASI8KJP0CfvCwobRrTBlO8BDsdiITO4SNyalI28kLXpCzxiiFG yDzOZx8FcjEpHZLmctgeCWkCgYAO0rDCM0FNZBl8WOH41tt47g16mBT/Yi1XJgqy upbs+4xa8XtwFZyjrFVKyNIBzxuNHLPyx4olsYYfGhrIKoP0a+0yIMKRva7/nNQF Of+xePBeIo5X6XMyPZ7DrTv3d/+fw0maqbsX2mKMQE4KAIGlFQXnxMTjuZP1khiX 44zG0QKBgGwQ8T4DGZK5ukLQmhLi9npCaAW99s/uuKArMzAG9xd/I8YntM/kVY0V VUi3lKqwXhtReYdrqVTPdjnyGIYIGGNRD7EKqQe15IRfbpy536DSN+LvL65Fdyis iNITDKNP1H3hedFNFfbTGpueYdRX6QaptK4+NB4+dOm7hn8iqq7U -----END RSA PRIVATE KEY-----""" def test_secrets(admin_pc): client = admin_pc.client name = random_str() secret = client.create_secret(name=name, stringData={ 'foo': 'bar' }) assert secret.type == 'secret' assert secret.kind == 'Opaque' assert secret.name == name assert secret.data.foo == 'YmFy' secret.data.baz = 'YmFy' secret = client.update(secret, data=secret.data) secret = client.reload(secret) assert secret.baseType == 'secret' assert secret.type == 'secret' assert secret.kind == 'Opaque' assert secret.name == name assert secret.data.foo == 'YmFy' assert secret.data.baz == 'YmFy' assert secret.namespaceId is None assert 'namespace' not in secret.data assert secret.projectId == admin_pc.project.id found = False for i in client.list_secret(): if i.id == secret.id: found = True break assert found client.delete(secret) def test_certificates(admin_pc): client = admin_pc.client name = random_str() cert = client.create_certificate(name=name, key=KEY, certs=CERT) assert cert.baseType == 'secret' assert cert.expiresAt == '2026-06-28T01:13:32Z' assert cert.type == 'certificate' assert cert.name == name assert cert.certs == CERT assert cert.namespaceId is None assert 'namespace' not in cert # cert = client.update(cert, certs='certdata2') # cert = client.reload(cert) # # assert cert.baseType == 'secret' # assert cert.type == 'certificate' # assert cert.name == name # assert cert.certs == 'certdata2' # assert cert.namespaceId is None # assert 'namespace' not in cert # assert cert.projectId == pc.project.id found = False for i in client.list_certificate(): if i.id == cert.id: found = True break assert found cert = client.by_id_certificate(cert.id) assert cert is not None client.delete(cert) def test_docker_credential(admin_pc): client = admin_pc.client name = random_str() registries = {'index.docker.io': { 'username': 'foo', 'password': 'bar', }} cert = client.create_docker_credential(name=name, registries=registries) assert cert.baseType == 'secret' assert cert.type == 'dockerCredential' assert cert.name == name assert cert.registries['index.docker.io'].username == 'foo' assert 'password' in cert.registries['index.docker.io'] assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id registries['two'] = { 'username': 'blah' } cert = client.update(cert, registries=registries) cert = client.reload(cert) assert cert.baseType == 'secret' assert cert.type == 'dockerCredential' assert cert.name == name assert cert.registries['index.docker.io'].username == 'foo' assert cert.registries.two.username == 'blah' assert 'password' not in cert.registries['index.docker.io'] assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id found = False for i in client.list_docker_credential(): if i.id == cert.id: found = True break assert found cert = client.by_id_docker_credential(cert.id) assert cert is not None client.delete(cert) def test_basic_auth(admin_pc): client = admin_pc.client name = random_str() cert = client.create_basic_auth(name=name, username='foo', password='bar') assert cert.baseType == 'secret' assert cert.type == 'basicAuth' assert cert.name == name assert cert.username == 'foo' assert 'password' in cert assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id cert = client.update(cert, username='foo2') cert = client.reload(cert) assert cert.baseType == 'secret' assert cert.type == 'basicAuth' assert cert.name == name assert cert.username == 'foo2' assert 'password' not in cert assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id found = False for i in client.list_basic_auth(): if i.id == cert.id: found = True break assert found cert = client.by_id_basic_auth(cert.id) assert cert is not None client.delete(cert) def test_ssh_auth(admin_pc): client = admin_pc.client name = random_str() cert = client.create_ssh_auth(name=name, privateKey='foo') assert cert.baseType == 'secret' assert cert.type == 'sshAuth' assert cert.name == name assert 'privateKey' in cert assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id cert = client.update(cert, privateKey='foo2') cert = client.reload(cert) assert cert.baseType == 'secret' assert cert.type == 'sshAuth' assert cert.name == name assert 'privateKey' not in cert assert cert.namespaceId is None assert 'namespace' not in cert assert cert.projectId == admin_pc.project.id found = False for i in client.list_ssh_auth(): if i.id == cert.id: found = True break assert found cert = client.by_id_ssh_auth(cert.id) assert cert is not None client.delete(cert)

33.039063

68

0.731497

8f037fb6cbc1907357d41dbef7cebde6c3f2bba5

594

py

Python

modoboa/admin/migrations/0011_domain_transport.py

Makakken-Crew/modoboa

faac20fe36e26bd7c01edc2adb12312dda4cfaa2

[ "ISC" ]

2

2021-04-20T19:40:09.000Z

2021-04-20T20:23:57.000Z

modoboa/admin/migrations/0011_domain_transport.py

vavilon/modoboa

9fcd133bc883a94cbf66c5bc9687787caadc8ca2

[ "0BSD" ]

19

2021-05-05T03:31:52.000Z

2021-12-11T09:58:52.000Z

modoboa/admin/migrations/0011_domain_transport.py

vavilon/modoboa

9fcd133bc883a94cbf66c5bc9687787caadc8ca2

[ "0BSD" ]

1

2020-01-10T11:43:00.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.7 on 2017-12-20 14:17 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('transport', '0001_initial'), ('admin', '0010_auto_20170215_1328'), ] operations = [ migrations.AddField( model_name='domain', name='transport', field=models.OneToOneField(null=True, on_delete=django.db.models.deletion.SET_NULL, to='transport.Transport'), ), ]

25.826087

122

0.648148

61d077aa9699b010c0112c50d2bb5210c65b0e8b

556

py

Python

firehose/functions/firehose/main.py

pei0804/serverless-sample

6ac55469aed7cf0d6e9f57109f0852122a951906

[ "MIT" ]

null

firehose/functions/firehose/main.py

pei0804/serverless-sample

6ac55469aed7cf0d6e9f57109f0852122a951906

[ "MIT" ]

null

firehose/functions/firehose/main.py

pei0804/serverless-sample

6ac55469aed7cf0d6e9f57109f0852122a951906

[ "MIT" ]

null

import boto3 def lambda_handler(event, context): firehose = boto3.client('firehose') firehose.put_record( DeliveryStreamName='firehose', Record={ 'Data': b'{"user_id": "a", "event": "AAA"}\n' } ) firehose.put_record( DeliveryStreamName='firehose', Record={ 'Data': b'{"user_id": "b", "event": "BBB"}\n' } ) firehose.put_record( DeliveryStreamName='firehose', Record={ 'Data': b'{"user_id": "c", "event": "CCC"}\n' } )

23.166667

57

0.508993

6b0be06b15cb9e6ce1469770bcfd2a0046cd5520

404

py

Python

core/migrations/0009_auto_20220115_2012.py

timehaswings/aoi_backend

17fa315d250022455e38235a10656ddaae4db655

[ "Apache-2.0" ]

1

2021-12-22T01:48:02.000Z

core/migrations/0009_auto_20220115_2012.py

timehaswings/aoi_backend

17fa315d250022455e38235a10656ddaae4db655

[ "Apache-2.0" ]

null

core/migrations/0009_auto_20220115_2012.py

timehaswings/aoi_backend

17fa315d250022455e38235a10656ddaae4db655

[ "Apache-2.0" ]

null

# Generated by Django 3.1.7 on 2022-01-15 20:12 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0008_groupmenu'), ] operations = [ migrations.AlterField( model_name='menu', name='url', field=models.CharField(blank=True, max_length=160, verbose_name='菜单URL'), ), ]

21.263158

85

0.596535

fd21a1d60885c7ee0db39f822ef0fb571f20567b

24,169

py

Python

code/run_fewrel.py

CJWBW/my_erine

9cedbcb2efcdfe4e7cb0aabaff3a53e971131ebb

[ "MIT" ]

null

code/run_fewrel.py

CJWBW/my_erine

9cedbcb2efcdfe4e7cb0aabaff3a53e971131ebb

[ "MIT" ]

null

code/run_fewrel.py

CJWBW/my_erine

9cedbcb2efcdfe4e7cb0aabaff3a53e971131ebb

[ "MIT" ]

null

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import csv import os import logging import argparse import random from tqdm import tqdm, trange import simplejson as json import numpy as np import torch from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from knowledge_bert.tokenization import BertTokenizer from knowledge_bert.modeling import BertForSequenceClassification from knowledge_bert.optimization import BertAdam from knowledge_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO) logger = logging.getLogger(__name__) class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, input_ent, ent_mask, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id self.input_ent = input_ent self.ent_mask = ent_mask class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_json(cls, input_file): with open(input_file, "r", encoding='utf-8') as f: return json.loads(f.read()) class FewrelProcessor(DataProcessor): """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" examples = self._create_examples( self._read_json(os.path.join(data_dir, "train.json")), "train") labels = set([x.label for x in examples]) return examples, list(labels) def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_json(os.path.join(data_dir, "dev.json")), "dev") def get_labels(self): """Useless""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) for x in line['ents']: if x[1] == 1: x[1] = 0 text_a = (line['text'], line['ents']) label = line['label'] examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, threshold): """Loads a data file into a list of `InputBatch`s.""" label_list = sorted(label_list) label_map = {label: i for i, label in enumerate(label_list)} entity2id = {} with open("kg_embed/entity2id.txt") as fin: fin.readline() for line in fin: qid, eid = line.strip().split('\t') entity2id[qid] = int(eid) features = [] for (ex_index, example) in enumerate(examples): ex_text_a = example.text_a[0] h, t = example.text_a[1] h_name = ex_text_a[h[1]:h[2]] t_name = ex_text_a[t[1]:t[2]] # Add [HD] and [TL], which are "#" and "$" respectively. if h[1] < t[1]: ex_text_a = ex_text_a[:h[1]] + "# " + h_name + " #" + ex_text_a[ h[2]:t[1]] + "$ " + t_name + " $" + ex_text_a[t[2]:] else: ex_text_a = ex_text_a[:t[1]] + "$ " + t_name + " $" + ex_text_a[ t[2]:h[1]] + "# " + h_name + " #" + ex_text_a[h[2]:] if h[1] < t[1]: h[1] += 2 h[2] += 2 t[1] += 6 t[2] += 6 else: h[1] += 6 h[2] += 6 t[1] += 2 t[2] += 2 tokens_a, entities_a = tokenizer.tokenize(ex_text_a, [h, t]) if len([x for x in entities_a if x != "UNK"]) != 2: print(entities_a, len([x for x in entities_a if x[0] != "UNK"])) exit(1) tokens_b = None if example.text_b: tokens_b, entities_b = tokenizer.tokenize(example.text_b[0], [x for x in example.text_b[1] if x[-1] > threshold]) # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" _truncate_seq_pair(tokens_a, tokens_b, entities_a, entities_b, max_seq_length - 3) else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[:(max_seq_length - 2)] entities_a = entities_a[:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambigiously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. tokens = ["[CLS]"] + tokens_a + ["[SEP]"] ents = ["UNK"] + entities_a + ["UNK"] segment_ids = [0] * len(tokens) if tokens_b: tokens += tokens_b + ["[SEP]"] ents += entities_b + ["UNK"] segment_ids += [1] * (len(tokens_b) + 1) input_ids = tokenizer.convert_tokens_to_ids(tokens) input_ent = [] ent_mask = [] for ent in ents: if ent != "UNK" and ent in entity2id: input_ent.append(entity2id[ent]) ent_mask.append(1) else: input_ent.append(-1) ent_mask.append(0) ent_mask[0] = 1 # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. padding = [0] * (max_seq_length - len(input_ids)) padding_ = [-1] * (max_seq_length - len(input_ids)) input_ids += padding input_mask += padding segment_ids += padding input_ent += padding_ ent_mask += padding assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(input_ent) == max_seq_length assert len(ent_mask) == max_seq_length label_id = label_map[example.label] if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("tokens: %s" % " ".join( [str(x) for x in tokens])) logger.info("ents: %s" % " ".join( [str(x) for x in ents])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) logger.info("label: %s (id = %d)" % (example.label, label_id)) features.append( InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, input_ent=input_ent, ent_mask=ent_mask, label_id=label_id)) return features def _truncate_seq_pair(tokens_a, tokens_b, ents_a, ents_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() ents_a.pop() else: tokens_b.pop() ents_b.pop() def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) def warmup_linear(x, warmup=0.002): if x < warmup: return x / warmup return 1.0 def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--ernie_model", default=None, type=str, required=True, help="Ernie pre-trained model") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", default=False, action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', type=float, default=0, help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--threshold', type=float, default=.3) args = parser.parse_args() processors = FewrelProcessor num_labels_task = 80 if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( args.gradient_accumulation_steps)) args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train: raise ValueError("At least one of `do_train` or `do_eval` must be True.") if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) processor = processors() num_labels = num_labels_task label_list = None tokenizer = BertTokenizer.from_pretrained(args.ernie_model, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None train_examples, label_list = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model model, _ = BertForSequenceClassification.from_pretrained(args.ernie_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format( args.local_rank), num_labels=num_labels) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_grad = ['bert.encoder.layer.11.output.dense_ent', 'bert.encoder.layer.11.output.LayerNorm_ent'] param_optimizer = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_grad)] no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=t_total) global_step = 0 if args.do_train: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer, args.threshold) vecs = [] vecs.append([0] * 100) with open("kg_embed/entity2vec.vec", 'r') as fin: for line in fin: vec = line.strip().split('\t') vec = [float(x) for x in vec] vecs.append(vec) embed = torch.FloatTensor(vecs) embed = torch.nn.Embedding.from_pretrained(embed) # embed = torch.nn.Embedding(5041175, 100) logger.info("Shape of entity embedding: " + str(embed.weight.size())) del vecs logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) all_ent = torch.tensor([f.input_ent for f in train_features], dtype=torch.long) all_ent_masks = torch.tensor([f.ent_mask for f in train_features], dtype=torch.long) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ent, all_ent_masks, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) output_loss_file = os.path.join(args.output_dir, "loss") loss_fout = open(output_loss_file, 'w') model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) if i != 3 else t for i, t in enumerate(batch)) input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids = batch input_ent = embed(input_ent + 1).to(device) # -1 -> 0 loss = model(input_ids, segment_ids, input_mask, input_ent.half(), ent_mask, label_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: loss.backward() loss_fout.write("{}\n".format(loss.item())) tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: # modify learning rate with special warm up BERT uses lr_this_step = args.learning_rate * warmup_linear(global_step / t_total, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 model_to_save = model.module if hasattr(model, 'module') else model output_model_file = os.path.join(args.output_dir, "pytorch_model.bin_{}".format(global_step)) torch.save(model_to_save.state_dict(), output_model_file) # Save a trained model model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") torch.save(model_to_save.state_dict(), output_model_file) if __name__ == "__main__": main()

42.776991

127

0.582771

18a22f9ecd12b8cd2ba070dcb05f2e55ef3f8d64

86

py

Python

mne/datasets/kiloword/__init__.py

fmamashli/mne-python

52f064415e7c9fa8fe243d22108dcdf3d86505b9

[ "BSD-3-Clause" ]

1,953

2015-01-17T20:33:46.000Z

2022-03-30T04:36:34.000Z

mne/datasets/kiloword/__init__.py

fmamashli/mne-python

52f064415e7c9fa8fe243d22108dcdf3d86505b9

[ "BSD-3-Clause" ]

8,490

2015-01-01T13:04:18.000Z

2022-03-31T23:02:08.000Z

mne/datasets/kiloword/__init__.py

fmamashli/mne-python

52f064415e7c9fa8fe243d22108dcdf3d86505b9

[ "BSD-3-Clause" ]

1,130

2015-01-08T22:39:27.000Z

2022-03-30T21:44:26.000Z

"""MNE visual_92_categories dataset.""" from .kiloword import data_path, get_version

21.5

44

0.790698

9fe809b289dcd40d88c83e33a194fe713490761a

6,367

py

Python

redis_wikicache.py

radomd92/botjagwar

1dc96600c40041057a9f9afde38c31ca34b8db38

[ "MIT" ]

7

2015-01-23T17:24:04.000Z

2022-01-12T16:54:24.000Z

redis_wikicache.py

radomd92/botjagwar

1dc96600c40041057a9f9afde38c31ca34b8db38

[ "MIT" ]

18

2017-12-09T01:11:23.000Z

2021-09-22T13:26:24.000Z

redis_wikicache.py

radomd92/botjagwar

1dc96600c40041057a9f9afde38c31ca34b8db38

[ "MIT" ]

1

2015-06-22T02:17:55.000Z

import pywikibot import redis from api.config import BotjagwarConfig from api.decorator import separate_process from import_wiktionary import EnWiktionaryDumpImporter config = BotjagwarConfig() class NoPage(Exception): pass class RedisSite(object): def __init__( self, language: str, wiki: str, host='default', port=6379, password='default'): self.language = language self.wiki = wiki if host == 'default': self.host = config.get('host', 'redis') else: self.host = host if password == 'default': self.password = config.get('password', 'redis') if not self.password: self.password = None else: self.password = None self.port = port self.instance = redis.Redis( self.host, self.port, password=self.password, socket_timeout=3) def random_page(self): rkey = self.instance.randomkey() while not rkey.startswith( bytes( f'{self.wiki}.{self.language}/', 'utf8')): rkey = self.instance.randomkey() page_name = str( rkey, encoding='utf8').replace( f'{self.wiki}.{self.language}/', '') return RedisPage(self, page_name) @separate_process def load_xml_dump(self, dump='user_data/dumps/enwikt.xml'): importer = EnWiktionaryDumpImporter(dump) for xml_page in importer.load(): try: title, content = importer.get_page_from_xml(xml_page) self.push_page(title, content) except redis.ConnectionError as error: print(error) except Exception as error: print('Unknown error ', error) def push_page(self, title: str, content: str): if title is not None and content is not None: self.instance.set(f'{self.wiki}.{self.language}/{title}', content) def __str__(self): return f'{self.wiki}.{self.language}' class RedisPage(object): def __init__(self, site: RedisSite, title: str, offline=True): self.offline = offline self.site = site self._title = title def title(self, *args): return self._title def __repr__(self): return f'Page({self.site}/{self.title()})' def isEmpty(self): return self.get() == '' def get(self): if self._title is None: return '' cache_contents = self.site.instance.get( f'{self.site.wiki}.{self.site.language}/{self._title}') if not cache_contents: if not self.offline: wikisite = pywikibot.Site(self.site.language, self.site.wiki) wikipage = pywikibot.Page(wikisite, self._title) if wikipage.exists(): content = wikipage.get() self.site.push_page(self._title, content) return content else: raise NoPage( f'Page {self._title} at {self.site} not found ' f'neither in-redis nor on-wiki') else: raise NoPage( f'Page {self._title} at {self.site} not found in redis. ' f'Offline mode is OFF so no on-wiki fetching.') else: cache_contents = str(cache_contents, encoding='utf8') return cache_contents def exists(self): cache_contents = self.site.instance.get( f'{self.site.wiki}.{self.site.language}/{self._title}') if not cache_contents: if self.offline: return False else: wikisite = pywikibot.Site(self.site.language, self.site.wiki) wikipage = pywikibot.Page(wikisite, self._title) return wikipage.exists() else: return True def namespace(self): if self.offline: class Namespace(object): content = self.get() return Namespace() else: wikisite = pywikibot.Site(self.site.language, self.site.wiki) wikipage = pywikibot.Page(wikisite, self._title) return getattr(wikipage, 'namespace')() def isRedirectPage(self): if self.exists(): return '#REDIRECT [[' in self.get() else: if not self.offline: wikisite = pywikibot.Site(self.site.language, self.site.wiki) wikipage = pywikibot.Page(wikisite, self._title) return wikipage.isRedirectPage() return False def __getattr__(self, item): if hasattr(RedisPage, item): return getattr(self, item) else: wikisite = pywikibot.Site(self.site.language, self.site.wiki) wikipage = pywikibot.Page(wikisite, self._title) return getattr(wikipage, item) if __name__ == '__main__': print(""" Download the en.wiktionary page dumps, split it into several chunks (e.g. using split) and run this script. All en.wiktionary pages will have their latest version uploaded in your Redis. Using RedisSite and RedisPage, you'll have a much faster read and offline access. """) site = RedisSite('en', 'wiktionary') site.load_xml_dump('user_data/dumps/enwikt.xml') # site.load_xml_dump('user_data/dumps/enwikt_2.xml') # site.load_xml_dump('user_data/dumps/enwikt_3.xml') # site.load_xml_dump('user_data/dumps/enwikt_4.xml') # site.load_xml_dump('user_data/dumps/enwikt_5.xml') # site.load_xml_dump('user_data/dumps/enwikt_6.xml') # site.load_xml_dump('user_data/dumps/enwikt_7.xml') # site.load_xml_dump('user_data/dumps/enwikt_8.xml') # site.load_xml_dump('user_data/dumps/enwikt_9.xml') # site.load_xml_dump('user_data/dumps/enwikt_10.xml') # site.load_xml_dump('user_data/dumps/enwikt_11.xml') # site.load_xml_dump('user_data/dumps/enwikt_13.xml') # site.load_xml_dump('user_data/dumps/enwikt_14.xml') # site.load_xml_dump('user_data/dumps/enwikt_15.xml') # site.load_xml_dump('user_data/dumps/enwikt_16.xml') # site.load_xml_dump('user_data/dumps/enwikt_17.xml')

34.416216

85

0.586619

b2329006106df9a6fd2a3a4aef8a9cca3bde2071

59

py

Python

masonite/__init__.py

josephmancuso/masonite-azure-driver

06e766f9833b48d28a6ffd4afb24114be1a60a1c

[ "MIT" ]

1

2018-12-08T07:07:37.000Z

masonite/__init__.py

vaibhavmule/masonite-cloudinary-driver

866b073717144b8e4755495a01cd4da20d295eaf

[ "MIT" ]

null

masonite/__init__.py

vaibhavmule/masonite-cloudinary-driver

866b073717144b8e4755495a01cd4da20d295eaf

[ "MIT" ]

1

2019-08-07T16:53:09.000Z

from .contrib import drivers from .contrib import providers

29.5

30

0.847458

af0e056d6f71bb1927c037ea383c3b625321d406

2,180

py

Python

topaz/modules/ffi/function.py

mswart/topaz

4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae

[ "BSD-3-Clause" ]

6

2015-04-10T20:11:03.000Z

2021-11-10T07:03:46.000Z

topaz/modules/ffi/function.py

mswart/topaz

4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae

[ "BSD-3-Clause" ]

1

2017-01-24T10:17:55.000Z

topaz/modules/ffi/function.py

mswart/topaz

4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae

[ "BSD-3-Clause" ]

2

2017-01-23T18:47:35.000Z

2019-11-13T08:31:18.000Z

import sys from topaz.module import ClassDef from topaz.modules.ffi import type as ffitype from topaz.modules.ffi.pointer import W_PointerObject from topaz.modules.ffi.dynamic_library import coerce_dl_symbol from topaz.modules.ffi.function_type import W_FunctionTypeObject from topaz.objects.moduleobject import W_FunctionObject from rpython.rtyper.lltypesystem import rffi, lltype from rpython.rlib import jit from rpython.rlib.jit_libffi import CIF_DESCRIPTION from rpython.rlib.jit_libffi import FFI_TYPE_PP for i, name in enumerate(ffitype.type_names): globals()[name] = i class W_FFIFunctionObject(W_PointerObject): classdef = ClassDef('FFI::Function', W_PointerObject.classdef) _immutable_fields_ = ['ptr'] @classdef.singleton_method('allocate') def singleton_method_allocate(self, space, args_w): return W_FFIFunctionObject(space) def __init__(self, space): W_PointerObject.__init__(self, space) self.ptr = lltype.nullptr(rffi.VOIDP.TO) @classdef.method('initialize') def method_initialize(self, space, w_ret_type, w_arg_types, w_handle=None, w_options=None): self.w_info = space.send(space.getclassfor(W_FunctionTypeObject), 'new', [w_ret_type, w_arg_types, w_options]) self.setup(space, w_handle) def setup(self, space, w_handle): self.ptr = (coerce_dl_symbol(space, w_handle) if w_handle else lltype.nullptr(rffi.VOIDP.TO)) @classdef.method('call') def method_call(self, space, args_w, block=None): return self.w_info.invoke(space, self.ptr, args_w, block) @classdef.method('attach', name='str') def method_attach(self, space, w_lib, name): w_lib.attach_method(space, name, W_MethodAdapter(name, self)) class W_MethodAdapter(W_FunctionObject): _immutable_fields_ = ['name', 'w_ffi_func'] def __init__(self, name, w_ffi_func): W_FunctionObject.__init__(self, name) self.name = name self.w_ffi_func = w_ffi_func def call(self, space, w_receiver, args_w, block): return space.send(self.w_ffi_func, 'call', args_w, block)

36.949153

77

0.713761

d75b4cbc06c6d60918f9ffe5918cd5ca78a252c1

1,286

py

Python

tests/utils/model_space_test.py

HSE-LAMBDA/modelgym

7f8086c716a014852c1c91c6871bf75da6463ceb

[ "Apache-2.0" ]

18

2017-06-11T01:04:19.000Z

2022-03-09T04:45:19.000Z

tests/utils/model_space_test.py

HSE-LaMBDA/modelgym

7f8086c716a014852c1c91c6871bf75da6463ceb

[ "Apache-2.0" ]

37

2017-06-12T22:33:12.000Z

2019-03-07T08:20:43.000Z

tests/utils/model_space_test.py

HSE-LaMBDA/modelgym

7f8086c716a014852c1c91c6871bf75da6463ceb

[ "Apache-2.0" ]

20

2017-07-18T17:19:02.000Z

2019-04-09T16:22:53.000Z

import pytest from modelgym.utils import ModelSpace, process_model_spaces from modelgym.models import LGBMClassifier, RFClassifier def test_process_model_spaces(): rf_model_space = ModelSpace(RFClassifier, name="RF") processed = process_model_spaces([LGBMClassifier, rf_model_space]) assert len(processed) == 2 assert all(map(lambda ms: isinstance(ms, ModelSpace), processed.values())) assert all(map(lambda kv: kv[0] == kv[1].name, processed.items())) assert processed["LGBMClassifier"].model_class == LGBMClassifier assert processed["RF"] == rf_model_space def test_process_model_spaces_unwrap(): processed = process_model_spaces(LGBMClassifier) assert isinstance(processed, dict) assert len(processed) == 1 assert all(map(lambda ms: isinstance(ms, ModelSpace), processed.values())) assert all(map(lambda kv: kv[0] == kv[1].name, processed.items())) assert processed["LGBMClassifier"].model_class == LGBMClassifier def test_process_model_space_value_errors(): with pytest.raises(ValueError): process_model_spaces([RFClassifier()]) with pytest.raises(ValueError): process_model_spaces(LGBMClassifier()) with pytest.raises(ValueError): process_model_spaces([LGBMClassifier, LGBMClassifier])

38.969697

78

0.746501

7a52e70c3728755daa51b7cbe7ac413e27f720e3

4,988

py

Python

ooobuild/lo/sdbc/row_set.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/sdbc/row_set.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/sdbc/row_set.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Service Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.sdbc import typing from abc import abstractproperty from .result_set import ResultSet as ResultSet_8ecf0a4f from .x_parameters import XParameters as XParameters_a36c0b10 from .x_row_set import XRowSet as XRowSet_7a090960 if typing.TYPE_CHECKING: from ..container.x_name_access import XNameAccess as XNameAccess_e2ab0cf6 class RowSet(ResultSet_8ecf0a4f, XParameters_a36c0b10, XRowSet_7a090960): """ Service Class is a client side ResultSet, which combines the characteristics of a com.sun.star.sdbc.Statement and a com.sun.star.sdbc.ResultSet. It acts like a typical bean. Before you use the RowSet, you have to specify a set of properties like a DataSource and a Command and other properties known of Statement. Afterwards, you can populate the RowSet by its execute method to fill the set with data. On the one hand, a RowSet can be used as a short cut to retrieve the data of a DataSource. You don't have to establish a connection, create a Statement, and then create a ResultSet. On the other hand, a row set can be used to implement capabilities for a result set, which are not supported by a driver result set, like caching strategies or update capabilities. See Also: `API RowSet <https://api.libreoffice.org/docs/idl/ref/servicecom_1_1sun_1_1star_1_1sdbc_1_1RowSet.html>`_ """ __ooo_ns__: str = 'com.sun.star.sdbc' __ooo_full_ns__: str = 'com.sun.star.sdbc.RowSet' __ooo_type_name__: str = 'service' @abstractproperty def Command(self) -> str: """ is the command which should be executed. """ @abstractproperty def DataSourceName(self) -> str: """ is the name of a named datasource to use. """ @abstractproperty def EscapeProcessing(self) -> bool: """ returns if escape processing is on or off. If escape scanning is on (the default), the driver will do escape substitution before sending the SQL to the database. This is only evaluated, if the CommandType is COMMAND. """ @abstractproperty def MaxFieldSize(self) -> int: """ returns the maximum number of bytes allowed for any column value. This limit is the maximum number of bytes that can be returned for any column value. The limit applies only to com.sun.star.sdbc.DataType.BINARY , com.sun.star.sdbc.DataType.VARBINARY , com.sun.star.sdbc.DataType.LONGVARBINARY , com.sun.star.sdbc.DataType.CHAR , com.sun.star.sdbc.DataType.VARCHAR , and com.sun.star.sdbc.DataType.LONGVARCHAR columns. If the limit is exceeded, the excess data is silently discarded. There is no limitation, if set to zero. """ @abstractproperty def MaxRows(self) -> int: """ retrieves the maximum number of rows that a ResultSet can contain. If the limit is exceeded, the excess rows are silently dropped. There is no limitation, if set to zero. """ @abstractproperty def Password(self) -> str: """ determines the user for whom to open the connection. """ @abstractproperty def QueryTimeOut(self) -> int: """ retrieves the number of seconds the driver will wait for a Statement to execute. If the limit is exceeded, a com.sun.star.sdbc.SQLException is thrown. There is no limitation, if set to zero. """ @abstractproperty def ResultSetType(self) -> int: """ determine the result set type. """ @abstractproperty def TransactionIsolation(self) -> int: """ indicates the transaction isolation level, which should be used for the connection. """ @abstractproperty def TypeMap(self) -> 'XNameAccess_e2ab0cf6': """ is the type map that will be used for the custom mapping of SQL structured types and distinct types. """ @abstractproperty def URL(self) -> str: """ is the connection URL. Could be used instead of the DataSourceName. """ @abstractproperty def User(self) -> str: """ determines the user for whom to open the connection. """ __all__ = ['RowSet']

36.948148

424

0.685245

f322de074b3ea4b3fd5d3377bcfba25f5a9920b3

5,040

py

Python

talon_one/models/inline_response2006.py

talon-one/talon_one.py

f863bb3c2cc5ddc94d9227adcf14947b2ea7db41

[ "MIT" ]

1

2021-03-05T06:41:26.000Z

talon_one/models/inline_response2006.py

talon-one/talon_one.py

f863bb3c2cc5ddc94d9227adcf14947b2ea7db41

[ "MIT" ]

1

2021-09-07T08:56:58.000Z

talon_one/models/inline_response2006.py

talon-one/talon_one.py

f863bb3c2cc5ddc94d9227adcf14947b2ea7db41

[ "MIT" ]

1

2019-05-21T10:27:54.000Z

# coding: utf-8 """ Talon.One API The Talon.One API is used to manage applications and campaigns, as well as to integrate with your application. The operations in the _Integration API_ section are used to integrate with our platform, while the other operations are used to manage applications and campaigns. ### Where is the API? The API is available at the same hostname as these docs. For example, if you are reading this page at `https://mycompany.talon.one/docs/api/`, the URL for the [updateCustomerProfile][] operation is `https://mycompany.talon.one/v1/customer_profiles/id` [updateCustomerProfile]: #operation--v1-customer_profiles--integrationId--put # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from talon_one.configuration import Configuration class InlineResponse2006(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'total_result_size': 'int', 'data': 'list[Referral]' } attribute_map = { 'total_result_size': 'totalResultSize', 'data': 'data' } def __init__(self, total_result_size=None, data=None, local_vars_configuration=None): # noqa: E501 """InlineResponse2006 - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._total_result_size = None self._data = None self.discriminator = None self.total_result_size = total_result_size self.data = data @property def total_result_size(self): """Gets the total_result_size of this InlineResponse2006. # noqa: E501 :return: The total_result_size of this InlineResponse2006. # noqa: E501 :rtype: int """ return self._total_result_size @total_result_size.setter def total_result_size(self, total_result_size): """Sets the total_result_size of this InlineResponse2006. :param total_result_size: The total_result_size of this InlineResponse2006. # noqa: E501 :type: int """ if self.local_vars_configuration.client_side_validation and total_result_size is None: # noqa: E501 raise ValueError("Invalid value for `total_result_size`, must not be `None`") # noqa: E501 self._total_result_size = total_result_size @property def data(self): """Gets the data of this InlineResponse2006. # noqa: E501 :return: The data of this InlineResponse2006. # noqa: E501 :rtype: list[Referral] """ return self._data @data.setter def data(self, data): """Sets the data of this InlineResponse2006. :param data: The data of this InlineResponse2006. # noqa: E501 :type: list[Referral] """ if self.local_vars_configuration.client_side_validation and data is None: # noqa: E501 raise ValueError("Invalid value for `data`, must not be `None`") # noqa: E501 self._data = data def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, InlineResponse2006): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, InlineResponse2006): return True return self.to_dict() != other.to_dict()

33.825503

647

0.624405

2e69639822990ba77ef76b36bc7df273573566e6

3,208

py

Python

fn_scheduler/fn_scheduler/components/scheduled_rule_pause_resume.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2020-08-25T03:43:07.000Z

fn_scheduler/fn_scheduler/components/scheduled_rule_pause_resume.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2019-07-08T16:57:48.000Z

fn_scheduler/fn_scheduler/components/scheduled_rule_pause_resume.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

null

# -*- coding: utf-8 -*- # pragma pylint: disable=unused-argument, no-self-use """Function implementation""" import logging from resilient_circuits import ResilientComponent, function, handler, StatusMessage, FunctionResult, FunctionError from resilient_lib import ResultPayload from fn_scheduler.components import SECTION_SCHEDULER from fn_scheduler.lib.scheduler_helper import ResilientScheduler class FunctionComponent(ResilientComponent): """Component that implements Resilient function 'scheduled_rule_pause""" def __init__(self, opts): """constructor provides access to the configuration options""" super(FunctionComponent, self).__init__(opts) self.options = opts.get(SECTION_SCHEDULER, {}) @handler("reload") def _reload(self, event, opts): """Configuration options have changed, save new values""" self.options = opts.get(SECTION_SCHEDULER, {}) @function("scheduled_rule_pause") def _scheduled_rule_pause_function(self, event, *args, **kwargs): """Function: Pause a scheduled rule""" try: # Get the function parameters: scheduler_label = kwargs.get("scheduler_label") # text log = logging.getLogger(__name__) log.info("scheduler_label: %s", scheduler_label) rc = ResultPayload(SECTION_SCHEDULER, **kwargs) job = self.find_job_by_label(scheduler_label) if job is None: raise KeyError("Job not found: {}".format(scheduler_label)) job.pause() yield StatusMessage("Job paused: {}".format(scheduler_label)) results = rc.done(True, ResilientScheduler.sanitize_job(job)) # Produce a FunctionResult with the results yield FunctionResult(results) except Exception: yield FunctionError() @function("scheduled_rule_resume") def _scheduled_rule_resume_function(self, event, *args, **kwargs): """Function: Resume a scheduled job""" try: # Get the function parameters: scheduler_label = kwargs.get("scheduler_label") # text log = logging.getLogger(__name__) log.info("scheduler_label: %s", scheduler_label) rc = ResultPayload(SECTION_SCHEDULER, **kwargs) job = self.find_job_by_label(scheduler_label) if job is None: raise KeyError("Job not found: {}".format(scheduler_label)) job.resume() yield StatusMessage("Job resumed: {}".format(scheduler_label)) results = rc.done(True, ResilientScheduler.sanitize_job(job)) # Produce a FunctionResult with the results yield FunctionResult(results) except Exception: yield FunctionError() def find_job_by_label(self, scheduler_label): """ find the job by it's label :param scheduler_label: :return: job found or None """ scheduler = ResilientScheduler.get_scheduler() jobs = scheduler.get_jobs() for job in jobs: if job.id.lower() == scheduler_label.lower(): return job return None

35.252747

114

0.644638

d68650fff76d8fe33b5db8606628d7484753a51e

10,565

py

Python

support/data_upload.py

ErinBryson/Robs_Data_Collector

8f2de121ee821f09ba5ff274fc13258a9d571186

[ "MIT" ]

null

support/data_upload.py

ErinBryson/Robs_Data_Collector

8f2de121ee821f09ba5ff274fc13258a9d571186

[ "MIT" ]

6

2021-06-06T19:38:38.000Z

2021-08-11T19:44:17.000Z

support/data_upload.py

ErinBryson/robs_data_collector

8f2de121ee821f09ba5ff274fc13258a9d571186

[ "MIT" ]

null

######################################################################################################################## # FILE DETAILS # # -------------------------------------------------------------------------------------------------------------------- # # PROGRAM: Fixed Gas Data Aggregator # # Author: Erin Bryson # ######################################################################################################################## ######################################################################################################################## # Imports # # -------------------------------------------------------------------------------------------------------------------- # import json as js import pandas as pd import glob from typing import Callable ######################################################################################################################## # GLOBAL FUNCTIONS # # -------------------------------------------------------------------------------------------------------------------- # def re_index_df(df): return df.reset_index(drop=True) def add_data_row(export_df: pd.DataFrame, temp_data: pd.DataFrame): return pd.concat([export_df, temp_data]) def get_input_data_file_paths(input_data_dir, input_data_file_name) -> list: return glob.glob(f'{input_data_dir}/**/{input_data_file_name}', recursive=True) ######################################################################################################################## # CLASSES # # -------------------------------------------------------------------------------------------------------------------- # class Config: def __init__(self): self._config = None self.file_name = None self.report_name = None self.report_ext = None self.datasheets_metadata = None self.datasheets_names = [] @property def config(self): return self._config @config.setter def config(self, config_file_name: str) -> None: with open(config_file_name) as config_file: self._config = js.load(config_file) def config_settings(self): self.report_name = self.config["report_name"] self.report_ext = self.config["report_ext"] self.datasheets_metadata = self.config["data_sheets"] # Store datasheet names for key in self.config["data_sheets"].keys(): self.datasheets_names.append(key) def reset(self): self.__init__() class DataSheet: def __init__(self, name: str, sheet_dict: dict): self.name = name self.sheet_dict = sheet_dict @property def header_bool(self): bool_val = self.sheet_dict["headers_bool"] if bool_val: return True return False @property def header(self): """This property sets 'header' parameter in read_excel() either to '0' (headers located on top row of excel sheet) or 'None'(no headers used in excel sheet)""" if self.header_bool: return 0 return None @property def transpose_bool(self): """Store whether data in sheet should be transposed.""" if self.sheet_dict["transpose_bool"] == 1: return True return False # If data filter columns if specified @property def use_cols(self): if self.header_bool: try: return self.sheet_dict["headers_nm"] except KeyError: print("ERROR! Use Cols list missing from config file!") return KeyError return None # Get row data map if specified @property def data_map(self): return self.sheet_dict["data_map"] @property def data_map_keys(self): data_map_keys = [] for key in self.data_map: if key.isnumeric(): final_key = int(key) else: final_key = key data_map_keys.append(final_key) return data_map_keys @property def data_map_values(self): data_map_values = [] for key in self.data_map: data_map_values.append(self.data_map[key]) return data_map_values def import_data(self, file_name) -> pd.DataFrame: return pd.read_excel(file_name, sheet_name=self.name, header=self.header, usecols=self.use_cols) def clean_data(self, data_df: pd.DataFrame) -> pd.DataFrame: if self.use_cols is None: use_col_num = 0 else: use_col_num = len(self.use_cols) # Drop NA values data_df = data_df.dropna() # Transpose data, if needed if self.transpose_bool: data_df = data_df.T.reset_index(drop=True) if use_col_num != 1: # Set column names to equal to row 1 data_df.columns = data_df.iloc[0] # Filter out all undesired columns data_df = data_df.filter(items=self.data_map_keys, axis=1) # print(data_df) data_df.columns = self.data_map_values if use_col_num != 1: # Drop the now-redundant first row data_df = data_df.drop(index=0).reset_index(drop=True) # Reset Index data_df = data_df.reset_index(drop=True) data_df.columns = self.data_map_values return data_df class DataImporter: def __init__(self): self._config = None self._root_path = "" self.import_file_path_list = None self.data_map = {} self.agg_data_df_cols = [] self.datasheet_object_list = [] self.agg_data_df = None self.file_count = None @property def config(self): return self._config @config.setter def config(self, config: "Config"): self._config = config @property def root_path(self): return self._root_path @root_path.setter def root_path(self, root_path): self._root_path = root_path def config_import_settings(self): self.import_file_path_list = get_input_data_file_paths( self.root_path, f"{self.config.report_name}.{self.config.report_ext}" ) self.file_count = len(self.import_file_path_list) # Populate sheet_obj_list with constructed sheet objects for datasheet_name in self.config.datasheets_names: # Get metadata for specific sheet datasheet_metadata = self.config.datasheets_metadata[datasheet_name] # Create a datasheet object for each datasheet sheet_obj = DataSheet(datasheet_name, datasheet_metadata) # Build list of aggregated data DataFrame columns for data_map_value in sheet_obj.data_map_values: self.agg_data_df_cols.append(data_map_value) # Add datasheet object to the list of datasheets self.datasheet_object_list.append(sheet_obj) self.agg_data_df = pd.DataFrame(columns=self.agg_data_df_cols) def concat_import_data(self, data_file_path): # Create an empty DataFrame to store input data # NOTE: Creating an empty DataFrame with a column allows data to be appended below temp_df = pd.DataFrame({'temp_col': []}) # Import data from each sheet for datasheet_object in self.datasheet_object_list: # print(sheet_obj.name) sheet_df = datasheet_object.import_data(data_file_path) # Clean sheet data sheet_df = datasheet_object.clean_data(sheet_df) # Add imported data to new data row temp_df = pd.concat([temp_df, sheet_df], axis=1) # Clean up del sheet_df # Drop the temporary column used for work-around temp_df = temp_df.drop(columns='temp_col') # Add new data row self.agg_data_df = add_data_row(self.agg_data_df, temp_df) # Clean up del temp_df def aggregate_data(self, status_message_func: Callable = None, download_button=None, progress_bar=None): progress_num = 0 # progress_bar['maximum'] = self.file_count if progress_bar: progress_bar['maximum'] = self.file_count for data_file_path in self.import_file_path_list: # Concat datasheet data self.concat_import_data(data_file_path) # Increment file_counter by 1 progress_num += 1 # Pass file number into Callable Status Message Function if status_message_func: status_message_func(progress_num) # Re-Index Aggregated Data self.agg_data_df = re_index_df(self.agg_data_df) download_button['state'] = 'active' def reset(self): self.__init__() class DataExporter: def __init__(self, export_df, export_data_full_file_name): self.export_df = export_df self.export_data_full_file_name = export_data_full_file_name # Fine position of final "." ext_start_pos = self.export_data_full_file_name.rfind(".") # Get the file extension file_ext = self.export_data_full_file_name[ext_start_pos + 1:] # IF file extension is supported, set the file_ext property value if file_ext in self.supported_ext.keys(): self.file_ext = file_ext @property def supported_ext(self): return { "xlsx": self.export_df.to_excel, "csv": self.export_df.to_csv } def export(self): self.supported_ext[self.file_ext](self.export_data_full_file_name) ######################################################################################################################## # END FILE # ########################################################################################################################

33.22327

120

0.51027

0fa47268d9d628afe49da8d4a996c283e29911ea

12,996

py

Python

multiagent-particle-envs/multiagent/environment.py

ScorpioPeng/maddpg

d4b109da6f4a21958b20825d37ade86568c48237

[ "MIT" ]

null

multiagent-particle-envs/multiagent/environment.py

ScorpioPeng/maddpg

d4b109da6f4a21958b20825d37ade86568c48237

[ "MIT" ]

null

multiagent-particle-envs/multiagent/environment.py

ScorpioPeng/maddpg

d4b109da6f4a21958b20825d37ade86568c48237

[ "MIT" ]

null

import gym from gym import spaces from gym.envs.registration import EnvSpec import numpy as np from multiagent.multi_discrete import MultiDiscrete # environment for all agents in the multiagent world # currently code assumes that no agents will be created/destroyed at runtime! class MultiAgentEnv(gym.Env): metadata = { 'render.modes' : ['human', 'rgb_array'] } def __init__(self, world, reset_callback=None, reward_callback=None, observation_callback=None, info_callback=None, done_callback=None, shared_viewer=True): self.world = world self.agents = self.world.policy_agents # set required vectorized gym env property self.n = len(world.policy_agents) # scenario callbacks self.reset_callback = reset_callback self.reward_callback = reward_callback self.observation_callback = observation_callback self.info_callback = info_callback self.done_callback = done_callback # environment parameters self.discrete_action_space = True # if true, action is a number 0...N, otherwise action is a one-hot N-dimensional vector self.discrete_action_input = False # if true, even the action is continuous, action will be performed discretely self.force_discrete_action = world.discrete_action if hasattr(world, 'discrete_action') else False # if true, every agent has the same reward self.shared_reward = world.collaborative if hasattr(world, 'collaborative') else False self.time = 0 # configure spaces self.action_space = [] self.observation_space = [] for agent in self.agents: total_action_space = [] # physical action space if self.discrete_action_space: u_action_space = spaces.Discrete(world.dim_p * 2 + 1) else: u_action_space = spaces.Box(low=-agent.u_range, high=+agent.u_range, shape=(world.dim_p,), dtype=np.float32) if agent.movable: total_action_space.append(u_action_space) # communication action space if self.discrete_action_space: c_action_space = spaces.Discrete(world.dim_c) else: c_action_space = spaces.Box(low=0.0, high=1.0, shape=(world.dim_c,), dtype=np.float32) if not agent.silent: total_action_space.append(c_action_space) # total action space if len(total_action_space) > 1: # all action spaces are discrete, so simplify to MultiDiscrete action space if all([isinstance(act_space, spaces.Discrete) for act_space in total_action_space]): act_space = MultiDiscrete([[0, act_space.n - 1] for act_space in total_action_space]) else: act_space = spaces.Tuple(total_action_space) self.action_space.append(act_space) else: self.action_space.append(total_action_space[0]) # observation space obs_dim = len(observation_callback(agent, self.world)) self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,), dtype=np.float32)) agent.action.c = np.zeros(self.world.dim_c) # rendering self.shared_viewer = shared_viewer if self.shared_viewer: self.viewers = [None] else: self.viewers = [None] * self.n self._reset_render() def step(self, action_n): obs_n = [] reward_n = [] done_n = [] info_n = {'n': []} self.agents = self.world.policy_agents # set action for each agent for i, agent in enumerate(self.agents): self._set_action(action_n[i], agent, self.action_space[i]) # advance world state self.world.step() # record observation for each agent for agent in self.agents: obs_n.append(self._get_obs(agent)) reward_n.append(self._get_reward(agent)) done_n.append(self._get_done(agent)) info_n['n'].append(self._get_info(agent)) # all agents get total reward in cooperative case reward = np.sum(reward_n) if self.shared_reward: reward_n = [reward] * self.n return obs_n, reward_n, done_n, info_n def reset(self): # reset world self.reset_callback(self.world) # reset renderer self._reset_render() # record observations for each agent obs_n = [] self.agents = self.world.policy_agents for agent in self.agents: obs_n.append(self._get_obs(agent)) return obs_n # get info used for benchmarking def _get_info(self, agent): if self.info_callback is None: return {} return self.info_callback(agent, self.world) # get observation for a particular agent def _get_obs(self, agent): if self.observation_callback is None: return np.zeros(0) return self.observation_callback(agent, self.world) # get dones for a particular agent # unused right now -- agents are allowed to go beyond the viewing screen def _get_done(self, agent): if self.done_callback is None: return False return self.done_callback(agent, self.world) # get reward for a particular agent def _get_reward(self, agent): if self.reward_callback is None: return 0.0 return self.reward_callback(agent, self.world) # set env action for a particular agent def _set_action(self, action, agent, action_space, time=None): agent.action.u = np.zeros(self.world.dim_p) agent.action.c = np.zeros(self.world.dim_c) # process action if isinstance(action_space, MultiDiscrete): act = [] size = action_space.high - action_space.low + 1 index = 0 for s in size: act.append(action[index:(index+s)]) index += s action = act else: action = [action] if agent.movable: # physical action if self.discrete_action_input: #False agent.action.u = np.zeros(self.world.dim_p) # process discrete action if action[0] == 1: agent.action.u[0] = -1.0 if action[0] == 2: agent.action.u[0] = +1.0 if action[0] == 3: agent.action.u[1] = -1.0 if action[0] == 4: agent.action.u[1] = +1.0 else: if self.force_discrete_action: d = np.argmax(action[0]) action[0][:] = 0.0 action[0][d] = 1.0 if self.discrete_action_space: agent.action.u[0] += action[0][1] - action[0][2] agent.action.u[1] += action[0][3] - action[0][4] else: agent.action.u = action[0] sensitivity = 5.0 if agent.accel is not None: sensitivity = agent.accel agent.action.u *= sensitivity action = action[1:] if not agent.silent: # communication action if self.discrete_action_input: agent.action.c = np.zeros(self.world.dim_c) agent.action.c[action[0]] = 1.0 else: agent.action.c = action[0] action = action[1:] # make sure we used all elements of action assert len(action) == 0 # reset rendering assets def _reset_render(self): self.render_geoms = None self.render_geoms_xform = None # render environment def render(self, mode='human'): if mode == 'human': alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' message = '' for agent in self.world.agents: comm = [] for other in self.world.agents: if other is agent: continue if np.all(other.state.c == 0): word = '_' else: word = alphabet[np.argmax(other.state.c)] message += (other.name + ' to ' + agent.name + ': ' + word + ' ') print(message) for i in range(len(self.viewers)): # create viewers (if necessary) if self.viewers[i] is None: # import rendering only if we need it (and don't import for headless machines) #from gym.envs.classic_control import rendering from multiagent import rendering self.viewers[i] = rendering.Viewer(700,700) # create rendering geometry if self.render_geoms is None: # import rendering only if we need it (and don't import for headless machines) #from gym.envs.classic_control import rendering from multiagent import rendering self.render_geoms = [] self.render_geoms_xform = [] for entity in self.world.entities: geom = rendering.make_circle(entity.size) xform = rendering.Transform() if 'agent' in entity.name: geom.set_color(*entity.color, alpha=0.5) else: geom.set_color(*entity.color) # if 'boat' in entity.name: # geom.set_color(*entity.color) geom.add_attr(xform) self.render_geoms.append(geom) self.render_geoms_xform.append(xform) # add geoms to viewer for viewer in self.viewers: viewer.geoms = [] for geom in self.render_geoms: viewer.add_geom(geom) results = [] for i in range(len(self.viewers)): from multiagent import rendering # update bounds to center around agent cam_range = 1 if self.shared_viewer: pos = np.zeros(self.world.dim_p) else: pos = self.agents[i].state.p_pos self.viewers[i].set_bounds(pos[0]-cam_range,pos[0]+cam_range,pos[1]-cam_range,pos[1]+cam_range) # update geometry positions for e, entity in enumerate(self.world.entities): self.render_geoms_xform[e].set_translation(*entity.state.p_pos) # render to display or array results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array')) return results # create receptor field locations in local coordinate frame def _make_receptor_locations(self, agent): receptor_type = 'polar' range_min = 0.05 * 2.0 range_max = 1.00 dx = [] # circular receptive field if receptor_type == 'polar': for angle in np.linspace(-np.pi, +np.pi, 8, endpoint=False): for distance in np.linspace(range_min, range_max, 3): dx.append(distance * np.array([np.cos(angle), np.sin(angle)])) # add origin dx.append(np.array([0.0, 0.0])) # grid receptive field if receptor_type == 'grid': for x in np.linspace(-range_max, +range_max, 5): for y in np.linspace(-range_max, +range_max, 5): dx.append(np.array([x,y])) return dx # vectorized wrapper for a batch of multi-agent environments # assumes all environments have the same observation and action space class BatchMultiAgentEnv(gym.Env): metadata = { 'runtime.vectorized': True, 'render.modes' : ['human', 'rgb_array'] } def __init__(self, env_batch): self.env_batch = env_batch @property def n(self): return np.sum([env.n for env in self.env_batch]) @property def action_space(self): return self.env_batch[0].action_space @property def observation_space(self): return self.env_batch[0].observation_space def step(self, action_n, time): obs_n = [] reward_n = [] done_n = [] info_n = {'n': []} i = 0 for env in self.env_batch: obs, reward, done, _ = env.step(action_n[i:(i+env.n)], time) i += env.n obs_n += obs # reward = [r / len(self.env_batch) for r in reward] reward_n += reward done_n += done return obs_n, reward_n, done_n, info_n def reset(self): obs_n = [] for env in self.env_batch: obs_n += env.reset() return obs_n # render environment def render(self, mode='human', close=True): results_n = [] for env in self.env_batch: results_n += env.render(mode, close) return results_n

38.449704

124

0.573176

99f3850abb8be151725bc753504a25abee5542b0

111,507

py

Python

jax/_src/lax/control_flow.py

erwincoumans/jax

f0e55e3ce241f997b097bd9381504c08a9d35cf8

[ "ECL-2.0", "Apache-2.0" ]

14

2021-04-24T03:26:39.000Z

2022-01-28T14:25:13.000Z

jax/_src/lax/control_flow.py

erwincoumans/jax

f0e55e3ce241f997b097bd9381504c08a9d35cf8

[ "ECL-2.0", "Apache-2.0" ]

null

jax/_src/lax/control_flow.py

erwincoumans/jax

f0e55e3ce241f997b097bd9381504c08a9d35cf8

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Control flow primitives. """ import collections import functools import inspect import itertools import operator import os from typing import Any, Callable, Optional, Sequence, Tuple, TypeVar import numpy as np import jax from jax._src import api from jax import core from jax._src import dtypes from jax._src import source_info_util from jax._src import util from jax._src.lax import lax from jax import linear_util as lu from jax.core import ConcreteArray, ShapedArray, raise_to_shaped from jax.api_util import flatten_fun_nokwargs from jax.interpreters import ad from jax.interpreters import partial_eval as pe from jax.interpreters import xla from jax.interpreters import batching from jax.interpreters import masking from jax.lib import xla_bridge as xb from jax.lib import xla_client from jax._src.traceback_util import api_boundary from jax._src.util import (partial, unzip2, unzip3, safe_map, safe_zip, split_list, cache, extend_name_stack) from jax.tree_util import (tree_flatten, tree_unflatten, treedef_is_leaf, treedef_children, treedef_tuple, tree_multimap, tree_leaves, tree_structure) from jax import ad_util from jax.config import config xops = xla_client.ops _map = safe_map zip = safe_zip _reduce = functools.reduce T = TypeVar('T') Array = Any @cache() def _initial_style_open_jaxpr(fun: Callable, in_tree, in_avals, primitive_name: Optional[str] = None): wrapped_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree) debug = pe.debug_info(fun, in_tree, False, primitive_name or "<unknown>") jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(wrapped_fun, in_avals, debug) return jaxpr, consts, out_tree() @cache() def _initial_style_jaxpr(fun: Callable, in_tree, in_avals, primitive_name: Optional[str] = None): jaxpr, consts, out_tree = _initial_style_open_jaxpr( fun, in_tree, in_avals, primitive_name) closed_jaxpr = core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ()) return closed_jaxpr, consts, out_tree @cache() def _initial_style_jaxprs_with_common_consts( funs: Sequence[Callable], in_tree, in_avals, primitive_name: str): # When staging the branches of a conditional into jaxprs, constants are # extracted from each branch and converted to jaxpr arguments. To use the # staged jaxprs as the branches to a conditional *primitive*, we need for # their (input) signatures to match. This function "joins" the staged jaxprs: # for each one, it makes another that accepts *all* constants, but only uses # those that it needs (dropping the rest). jaxprs, all_consts, all_out_trees = \ unzip3(_initial_style_open_jaxpr(fun, in_tree, in_avals, primitive_name) for fun in funs) newvar = core.gensym(jaxprs, suffix='_') all_const_avals = [[raise_to_shaped(core.get_aval(c)) for c in consts] for consts in all_consts] unused_const_vars = [[newvar(aval) for aval in const_avals] for const_avals in all_const_avals] def pad_jaxpr_constvars(i, jaxpr): prefix = util.concatenate(unused_const_vars[:i]) suffix = util.concatenate(unused_const_vars[i + 1:]) constvars = [*prefix, *jaxpr.constvars, *suffix] return core.Jaxpr(constvars=constvars, invars=jaxpr.invars, outvars=jaxpr.outvars, eqns=jaxpr.eqns) consts = util.concatenate(all_consts) jaxprs = [pad_jaxpr_constvars(i, jaxpr) for i, jaxpr in enumerate(jaxprs)] closed_jaxprs = [core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ()) for jaxpr in jaxprs] return closed_jaxprs, consts, all_out_trees def _abstractify(x): return raise_to_shaped(core.get_aval(x)) def _typecheck_param(prim, param, name, msg_required, pred): msg = (f'invalid {prim} param {name} of type {type(param).__name__}, ' f'{msg_required} required:') param_str = str(param) sep = os.linesep if os.linesep in param_str else ' ' msg = sep.join([msg, param_str]) core.typecheck_assert(pred, msg) ### fori_loop and while_loop def _fori_cond_fun(loop_carry): i, upper, _ = loop_carry return lax.lt(i, upper) @cache() def _fori_body_fun(body_fun): def while_body_fun(loop_carry): i, upper, x = loop_carry return lax.add(i, lax._const(i, 1)), upper, body_fun(i, x) return while_body_fun @cache() def _fori_scan_body_fun(body_fun): def scanned_fun(loop_carry, _): i, x = loop_carry return (i + 1, body_fun(i, x)), None return scanned_fun @api_boundary def fori_loop(lower, upper, body_fun, init_val): """Loop from ``lower`` to ``upper`` by reduction to :func:`jax.lax.while_loop`. The type signature in brief is .. code-block:: haskell fori_loop :: Int -> Int -> ((int, a) -> a) -> a -> a The semantics of ``fori_loop`` are given by this Python implementation:: def fori_loop(lower, upper, body_fun, init_val): val = init_val for i in range(lower, upper): val = body_fun(i, val) return val Unlike that Python version, ``fori_loop`` is implemented in terms of either a call to :func:`jax.lax.while_loop` or a call to :func:`jax.lax.scan`. If the trip count is static (meaning known at tracing time, perhaps because ``lower`` and ``upper` are Python integer literals) then the ``fori_loop`` is implemented in terms of ``scan`` and reverse-mode autodiff is supported; otherwise, a ``while_loop`` is used and reverse-mode autodiff is not supported. See those functions' docstrings for more information. Also unlike the Python analogue, the loop-carried value ``val`` must hold a fixed shape and dtype across all iterations (and not just be consistent up to NumPy rank/shape broadcasting and dtype promotion rules, for example). In other words, the type ``a`` in the type signature above represents an array with a fixed shape and dtype (or a nested tuple/list/dict container data structure with a fixed structure and arrays with fixed shape and dtype at the leaves). Args: lower: an integer representing the loop index lower bound (inclusive) upper: an integer representing the loop index upper bound (exclusive) body_fun: function of type ``(int, a) -> a``. init_val: initial loop carry value of type ``a``. Returns: Loop value from the final iteration, of type ``a``. """ # TODO(phawkins): perhaps do more type checking here, better error messages. lower_dtype = dtypes.canonicalize_dtype(lax.dtype(lower)) upper_dtype = dtypes.canonicalize_dtype(lax.dtype(upper)) if lower_dtype != upper_dtype: msg = ("lower and upper arguments to fori_loop must have equal types, " "got {} and {}") raise TypeError(msg.format(lower_dtype.name, upper_dtype.name)) # If we can specialize on the trip count, call scan instead of a while_loop # to enable efficient reverse-mode differentiation. if (isinstance(core.get_aval(lower), ConcreteArray) and isinstance(core.get_aval(upper), ConcreteArray)): try: lower_ = int(lower) upper_ = int(upper) except TypeError: use_scan = False else: use_scan = True else: use_scan = False if use_scan: (_, result), _ = scan(_fori_scan_body_fun(body_fun), (lower_, init_val), None, length=upper_ - lower_) else: _, _, result = while_loop(_fori_cond_fun, _fori_body_fun(body_fun), (lower, upper, init_val)) return result @api_boundary def while_loop(cond_fun: Callable[[T], bool], body_fun: Callable[[T], T], init_val: T) -> T: """Call ``body_fun`` repeatedly in a loop while ``cond_fun`` is True. The type signature in brief is .. code-block:: haskell while_loop :: (a -> Bool) -> (a -> a) -> a -> a The semantics of ``while_loop`` are given by this Python implementation:: def while_loop(cond_fun, body_fun, init_val): val = init_val while cond_fun(val): val = body_fun(val) return val Unlike that Python version, ``while_loop`` is a JAX primitive and is lowered to a single XLA While HLO. That makes it useful for reducing compilation times for jit-compiled functions, since native Python loop constructs in an ``@jit`` function are unrolled, leading to large XLA computations. Also unlike the Python analogue, the loop-carried value ``val`` must hold a fixed shape and dtype across all iterations (and not just be consistent up to NumPy rank/shape broadcasting and dtype promotion rules, for example). In other words, the type ``a`` in the type signature above represents an array with a fixed shape and dtype (or a nested tuple/list/dict container data structure with a fixed structure and arrays with fixed shape and dtype at the leaves). Another difference from using Python-native loop constructs is that ``while_loop`` is not reverse-mode differentiable because XLA computations require static bounds on memory requirements. Args: cond_fun: function of type ``a -> Bool``. body_fun: function of type ``a -> a``. init_val: value of type ``a``, a type that can be a scalar, array, or any pytree (nested Python tuple/list/dict) thereof, representing the initial loop carry value. Returns: The output from the final iteration of body_fun, of type ``a``. """ if config.jax_disable_jit: try: val = init_val while cond_fun(val): val = body_fun(val) return val except core.ConcretizationTypeError: # Can't run this while_loop in Python (e.g. because there's a vmap # transformation on it), so we fall back to the primitive version. pass def _create_jaxpr(init_val): init_vals, in_tree = tree_flatten((init_val,)) init_avals = tuple(_map(_abstractify, init_vals)) cond_jaxpr, cond_consts, cond_tree = _initial_style_jaxpr( cond_fun, in_tree, init_avals, "while_cond") body_jaxpr, body_consts, body_tree = _initial_style_jaxpr( body_fun, in_tree, init_avals, "while_loop") if not treedef_is_leaf(cond_tree) or len(cond_jaxpr.out_avals) != 1: msg = "cond_fun must return a boolean scalar, but got pytree {}." raise TypeError(msg.format(cond_tree)) pred_aval = cond_jaxpr.out_avals[0] if (not isinstance(pred_aval, ShapedArray) or pred_aval.strip_weak_type().strip_named_shape() != ShapedArray((), np.bool_)): msg = "cond_fun must return a boolean scalar, but got output type(s) {}." raise TypeError(msg.format(cond_jaxpr.out_avals)) return init_vals, init_avals, body_jaxpr, in_tree, cond_jaxpr, cond_consts, body_consts, body_tree # The body input and output avals must match exactly. However, we want to account for # the case when init contains weakly-typed values (e.g. Python scalars), with avals that # may not match the output despite being compatible by virtue of their weak type. # To do this, we compute the jaxpr in two passes: first with the raw inputs, and if # necessary, a second time with modified init values. init_vals, init_avals, body_jaxpr, in_tree, *rest = _create_jaxpr(init_val) new_init_vals, changed = _promote_weak_typed_inputs(init_vals, init_avals, body_jaxpr.out_avals) if changed: new_init_val, = tree_unflatten(in_tree, new_init_vals) init_vals, init_avals, body_jaxpr, in_tree, *rest = _create_jaxpr(new_init_val) cond_jaxpr, cond_consts, body_consts, body_tree = rest in_tree_children = in_tree.children() assert len(in_tree_children) == 1 _check_tree_and_avals("body_fun output and input", body_tree, body_jaxpr.out_avals, in_tree_children[0], init_avals) outs = while_p.bind(*itertools.chain(cond_consts, body_consts, init_vals), cond_nconsts=len(cond_consts), cond_jaxpr=cond_jaxpr, body_nconsts=len(body_consts), body_jaxpr=body_jaxpr) return tree_unflatten(body_tree, outs) def _while_loop_abstract_eval(*args, **kwargs): return _map(raise_to_shaped, kwargs["body_jaxpr"].out_avals) def _while_loop_translation_rule(c, axis_env, name_stack, avals, backend, *args, cond_jaxpr, body_jaxpr, cond_nconsts, body_nconsts): cond_consts, body_consts, init_vals = split_list(args, [cond_nconsts, body_nconsts]) batched = bool(cond_jaxpr.out_avals[0].shape) # Since jaxprs don't have tuples and have multiple return values, but we need # the HLO While loop to take a single tuple input and output a single boolean # (for the cond computation) or a single tuple output (for the body # computation), we build XLA computations that handle the tuple munging before # generating a Call into the computations formed from the jaxprs. init_carry = xops.Tuple(c, cond_consts + body_consts + init_vals) cond_c = xb.make_computation_builder("cond_computation") cond_carry = xb.parameter(cond_c, 0, c.get_shape(init_carry)) cond_carry_elts = [xops.GetTupleElement(cond_carry, i) for i in range(len(args))] x, _, z = split_list(cond_carry_elts, [cond_nconsts, body_nconsts]) pred, = xla.jaxpr_subcomp(cond_c, cond_jaxpr.jaxpr, backend, axis_env, _map(partial(xb.constant, cond_c), cond_jaxpr.consts), extend_name_stack(name_stack, 'cond'), *(x + z)) if batched: scalar = ShapedArray((), np.bool_) or_ = xla.primitive_subcomputation(lax.or_p, scalar, scalar) pred = xops.Reduce(cond_c, [pred], [xb.constant(cond_c, np.array(False))], or_, list(range(cond_jaxpr.out_avals[0].ndim))) body_c = xb.make_computation_builder("body_computation") body_carry = xb.parameter(body_c, 0, c.get_shape(init_carry)) body_carry_elts = [xops.GetTupleElement(body_carry, i) for i in range(len(args))] x, y, z = split_list(body_carry_elts, [cond_nconsts, body_nconsts]) new_z = xla.jaxpr_subcomp(body_c, body_jaxpr.jaxpr, backend, axis_env, _map(partial(xb.constant, body_c), body_jaxpr.consts), extend_name_stack(name_stack, 'body'), *(y + z)) if batched: body_pred, = xla.jaxpr_subcomp(body_c, cond_jaxpr.jaxpr, backend, axis_env, _map(partial(xb.constant, body_c), cond_jaxpr.consts), extend_name_stack(name_stack, 'body_pred'), *(x + z)) new_z = _map(partial(_pred_bcast_select, body_c, body_pred), new_z, z, body_jaxpr.out_avals) assert _map(body_c.get_shape, new_z) == _map(body_c.get_shape, z) # no broadcast new_carry = xops.Tuple(body_c, list(itertools.chain(x, y, new_z))) ans = xops.While(cond_c.build(pred), body_c.build(new_carry), init_carry) ans_elts = [xops.GetTupleElement(ans, i) for i in range(len(args))] _, _, z = split_list(ans_elts, [cond_nconsts, body_nconsts]) return xops.Tuple(c, z) def _pred_bcast_select(c, pred, x, y, x_y_aval: core.AbstractValue): pred_shape = c.get_shape(pred).dimensions() x_shape = c.get_shape(x).dimensions() y_shape = c.get_shape(y).dimensions() assert x_shape == y_shape if x_y_aval is core.abstract_unit: return x elif x_y_aval is core.abstract_token: return xops.AfterAll(c, [x, y]) else: assert pred_shape == x_shape[:len(pred_shape)] == y_shape[:len(pred_shape)] bcast_pred = xops.BroadcastInDim(pred, x_shape, list(range(len(pred_shape)))) return xops.Select(bcast_pred, x, y) def _while_loop_batching_rule(args, dims, axis_name, main_type, cond_nconsts, cond_jaxpr, body_nconsts, body_jaxpr): size, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped} orig_batched = [d is not batching.not_mapped for d in dims] cconst_bat, bconst_bat, init_bat = split_list(orig_batched, [cond_nconsts, body_nconsts]) # Fixpoint computation of which carry are batched: either # batched from init, or the carry out is batched. Each iteration promotes # at least one carry to batched. We need at most len(carry) iterations, # but we need one last iteration to prepare the jaxpr based on the final # carry_bat. carry_bat = init_bat for _ in range(1 + len(carry_bat)): batched = bconst_bat + carry_bat body_jaxpr_batched, carry_bat_out = batching.batch_jaxpr( body_jaxpr, size, batched, instantiate=carry_bat, axis_name=axis_name, main_type=main_type) cond_jaxpr_batched, (pred_bat,) = batching.batch_jaxpr( cond_jaxpr, size, cconst_bat + carry_bat, instantiate=bool(cond_jaxpr.out_avals[0].shape), axis_name=axis_name, main_type=main_type) carry_bat_out = _map(partial(operator.or_, pred_bat), carry_bat_out) if carry_bat_out == carry_bat: break else: carry_bat = _map(operator.or_, carry_bat, carry_bat_out) else: assert False, "Fixpoint not reached" consts, init = split_list(args, [cond_nconsts + body_nconsts]) const_dims, init_dims = split_list(dims, [cond_nconsts + body_nconsts]) new_consts = [batching.moveaxis(x, d, 0) if d is not batching.not_mapped and d != 0 else x for x, d in zip(consts, const_dims)] new_init = [batching.broadcast(x, size, 0) if now_bat and not was_bat else batching.moveaxis(x, d, 0) if now_bat and d != 0 else x for x, d, was_bat, now_bat in zip(init, init_dims, init_bat, carry_bat)] outs = while_p.bind(*(new_consts + new_init), cond_nconsts=cond_nconsts, cond_jaxpr=cond_jaxpr_batched, body_nconsts=body_nconsts, body_jaxpr=body_jaxpr_batched) out_bdims = [0 if b else batching.not_mapped for b in carry_bat] return outs, out_bdims def _while_loop_jvp(primals, tangents, cond_nconsts, cond_jaxpr, body_nconsts, body_jaxpr): nonzeros = [type(t) is not ad_util.Zero for t in tangents] cconst_nz, bconst_nz, init_nz = split_list(nonzeros, [cond_nconsts, body_nconsts]) carry_nz = init_nz for _ in range(1 + len(carry_nz)): body_nonzeros = bconst_nz + carry_nz body_jvp, nonzeros_out = ad.jvp_jaxpr( body_jaxpr, body_nonzeros, instantiate=carry_nz) if nonzeros_out == carry_nz: break carry_nz = _map(operator.or_, carry_nz, nonzeros_out) else: assert False, "Fixpoint not reached" nonzeros = cconst_nz + body_nonzeros tangents = [ad.instantiate_zeros(t) if nz else t for t, nz in zip(tangents, nonzeros)] cconst, bconst, init = split_list(primals, [cond_nconsts, body_nconsts]) _, bconst_dot, init_dot = split_list(tangents, [cond_nconsts, body_nconsts]) bconst_dot = _prune_zeros(bconst_dot) init_dot = _prune_zeros(init_dot) num_carry = len(primals) - cond_nconsts - body_nconsts body_jvp_rearranged = ad.rearrange_binders( body_jvp, [body_nconsts, num_carry], [len(bconst_dot), len(init_dot)], [num_carry], [len(init_dot)]) newvar = core.gensym([cond_jaxpr.jaxpr]) invars_aug = ( cond_jaxpr.jaxpr.invars + [newvar(core.get_aval(x)) for x in init_dot]) cond_jaxpr_augmented = core.Jaxpr(cond_jaxpr.jaxpr.constvars, invars_aug, cond_jaxpr.jaxpr.outvars, cond_jaxpr.jaxpr.eqns) cond_jaxpr_augmented = core.ClosedJaxpr(cond_jaxpr_augmented, cond_jaxpr.consts) out = while_p.bind( *(cconst + bconst + bconst_dot + init + init_dot), cond_nconsts=cond_nconsts, cond_jaxpr=cond_jaxpr_augmented, body_nconsts=len(bconst) + len(bconst_dot), body_jaxpr=body_jvp_rearranged) out_carry, out_carry_dot = split_list(out, [num_carry]) out_tangents_iter = iter(out_carry_dot) out_tangents = [next(out_tangents_iter) if nz else ad_util.Zero.from_value(p) for p, nz in zip(out_carry, nonzeros_out)] return out_carry, out_tangents def _while_partial_eval(trace: pe.JaxprTrace, *tracers: pe.Tracer, cond_nconsts: int, cond_jaxpr: pe.ClosedJaxpr, body_nconsts: int, body_jaxpr: pe.ClosedJaxpr) -> Sequence[pe.Tracer]: """An implementation of partial evaluation for while. As long as some carry (and hence output) are known and the output of `cond_jaxpr` is known, we use a portion of the loop body to compute the known outputs of the `while_loop`. For the unknown outputs we generate Jaxpr to run the whole while, including recomputing the known parts. This means that we don't actually save any computation by partial evaluation if there are unknown outputs. What this achieves is that we can give a proper error for reverse differentiation of `while`, because in that use of partial evaluation the primal inputs are considered "known", and only the tangent computation is unknown (see issue #2129). """ unknowns = [not t.pval.is_known() for t in tracers] params = dict(cond_nconsts=cond_nconsts, cond_jaxpr=cond_jaxpr, body_nconsts=body_nconsts, body_jaxpr=body_jaxpr) cond_consts_uk, body_consts_uk, carry_init_uk = split_list(unknowns, [cond_nconsts, body_nconsts]) # Fixpoint computation of unknown carry. Each iteration promotes # at least one carry to unknown. We need one last iteration to prepare the jaxpr. carry_uk = carry_init_uk for _ in range(1 + len(carry_uk)): body_jaxpr_known, _, carry_out_uk = pe.partial_eval_jaxpr( # type: ignore body_jaxpr, body_consts_uk + carry_uk, instantiate=carry_uk) if carry_out_uk == carry_uk: break else: carry_uk = _map(operator.or_, carry_uk, carry_out_uk) else: assert False, "Fixpoint not reached" cond_jaxpr_known, _, cond_uk = pe.partial_eval_jaxpr( # type: ignore cond_jaxpr, cond_consts_uk + carry_uk, instantiate=False) if cond_uk[0] or all([not uk for uk in unknowns]) or all(unknowns): # If conditional is unknown, or all inputs are known, or all are unknown, # just do the default processing. return trace.default_process_primitive(while_p, tracers, params) # Run the known part of the while. Prepare the inputs, as constants (if known), or # as core.unit. in_consts = [ core.unit if uk else t.pval.get_known() for uk, t in zip(cond_consts_uk + body_consts_uk + carry_uk, tracers)] # There should be no residuals for the cond_jaxpr_known assert 1 == len(cond_jaxpr_known.out_avals) # We ignore the residuals from the body_jaxpr_known, so the type of inputs matches # the type of outputs; residuals are at the end if len(body_jaxpr_known.out_avals) > len(body_jaxpr.out_avals): # TODO(necula): this is not quite enough; we should drop the residual computations also body_jaxpr_known.jaxpr.outvars = body_jaxpr_known.jaxpr.outvars[:len(body_jaxpr.out_avals)] out_known = while_p.bind( *in_consts, cond_nconsts=cond_nconsts, cond_jaxpr=cond_jaxpr_known, body_nconsts=body_nconsts, body_jaxpr=body_jaxpr_known) # Run the whole while_loop to get all the outputs, then merge with known ones out_all: Sequence[pe.Tracer] = trace.default_process_primitive(while_p, tracers, params) out_tracers: Sequence[pe.Tracer] = [ out_unknown if uk else pe.JaxprTracer(trace, pe.PartialVal.known(known), out_unknown.recipe) for uk, out_unknown, known in zip(carry_uk, out_all, out_known)] return out_tracers def _while_transpose_error(*_, **kwargs): raise ValueError("Reverse-mode differentiation does not work for " "lax.while_loop or lax.fori_loop. " "Try using lax.scan instead.") while_p = lax.Primitive('while') while_p.multiple_results = True while_p.def_impl(partial(xla.apply_primitive, while_p)) while_p.def_abstract_eval(_while_loop_abstract_eval) ad.primitive_jvps[while_p] = _while_loop_jvp pe.custom_partial_eval_rules[while_p] = _while_partial_eval xla.initial_style_translations[while_p] = _while_loop_translation_rule ad.primitive_transposes[while_p] = _while_transpose_error batching.initial_style_batchers[while_p] = _while_loop_batching_rule ### cond and switch @api_boundary def switch(index, branches: Sequence[Callable], operand): """Apply exactly one of ``branches`` given by ``index``. If ``index`` is out of bounds, it is clamped to within bounds. Has the semantics of the following Python:: def switch(index, branches, operand): index = clamp(0, index, len(branches) - 1) return branches[index](operand) Args: index: Integer scalar type, indicating which branch function to apply. branches: Sequence of functions (A -> B) to be applied based on `index`. operand: Operand (A) input to whichever branch is applied. """ if len(np.shape(index)) != 0: raise TypeError( f"Branch index must be scalar, " f"got {index} of shape {np.shape(index)}.") try: index_dtype = dtypes.result_type(index) except TypeError as err: msg = f"Index type must be an integer, got {index}." raise TypeError(msg) from err if index_dtype.kind not in 'iu': raise TypeError( f"Index type must be an integer, got {index} as {index_dtype}") branches = tuple(branches) if len(branches) == 0: raise ValueError("Empty branch sequence") elif len(branches) == 1: return branches[0](operand) index = lax.convert_element_type(index, np.int32) lo = np.array(0, np.int32) hi = np.array(len(branches) - 1, np.int32) index = lax.clamp(lo, index, hi) if (config.jax_disable_jit and isinstance(core.get_aval(index), ConcreteArray)): return branches[int(index)](operand) ops, ops_tree = tree_flatten((operand,)) ops_avals = tuple(_map(_abstractify, ops)) jaxprs, consts, out_trees = _initial_style_jaxprs_with_common_consts( branches, ops_tree, ops_avals, primitive_name='switch') for i, (out_tree, jaxpr) in enumerate(zip(out_trees[1:], jaxprs[1:])): _check_tree_and_avals(f"branch 0 and {i + 1} outputs", out_trees[0], jaxprs[0].out_avals, out_tree, jaxpr.out_avals) linear = (False,) * (len(consts) + len(ops)) out = cond_p.bind( index, *consts, *ops, branches=tuple(jaxprs), linear=linear) return tree_unflatten(out_trees[0], out) def _cond(pred, true_fun: Callable, false_fun: Callable, operand): """Conditionally apply ``true_fun`` or ``false_fun``. ``cond()`` has equivalent semantics to this Python implementation:: def cond(pred, true_fun, false_fun, operand): if pred: return true_fun(operand) else: return false_fun(operand) ``pred`` must be a scalar type. Functions ``true_fun``/``false_fun`` may not need to refer to an ``operand`` to compute their result, but one must still be provided to the ``cond`` call and be accepted by both the branch functions, e.g.:: jax.lax.cond( get_predicate_value(), lambda _: 23, lambda _: 42, operand=None) Args: pred: Boolean scalar type, indicating which branch function to apply. true_fun: Function (A -> B), to be applied if ``pred`` is True. false_fun: Function (A -> B), to be applied if ``pred`` is False. operand: Operand (A) input to either branch depending on ``pred``. The type can be a scalar, array, or any pytree (nested Python tuple/list/dict) thereof. Returns: Value (B) of either ``true_fun(operand)`` or ``false_fun(operand)``, depending on the value of ``pred``. The type can be a scalar, array, or any pytree (nested Python tuple/list/dict) thereof. """ if isinstance(pred, Sequence) or np.ndim(pred) != 0: raise TypeError( f"Pred must be a scalar, got {pred} of " + (f"type {type(pred)}" if isinstance(pred, Sequence) else f"shape {np.shape(pred)}.")) try: pred_dtype = dtypes.result_type(pred) except TypeError as err: msg = ("Pred type must be either boolean or number, got {}.") raise TypeError(msg.format(pred)) from err if pred_dtype.kind != 'b': if pred_dtype.kind in 'iuf': pred = pred != 0 else: msg = ("Pred type must be either boolean or number, got {}.") raise TypeError(msg.format(pred_dtype)) if config.jax_disable_jit and isinstance(core.get_aval(pred), ConcreteArray): if pred: return true_fun(operand) else: return false_fun(operand) ops, ops_tree = tree_flatten((operand,)) ops_avals = tuple(_map(_abstractify, ops)) jaxprs, consts, out_trees = _initial_style_jaxprs_with_common_consts( (true_fun, false_fun), ops_tree, ops_avals, 'cond') true_jaxpr, false_jaxpr = jaxprs out_tree, false_out_tree = out_trees _check_tree_and_avals("true_fun and false_fun output", out_tree, true_jaxpr.out_avals, false_out_tree, false_jaxpr.out_avals) index = lax.convert_element_type(pred, np.int32) linear = (False,) * (len(consts) + len(ops)) out = cond_p.bind( index, *consts, *ops, branches=(false_jaxpr, true_jaxpr), linear=linear) return tree_unflatten(out_tree, out) @api_boundary @functools.wraps(_cond) def cond(*args, **kwargs): # detect an attempt to call the former, deprecated cond try: ba = inspect.signature(_cond_with_per_branch_args).bind(*args, **kwargs) except TypeError: pass else: return _cond_with_per_branch_args(*ba.args) return _cond(*args, **kwargs) def _cond_with_per_branch_args(pred, true_operand, true_fun: Callable, false_operand, false_fun: Callable): """Conditionally apply ``true_fun`` or ``false_fun``. Has equivalent semantics to this Python implementation:: def cond(pred, true_operand, true_fun, false_operand, false_fun): if pred: return true_fun(true_operand) else: return false_fun(false_operand) Pred has to be a scalar type, collection types (list, tuple) are not supported """ return _cond(pred, lambda op: true_fun(op[0]), lambda op: false_fun(op[1]), (true_operand, false_operand)) def _cond_abstract_eval(*args, **kwargs): return _map(raise_to_shaped, kwargs["branches"][0].out_avals) def _cond_translation_rule(c, axis_env, name_stack, avals, backend, index, *args, branches, linear): del linear # Unused. def make_computation(name, jaxpr, op_shape): c = xb.make_computation_builder(name + '_comp') op = xb.parameter(c, 0, op_shape) ops = [xops.GetTupleElement(op, i) for i in range(len(jaxpr.in_avals))] outs = xla.jaxpr_subcomp(c, jaxpr.jaxpr, backend, axis_env, _map(partial(xb.constant, c), jaxpr.consts), extend_name_stack(name_stack, name + '_fun'), *ops) return c.build(xops.Tuple(c, outs)) op = xops.Tuple(c, args) op_shape = c.get_shape(op) branch_computations = [ make_computation(f'branch_{i}', jaxpr, op_shape) for i, jaxpr in enumerate(branches)] return xops.Conditional(index, branch_computations, [op] * len(branches)) def _select_tree(indices, branch_vals): assert len(branch_vals) > 0 if len(branch_vals) == 1: return branch_vals[0] mid = lax._const(indices, len(branch_vals) // 2) return _bcast_select(lax.lt(indices, mid), _select_tree(indices, branch_vals[:mid]), _select_tree(indices - mid, branch_vals[mid:])) def _bcast_select(pred, on_true, on_false): if np.ndim(pred) != np.ndim(on_true): idx = list(range(np.ndim(pred))) pred = lax.broadcast_in_dim(pred, np.shape(on_true), idx) return lax.select(pred, on_true, on_false) def _cond_batching_rule(args, dims, axis_name, main_type, branches, linear): size, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped} index, *ops = args index_dim, *op_dims = dims if index_dim is not batching.not_mapped: # Convert to a lax.select. While we could get away with not broadcasting # some operands yet, because all outputs must be broadcast together anyway # for the select we broadcast the input operands for simplicity and leave # optimizations to XLA. # TODO(mattjj,frostig): assumes branches are side-effect-free, revise! index, *ops = [batching.bdim_at_front(x, d, size) for x, d in zip(args, dims)] branches_batched = [ batching.batch_jaxpr(jaxpr, size, [True] * len(ops), True, axis_name, main_type)[0] for jaxpr in branches] branch_outs = [] for i, jaxpr in enumerate(branches_batched): # Perform a select on the inputs for safety of reverse-mode autodiff; see # https://github.com/google/jax/issues/1052 ops_ = [_bcast_select(lax.eq(index, lax._const(index, i)), x, lax.stop_gradient(x)) if x is not core.unit else x for x in ops] branch_outs.append(core.jaxpr_as_fun(jaxpr)(*ops_)) out = [_select_tree(index, outs) if outs[0] is not core.unit else outs[0] for outs in zip(*branch_outs)] return out, [0] * len(branch_outs[0]) else: ops_bat = [d is not batching.not_mapped for d in op_dims] ops = [batching.moveaxis(x, d, 0) if b else x for b, x, d in zip(ops_bat, ops, op_dims)] branches_out_bat = [ batching.batch_jaxpr(jaxpr, size, ops_bat, False, axis_name, main_type)[1] for jaxpr in branches] out_bat = [any(bat) for bat in zip(*branches_out_bat)] branches_batched = tuple( batching.batch_jaxpr(jaxpr, size, ops_bat, out_bat, axis_name, main_type)[0] for jaxpr in branches) out_dims = [0 if b else batching.not_mapped for b in out_bat] out = cond_p.bind( index, *ops, branches=branches_batched, linear=linear) return out, out_dims def _cond_jvp(primals, tangents, branches, linear): nonzeros = [type(t) is not ad_util.Zero for t in tangents] index_nz, *ops_nz = nonzeros assert index_nz is False branches_out_nz = [ad.jvp_jaxpr(jaxpr, ops_nz, instantiate=False)[1] for jaxpr in branches] out_nz = [any(nz) for nz in zip(*branches_out_nz)] branches_jvp = tuple(ad.jvp_jaxpr(jaxpr, ops_nz, instantiate=out_nz)[0] for jaxpr in branches) index, *ops = primals _, *ops_dot = tangents ops_dot = _prune_zeros(ops_dot) ops_lin = tuple(linear) linear_jvp = ops_lin + (True,) * len(ops_dot) out = cond_p.bind( index, *ops, *ops_dot, branches=branches_jvp, linear=linear_jvp) out_primals, out_tangents = split_list(out, [len(out_nz)]) out_tangents_iter = iter(out_tangents) out_tangents = [next(out_tangents_iter) if nz else ad_util.Zero.from_value(p) for p, nz in zip(out_primals, out_nz)] return out_primals, out_tangents def _cond_partial_eval(trace, *tracers, branches, linear): unknowns = [t.pval[0] is not None for t in tracers] index_uk, *ops_uk = unknowns if index_uk: # When the branch index is unknown, we stage out the whole cond. params = dict(branches=branches, linear=linear) return trace.default_process_primitive(cond_p, tracers, params) branches_out_uks = [] for branch_jaxpr in branches: _, _, out_uks = pe.partial_eval_jaxpr(branch_jaxpr, ops_uk, instantiate=False) branches_out_uks.append(out_uks) out_uks = [any(uks) for uks in zip(*branches_out_uks)] branches_1, branches_2, branch_res_avals = [], [], [] for branch_jaxpr in branches: branch_jaxpr_1, branch_jaxpr_2, _ = pe.partial_eval_jaxpr( branch_jaxpr, ops_uk, instantiate=out_uks) branch_num_res = len(branch_jaxpr_1.out_avals) - len(out_uks) # move residuals to the front move = [False] * len(ops_uk) + [True] * branch_num_res branch_jaxpr_2 = pe.move_binders_to_front(branch_jaxpr_2, move) # TODO(frostig,mattjj): pe.partial_eval_jaxpr should raise to shaped avals res_avals = _map( raise_to_shaped, branch_jaxpr_2.in_avals[:branch_num_res]) branches_1.append(branch_jaxpr_1) branches_2.append(branch_jaxpr_2) branch_res_avals.append(res_avals) branches_1 = tuple(branches_1) branches_2 = tuple(branches_2) for jaxpr in branches_2[1:]: assert len(jaxpr.out_avals) == len(branches_2[0].out_avals) num_outs = len(branches_2[0].out_avals) all_res_avals, res_avals_per_branch = _merge_branch_residuals( branch_res_avals) branches_1 = _join_cond_outputs( branches_1, all_res_avals, res_avals_per_branch, num_outs) branches_2 = _join_cond_pe_staged_jaxpr_inputs( branches_2, all_res_avals, res_avals_per_branch) # TODO(frostig,mattjj): reinstate this assertion once pe.partial_eval_jaxpr # raises to shaped avals # for j in branches_1[1:]: # assert j.out_avals == branches_1[0].out_avals num_res = len(all_res_avals) _, in_consts = unzip2([t.pval for t in tracers]) out_consts_res = cond_p.bind(*in_consts, branches=branches_1, linear=linear) out_consts, res = split_list(out_consts_res, [len(out_consts_res) - num_res]) # TODO(frostig,mattjj): remove raised_to_shaped of avals once # pe.partial_eval_jaxpr handles it out_avals = _map(raise_to_shaped, branches_2[0].out_avals) out_pvs = [aval if uk else None for aval, uk in zip(out_avals, out_uks)] index_tracer = trace.instantiate_const(tracers[0]) ops_tracers = [trace.instantiate_const(t) if uk else trace.new_instantiated_literal(core.unit) for uk, t in zip(unknowns[1:], tracers[1:])] res_tracers = _map(trace.new_instantiated_const, res) out_tracers = [pe.JaxprTracer(trace, pe.PartialVal((pv, const)), None) for pv, const in zip(out_pvs, out_consts)] linear_2 = (False,) * num_res + linear params = dict(branches=branches_2, linear=linear_2) eqn = pe.new_eqn_recipe( [index_tracer] + res_tracers + ops_tracers, out_tracers, cond_p, params, source_info_util.current()) for t in out_tracers: t.recipe = eqn return out_tracers # When partially evaluating conditionals, each branch produces residuals # depending on the computation carried out by the branch, and a corresponding # staged jaxpr that accepts those residuals as its first few inputs. The # residual-producing branches are staged as jaxprs and bound right away in a # conditional. The residual-consuming jaxprs are assembled together in a jaxpr # conditional. The following helper functions ensure that both collections of # jaxprs (those evaluated and those staged) are valid for joint use under their # respective conditionals. # # In particular, the residuals derived from each original branch may have # distinct types. Because the branches of conditionals must have identical type # signatures, we join residuals together across branches into a common format. # In order to set up a type signature that all branches can conform to, it would # suffice to concatenate all branches' residuals. But concatenation can result # in redundant inputs and outputs, and might lead to memory allocation that # scales unnecessarily with the branch count. This function finds common # residual types across branches for reuse, so as to avoid redundant # allocation. It returns a list L of types (avals) representing the collection # of residuals merged according to type, and, for each branch, a lookup table to # match its residuals to their positions/types in L. Example input/output: # # [x], [y], [x, x] -> [x, y, x], [[0], [1], [0, 2]] # [x], [x], [x, x] -> [x, x], [[0], [0], [0, 1]] # [y, x, x], [x, z, y], [z, x] -> [y, x, x, z], [[0, 1, 2], [1, 3, 0], [3, 1]] def _merge_branch_residuals(branch_res_avals): def enumerate_equal(xs): counts = {v: itertools.count() for v in set(xs)} return [(x, next(counts[x])) for x in xs] branch_res_tagged_avals = _map(enumerate_equal, branch_res_avals) all_tagged_avals = _ordered_unique(util.concatenate(branch_res_tagged_avals)) indices = {v: i for i, v in enumerate(all_tagged_avals)} branch_indices = [ [indices[aval] for aval in avals] for avals in branch_res_tagged_avals] all_avals = [x for x, _ in all_tagged_avals] return all_avals, branch_indices # This function augments branch outputs to agree with the merged residual # format: each branch is made to return zero-filled values in the places of # residual outputs that it does not populate. def _join_cond_outputs(jaxprs, all_res_avals, res_aval_indices_per_jaxpr, num_non_res_outputs): def augment_jaxpr(jaxpr, res_indices): @lu.wrap_init def f_aug(*args): outs_and_residuals = core.jaxpr_as_fun(jaxpr)(*args) outs, residuals = split_list(outs_and_residuals, [num_non_res_outputs]) aug_residuals = _map(ad_util.zeros_like_aval, all_res_avals) aug_residuals = util.subvals(aug_residuals, zip(res_indices, residuals)) return outs + list(aug_residuals) return _make_closed_jaxpr(f_aug, jaxpr.in_avals) return tuple(_map(augment_jaxpr, jaxprs, res_aval_indices_per_jaxpr)) # This function augments branch inputs to agree with the merged residual format: # each branch is made to accept all residuals, even though it will ignore those # that it does not read. def _join_cond_pe_staged_jaxpr_inputs(jaxprs, all_res_avals, res_aval_indices_per_jaxpr): newvar = core.gensym([j.jaxpr for j in jaxprs], suffix='_') all_res_vars = _map(newvar, all_res_avals) def augment_jaxpr(jaxpr, res_indices): num_res = len(res_indices) res_vars = jaxpr.jaxpr.invars[:num_res] non_res_vars = jaxpr.jaxpr.invars[num_res:] aug_res_vars = list(util.subvals(all_res_vars, zip(res_indices, res_vars))) aug_invars = aug_res_vars + non_res_vars jaxpr_aug = core.Jaxpr(jaxpr.jaxpr.constvars, aug_invars, jaxpr.jaxpr.outvars, jaxpr.jaxpr.eqns) jaxpr_aug = core.ClosedJaxpr(jaxpr_aug, jaxpr.consts) return jaxpr_aug return tuple(_map(augment_jaxpr, jaxprs, res_aval_indices_per_jaxpr)) def _ordered_unique(xs): d = collections.OrderedDict((x, None) for x in xs) return list(d.keys()) def _transpose_cond_jaxpr(jaxpr, num_res): res_avals, primal_avals = split_list(jaxpr.in_avals, [num_res]) primal_avals = _map(raise_to_shaped, primal_avals) @lu.wrap_init def transposed(*args): res, cts_out = split_list(args, [num_res]) primals = res + [ad.UndefinedPrimal(aval) for aval in primal_avals] cts_in = ad.backward_pass( jaxpr.jaxpr, jaxpr.consts, primals, cts_out) _, cts_in = split_list(cts_in, [num_res]) return _map(ad.instantiate_zeros_aval, primal_avals, cts_in) return _make_closed_jaxpr(transposed, res_avals + jaxpr.out_avals) def _cond_transpose(cts, *args, branches, linear): index, *ops = args in_avals = _map(raise_to_shaped, branches[0].in_avals) num_res = len(ops) - sum(linear) branches_trans = tuple( _transpose_cond_jaxpr(jaxpr, num_res) for jaxpr in branches) lin_in_avals = [raise_to_shaped(a, weak_type=False) for a, l in zip(in_avals, linear) if l] assert all(core.typematch(out_aval, lin_in_aval) for jaxpr in branches_trans for out_aval, lin_in_aval in zip(jaxpr.out_avals, lin_in_avals)) res = ops[:num_res] cts = _map(ad.instantiate_zeros_aval, branches[0].out_avals, cts) linear_trans = (False,) * num_res + (True,) * len(cts) out = cond_p.bind( index, *res, *cts, branches=branches_trans, linear=linear_trans) assert all(_map(core.typecheck, lin_in_avals, out)) out_iter = iter(out) out = [next(out_iter) if l else None for l in linear] assert next(out_iter, None) is None return [None] + out def _avals_short(avals): to_str = lambda aval: getattr(aval, 'str_short', partial(str, aval))() return ' '.join(_map(to_str, avals)) def _cond_typecheck(*avals, branches, linear): tc = partial(_typecheck_param, 'cond') tc(branches, 'branches', 'tuple of ClosedJaxpr', type(branches) is tuple and all(type(x) is core.ClosedJaxpr for x in branches)) tc(linear, 'linear', 'tuple of bool', type(linear) is tuple and all(type(x) is bool for x in linear)) core.typecheck_assert( len(branches) > 0, 'cond requires at least one branch function') core.typecheck_assert( len(linear) + 1 == len(avals), f'cond given {len(linear)} linear flags for ' f'{len(avals) - 1} non-predicate operands') jaxpr0 = branches[0] jaxpr0_in_avals_str = _avals_short(jaxpr0.in_avals) jaxpr0_out_avals_str = _avals_short(jaxpr0.out_avals) for i, jaxpr in enumerate(branches[1:]): core.typecheck_assert( len(jaxpr0.in_avals) == len(jaxpr.in_avals), f'cond branch 0 takes {len(jaxpr0.in_avals)} inputs, ' f'branch {i+1} takes {len(jaxpr.in_avals)}') core.typecheck_assert( len(jaxpr0.out_avals) == len(jaxpr.out_avals), f'cond branch 0 outputs {len(jaxpr0.out_avals)} values, ' f'branch {i+1} outputs {len(jaxpr.out_avals)}') core.typecheck_assert( all(_map(core.typematch, jaxpr0.in_avals, jaxpr.in_avals)), f'cond branches 0 and {i+1} have mismatching input types: ' f'{jaxpr0_in_avals_str} vs {_avals_short(jaxpr.in_avals)}') core.typecheck_assert( all(_map(core.typematch, jaxpr0.out_avals, jaxpr.out_avals)), f'cond branches 0 and {i+1} have mismatching output types: ' f'{jaxpr0_out_avals_str} vs {_avals_short(jaxpr.out_avals)}') core.typecheck_assert( len(avals) == 1 + len(jaxpr0.in_avals), f'cond called with {len(avals) - 1} non-predicate operands, ' f'but branches take {len(jaxpr0.in_avals)} inputs') index_aval, *op_avals = avals core.typecheck_assert( index_aval.dtype == np.int32, f'cond called with index of type {index_aval.dtype} instead of int32') core.typecheck_assert( all(_map(core.typecompat, jaxpr0.in_avals, op_avals)), f'cond branches take input types {jaxpr0_in_avals_str}, ' f'called with operands of type {_avals_short(op_avals)}') def cond_bind(*args, branches, linear): if config.jax_enable_checks: avals = _map(core.get_aval, args) _cond_typecheck(*avals, branches=branches, linear=linear) for jaxpr in branches: core.check_jaxpr(jaxpr.jaxpr) return core.Primitive.bind(cond_p, *args, branches=branches, linear=linear) cond_p = lax.Primitive('cond') cond_p.multiple_results = True cond_p.def_impl(partial(xla.apply_primitive, cond_p)) cond_p.def_abstract_eval(_cond_abstract_eval) cond_p.def_custom_bind(cond_bind) ad.primitive_jvps[cond_p] = _cond_jvp ad.primitive_transposes[cond_p] = _cond_transpose pe.custom_partial_eval_rules[cond_p] = _cond_partial_eval batching.initial_style_batchers[cond_p] = _cond_batching_rule xla.initial_style_translations[cond_p] = _cond_translation_rule core.custom_typechecks[cond_p] = _cond_typecheck ### scan Carry = TypeVar('Carry') X = TypeVar('X') Y = TypeVar('Y') @api_boundary def scan(f: Callable[[Carry, X], Tuple[Carry, Y]], init: Carry, xs: X, length: Optional[int] = None, reverse: bool = False, unroll: int = 1) -> Tuple[Carry, Y]: """Scan a function over leading array axes while carrying along state. The type signature in brief is .. code-block:: haskell scan :: (c -> a -> (c, b)) -> c -> [a] -> (c, [b]) where we use [t] here to denote the type t with an additional leading axis. That is, if t is an array type then [t] represents the type with an additional leading axis, and if t is a pytree (container) type with array leaves then [t] represents the type with the same pytree structure and corresponding leaves each with an additional leading axis. When ``a`` is an array type or None, and ``b`` is an array type, the semantics of ``scan`` are given roughly by this Python implementation:: def scan(f, init, xs, length=None): if xs is None: xs = [None] * length carry = init ys = [] for x in xs: carry, y = f(carry, x) ys.append(y) return carry, np.stack(ys) Unlike that Python version, both ``a`` and ``b`` may be arbitrary pytree types, and so multiple arrays can be scanned over at once and produce multiple output arrays. (None is actually an empty pytree.) Also unlike that Python version, ``scan`` is a JAX primitive and is lowered to a single XLA While HLO. That makes it useful for reducing compilation times for jit-compiled functions, since native Python loop constructs in an ``@jit`` function are unrolled, leading to large XLA computations. Finally, the loop-carried value ``carry`` must hold a fixed shape and dtype across all iterations (and not just be consistent up to NumPy rank/shape broadcasting and dtype promotion rules, for example). In other words, the type ``c`` in the type signature above represents an array with a fixed shape and dtype (or a nested tuple/list/dict container data structure with a fixed structure and arrays with fixed shape and dtype at the leaves). Args: f: a Python function to be scanned of type ``c -> a -> (c, b)``, meaning that ``f`` accepts two arguments where the first is a value of the loop carry and the second is a slice of ``xs`` along its leading axis, and that ``f`` returns a pair where the first element represents a new value for the loop carry and the second represents a slice of the output. init: an initial loop carry value of type ``c``, which can be a scalar, array, or any pytree (nested Python tuple/list/dict) thereof, representing the initial loop carry value. This value must have the same structure as the first element of the pair returned by ``f``. xs: the value of type ``[a]`` over which to scan along the leading axis, where ``[a]`` can be an array or any pytree (nested Python tuple/list/dict) thereof with consistent leading axis sizes. length: optional integer specifying the number of loop iterations, which must agree with the sizes of leading axes of the arrays in ``xs`` (but can be used to perform scans where no input ``xs`` are needed). reverse: optional boolean specifying whether to run the scan iteration forward (the default) or in reverse, equivalent to reversing the leading axes of the arrays in both ``xs`` and in ``ys``. unroll: optional positive int specifying, in the underlying operation of the scan primitive, how many scan iterations to unroll within a single iteration of a loop. Returns: A pair of type ``(c, [b])`` where the first element represents the final loop carry value and the second element represents the stacked outputs of the second output of ``f`` when scanned over the leading axis of the inputs. """ xs_flat, xs_tree = tree_flatten(xs) try: lengths = [x.shape[0] for x in xs_flat] except AttributeError as err: msg = "scan got value with no leading axis to scan over: {}." raise ValueError( msg.format(', '.join(str(x) for x in xs_flat if not hasattr(x, 'shape')))) from err if length is not None: length = int(length) if not all(length == l for l in lengths): msg = ("scan got `length` argument of {} which disagrees with " "leading axis sizes {}.") raise ValueError(msg.format(length, [x.shape[0] for x in xs_flat])) else: unique_lengths = set(lengths) if len(unique_lengths) > 1: msg = "scan got values with different leading axis sizes: {}." raise ValueError(msg.format(', '.join(str(x.shape[0]) for x in xs_flat))) elif len(unique_lengths) == 0: msg = "scan got no values to scan over and `length` not provided." raise ValueError(msg) else: length, = unique_lengths if config.jax_disable_jit: carry = init ys = [] maybe_reversed = reversed if reverse else lambda x: x for i in maybe_reversed(range(length)): xs_slice = [_index_array(i, core.get_aval(x), x) for x in xs_flat] carry, y = f(carry, tree_unflatten(xs_tree, xs_slice)) ys.append(y) stack = lambda y, *ys: (y if core.get_aval(y) is core.abstract_unit else jax.numpy.stack((y, *ys))) stacked_y = tree_multimap(stack, *maybe_reversed(ys)) return carry, stacked_y x_shapes = [masking.padded_shape_as_value(x.shape[1:]) for x in xs_flat] x_dtypes = [x.dtype for x in xs_flat] x_avals = tuple(_map(ShapedArray, x_shapes, x_dtypes)) def _create_jaxpr(init): init_flat, init_tree = tree_flatten(init) in_flat, in_tree = tree_flatten((init, xs)) carry_avals = tuple(_map(_abstractify, init_flat)) jaxpr, consts, out_tree = _initial_style_jaxpr( f, in_tree, carry_avals + x_avals, "scan") out_tree_children = out_tree.children() if len(out_tree_children) != 2: msg = "scan body output must be a pair, got {}." raise TypeError(msg.format(tree_unflatten(out_tree, jaxpr.out_avals))) carry_avals_out = jaxpr.out_avals[:out_tree_children[0].num_leaves] return init_flat, carry_avals, carry_avals_out, init_tree, in_flat, jaxpr, consts, out_tree, out_tree_children # The carry input and output avals must match exactly. However, we want to account for # the case when init contains weakly-typed values (e.g. Python scalars), with avals that # may not match the output despite being compatible by virtue of their weak type. # To do this, we compute the jaxpr in two passes: first with the raw inputs, and if # necessary, a second time with modified init values. init_flat, carry_avals, carry_avals_out, init_tree, *rest = _create_jaxpr(init) new_init_flat, changed = _promote_weak_typed_inputs(init_flat, carry_avals, carry_avals_out) if changed: new_init = tree_unflatten(init_tree, new_init_flat) init_flat, carry_avals, carry_avals_out, init_tree, *rest = _create_jaxpr(new_init) in_flat, jaxpr, consts, out_tree, out_tree_children = rest _check_tree_and_avals("scan carry output and input", # Extract the subtree and avals for the first element of the return tuple out_tree_children[0], carry_avals_out, init_tree, carry_avals) out = scan_p.bind(*itertools.chain(consts, in_flat), reverse=reverse, length=length, jaxpr=jaxpr, num_consts=len(consts), num_carry=len(init_flat), linear=(False,) * (len(consts) + len(in_flat)), unroll=unroll) return tree_unflatten(out_tree, out) def _scan_impl_unrolled(*args, reverse, length, num_consts, num_carry, linear, f_impl, x_avals, y_avals): consts, init, xs = split_list(args, [num_consts, num_carry]) carry = init ys = [] for i in range(length): i_ = length - i - 1 if reverse else i x = _map(partial(_index_array, i_), x_avals, xs) out = f_impl(*consts, *carry, *x) carry, y = split_list(out, [num_carry]) ys.append(y) ys = list(reversed(ys)) if reverse else ys ys = list(zip(*ys)) ys = _map(_stack, y_avals, ys) return (*carry, *ys) def _scan_impl_loop(*args, reverse, length, num_consts, num_carry, linear, f_impl, x_avals, y_avals): consts, init, xs = split_list(args, [num_consts, num_carry]) def cond_fun(vals): i, *_ = vals return i < length def body_fun(vals): [i], carry, ys = split_list(vals, [1, num_carry]) i_ = length - i - 1 if reverse else i x = _map(partial(_dynamic_index_array, i_), x_avals, xs) out_flat = f_impl(*consts, *carry, *x) carry_out, y_updates = split_list(out_flat, [num_carry]) ys_out = _map(partial(_update_array, i_), y_avals, ys, y_updates) return [i + 1] + carry_out + ys_out ys_init = _map(partial(_empty_array, length), y_avals) if length == 0: return init + ys_init else: init_val = [lax._const(length, 0)] + init + ys_init _, *outs = while_loop(cond_fun, body_fun, init_val) return outs def _scan_impl_block_unrolled(*args, reverse, length, num_consts, num_carry, linear, block_length, f_impl, x_avals, y_avals): consts, init, xs = split_list(args, [num_consts, num_carry]) num_blocks, rem = divmod(length, block_length) assert rem == 0 partition = partial(_partition_leading, num_blocks, block_length) xs_block = _map(partition, x_avals, xs) prepend_aval = partial(_prepend_dim_to_aval, block_length) x_block_avals = _map(prepend_aval, x_avals) y_block_avals = _map(prepend_aval, y_avals) f_impl_block = partial( _scan_impl_unrolled, reverse=reverse, length=block_length, num_consts=num_consts, num_carry=num_carry, linear=linear, f_impl=f_impl, x_avals=x_avals, y_avals=y_avals) outs = _scan_impl_loop( *consts, *init, *xs_block, reverse=reverse, length=num_blocks, num_consts=num_consts, num_carry=num_carry, linear=linear, f_impl=f_impl_block, x_avals=x_block_avals, y_avals=y_block_avals) carry, ys_blocks = split_list(outs, [num_carry]) combine = partial(_combine_leading, num_blocks, block_length) ys = _map(combine, y_avals, ys_blocks) return (*carry, *ys) def _scan_impl(*args, reverse, length, num_consts, num_carry, jaxpr, linear, unroll): _, _, x_avals = split_list(jaxpr.in_avals, [num_consts, num_carry]) _, y_avals = split_list(jaxpr.out_avals, [num_carry]) f_impl = core.jaxpr_as_fun(jaxpr) if unroll == 1: return _scan_impl_loop( *args, reverse=reverse, length=length, num_consts=num_consts, num_carry=num_carry, linear=linear, f_impl=f_impl, x_avals=x_avals, y_avals=y_avals) consts, init, xs = split_list(args, [num_consts, num_carry]) num_blocks, rem = divmod(length, unroll) length_div = num_blocks * unroll if rem > 0: if reverse: split = partial(_split_leading_dim, rem) xs_rem, xs = unzip2(_map(split, x_avals, xs)) else: split = partial(_split_leading_dim, length_div) xs, xs_rem = unzip2(_map(split, x_avals, xs)) outs = _scan_impl_block_unrolled( *consts, *init, *xs, reverse=reverse, length=length_div, num_consts=num_consts, num_carry=num_carry, linear=linear, block_length=unroll, f_impl=f_impl, x_avals=x_avals, y_avals=y_avals) carry, ys = split_list(outs, [num_carry]) if rem > 0: outs = _scan_impl_unrolled( *consts, *carry, *xs_rem, reverse=reverse, length=rem, num_consts=num_consts, num_carry=num_carry, linear=linear, f_impl=f_impl, x_avals=x_avals, y_avals=y_avals) carry, ys_rem = split_list(outs, [num_carry]) if reverse: ys = _map(_concatenate, y_avals, ys_rem, ys) else: ys = _map(_concatenate, y_avals, ys, ys_rem) return (*carry, *ys) def _stack(aval, vals): if aval is core.abstract_unit: return core.unit else: vals = [lax.expand_dims(x, (0,)) for x in vals] return lax.concatenate(vals, 0) def _concatenate(aval, x1, x2): if aval is core.abstract_unit: return core.unit else: return lax.concatenate([x1, x2], 0) def _split_leading_dim(i, aval, x): if aval is core.abstract_unit: return (core.unit, core.unit) else: assert x.ndim >= 1 return (lax.slice_in_dim(x, 0, i), lax.slice_in_dim(x, i, x.shape[0])) def _dynamic_index_array(i, aval, x): if aval is core.abstract_unit: return core.unit else: return lax.dynamic_index_in_dim(x, i, keepdims=False) def _index_array(i, aval, x): if aval is core.abstract_unit: return core.unit else: return lax.index_in_dim(x, i, keepdims=False) def _empty_array(sz, aval): if aval is core.abstract_unit: return core.unit else: return lax.full((sz,) + aval.shape, 0, aval.dtype) def _update_array(i, aval, xs, x): if aval is core.abstract_unit: return core.unit else: return lax.dynamic_update_index_in_dim(xs, x, i, 0) def _partition_leading(sz0, sz1, aval, x): if aval is core.abstract_unit: return core.unit else: assert x.ndim >= 1 assert x.shape[0] == sz0 * sz1 return lax.reshape(x, (sz0, sz1, *x.shape[1:])) def _combine_leading(sz0, sz1, aval, x): if aval is core.abstract_unit: return core.unit else: assert x.ndim >= 2 assert x.shape[0] == sz0 assert x.shape[1] == sz1 return lax.collapse(x, 0, 2) def _prepend_dim_to_aval(sz, aval): if aval is core.abstract_unit: return aval elif isinstance(aval, ShapedArray): return aval.update(shape=(sz, *aval.shape), weak_type=False) else: raise TypeError(f'Prepending dim {sz} to aval {aval}') def _scan_abstract_eval(*args, reverse, length, num_consts, num_carry, jaxpr, linear, unroll): carry_avals, y_avals = split_list(jaxpr.out_avals, [num_carry]) ys_avals = _map(partial(_prepend_dim_to_aval, length), y_avals) return carry_avals + ys_avals def _scan_jvp(primals, tangents, reverse, length, jaxpr, num_consts, num_carry, linear, unroll): num_xs = len(jaxpr.in_avals) - num_carry - num_consts num_ys = len(jaxpr.out_avals) - num_carry nonzeros = [type(t) is not ad_util.Zero for t in tangents] const_nz, init_nz, xs_nz = split_list(nonzeros, [num_consts, num_carry]) # Fixpoint computation of which carry are not ad.zero: either # non-zero from init, or the carry out is non-zero. Each iteration promotes # at least one carry to non-zero. We need at most len(carry) iterations, # but we need one last iteration to prepare the jaxpr based on the final # carry_nz. carry_nz = init_nz for _ in range(1 + len(carry_nz)): nonzeros = const_nz + carry_nz + xs_nz jaxpr_jvp, nonzeros_out = ad.jvp_jaxpr( jaxpr, nonzeros, instantiate=carry_nz + [False] * num_ys) carry_nz_out, _ = nonzeros_out[:num_carry], nonzeros_out[num_carry:] if carry_nz_out == carry_nz: break else: carry_nz = _map(operator.or_, carry_nz, carry_nz_out) else: assert False, "Fixpoint not reached" tangents = [ad.instantiate_zeros(t) if nz else t for t, nz in zip(tangents, nonzeros)] consts, init, xs = split_list(primals, [num_consts, num_carry]) all_tangents = split_list(tangents, [num_consts, num_carry]) consts_dot, init_dot, xs_dot = _map(_prune_zeros, all_tangents) jaxpr_jvp_rearranged = ad.rearrange_binders( jaxpr_jvp, [num_consts, num_carry, num_xs], [len(consts_dot), len(init_dot), len(xs_dot)], [num_carry, num_ys], [len(init_dot), sum(nonzeros_out) - len(init_dot)]) consts_linear, init_linear, xs_linear = split_list(linear, [num_consts, num_carry]) jaxpr_jvp_linear = tuple(consts_linear + [True] * len(consts_dot) + init_linear + [True] * len(init_dot) + xs_linear + [True] * len(xs_dot)) out_flat = scan_p.bind( *(consts + consts_dot + init + init_dot + xs + xs_dot), reverse=reverse, length=length, jaxpr=jaxpr_jvp_rearranged, num_consts=num_consts + len(consts_dot), num_carry=num_carry + len(init_dot), linear=jaxpr_jvp_linear, unroll=unroll) carry, carry_dot, ys, ys_dot = split_list(out_flat, [num_carry, len(init_dot), num_ys]) primals_out = carry + ys tangents_out_iter = iter(carry_dot + ys_dot) tangents_out = [next(tangents_out_iter) if nz else ad_util.Zero.from_value(p) for p, nz in zip(primals_out, nonzeros_out)] return primals_out, tangents_out def _prune_zeros(ts): return [t for t in ts if type(t) is not ad_util.Zero] def _scan_partial_eval(trace, *tracers, reverse, length, num_consts, num_carry, jaxpr, linear, unroll): num_ys = len(jaxpr.out_avals) - num_carry unknowns = [t.pval[0] is not None for t in tracers] const_uk, init_uk, xs_uk = split_list(unknowns, [num_consts, num_carry]) # Fixpoint computation of which carry are unknown (not a constant): either # unknown from init, or the carry out is unknown. Each iteration promotes # at least one carry to unknown. We need at most len(carry) iterations, # but we need one last iteration to prepare the jaxpr based on the final # carry_uk. carry_uk = init_uk for _ in range(1 + len(carry_uk)): unknowns = const_uk + carry_uk + xs_uk jaxpr_1, jaxpr_2, out_uk = pe.partial_eval_jaxpr( jaxpr, unknowns, instantiate=carry_uk + [False] * num_ys) carry_uk_out = out_uk[:num_carry] if carry_uk_out == carry_uk: break else: carry_uk = _map(operator.or_, carry_uk, carry_uk_out) else: assert False, "Fixpoint not reached" num_res = len(jaxpr_1.out_avals) - len(jaxpr_2.out_avals) # The residuals are treated as extensive outputs of jaxpr_1 (and extensive # inputs to jaxpr_2), but residuals that are loop-invariant can be hoisted. # TODO(mattjj): hoist other loop-invariant values here too (instantiate=False) invariant_pvals = [pe.PartialVal.known(core.unit if uk else t.pval[1]) for uk, t in zip(unknowns[:num_consts], tracers[:num_consts])] other_pvals = [pe.PartialVal.unknown(a) for a in jaxpr_1.in_avals[num_consts:]] in_pvals_1 = invariant_pvals + other_pvals jaxpr_1_opt, out_pvals_1, consts_1 = pe.trace_to_jaxpr( lu.wrap_init(core.jaxpr_as_fun(jaxpr_1)), in_pvals_1, instantiate=[True] * (num_carry + num_ys) + [False] * num_res) jaxpr_1_opt = pe.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr_1_opt), ()) num_consts_1 = num_consts + len(consts_1) # any now-known residuals are intensive, so we want to revise jaxpr_2 to take # those inputs as constants rather than as extensive inputs _, _, res_pvals = split_list(out_pvals_1, [num_carry, num_ys]) intensive_residuals = [const for pv, const in res_pvals if pv is None] move = [False] * len(jaxpr_1.in_avals) + [pv is None for pv, _ in res_pvals] jaxpr_2_opt = pe.move_binders_to_front(jaxpr_2, move) num_consts_2 = num_consts + len(intensive_residuals) # As another optimization, for any extensive inputs that are just forwarded to # extensive outputs, to avoid a copy (looping over dynamic-update-slice) we'd # rather just forward the input tracer. That means pruning some extensive # outputs from the jaxpr here, and updating out_flat below. extensive_invars = jaxpr_1_opt.jaxpr.invars[num_consts_1 + num_carry:] extensive_outvars = jaxpr_1_opt.jaxpr.outvars[num_carry:] extensive_avals = [core.unmapped_aval(length, 0, core.raise_to_shaped(v.aval)) for v in extensive_outvars] fwd_extensive = [num_consts + num_carry + extensive_invars.index(v) if v in extensive_invars else None for v in extensive_outvars] jaxpr_1_opt.jaxpr.outvars = ( jaxpr_1_opt.jaxpr.outvars[:num_carry] + [v for i, v in zip(fwd_extensive, extensive_outvars) if i is None]) in_consts = (list(consts_1) + [core.unit] * num_consts + [core.unit if uk else t.pval[1] for uk, t in zip(unknowns[num_consts:], tracers[num_consts:])]) linear_1 = ([False] * len(consts_1) + [True] * num_consts + [lin or uk for uk, lin in zip(unknowns[num_consts:], linear[num_consts:])]) out_flat = scan_p.bind( *in_consts, reverse=reverse, length=length, jaxpr=jaxpr_1_opt, num_consts=num_consts_1, num_carry=num_carry, linear=tuple(linear_1), unroll=unroll) # Propagate the forwarded extensive outputs using fwd_extensive. Any # numpy.ndarray inputs should be converted to JAX DeviceArrays. out_carry, out_extensive = split_list(out_flat, [num_carry]) out_extensive_iter = iter(out_extensive) out_extensive = [next(out_extensive_iter) if i is None else _maybe_device_put(tracers[i].pval[1]) if tracers[i].is_known() else tracers[i] for i in fwd_extensive] assert all(a.strip_named_shape() == core.raise_to_shaped( core.get_aval(out)).strip_named_shape() for a, out in zip(extensive_avals, out_extensive)) out_flat = out_carry + out_extensive out_carry, ys, res_and_units = split_list(out_flat, [num_carry, num_ys]) extensive_residuals = [r for r, (pv, _) in zip(res_and_units, res_pvals) if pv is not None] new_tracers = [trace.instantiate_const(t) if uk else trace.new_instantiated_literal(core.unit) for uk, t in zip(unknowns, tracers)] carry_avals, y_avals = split_list(jaxpr.out_avals, [num_carry]) ys_avals = _map(partial(_prepend_dim_to_aval, length), y_avals) out_avals = carry_avals + ys_avals out_pvs = [aval if uk else None for aval, uk in zip(out_avals, out_uk)] out_consts = out_carry + ys int_res_tracers = _map(trace.new_instantiated_const, intensive_residuals) ext_res_tracers = _map(trace.new_instantiated_const, extensive_residuals) out_tracers = [pe.JaxprTracer(trace, pe.PartialVal((pv, const)), None) for pv, const in zip(out_pvs, out_consts)] linear_2 = ([False] * len(int_res_tracers) + [lin or not uk for uk, lin in zip(unknowns, linear)] + [False] * len(ext_res_tracers)) eqn = pe.new_eqn_recipe(int_res_tracers + new_tracers + ext_res_tracers, out_tracers, scan_p, dict(reverse=reverse, length=length, jaxpr=jaxpr_2_opt, num_consts=num_consts_2, num_carry=num_carry, linear=tuple(linear_2), unroll=unroll), source_info_util.current()) for t in out_tracers: t.recipe = eqn return out_tracers def _maybe_device_put(x): if isinstance(x, np.ndarray): return lax._device_put_raw(x) else: return x def _scan_transpose(cts, *args, reverse, length, num_consts, num_carry, jaxpr, linear, unroll): # we've only implemented transposing scans with specific lin/nonlin patterns consts_lin, init_lin, xs_lin = split_list(linear, [num_consts, num_carry]) num_ires = len(consts_lin) - sum(consts_lin) num_eres = len(xs_lin) - sum(xs_lin) if consts_lin != [False] * num_ires + [True] * (len(consts_lin) - num_ires): raise NotImplementedError if xs_lin != [True] * (len(xs_lin) - num_eres) + [False] * num_eres: raise NotImplementedError if not all(init_lin): pass # TODO(mattjj): error check https://github.com/google/jax/issues/1963 consts, _, xs = split_list(args, [num_consts, num_carry]) ires, _ = split_list(consts, [num_ires]) _, eres = split_list(xs, [sum(xs_lin)]) assert not any(ad.is_undefined_primal(r) for r in ires) assert not any(ad.is_undefined_primal(r) for r in eres) carry_avals, y_avals = split_list(jaxpr.out_avals, [num_carry]) ys_avals = _map(partial(_prepend_dim_to_aval, length), y_avals) ct_carry, ct_ys = split_list(cts, [num_carry]) ct_carry = _map(ad.instantiate_zeros_aval, carry_avals, ct_carry) ct_ys = _map(ad.instantiate_zeros_aval, ys_avals, ct_ys) ct_consts = _map(ad_util.zeros_like_aval, jaxpr.in_avals[num_ires:num_consts]) # jaxpr :: [ires, T d] -> [T c] -> [T a, eres] -> ([T c], [T b]) # jaxpr_trans :: [ires] -> [CT d, CT c] -> [CT b, eres] -> ([CT d, CT c], [CT a]) jaxpr_trans = _transpose_scan_jaxpr( num_ires, num_consts - num_ires, num_eres, jaxpr) linear_trans = ([False] * num_ires + [True] * (len(ct_consts) + len(ct_carry) + len(ct_ys)) + [False] * num_eres) outs = scan_p.bind( *(ires + ct_consts + ct_carry + ct_ys + eres), reverse=not reverse, length=length, jaxpr=jaxpr_trans, num_consts=num_ires, num_carry=num_consts-num_ires+num_carry, linear=tuple(linear_trans), unroll=unroll) ct_consts, ct_init, ct_xs = split_list(outs, [num_consts - num_ires, num_carry]) return [None] * num_ires + ct_consts + ct_init + ct_xs + [None] * num_eres # transpose_scan_jaxpr :: ([res1, c, a, res2] -> b) # -> ([res1, CT c, CT b, res2] -> [CT c, CT a]) def _transpose_scan_jaxpr(num_res1, num_c, num_res2, jaxpr): num_a = len(jaxpr.in_avals) - num_res1 - num_c - num_res2 res1_avals, c_avals, a_avals, res2_avals = split_list( jaxpr.in_avals, [num_res1, num_c, num_a]) num_b = len(jaxpr.out_avals) b_avals = list(jaxpr.out_avals) @lu.wrap_init def transposed(*res1_cbar_bbar_res2): res1, c_bar, b_bar, res2 = split_list( res1_cbar_bbar_res2, [num_res1, num_c, num_b]) primals = (res1 + [ad.UndefinedPrimal(aval) for aval in c_avals] + [ad.UndefinedPrimal(aval) for aval in a_avals] + res2) cbar_abar = ad.backward_pass(jaxpr.jaxpr, jaxpr.consts, primals, b_bar) _, new_c_bar, a_bar, _ = split_list(cbar_abar, [num_res1, num_c, num_a]) a_bar = _map(ad.instantiate_zeros_aval, a_avals, a_bar) c_bar = _map(ad.instantiate_zeros_aval, c_avals, _map(ad.add_tangents, c_bar, new_c_bar)) return c_bar + a_bar return _make_closed_jaxpr(transposed, res1_avals + c_avals + b_avals + res2_avals) def _make_closed_jaxpr(traceable: lu.WrappedFun, in_avals: Sequence[core.AbstractValue]): jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(traceable, in_avals) return core.ClosedJaxpr(jaxpr, consts) def _scan_batching_rule(args, dims, axis_name, main_type, reverse, length, jaxpr, num_consts, num_carry, linear, unroll): num_ys = len(jaxpr.out_avals) - num_carry size, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped} orig_batched = [d is not batching.not_mapped for d in dims] const_batched, init_batched, xs_batched = split_list(orig_batched, [num_consts, num_carry]) # Fixpoint computation of which carry are batched: either # batched from init, or the carry out is batched. Each iteration promotes # at least one carry to batched. We need at most len(carry) iterations, # but we need one last iteration to prepare the jaxpr based on the final # carry_batched. carry_batched = init_batched for _ in range(1 + len(carry_batched)): batched = const_batched + carry_batched + xs_batched jaxpr_batched, batched_out = batching.batch_jaxpr( jaxpr, size, batched, instantiate=carry_batched + [False] * num_ys, axis_name=axis_name, main_type=main_type) carry_batched_out, ys_batched = batched_out[:num_carry], batched_out[num_carry:] if carry_batched_out == carry_batched: break else: carry_batched = _map(operator.or_, carry_batched, carry_batched_out) else: assert False, "Fixpoint not reached" consts, init, xs = split_list(args, [num_consts, num_carry]) consts_bdims, init_bdims, xs_bdims = split_list(dims, [num_consts, num_carry]) new_consts = [batching.moveaxis(x, d, 0) if d is not batching.not_mapped and d != 0 else x for x, d in zip(consts, consts_bdims)] new_init = [batching.broadcast(x, size, 0) if now_batched and not was_batched else batching.moveaxis(x, d, 0) if now_batched else x for x, d, was_batched, now_batched in zip(init, init_bdims, init_batched, carry_batched)] new_xs = [batching.moveaxis(x, d, 1) if d is not batching.not_mapped and d != 1 else x for x, d in zip(xs, xs_bdims)] new_args = new_consts + new_init + new_xs outs = scan_p.bind( *new_args, reverse=reverse, length=length, jaxpr=jaxpr_batched, num_consts=num_consts, num_carry=num_carry, linear=linear, unroll=unroll) carry_bdims = [0 if b else batching.not_mapped for b in carry_batched] ys_bdims = [1 if b else batching.not_mapped for b in ys_batched] return outs, carry_bdims + ys_bdims def _scan_masking_rule(padded_vals, logical_shapes, reverse, length, jaxpr, num_consts, num_carry, linear, unroll): dynamic_length, = masking.shape_as_value((length,)) masked_jaxpr = _masked_scan_jaxpr(jaxpr, num_consts, num_carry) consts, init, xs = split_list(padded_vals, [num_consts, num_carry]) max_length, = {x.shape[0] for x in xs} const_linear, init_linear, xs_linear = split_list(linear, [num_consts, num_carry]) out_vals = scan_p.bind( *itertools.chain([dynamic_length] + consts, [0], init, xs), reverse=reverse, length=max_length, jaxpr=masked_jaxpr, num_consts=1 + num_consts, num_carry=1 + num_carry, linear=tuple([False] + const_linear + [False] + init_linear + xs_linear), unroll=unroll) return out_vals[1:] def _masked_scan_jaxpr(jaxpr, num_consts, num_carry): fun = core.jaxpr_as_fun(jaxpr) @lu.wrap_init def masked(*args): [dynamic_length], consts, [i], carry, xs = split_list( args, [1, num_consts, 1, num_carry]) out = fun(*(consts + carry + xs)) new_carry, ys = split_list(out, [num_carry]) new_carry = [lax.select(i < dynamic_length, new_c, c) for new_c, c in zip(new_carry, carry)] return [i + 1] + new_carry + ys aval = ShapedArray((), dtypes.int_) const_avals, carry_avals, x_avals = split_list(jaxpr.in_avals, [num_consts, num_carry]) return _make_closed_jaxpr(masked, [aval] + const_avals + [aval] + carry_avals + x_avals) def _scan_typecheck(bind_time, *avals, reverse, length, num_consts, num_carry, jaxpr, linear, unroll): tc = partial(_typecheck_param, 'scan') tc(reverse, 'reverse', 'bool', type(reverse) is bool) tc(num_consts, 'num_consts', 'non-negative int', type(num_consts) is int and num_consts >= 0) tc(num_carry, 'num_carry', 'non-negative int', type(num_carry) is int and num_carry >= 0) tc(jaxpr, 'jaxpr', 'ClosedJaxpr', type(jaxpr) is core.ClosedJaxpr) tc(linear, 'linear', 'tuple of bool', type(linear) is tuple and all(type(x) is bool for x in linear)) tc(unroll, 'unroll', 'positive int', type(unroll) is int and unroll > 0) length_types = (int, masking.Poly) if bind_time else (int,) tc(length, 'length', 'non-negative int', type(length) in length_types and length >= 0) core.typecheck_assert( len(linear) == len(avals), f'scan param linear has length {len(linear)} for {len(avals)} operands') const_avals, init_avals, x_avals = split_list(avals, [num_consts, num_carry]) const_avals_jaxpr, init_avals_jaxpr, x_avals_jaxpr = split_list( jaxpr.in_avals, [num_consts, num_carry]) carry_avals_jaxpr, _ = split_list(jaxpr.out_avals, [num_carry]) x_avals_mapped = _map(partial(core.mapped_aval, length, 0), x_avals) core.typecheck_assert( all(_map(core.typematch, init_avals_jaxpr, carry_avals_jaxpr)), f'scan input carry input and output types mismatch: ' f'\n{_avals_short(init_avals_jaxpr)}\nvs\n{_avals_short(carry_avals_jaxpr)}') core.typecheck_assert( all(_map(core.typecompat, const_avals_jaxpr, const_avals)), f'scan jaxpr takes input const types\n{_avals_short(const_avals_jaxpr)},\n' f'called with consts of type\n{_avals_short(const_avals)}') core.typecheck_assert( all(_map(core.typecompat, init_avals_jaxpr, init_avals)), f'scan jaxpr takes input carry types\n{_avals_short(init_avals_jaxpr)},\n' f'called with initial carry of type\n{_avals_short(init_avals)}') core.typecheck_assert( all(_map(core.typecompat, x_avals_jaxpr, x_avals_mapped)), f'scan jaxpr takes input sequence types\n{_avals_short(x_avals_jaxpr)},\n' f'called with sequence of type\n{_avals_short(x_avals)}') def scan_bind(*args, **params): if config.jax_enable_checks: avals = _map(core.get_aval, args) _scan_typecheck(True, *avals, **params) core.check_jaxpr(params['jaxpr'].jaxpr) return core.Primitive.bind(scan_p, *args, **params) scan_p = core.Primitive("scan") scan_p.multiple_results = True scan_p.def_custom_bind(scan_bind) scan_p.def_impl(partial(xla.apply_primitive, scan_p)) scan_p.def_abstract_eval(_scan_abstract_eval) ad.primitive_jvps[scan_p] = _scan_jvp ad.primitive_transposes[scan_p] = _scan_transpose pe.custom_partial_eval_rules[scan_p] = _scan_partial_eval xla.initial_style_translations[scan_p] = xla.lower_fun_initial_style(_scan_impl) batching.initial_style_batchers[scan_p] = _scan_batching_rule masking.masking_rules[scan_p] = _scan_masking_rule core.custom_typechecks[scan_p] = partial(_scan_typecheck, False) @api_boundary def map(f, xs): """Map a function over leading array axes. Like Python's builtin map, except inputs and outputs are in the form of stacked arrays. Consider using the ``jax.vmap`` transform instead, unless you need to apply a function element by element for reduced memory usage or heterogeneous computation with other control flow primitives. When ``xs`` is an array type, the semantics of ``map`` are given by this Python implementation:: def map(f, xs): return np.stack([f(x) for x in xs]) Like ``scan``, ``map`` is implemented in terms of JAX primitives so many of the same advantages over a Python loop apply: ``xs`` may be an arbitrary nested pytree type, and the mapped computation is compiled only once. Args: f: a Python function to apply element-wise over the first axis or axes of ``xs``. xs: values over which to map along the leading axis. Returns: Mapped values. """ g = lambda _, x: ((), f(x)) _, ys = scan(g, (), xs) return ys def _concat_masking_rule(padded_vals, logical_shapes, dimension): result = lax.concatenate(padded_vals, dimension) # fragmented offset = 0 for padded_val, logical_shape in zip(padded_vals, logical_shapes): result = _memcpy(dimension, logical_shape[dimension], padded_val, result, offset) offset = offset + logical_shape[dimension] return result def _memcpy(axis, num, src, dst, offset): def body(i, dst): update = lax.dynamic_index_in_dim(src, i, axis) return lax.dynamic_update_index_in_dim(dst, update, i + offset, axis) return fori_loop(0, num, body, dst) masking.masking_rules[lax.concatenate_p] = _concat_masking_rule # type: ignore def _check_tree_and_avals(what, tree1, avals1, tree2, avals2): """Raises TypeError if (tree1, avals1) does not match (tree2, avals2). Corresponding `tree` and `avals` must match in the sense that the number of leaves in `tree` must be equal to the length of `avals`. `what` will be prepended to details of the mismatch in TypeError. """ if tree1 != tree2: raise TypeError( f"{what} must have same type structure, got {tree1} and {tree2}.") if not all(_map(core.typematch, avals1, avals2)): raise TypeError( f"{what} must have identical types, got\n" f"{tree_unflatten(tree1, avals1)}\nand\n" f"{tree_unflatten(tree2, avals2)}.") def _check_tree(func_name, expected_name, actual_tree, expected_tree, has_aux=False): if has_aux: actual_tree_children = actual_tree.children() if len(actual_tree_children) == 2: # select first child as result tree actual_tree = tree_structure(actual_tree_children[0]) else: raise ValueError( f"{func_name}() produced a pytree with structure " f"{actual_tree}, but a pytree tuple with auxiliary " f"output was expected because has_aux was set to True.") if actual_tree != expected_tree: raise TypeError( f"{func_name}() output pytree structure must match {expected_name}, " f"got {actual_tree} and {expected_tree}.") def _promote_weak_typed_inputs(in_vals, in_avals, out_avals): """Promote weakly-typed in_vals to be compatible with out_avals. Args: in_vals : flattened list of input values. in_avals : corresponding list of avals. out_avals : list of target output avals. Returns: in_vals_new : flattened list of modified in_vals with no weak types. changed : bool; true if in_vals required modification. """ if len(in_vals) != len(in_avals) or len(in_avals) != len(out_avals): # Calling function is responsible for catching this. return in_vals, False weak_mismatches = [i for i, (a1, a2) in enumerate(zip(in_avals, out_avals)) if getattr(a1, 'weak_type', False) and not core.typematch(a1, a2)] if not weak_mismatches: return in_vals, False for i in weak_mismatches: new_dtype = dtypes.result_type(in_vals[i], out_avals[i]) in_vals[i] = lax.convert_element_type(in_vals[i], new_dtype) return in_vals, True def _stop_gradient_fun(f): """Create a version of f() that stops all gradients.""" def wrapper(*args, **kwargs): args_flat, in_args_tree = tree_flatten((args, kwargs)) args_avals = tuple(_map(_abstractify, args_flat)) g = lambda a, b: f(*a, **b) jaxpr, consts, out_tree = _initial_style_jaxpr(g, in_args_tree, args_avals) all_args = _map(lax.stop_gradient, (*consts, *args_flat)) out = core.jaxpr_as_fun(jaxpr)(*all_args) return tree_unflatten(out_tree, out) return wrapper _RootTuple = collections.namedtuple('_RootTuple', 'f, solve, l_and_s') def _split_root_args(args, const_lengths): params_list = split_list(args, list(const_lengths)) return _RootTuple(*params_list[:-1]), params_list[-1] @api_boundary def custom_root(f, initial_guess, solve, tangent_solve): """Differentiably solve for a roots of a function. This is a low-level routine, mostly intended for internal use in JAX. Gradients of custom_root() are defined with respect to closed-over variables from the provided function ``f`` via the implicit function theorem: https://en.wikipedia.org/wiki/Implicit_function_theorem Args: f: function for which to find a root. Should accept a single argument, return a tree of arrays with the same structure as its input. initial_guess: initial guess for a zero of f. solve: function to solve for the roots of f. Should take two positional arguments, f and initial_guess, and return a solution with the same structure as initial_guess such that func(solution) = 0. In other words, the following is assumed to be true (but not checked):: solution = solve(f, initial_guess) error = f(solution) assert all(error == 0) tangent_solve: function to solve the tangent system. Should take two positional arguments, a linear function ``g`` (the function ``f`` linearized at its root) and a tree of array(s) ``y`` with the same structure as initial_guess, and return a solution ``x`` such that ``g(x)=y``: - For scalar ``y``, use ``lambda g, y: y / g(1.0)``. - For vector ``y``, you could use a linear solve with the Jacobian, if dimensionality of ``y`` is not too large: ``lambda g, y: np.linalg.solve(jacobian(g)(y), y)``. Returns: The result of calling solve(f, initial_guess) with gradients defined via implicit differentiation assuming ``f(solve(f, initial_guess)) == 0``. """ guess_flat, in_args_tree = tree_flatten((initial_guess,)) guess_avals = tuple(_map(_abstractify, guess_flat)) f_jaxpr, f_consts, out_tree = _initial_style_jaxpr( f, in_args_tree, guess_avals) in_tree, = treedef_children(in_args_tree) _check_tree("f", "initial_guess", out_tree, in_tree) solve_jaxpr, solve_consts, solution_tree = _initial_style_jaxpr( partial(solve, _stop_gradient_fun(f)), in_args_tree, guess_avals) _check_tree("solve", "initial_guess", solution_tree, in_tree) def linearize_and_solve(x, b): unchecked_zeros, f_jvp = jax.linearize(f, x) return tangent_solve(f_jvp, b) l_and_s_jaxpr, l_and_s_consts, out_tree = _initial_style_jaxpr( linearize_and_solve, treedef_tuple((in_tree,) * 2), guess_avals * 2) _check_tree("tangent_solve", "x", out_tree, in_tree) all_consts = [f_consts, solve_consts, l_and_s_consts] const_lengths = _RootTuple(*_map(len, all_consts)) jaxprs = _RootTuple(f_jaxpr, solve_jaxpr, l_and_s_jaxpr) out_flat = _custom_root( const_lengths, jaxprs, *(_flatten(all_consts) + guess_flat)) return tree_unflatten(out_tree, out_flat) @partial(jax.custom_jvp, nondiff_argnums=(0, 1)) def _custom_root(const_lengths, jaxprs, *args): params, initial_guess = _split_root_args(args, const_lengths) solution = core.jaxpr_as_fun(jaxprs.solve)(*(params.solve + initial_guess)) return solution @_custom_root.defjvp def _root_jvp(const_lengths, jaxprs, primals, tangents): params, _ = _split_root_args(primals, const_lengths) solution = _custom_root(const_lengths, jaxprs, *primals) params_dot, _ = _split_root_args(tangents, const_lengths) # F(m, u) = 0 # system of equations in u, parameterized by m # # solution is u*(m) defined in a neighborhood # F(m, u*(m)) = 0 # satisfied in a neighborhood # # ∂_0 F(m, u*(m)) + ∂_1 F(m, u*(m)) ∂ u*(m) = 0 # implied by line above # ∂ u*(m) = - (∂_1 F(m, u*(m)))^{-1} ∂_0 F(m, u*(m)) # rearrange # # ∂ u*(m)[v] = - (∂_1 F(m, u*(m)))^{-1} [∂_0 F(m, u*(m))[v]] # jvp f = core.jaxpr_as_fun(jaxprs.f) linearize_and_solve = partial( core.jaxpr_as_fun(jaxprs.l_and_s), *params.l_and_s) f_at_solution = lambda *params: f(*itertools.chain(params, solution)) _, rhs = ad.jvp(lu.wrap_init(f_at_solution)).call_wrapped( params.f, params_dot.f) solution_dot = _map( operator.neg, linearize_and_solve(*itertools.chain(solution, rhs))) return solution, solution_dot class _LinearSolveTuple(collections.namedtuple( '_LinearSolveTuple', 'matvec, vecmat, solve, transpose_solve')): def transpose(self): return type(self)(self.vecmat, self.matvec, self.transpose_solve, self.solve) def _split_linear_solve_args(args, const_lengths): params_list = split_list(args, list(const_lengths)) return _LinearSolveTuple(*params_list[:-1]), params_list[-1] def _transpose_one_output(linear_fun, primals): transpose_fun = jax.linear_transpose(linear_fun, primals) def transposed_fun(x): (y,) = transpose_fun(x) return y return transposed_fun def _flatten(args): return [x for arg in args for x in arg] def _check_shapes(func_name, expected_name, actual, expected): actual_shapes = _map(np.shape, tree_leaves(actual)) expected_shapes = _map(np.shape, tree_leaves(expected)) if actual_shapes != expected_shapes: raise ValueError( f"{func_name}() output shapes must match {expected_name}, " f"got {actual_shapes} and {expected_shapes}") @api_boundary def custom_linear_solve( matvec, b, solve, transpose_solve=None, symmetric=False, has_aux=False): """Perform a matrix-free linear solve with implicitly defined gradients. This function allows for overriding or defining gradients for a linear solve directly via implicit differentiation at the solution, rather than by differentiating *through* the solve operation. This can sometimes be much faster or more numerically stable, or differentiating through the solve operation may not even be implemented (e.g., if ``solve`` uses ``lax.while_loop``). Required invariant:: x = solve(matvec, b) # solve the linear equation assert matvec(x) == b # not checked Args: matvec: linear function to invert. Must be differentiable. b: constant right handle side of the equation. May be any nested structure of arrays. solve: higher level function that solves for solution to the linear equation, i.e., ``solve(matvec, x) == x`` for all ``x`` of the same form as ``b``. This function need not be differentiable. transpose_solve: higher level function for solving the transpose linear equation, i.e., ``transpose_solve(vecmat, x) == x``, where ``vecmat`` is the transpose of the linear map ``matvec`` (computed automatically with autodiff). Required for backwards mode automatic differentiation, unless ``symmetric=True``, in which case ``solve`` provides the default value. symmetric: bool indicating if it is safe to assume the linear map corresponds to a symmetric matrix, i.e., ``matvec == vecmat``. has_aux: bool indicating whether the ``solve`` and ``transpose_solve`` functions return auxiliary data like solver diagnostics as a second argument. Returns: Result of ``solve(matvec, b)``, with gradients defined assuming that the solution ``x`` satisfies the linear equation ``matvec(x) == b``. """ if transpose_solve is None and symmetric: transpose_solve = solve b_flat, in_args_tree = tree_flatten((b,)) b_avals = tuple(_map(_abstractify, b_flat)) tree, = treedef_children(in_args_tree) def _shape_checked(fun, name, has_aux): def f(x): y = fun(x) _check_shapes(name, "b", y, b_flat) return y def f_aux(x): y, aux = fun(x) _check_shapes(name, "b", y, b_flat) return y, aux return f_aux if has_aux else f # no auxiliary data assumed for matvec matvec_jaxpr, matvec_consts, out_tree = _initial_style_jaxpr( _shape_checked(matvec, "matvec", False), in_args_tree, b_avals, 'custom_linear_solve') _check_tree("matvec", "b", out_tree, tree, False) solve_jaxpr, solve_consts, out_tree = _initial_style_jaxpr( _shape_checked(partial(solve, matvec), "solve", has_aux), in_args_tree, b_avals, 'custom_linear_solve') _check_tree("solve", "b", out_tree, tree, has_aux) if transpose_solve is None: vecmat_jaxpr = tr_solve_jaxpr = None vecmat_consts = tr_solve_consts = [] else: if symmetric: vecmat = matvec vecmat_jaxpr = matvec_jaxpr vecmat_consts = matvec_consts else: vecmat = _transpose_one_output(matvec, b) vecmat_jaxpr, vecmat_consts, out_tree = _initial_style_jaxpr( vecmat, in_args_tree, b_avals, 'custom_linear_solve') assert out_tree == tree tr_solve_jaxpr, tr_solve_consts, out_tree = _initial_style_jaxpr( _shape_checked(partial(transpose_solve, vecmat), "transpose_solve", has_aux), in_args_tree, b_avals, 'custom_linear_solve') _check_tree("transpose_solve", "b", out_tree, tree, has_aux) all_consts = [matvec_consts, vecmat_consts, solve_consts, tr_solve_consts] const_lengths = _LinearSolveTuple(*_map(len, all_consts)) jaxprs = _LinearSolveTuple( matvec_jaxpr, vecmat_jaxpr, solve_jaxpr, tr_solve_jaxpr) out_flat = linear_solve_p.bind( *(_flatten(all_consts) + b_flat), const_lengths=const_lengths, jaxprs=jaxprs) return tree_unflatten(out_tree, out_flat) def _linear_solve_abstract_eval(*args, const_lengths, jaxprs): args_to_raise = args[sum(const_lengths):] # raise aux_args to shaped arrays as well if present # number of aux args is the difference in out_avals # of solve and matvec (since they map to the same vector space) num_aux = len(jaxprs.solve.out_avals) - len(jaxprs.matvec.out_avals) if num_aux > 0: args_to_raise += tuple(jaxprs.solve.out_avals[-num_aux:]) return _map(raise_to_shaped, args_to_raise) def _custom_linear_solve_impl(*args, const_lengths, jaxprs): params, b = _split_linear_solve_args(args, const_lengths) x = core.jaxpr_as_fun(jaxprs.solve)(*(params.solve + b)) return x def _tangent_linear_map(func, params, params_dot, *x): """Compute the tangent of a linear map. Assuming ``func(*params, *x)`` is linear in ``x`` and computes ``A @ x``, this function computes ``∂A @ x``. """ assert any(type(p) is not ad_util.Zero for p in params_dot) zeros = _map(ad_util.Zero.from_value, x) _, out_tangent = ad.jvp(lu.wrap_init(func)).call_wrapped( params + list(x), params_dot + zeros) return out_tangent def _custom_linear_solve_jvp(primals, tangents, const_lengths, jaxprs): # A x - b = 0 # ∂A x + A ∂x - ∂b = 0 # ∂x = A^{-1} (∂b - ∂A x) kwargs = dict(const_lengths=const_lengths, jaxprs=jaxprs) x = linear_solve_p.bind(*primals, **kwargs) params, _ = _split_linear_solve_args(primals, const_lengths) params_dot, b_dot = _split_linear_solve_args(tangents, const_lengths) num_x_leaves = len(b_dot) # x is a flat tree with possible aux values appended # since x_tree == b_tree == b_dot_tree, we can cut off # aux values with len info provided by b_dot tree here x_leaves, _ = split_list(x, [num_x_leaves]) if all(type(p) is ad_util.Zero for p in params_dot.matvec): # no need to evaluate matvec_tangents rhs = b_dot else: matvec_tangents = _tangent_linear_map( core.jaxpr_as_fun(jaxprs.matvec), params.matvec, params_dot.matvec, *x_leaves) rhs = _map(ad.add_tangents, b_dot, _map(operator.neg, matvec_tangents)) x_dot = linear_solve_p.bind(*(_flatten(params) + rhs), **kwargs) # split into x tangents and aux tangents (these become zero) dx_leaves, daux_leaves = split_list(x_dot, [num_x_leaves]) daux_leaves = _map(ad_util.Zero.from_value, daux_leaves) x_dot = dx_leaves + daux_leaves return x, x_dot def _linear_solve_transpose_rule(cotangent, *primals, const_lengths, jaxprs): if jaxprs.transpose_solve is None: raise TypeError('transpose_solve required for backwards mode automatic ' 'differentiation of custom_linear_solve') params, b = _split_linear_solve_args(primals, const_lengths) # split off symbolic zeros in the cotangent if present x_cotangent, _ = split_list(cotangent, [len(b)]) assert all(ad.is_undefined_primal(x) for x in b) cotangent_b_full = linear_solve_p.bind( *(_flatten(params.transpose()) + x_cotangent), const_lengths=const_lengths.transpose(), jaxprs=jaxprs.transpose()) # drop aux values in cotangent computation cotangent_b, _ = split_list(cotangent_b_full, [len(b)]) return [None] * sum(const_lengths) + cotangent_b def _linear_solve_batching_rule(args, dims, axis_name, main_type, const_lengths, jaxprs): orig_bat = [d is not batching.not_mapped for d in dims] size, = { a.shape[d] for a, d in zip(args, dims) if d is not batching.not_mapped } params, b = _split_linear_solve_args(args, const_lengths) params_dims, b_dims = _split_linear_solve_args(dims, const_lengths) params_bat, orig_b_bat = _split_linear_solve_args(orig_bat, const_lengths) (matvec, vecmat, solve, solve_t) = jaxprs (matvec_bat, vecmat_bat, solve_bat, solve_t_bat) = params_bat num_aux = len(solve.out_avals) - len(matvec.out_avals) # Fixpoint computation of which parts of x and b are batched; we need to # ensure this is consistent between all four jaxprs b_bat = orig_b_bat x_bat = [False] * len(solve.out_avals) for i in range(1 + len(orig_b_bat) + len(solve.out_avals)): # Apply vecmat and solve -> new batched parts of x solve_jaxpr_batched, solve_x_bat = batching.batch_jaxpr( solve, size, solve_bat + b_bat, instantiate=x_bat, axis_name=axis_name, main_type=main_type) if vecmat is None: vecmat_jaxpr_batched = None x_bat_out = solve_x_bat else: vecmat_jaxpr_batched, vecmat_x_bat = batching.batch_jaxpr( vecmat, size, vecmat_bat + b_bat, instantiate=x_bat, axis_name=axis_name, main_type=main_type) # batch all aux data by default x_bat_out = _map(operator.or_, vecmat_x_bat + [True] * num_aux, solve_x_bat) # Apply matvec and solve_t -> new batched parts of b matvec_jaxpr_batched, matvec_b_bat = batching.batch_jaxpr( matvec, size, matvec_bat + x_bat_out, instantiate=b_bat, axis_name=axis_name, main_type=main_type) if solve_t is None: solve_t_jaxpr_batched = None b_bat_out = _map(operator.or_, matvec_b_bat, orig_b_bat) else: solve_t_jaxpr_batched, solve_t_b_bat = batching.batch_jaxpr( solve_t, size, solve_t_bat + x_bat_out, instantiate=b_bat, axis_name=axis_name, main_type=main_type) b_bat_out = _map(lambda m, s, o: m or s or o, matvec_b_bat, solve_t_b_bat, orig_b_bat) if x_bat_out == x_bat and b_bat_out == b_bat: break else: x_bat = x_bat_out b_bat = b_bat_out else: assert False, "Fixedpoint not reached" batched_jaxprs = _LinearSolveTuple(matvec_jaxpr_batched, vecmat_jaxpr_batched, solve_jaxpr_batched, solve_t_jaxpr_batched) # Move batched axes to the front new_params = [ batching.moveaxis(x, d, 0) if d is not batching.not_mapped and d != 0 else x for x, d in zip(_flatten(params), _flatten(params_dims)) ] # Broadcast out b if necessary new_b = [ batching.broadcast(x, size, 0) if now_bat and not was_bat else batching.moveaxis(x, d, 0) if now_bat and d != 0 else x for x, d, was_bat, now_bat in zip(b, b_dims, orig_b_bat, b_bat) ] outs = linear_solve_p.bind( *(new_params + new_b), const_lengths=const_lengths, jaxprs=batched_jaxprs) out_dims = [0 if batched else batching.not_mapped for batched in solve_x_bat] return outs, out_dims linear_solve_p = core.Primitive('custom_linear_solve') linear_solve_p.multiple_results = True linear_solve_p.def_impl(_custom_linear_solve_impl) linear_solve_p.def_abstract_eval(_linear_solve_abstract_eval) ad.primitive_jvps[linear_solve_p] = _custom_linear_solve_jvp xla.initial_style_translations[linear_solve_p] = \ xla.lower_fun_initial_style(_custom_linear_solve_impl) ad.primitive_transposes[linear_solve_p] = _linear_solve_transpose_rule batching.initial_style_batchers[linear_solve_p] = _linear_solve_batching_rule def _interleave(a, b, axis): """Given two Tensors of static shape, interleave them along the first axis.""" assert a.shape[axis] == b.shape[axis] or a.shape[axis] == b.shape[axis] + 1 a_pad = [(0, 0, 0)] * a.ndim b_pad = [(0, 0, 0)] * b.ndim a_pad[axis] = (0, 1 if a.shape[axis] == b.shape[axis] else 0, 1) b_pad[axis] = (1, 0 if a.shape[axis] == b.shape[axis] else 1, 1) op = lax.bitwise_or if a.dtype == np.bool_ else lax.add return op(lax.pad(a, lax._const(a, 0), a_pad), lax.pad(b, lax._const(b, 0), b_pad)) @api_boundary def associative_scan(fn: Callable, elems, reverse: bool = False, axis: int = 0): """Performs a scan with an associative binary operation, in parallel. For an introduction to associative scans, see [BLE1990]_. Args: fn: A Python callable implementing an associative binary operation with signature ``r = fn(a, b)``. Function `fn` must be associative, i.e., it must satisfy the equation ``fn(a, fn(b, c)) == fn(fn(a, b), c)``. The inputs and result are (possibly nested Python tree structures of) array(s) matching ``elems``. Each array has a dimension in place of the ``axis`` dimension. `fn` should be applied elementwise over the ``axis`` dimension (for example, by using :func:`jax.vmap` over the elementwise function.) The result ``r`` has the same shape (and structure) as the two inputs ``a`` and ``b``. elems: A (possibly nested Python tree structure of) array(s), each with an ``axis`` dimension of size ``num_elems``. reverse: A boolean stating if the scan should be reversed with respect to the ``axis`` dimension. axis: an integer identifying the axis over which the scan should occur. Returns: A (possibly nested Python tree structure of) array(s) of the same shape and structure as ``elems``, in which the ``k``'th element of ``axis`` is the result of recursively applying ``fn`` to combine the first ``k`` elements of ``elems`` along ``axis``. For example, given ``elems = [a, b, c, ...]``, the result would be ``[a, fn(a, b), fn(fn(a, b), c), ...]``. Example 1: partial sums of an array of numbers: >>> lax.associative_scan(jnp.add, jnp.arange(0, 4)) DeviceArray([0, 1, 3, 6], dtype=int32) Example 2: partial products of an array of matrices >>> mats = jax.random.uniform(jax.random.PRNGKey(0), (4, 2, 2)) >>> partial_prods = lax.associative_scan(jnp.matmul, mats) >>> partial_prods.shape (4, 2, 2) Example 3: reversed partial sums of an array of numbers >>> lax.associative_scan(jnp.add, jnp.arange(0, 4), reverse=True) DeviceArray([6, 6, 5, 3], dtype=int32) .. [BLE1990] Blelloch, Guy E. 1990. "Prefix Sums and Their Applications.", Technical Report CMU-CS-90-190, School of Computer Science, Carnegie Mellon University. """ elems_flat, tree = tree_flatten(elems) if reverse: elems_flat = [lax.rev(elem, [axis]) for elem in elems_flat] def combine(a_flat, b_flat): # Lower `fn` to operate on flattened sequences of elems. a = tree_unflatten(tree, a_flat) b = tree_unflatten(tree, b_flat) c = fn(a, b) c_flat, _ = tree_flatten(c) return c_flat # Check that all inputs have a consistent leading dimension `num_elems`. axis = lax._canonicalize_axis(axis, elems_flat[0].ndim) num_elems = int(elems_flat[0].shape[axis]) if not all(int(elem.shape[axis]) == num_elems for elem in elems_flat[1:]): raise ValueError('Array inputs to associative_scan must have the same ' 'first dimension. (saw: {})' .format([elem.shape for elem in elems_flat])) # Summary of algorithm: # # Consider elements of `_scan(elems)` at odd indices. That's the same as first # summing successive pairs of elements of `elems` and performing a scan on # that half sized tensor. We perform the latter scan by recursion. # # Now consider the even elements of `_scan(elems)`. These can be computed # from the odd elements of `_scan(elems)` by adding each odd element of # `_scan(elems)` to the matching even element in the original `elems`. # # We return the odd and even elements interleaved. # # For the base case of the recursion we return the first element # of `elems` followed by the sum of the first two elements computed as # a (small two-down-to-one) reduction step. def _scan(elems): """Perform scan on `elems`.""" num_elems = elems[0].shape[axis] if num_elems < 2: return elems # Combine adjacent pairs of elements. reduced_elems = combine( [lax.slice_in_dim(elem, 0, -1, stride=2, axis=axis) for elem in elems], [lax.slice_in_dim(elem, 1, None, stride=2, axis=axis) for elem in elems]) # Recursively compute scan for partially reduced tensors. odd_elems = _scan(reduced_elems) if num_elems % 2 == 0: even_elems = combine( [lax.slice_in_dim(e, 0, -1, axis=axis) for e in odd_elems], [lax.slice_in_dim(e, 2, None, stride=2, axis=axis) for e in elems]) else: even_elems = combine( odd_elems, [lax.slice_in_dim(e, 2, None, stride=2, axis=axis) for e in elems]) # The first element of a scan is the same as the first element # of the original `elems`. even_elems = [ lax.concatenate([lax.slice_in_dim(elem, 0, 1, axis=axis), result], dimension=axis) for (elem, result) in zip(elems, even_elems)] return list(_map(partial(_interleave, axis=axis), even_elems, odd_elems)) scans = _scan(elems_flat) if reverse: scans = [lax.rev(scanned, [axis]) for scanned in scans] return tree_unflatten(tree, scans) # Cumulative reductions. def cumsum(operand: Array, axis: int = 0, reverse: bool = False) -> Array: """Computes a cumulative sum along `axis`.""" return cumsum_p.bind(operand, axis=int(axis), reverse=bool(reverse)) def cumprod(operand: Array, axis: int = 0, reverse: bool = False) -> Array: """Computes a cumulative product along `axis`.""" return cumprod_p.bind(operand, axis=int(axis), reverse=bool(reverse)) def cummax(operand: Array, axis: int = 0, reverse: bool = False) -> Array: """Computes a cumulative maximum along `axis`.""" return cummax_p.bind(operand, axis=int(axis), reverse=bool(reverse)) def cummin(operand: Array, axis: int = 0, reverse: bool = False) -> Array: """Computes a cumulative minimum along `axis`.""" return cummin_p.bind(operand, axis=int(axis), reverse=bool(reverse)) def _cumred_shape_rule(x, *, axis: int, reverse: bool): if axis < 0 or axis >= x.ndim: raise ValueError( "axis {} is out of bounds for array of shape {}".format(axis, x.shape)) return x.shape def _cumsum_transpose_rule(t, operand, *, axis: int, reverse: bool): return [cumsum(t, axis=axis, reverse=not reverse)] def _cumred_tpu_translation_rule(window_reduce: Callable, x, *, axis: int, reverse: bool): # On TPU, an implementation using reduce_window is handled specially by the # compiler and is efficient. On other backends, it is O(n^2). n = x.shape[axis] if n == 0: return x padding = [(0, 0)] * x.ndim padding[axis] = (0, n - 1) if reverse else (n - 1, 0) strides = [1] * x.ndim window_dims = [1] * x.ndim window_dims[axis] = n return window_reduce(x, window_dims, strides, padding) def _cumred_batch_rule(prim, batched_args, batch_dims, *, axis: int, reverse: bool): operand, = batched_args bdim, = batch_dims axis = axis if axis < bdim else axis + 1 return prim.bind(operand, axis=axis, reverse=reverse), bdim def _cumred_dtype_rule(name, operand, *args, **kw): if not dtypes.issubdtype(operand.dtype, np.number): raise TypeError("{} does not accept dtype {}. Accepted dtypes are subtypes " "of number.".format(name, np.dtype(operand.dtype).name)) return dtypes.canonicalize_dtype(operand.dtype) def _cumulative_reduction_primitive(name, reduce_fn, tpu_reduce_window_fn): reducer_p = lax.standard_primitive( _cumred_shape_rule, partial(_cumred_dtype_rule, name), name, translation_rule=xla.lower_fun( partial(associative_scan, reduce_fn), multiple_results=False)) xla.backend_specific_translations['tpu'][reducer_p] = xla.lower_fun( partial(_cumred_tpu_translation_rule, tpu_reduce_window_fn), multiple_results=False) batching.primitive_batchers[reducer_p] = partial(_cumred_batch_rule, reducer_p) return reducer_p cumsum_p = _cumulative_reduction_primitive("cumsum", lax.add, lax._reduce_window_sum) ad.deflinear2(cumsum_p, _cumsum_transpose_rule) cumprod_p = _cumulative_reduction_primitive("cumprod", lax.mul, lax._reduce_window_prod) cummax_p = _cumulative_reduction_primitive("cummax", lax.max, lax._reduce_window_max) cummin_p = _cumulative_reduction_primitive("cummin", lax.min, lax._reduce_window_min) def _cumulative_jvp_rule(primals, tangents, *, axis: int, reverse: bool, combine_fn: Callable): # Irrespective of backend, we always use the parallel prefix scan # implementation when differentiating because reduce_window is not # arbitrarily differentiable. return api.jvp(partial(associative_scan, combine_fn, axis=axis, reverse=reverse), primals, tangents) ad.primitive_jvps[cumprod_p] = partial(_cumulative_jvp_rule, combine_fn=lax.mul) ad.primitive_jvps[cummin_p] = partial(_cumulative_jvp_rule, combine_fn=lax.min) ad.primitive_jvps[cummax_p] = partial(_cumulative_jvp_rule, combine_fn=lax.max)

41.88843

114

0.698916

258427bece34d944a7764f3f20d42bcaf4c45523

205

py

Python

2019/day1/day1p2.py

darkterbear/advent-of-code-2015

543d5a70c4b4c84081602cfa3d0ba05fe0693e54

[ "MIT" ]

null

2019/day1/day1p2.py

darkterbear/advent-of-code-2015

543d5a70c4b4c84081602cfa3d0ba05fe0693e54

[ "MIT" ]

2

2019-12-01T20:03:18.000Z

2021-05-11T22:41:00.000Z

2019/day1/day1p2.py

darkterbear/advent-of-code-2015

543d5a70c4b4c84081602cfa3d0ba05fe0693e54

[ "MIT" ]

null

file = open('./input') def findFuel(mass): raw = mass // 3 - 2 if raw <= 0: return 0 return raw + findFuel(raw) sum = 0 for line in file: sum += findFuel(int(line)) print(sum)

12.8125

30

0.55122

00176c24a883ff7f6aeb39eed299e4a096e6b950

707

py

Python

my_env/Lib/site-packages/sklearn/experimental/enable_iterative_imputer.py

obulrdy6881/Drowsinss

61cb9281d7dd22aee282b517e2fbf500f0ff9935

[ "MIT" ]

2

2021-05-02T07:59:56.000Z

2021-12-14T19:53:13.000Z

Web application/env/Lib/site-packages/sklearn/experimental/enable_iterative_imputer.py

arpit0891/Covid-19-and-Pneumonia-detection-from-X-Ray

6b2756e4672ab25083a0a50f44f36bec1833e789

[ "MIT" ]

7

2021-06-08T21:46:24.000Z

2022-03-12T00:35:31.000Z

my_env/Lib/site-packages/sklearn/experimental/enable_iterative_imputer.py

obulrdy6881/Drowsinss

61cb9281d7dd22aee282b517e2fbf500f0ff9935

[ "MIT" ]

1

2021-05-02T07:59:59.000Z

"""Enables IterativeImputer The API and results of this estimator might change without any deprecation cycle. Importing this file dynamically sets :class:`sklearn.impute.IterativeImputer` as an attribute of the impute module:: >>> # explicitly require this experimental feature >>> from sklearn.experimental import enable_iterative_imputer # noqa >>> # now you can import normally from impute >>> from sklearn.impute import IterativeImputer """ from ..impute._iterative import IterativeImputer from .. import impute # use settattr to avoid mypy errors when monkeypatching setattr(impute, 'IterativeImputer', IterativeImputer) impute.__all__ += ['IterativeImputer']

33.666667

78

0.755304

f07ba3569c6681ee886ae93348a466be5a66df13

29,056

py

Python

networkx/algorithms/threshold.py

kakila/networkx

a7c7e0bd2057acf8066d796e53f6ea1a77be803e

[ "BSD-3-Clause" ]

null

networkx/algorithms/threshold.py

kakila/networkx

a7c7e0bd2057acf8066d796e53f6ea1a77be803e

[ "BSD-3-Clause" ]

null

networkx/algorithms/threshold.py

kakila/networkx

a7c7e0bd2057acf8066d796e53f6ea1a77be803e

[ "BSD-3-Clause" ]

null

""" Threshold Graphs - Creation, manipulation and identification. """ __author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult (dschult@colgate.edu)""" # Copyright (C) 2004-2015 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. # __all__ = ['is_threshold_graph', 'find_threshold_graph'] import random # for swap_d from math import sqrt import networkx def is_threshold_graph(G): """ Returns True if G is a threshold graph. """ return is_threshold_sequence(list(d for n, d in G.degree())) def is_threshold_sequence(degree_sequence): """ Returns True if the sequence is a threshold degree seqeunce. Uses the property that a threshold graph must be constructed by adding either dominating or isolated nodes. Thus, it can be deconstructed iteratively by removing a node of degree zero or a node that connects to the remaining nodes. If this deconstruction failes then the sequence is not a threshold sequence. """ ds = degree_sequence[:] # get a copy so we don't destroy original ds.sort() while ds: if ds[0]==0: # if isolated node ds.pop(0) # remove it continue if ds[-1] != len(ds)-1: # is the largest degree node dominating? return False # no, not a threshold degree sequence ds.pop() # yes, largest is the dominating node ds=[ d-1 for d in ds ] # remove it and decrement all degrees return True def creation_sequence(degree_sequence,with_labels=False,compact=False): """ Determines the creation sequence for the given threshold degree sequence. The creation sequence is a list of single characters 'd' or 'i': 'd' for dominating or 'i' for isolated vertices. Dominating vertices are connected to all vertices present when it is added. The first node added is by convention 'd'. This list can be converted to a string if desired using "".join(cs) If with_labels==True: Returns a list of 2-tuples containing the vertex number and a character 'd' or 'i' which describes the type of vertex. If compact==True: Returns the creation sequence in a compact form that is the number of 'i's and 'd's alternating. Examples: [1,2,2,3] represents d,i,i,d,d,i,i,i [3,1,2] represents d,d,d,i,d,d Notice that the first number is the first vertex to be used for construction and so is always 'd'. with_labels and compact cannot both be True. Returns None if the sequence is not a threshold sequence """ if with_labels and compact: raise ValueError("compact sequences cannot be labeled") # make an indexed copy if isinstance(degree_sequence, dict): # labeled degree seqeunce ds = [ [degree,label] for (label,degree) in degree_sequence.items() ] else: ds=[ [d,i] for i,d in enumerate(degree_sequence) ] ds.sort() cs=[] # creation sequence while ds: if ds[0][0]==0: # isolated node (d,v)=ds.pop(0) if len(ds)>0: # make sure we start with a d cs.insert(0,(v,'i')) else: cs.insert(0,(v,'d')) continue if ds[-1][0]!=len(ds)-1: # Not dominating node return None # not a threshold degree sequence (d,v)=ds.pop() cs.insert(0,(v,'d')) ds=[ [d[0]-1,d[1]] for d in ds ] # decrement due to removing node if with_labels: return cs if compact: return make_compact(cs) return [ v[1] for v in cs ] # not labeled def make_compact(creation_sequence): """ Returns the creation sequence in a compact form that is the number of 'i's and 'd's alternating. Examples: [1,2,2,3] represents d,i,i,d,d,i,i,i. [3,1,2] represents d,d,d,i,d,d. Notice that the first number is the first vertex to be used for construction and so is always 'd'. Labeled creation sequences lose their labels in the compact representation. """ first=creation_sequence[0] if isinstance(first,str): # creation sequence cs = creation_sequence[:] elif isinstance(first,tuple): # labeled creation sequence cs = [ s[1] for s in creation_sequence ] elif isinstance(first,int): # compact creation sequence return creation_sequence else: raise TypeError("Not a valid creation sequence type") ccs=[] count=1 # count the run lengths of d's or i's. for i in range(1,len(cs)): if cs[i]==cs[i-1]: count+=1 else: ccs.append(count) count=1 ccs.append(count) # don't forget the last one return ccs def uncompact(creation_sequence): """ Converts a compact creation sequence for a threshold graph to a standard creation sequence (unlabeled). If the creation_sequence is already standard, return it. See creation_sequence. """ first=creation_sequence[0] if isinstance(first,str): # creation sequence return creation_sequence elif isinstance(first,tuple): # labeled creation sequence return creation_sequence elif isinstance(first,int): # compact creation sequence ccscopy=creation_sequence[:] else: raise TypeError("Not a valid creation sequence type") cs = [] while ccscopy: cs.extend(ccscopy.pop(0)*['d']) if ccscopy: cs.extend(ccscopy.pop(0)*['i']) return cs def creation_sequence_to_weights(creation_sequence): """ Returns a list of node weights which create the threshold graph designated by the creation sequence. The weights are scaled so that the threshold is 1.0. The order of the nodes is the same as that in the creation sequence. """ # Turn input sequence into a labeled creation sequence first=creation_sequence[0] if isinstance(first,str): # creation sequence if isinstance(creation_sequence,list): wseq = creation_sequence[:] else: wseq = list(creation_sequence) # string like 'ddidid' elif isinstance(first,tuple): # labeled creation sequence wseq = [ v[1] for v in creation_sequence] elif isinstance(first,int): # compact creation sequence wseq = uncompact(creation_sequence) else: raise TypeError("Not a valid creation sequence type") # pass through twice--first backwards wseq.reverse() w=0 prev='i' for j,s in enumerate(wseq): if s=='i': wseq[j]=w prev=s elif prev=='i': prev=s w+=1 wseq.reverse() # now pass through forwards for j,s in enumerate(wseq): if s=='d': wseq[j]=w prev=s elif prev=='d': prev=s w+=1 # Now scale weights if prev=='d': w+=1 wscale=1./float(w) return [ ww*wscale for ww in wseq] #return wseq def weights_to_creation_sequence(weights,threshold=1,with_labels=False,compact=False): """ Returns a creation sequence for a threshold graph determined by the weights and threshold given as input. If the sum of two node weights is greater than the threshold value, an edge is created between these nodes. The creation sequence is a list of single characters 'd' or 'i': 'd' for dominating or 'i' for isolated vertices. Dominating vertices are connected to all vertices present when it is added. The first node added is by convention 'd'. If with_labels==True: Returns a list of 2-tuples containing the vertex number and a character 'd' or 'i' which describes the type of vertex. If compact==True: Returns the creation sequence in a compact form that is the number of 'i's and 'd's alternating. Examples: [1,2,2,3] represents d,i,i,d,d,i,i,i [3,1,2] represents d,d,d,i,d,d Notice that the first number is the first vertex to be used for construction and so is always 'd'. with_labels and compact cannot both be True. """ if with_labels and compact: raise ValueError("compact sequences cannot be labeled") # make an indexed copy if isinstance(weights,dict): # labeled weights wseq = [ [w,label] for (label,w) in weights.items() ] else: wseq = [ [w,i] for i,w in enumerate(weights) ] wseq.sort() cs=[] # creation sequence cutoff=threshold-wseq[-1][0] while wseq: if wseq[0][0]<cutoff: # isolated node (w,label)=wseq.pop(0) cs.append((label,'i')) else: (w,label)=wseq.pop() cs.append((label,'d')) cutoff=threshold-wseq[-1][0] if len(wseq)==1: # make sure we start with a d (w,label)=wseq.pop() cs.append((label,'d')) # put in correct order cs.reverse() if with_labels: return cs if compact: return make_compact(cs) return [ v[1] for v in cs ] # not labeled # Manipulating NetworkX.Graphs in context of threshold graphs def threshold_graph(creation_sequence, create_using=None): """ Create a threshold graph from the creation sequence or compact creation_sequence. The input sequence can be a creation sequence (e.g. ['d','i','d','d','d','i']) labeled creation sequence (e.g. [(0,'d'),(2,'d'),(1,'i')]) compact creation sequence (e.g. [2,1,1,2,0]) Use cs=creation_sequence(degree_sequence,labeled=True) to convert a degree sequence to a creation sequence. Returns None if the sequence is not valid """ # Turn input sequence into a labeled creation sequence first=creation_sequence[0] if isinstance(first,str): # creation sequence ci = list(enumerate(creation_sequence)) elif isinstance(first,tuple): # labeled creation sequence ci = creation_sequence[:] elif isinstance(first,int): # compact creation sequence cs = uncompact(creation_sequence) ci = list(enumerate(cs)) else: print("not a valid creation sequence type") return None if create_using is None: G = networkx.Graph() elif create_using.is_directed(): raise networkx.NetworkXError("Directed Graph not supported") else: G = create_using G.clear() G.name="Threshold Graph" # add nodes and edges # if type is 'i' just add nodea # if type is a d connect to everything previous while ci: (v,node_type)=ci.pop(0) if node_type=='d': # dominating type, connect to all existing nodes # We use `for u in list(G):` instead of # `for u in G:` because we edit the graph `G` in # the loop. Hence using an iterator will result in # `RuntimeError: dictionary changed size during iteration` for u in list(G): G.add_edge(v, u) G.add_node(v) return G def find_alternating_4_cycle(G): """ Returns False if there aren't any alternating 4 cycles. Otherwise returns the cycle as [a,b,c,d] where (a,b) and (c,d) are edges and (a,c) and (b,d) are not. """ for (u,v) in G.edges(): for w in G.nodes(): if not G.has_edge(u,w) and u!=w: for x in G.neighbors(w): if not G.has_edge(v,x) and v!=x: return [u,v,w,x] return False def find_threshold_graph(G, create_using=None): """ Return a threshold subgraph that is close to largest in G. The threshold graph will contain the largest degree node in G. """ return threshold_graph(find_creation_sequence(G),create_using) def find_creation_sequence(G): """ Find a threshold subgraph that is close to largest in G. Returns the labeled creation sequence of that threshold graph. """ cs=[] # get a local pointer to the working part of the graph H=G while H.order()>0: # get new degree sequence on subgraph dsdict = dict(H.degree()) ds = [(d, v) for v,d in dsdict.items()] ds.sort() # Update threshold graph nodes if ds[-1][0]==0: # all are isolated cs.extend(zip(dsdict, ['i'] * (len(ds) - 1) + ['d'])) break # Done! # pull off isolated nodes while ds[0][0]==0: (d,iso)=ds.pop(0) cs.append((iso,'i')) # find new biggest node (d,bigv)=ds.pop() # add edges of star to t_g cs.append((bigv,'d')) # form subgraph of neighbors of big node H=H.subgraph(H.neighbors(bigv)) cs.reverse() return cs ### Properties of Threshold Graphs def triangles(creation_sequence): """ Compute number of triangles in the threshold graph with the given creation sequence. """ # shortcut algoritm that doesn't require computing number # of triangles at each node. cs=creation_sequence # alias dr=cs.count("d") # number of d's in sequence ntri=dr*(dr-1)*(dr-2)/6 # number of triangles in clique of nd d's # now add dr choose 2 triangles for every 'i' in sequence where # dr is the number of d's to the right of the current i for i,typ in enumerate(cs): if typ=="i": ntri+=dr*(dr-1)/2 else: dr-=1 return ntri def triangle_sequence(creation_sequence): """ Return triangle sequence for the given threshold graph creation sequence. """ cs=creation_sequence seq=[] dr=cs.count("d") # number of d's to the right of the current pos dcur=(dr-1)*(dr-2) // 2 # number of triangles through a node of clique dr irun=0 # number of i's in the last run drun=0 # number of d's in the last run for i,sym in enumerate(cs): if sym=="d": drun+=1 tri=dcur+(dr-1)*irun # new triangles at this d else: # cs[i]="i": if prevsym=="d": # new string of i's dcur+=(dr-1)*irun # accumulate shared shortest paths irun=0 # reset i run counter dr-=drun # reduce number of d's to right drun=0 # reset d run counter irun+=1 tri=dr*(dr-1) // 2 # new triangles at this i seq.append(tri) prevsym=sym return seq def cluster_sequence(creation_sequence): """ Return cluster sequence for the given threshold graph creation sequence. """ triseq=triangle_sequence(creation_sequence) degseq=degree_sequence(creation_sequence) cseq=[] for i,deg in enumerate(degseq): tri=triseq[i] if deg <= 1: # isolated vertex or single pair gets cc 0 cseq.append(0) continue max_size=(deg*(deg-1)) // 2 cseq.append(float(tri)/float(max_size)) return cseq def degree_sequence(creation_sequence): """ Return degree sequence for the threshold graph with the given creation sequence """ cs=creation_sequence # alias seq=[] rd=cs.count("d") # number of d to the right for i,sym in enumerate(cs): if sym=="d": rd-=1 seq.append(rd+i) else: seq.append(rd) return seq def density(creation_sequence): """ Return the density of the graph with this creation_sequence. The density is the fraction of possible edges present. """ N=len(creation_sequence) two_size=sum(degree_sequence(creation_sequence)) two_possible=N*(N-1) den=two_size/float(two_possible) return den def degree_correlation(creation_sequence): """ Return the degree-degree correlation over all edges. """ cs=creation_sequence s1=0 # deg_i*deg_j s2=0 # deg_i^2+deg_j^2 s3=0 # deg_i+deg_j m=0 # number of edges rd=cs.count("d") # number of d nodes to the right rdi=[ i for i,sym in enumerate(cs) if sym=="d"] # index of "d"s ds=degree_sequence(cs) for i,sym in enumerate(cs): if sym=="d": if i!=rdi[0]: print("Logic error in degree_correlation",i,rdi) raise ValueError rdi.pop(0) degi=ds[i] for dj in rdi: degj=ds[dj] s1+=degj*degi s2+=degi**2+degj**2 s3+=degi+degj m+=1 denom=(2*m*s2-s3*s3) numer=(4*m*s1-s3*s3) if denom==0: if numer==0: return 1 raise ValueError("Zero Denominator but Numerator is %s"%numer) return numer/float(denom) def shortest_path(creation_sequence,u,v): """ Find the shortest path between u and v in a threshold graph G with the given creation_sequence. For an unlabeled creation_sequence, the vertices u and v must be integers in (0,len(sequence)) refering to the position of the desired vertices in the sequence. For a labeled creation_sequence, u and v are labels of veritices. Use cs=creation_sequence(degree_sequence,with_labels=True) to convert a degree sequence to a creation sequence. Returns a list of vertices from u to v. Example: if they are neighbors, it returns [u,v] """ # Turn input sequence into a labeled creation sequence first=creation_sequence[0] if isinstance(first,str): # creation sequence cs = [(i,creation_sequence[i]) for i in range(len(creation_sequence))] elif isinstance(first,tuple): # labeled creation sequence cs = creation_sequence[:] elif isinstance(first,int): # compact creation sequence ci = uncompact(creation_sequence) cs = [(i,ci[i]) for i in range(len(ci))] else: raise TypeError("Not a valid creation sequence type") verts=[ s[0] for s in cs ] if v not in verts: raise ValueError("Vertex %s not in graph from creation_sequence"%v) if u not in verts: raise ValueError("Vertex %s not in graph from creation_sequence"%u) # Done checking if u==v: return [u] uindex=verts.index(u) vindex=verts.index(v) bigind=max(uindex,vindex) if cs[bigind][1]=='d': return [u,v] # must be that cs[bigind][1]=='i' cs=cs[bigind:] while cs: vert=cs.pop() if vert[1]=='d': return [u,vert[0],v] # All after u are type 'i' so no connection return -1 def shortest_path_length(creation_sequence,i): """ Return the shortest path length from indicated node to every other node for the threshold graph with the given creation sequence. Node is indicated by index i in creation_sequence unless creation_sequence is labeled in which case, i is taken to be the label of the node. Paths lengths in threshold graphs are at most 2. Length to unreachable nodes is set to -1. """ # Turn input sequence into a labeled creation sequence first=creation_sequence[0] if isinstance(first,str): # creation sequence if isinstance(creation_sequence,list): cs = creation_sequence[:] else: cs = list(creation_sequence) elif isinstance(first,tuple): # labeled creation sequence cs = [ v[1] for v in creation_sequence] i = [v[0] for v in creation_sequence].index(i) elif isinstance(first,int): # compact creation sequence cs = uncompact(creation_sequence) else: raise TypeError("Not a valid creation sequence type") # Compute N=len(cs) spl=[2]*N # length 2 to every node spl[i]=0 # except self which is 0 # 1 for all d's to the right for j in range(i+1,N): if cs[j]=="d": spl[j]=1 if cs[i]=='d': # 1 for all nodes to the left for j in range(i): spl[j]=1 # and -1 for any trailing i to indicate unreachable for j in range(N-1,0,-1): if cs[j]=="d": break spl[j]=-1 return spl def betweenness_sequence(creation_sequence,normalized=True): """ Return betweenness for the threshold graph with the given creation sequence. The result is unscaled. To scale the values to the iterval [0,1] divide by (n-1)*(n-2). """ cs=creation_sequence seq=[] # betweenness lastchar='d' # first node is always a 'd' dr=float(cs.count("d")) # number of d's to the right of curren pos irun=0 # number of i's in the last run drun=0 # number of d's in the last run dlast=0.0 # betweenness of last d for i,c in enumerate(cs): if c=='d': #cs[i]=="d": # betweennees = amt shared with eariler d's and i's # + new isolated nodes covered # + new paths to all previous nodes b=dlast + (irun-1)*irun/dr + 2*irun*(i-drun-irun)/dr drun+=1 # update counter else: # cs[i]="i": if lastchar=='d': # if this is a new run of i's dlast=b # accumulate betweenness dr-=drun # update number of d's to the right drun=0 # reset d counter irun=0 # reset i counter b=0 # isolated nodes have zero betweenness irun+=1 # add another i to the run seq.append(float(b)) lastchar=c # normalize by the number of possible shortest paths if normalized: order=len(cs) scale=1.0/((order-1)*(order-2)) seq=[ s*scale for s in seq ] return seq def eigenvectors(creation_sequence): """ Return a 2-tuple of Laplacian eigenvalues and eigenvectors for the threshold network with creation_sequence. The first value is a list of eigenvalues. The second value is a list of eigenvectors. The lists are in the same order so corresponding eigenvectors and eigenvalues are in the same position in the two lists. Notice that the order of the eigenvalues returned by eigenvalues(cs) may not correspond to the order of these eigenvectors. """ ccs=make_compact(creation_sequence) N=sum(ccs) vec=[0]*N val=vec[:] # get number of type d nodes to the right (all for first node) dr=sum(ccs[::2]) nn=ccs[0] vec[0]=[1./sqrt(N)]*N val[0]=0 e=dr dr-=nn type_d=True i=1 dd=1 while dd<nn: scale=1./sqrt(dd*dd+i) vec[i]=i*[-scale]+[dd*scale]+[0]*(N-i-1) val[i]=e i+=1 dd+=1 if len(ccs)==1: return (val,vec) for nn in ccs[1:]: scale=1./sqrt(nn*i*(i+nn)) vec[i]=i*[-nn*scale]+nn*[i*scale]+[0]*(N-i-nn) # find eigenvalue type_d=not type_d if type_d: e=i+dr dr-=nn else: e=dr val[i]=e st=i i+=1 dd=1 while dd<nn: scale=1./sqrt(i-st+dd*dd) vec[i]=[0]*st+(i-st)*[-scale]+[dd*scale]+[0]*(N-i-1) val[i]=e i+=1 dd+=1 return (val,vec) def spectral_projection(u,eigenpairs): """ Returns the coefficients of each eigenvector in a projection of the vector u onto the normalized eigenvectors which are contained in eigenpairs. eigenpairs should be a list of two objects. The first is a list of eigenvalues and the second a list of eigenvectors. The eigenvectors should be lists. There's not a lot of error checking on lengths of arrays, etc. so be careful. """ coeff=[] evect=eigenpairs[1] for ev in evect: c=sum([ evv*uv for (evv,uv) in zip(ev,u)]) coeff.append(c) return coeff def eigenvalues(creation_sequence): """ Return sequence of eigenvalues of the Laplacian of the threshold graph for the given creation_sequence. Based on the Ferrer's diagram method. The spectrum is integral and is the conjugate of the degree sequence. See:: @Article{degree-merris-1994, author = {Russel Merris}, title = {Degree maximal graphs are Laplacian integral}, journal = {Linear Algebra Appl.}, year = {1994}, volume = {199}, pages = {381--389}, } """ degseq=degree_sequence(creation_sequence) degseq.sort() eiglist=[] # zero is always one eigenvalue eig=0 row=len(degseq) bigdeg=degseq.pop() while row: if bigdeg<row: eiglist.append(eig) row-=1 else: eig+=1 if degseq: bigdeg=degseq.pop() else: bigdeg=0 return eiglist ### Threshold graph creation routines def random_threshold_sequence(n,p,seed=None): """ Create a random threshold sequence of size n. A creation sequence is built by randomly choosing d's with probabiliy p and i's with probability 1-p. s=nx.random_threshold_sequence(10,0.5) returns a threshold sequence of length 10 with equal probably of an i or a d at each position. A "random" threshold graph can be built with G=nx.threshold_graph(s) """ if not seed is None: random.seed(seed) if not (p<=1 and p>=0): raise ValueError("p must be in [0,1]") cs=['d'] # threshold sequences always start with a d for i in range(1,n): if random.random() < p: cs.append('d') else: cs.append('i') return cs # maybe *_d_threshold_sequence routines should # be (or be called from) a single routine with a more descriptive name # and a keyword parameter? def right_d_threshold_sequence(n,m): """ Create a skewed threshold graph with a given number of vertices (n) and a given number of edges (m). The routine returns an unlabeled creation sequence for the threshold graph. FIXME: describe algorithm """ cs=['d']+['i']*(n-1) # create sequence with n insolated nodes # m <n : not enough edges, make disconnected if m < n: cs[m]='d' return cs # too many edges if m > n*(n-1)/2: raise ValueError("Too many edges for this many nodes.") # connected case m >n-1 ind=n-1 sum=n-1 while sum<m: cs[ind]='d' ind -= 1 sum += ind ind=m-(sum-ind) cs[ind]='d' return cs def left_d_threshold_sequence(n,m): """ Create a skewed threshold graph with a given number of vertices (n) and a given number of edges (m). The routine returns an unlabeled creation sequence for the threshold graph. FIXME: describe algorithm """ cs=['d']+['i']*(n-1) # create sequence with n insolated nodes # m <n : not enough edges, make disconnected if m < n: cs[m]='d' return cs # too many edges if m > n*(n-1)/2: raise ValueError("Too many edges for this many nodes.") # Connected case when M>N-1 cs[n-1]='d' sum=n-1 ind=1 while sum<m: cs[ind]='d' sum += ind ind += 1 if sum>m: # be sure not to change the first vertex cs[sum-m]='i' return cs def swap_d(cs,p_split=1.0,p_combine=1.0,seed=None): """ Perform a "swap" operation on a threshold sequence. The swap preserves the number of nodes and edges in the graph for the given sequence. The resulting sequence is still a threshold sequence. Perform one split and one combine operation on the 'd's of a creation sequence for a threshold graph. This operation maintains the number of nodes and edges in the graph, but shifts the edges from node to node maintaining the threshold quality of the graph. """ if not seed is None: random.seed(seed) # preprocess the creation sequence dlist= [ i for (i,node_type) in enumerate(cs[1:-1]) if node_type=='d' ] # split if random.random()<p_split: choice=random.choice(dlist) split_to=random.choice(range(choice)) flip_side=choice-split_to if split_to!=flip_side and cs[split_to]=='i' and cs[flip_side]=='i': cs[choice]='i' cs[split_to]='d' cs[flip_side]='d' dlist.remove(choice) # don't add or combine may reverse this action # dlist.extend([split_to,flip_side]) # print >>sys.stderr,"split at %s to %s and %s"%(choice,split_to,flip_side) # combine if random.random()<p_combine and dlist: first_choice= random.choice(dlist) second_choice=random.choice(dlist) target=first_choice+second_choice if target >= len(cs) or cs[target]=='d' or first_choice==second_choice: return cs # OK to combine cs[first_choice]='i' cs[second_choice]='i' cs[target]='d' # print >>sys.stderr,"combine %s and %s to make %s."%(first_choice,second_choice,target) return cs

31.894621

115

0.608033

87e9c24c1b9dd8a0d152176d1baaf2d24870446a

4,910

py

Python

cogs/wikiScrape.py

LastAeon77/LibraryOfRuinaBot

87bbec4a796b6973a975e6eee3c2042754d8ae67

[ "MIT" ]

1

2020-08-26T12:16:22.000Z

cogs/wikiScrape.py

LastAeon77/LibraryOfRuinaBot

87bbec4a796b6973a975e6eee3c2042754d8ae67

[ "MIT" ]

null

cogs/wikiScrape.py

LastAeon77/LibraryOfRuinaBot

87bbec4a796b6973a975e6eee3c2042754d8ae67

[ "MIT" ]

null

from bs4 import BeautifulSoup import requests import discord from discord.ext import commands import pandas as pd class LibraryScrape: def __init__(self, searchTerm): df = pd.read_csv("./data/wiki.csv") row = df.loc[df["SearchTerms"].str.lower() == searchTerm.lower()] self.searchType = row.iloc[0]["Type"] self.link = "https://library-of-ruina.fandom.com/wiki/" + searchTerm source = requests.get(self.link).text self.soup = BeautifulSoup(source, "lxml") self.imageLink = "" self.contents = "" def Characters(self): img = self.soup.find_all("figure", attrs={"class": "pi-item pi-image"}) k = [] for imgs in img: k.append(imgs.find("img")) src = k[0].attrs["src"] self.imageLink = src words = self.soup.find_all("p") temp = [] for paragraphs in words: temp.append(paragraphs) self.contents += temp[1].get_text() self.contents += "\n" self.contents += temp[2].get_text() self.contents += "\n" self.contents += self.link final = [] final.append(self.imageLink) final.append(self.contents) return final def Mechanics(self): img = self.soup.find_all( "figure", attrs={"class": "article-thumb tright show-info-icon"} ) k = [] for imgs in img: k.append(imgs.find("img")) src = k[0].attrs["src"] self.imageLink = src words = self.soup.find_all("p") temp = [] for paragraphs in words: temp.append(paragraphs) self.contents += temp[1].get_text() self.contents += "\n" self.contents += temp[2].get_text() # self.contents += temp[3].get_text() final = [] final.append(self.imageLink) final.append(self.contents) return final def Floors(self): try: img = self.soup.find_all("a", attrs={"class": "image image-thumbnail"}) k = [] for imgs in img: k.append(imgs) src = k[0]["href"] self.imageLink = src except: self.imageLink = "https://i.imgflip.com/j69nf.jpg" words = self.soup.find_all("p") temp = [] for paragraphs in words: temp.append(paragraphs) self.contents += temp[1].get_text() self.contents += "\n" self.contents += temp[2].get_text() final = [] final.append(self.imageLink) final.append(self.contents) return final class LibraryStuff(commands.Cog): def __init__(self, bot): self.bot = bot @commands.command() async def wiki(self, ctx, arx: str): """Searches wiki for topics""" name = arx embed = discord.Embed() embed.color = 3066993 embed.set_author(name=str(arx)) link = "" content = "" try: temp = LibraryScrape(name) if temp.searchType == "Characters": link, content = temp.Characters() elif temp.searchType == "Floors": link, content = temp.Floors() elif temp.searchType == "Mechanics": link, content = temp.Mechanics() except: link = "https://i.imgflip.com/j69nf.jpg" content = "Content not found, either it doesn't" content += " exist or Master Last_Aeon was lazy" content += "\n If you typed floor, try Floor_of_History" embed.set_image(url=link) embed.set_author(name=name.capitalize()) embed.description = content await ctx.send(embed=embed) def setup(bot): bot.add_cog(LibraryStuff(bot)) # x = LibraryScrape("key_pages") # print(x.Mechanics()) # link = "https://library-of-ruina.fandom.com/wiki/Floor_of_History" # source = requests.get(link).text # soup = BeautifulSoup(source, "lxml") # img = soup.find_all("a", attrs={"class": "image image-thumbnail"}) # k = [] # for imgs in img: # k.append(imgs) # print(k[0]["href"]) # x = LibraryScrape("Invitations") # print(x.Mechanics()) # img = soup.find('div', class_=' image-WikiaSiteWrapper') # #find all stff with figure and class name this # summary = soup.find_all("figure", attrs={"class":"pi-item pi-image"}) # #print(summary) # k=[] # #find image # for imgs in summary: # k.append(imgs.find("img")) # #summary = img.find('div', class_='image-WikiaSiteWrapper') # #print(k[0].attrs) # #get the image src from attributes # src = k[0].attrs['src'] # #print(src) # #get link # img_link=src # img = requests.get(img_link) # #download # #with open("cogs/angela.png", "wb") as f: # #f.write(img.content) # words = soup.find_all("p") # temp=[] # for paragraphs in words: # temp.append(paragraphs) # temp[0] # print(temp[2].get_text())

28.71345

83

0.569654

4472399a5142731d50df281c0363aab60b7b92fe

404

py

Python

baselines/deepq/__init__.py

qwertyanonym789/anonym_code

d939b907badabbb01759a3677a0605194564ee96

[ "MIT" ]

282

2018-06-20T23:00:01.000Z

2022-03-30T06:25:17.000Z

baselines/deepq/__init__.py

qwertyanonym789/anonym_code

d939b907badabbb01759a3677a0605194564ee96

[ "MIT" ]

10

2019-03-25T08:19:51.000Z

2021-11-22T12:25:22.000Z

baselines/deepq/__init__.py

qwertyanonym789/anonym_code

d939b907badabbb01759a3677a0605194564ee96

[ "MIT" ]

106

2018-06-20T23:06:40.000Z

2022-03-03T05:04:03.000Z

from baselines.deepq import models # noqa from baselines.deepq.build_graph import build_act, build_train # noqa from baselines.deepq.simple import learn, load # noqa from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer # noqa def wrap_atari_dqn(env): from baselines.common.atari_wrappers import wrap_deepmind return wrap_deepmind(env, frame_stack=True, scale=True)

50.5

87

0.816832

f4cf823a11daf8fa4dbfb33f871bd4f608bd0841

2,052

py

Python

bindings/python/examples/return_region.py

mmccarty/legion

30e00fa6016527c4cf60025a461fb7865f8def6b

[ "Apache-2.0" ]

555

2015-01-19T07:50:27.000Z

2022-03-22T11:35:48.000Z

bindings/python/examples/return_region.py

mmccarty/legion

30e00fa6016527c4cf60025a461fb7865f8def6b

[ "Apache-2.0" ]

1,157

2015-01-07T18:34:23.000Z

2022-03-31T19:45:27.000Z

bindings/python/examples/return_region.py

mmccarty/legion

30e00fa6016527c4cf60025a461fb7865f8def6b

[ "Apache-2.0" ]

145

2015-02-03T02:31:42.000Z

2022-02-28T12:03:51.000Z

#!/usr/bin/env python3 # Copyright 2021 Stanford University # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import pygion from pygion import task, Region, RW @task def make_region(): # If you return a region from a task, the privileges to the region # will be automatically given to the calling task. R = Region([4, 4], {'x': pygion.float64}) pygion.fill(R, 'x', 0) print('returning from make_region with', R) return R @task def make_region_dict(): # It should also work if the region in question is returned as # part of a larger data structure. R = Region([4, 4], {'x': pygion.float64}) pygion.fill(R, 'x', 0) result = {'asdf': R} print('returning from make_region_dict with', result) return result @task(privileges=[RW]) def use_region(R): print('in use_region with', R) R.x.fill(123) @task(privileges=[RW]) def pass_region_nested(R, depth): # Passing and return region arguments also works, including to # recursive subtasks. if depth > 0: return pass_region_nested(R, depth-1).get() R.x.fill(456) return R @task def main(): R = make_region().get() use_region(R) print('in main with', R) R.x.fill(1) R2 = make_region_dict().get()['asdf'] use_region(R2) print('in main with', R2) R2.x.fill(124) R_copy = pass_region_nested(R, 5).get() # Check that this is the same region. assert R.handle[0].tree_id == R_copy.handle[0].tree_id if __name__ == '__main__': main()

27

74

0.681287

795af779c8632aff242bcdccdb44123e7a86ed56

14,133

py

Python

tests/test_minimalism.py

georgesdimitrov/arvo

86d33afc3f45d1f2e6f22aded8c2e2b12bc5db7d

[ "MIT" ]

11

2021-02-24T20:05:24.000Z

2022-03-13T14:27:04.000Z

tests/test_minimalism.py

georgesdimitrov/arvo

86d33afc3f45d1f2e6f22aded8c2e2b12bc5db7d

[ "MIT" ]

null

tests/test_minimalism.py

georgesdimitrov/arvo

86d33afc3f45d1f2e6f22aded8c2e2b12bc5db7d

[ "MIT" ]

3

2021-09-24T02:26:16.000Z

2022-03-22T12:34:36.000Z

import pytest from music21 import converter from arvo import minimalism from arvo import sequences @pytest.fixture def example_stream(): s = converter.parse("tinyNotation: C D E F G A B c d e f g") return s # Additive Process Tests def test_additive_process(example_stream): result = minimalism.additive_process(example_stream) intended_result = converter.parse( """tinyNotation: C C D C D E C D E F C D E F G C D E F G A C D E F G A B C D E F G A B c C D E F G A B c d C D E F G A B c d e C D E F G A B c d e f C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) @pytest.mark.parametrize( "direction,intended_result", [ ( minimalism.Direction.BACKWARD, converter.parse( """tinyNotation: g f g e f g d e f g c d e f g B c d e f g A B c d e f g G A B c d e f g F G A B c d e f g E F G A B c d e f g D E F G A B c d e f g C D E F G A B c d e f g """ ), ), ( minimalism.Direction.INWARD, converter.parse( """tinyNotation: C g C D f g C D E e f g C D E F d e f g C D E F G c d e f g C D E F G A B c d e f g """ ), ), ( minimalism.Direction.OUTWARD, converter.parse( """tinyNotation: A B G A B c F G A B c d E F G A B c d e D E F G A B c d e f C D E F G A B c d e f g """ ), ), ], ) def test_additive_process_direction(example_stream, direction, intended_result): result = minimalism.additive_process(example_stream, direction=direction) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_step_value_int(example_stream): result = minimalism.additive_process(example_stream, step_value=2) intended_result = converter.parse( """tinyNotation: C D C D E F C D E F G A C D E F G A B c C D E F G A B c d e C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_step_value_sequence(example_stream): result = minimalism.additive_process(example_stream, step_value=[1, 2, 3]) intended_result = converter.parse( """tinyNotation: C C D E C D E F G A C D E F G A B C D E F G A B c d C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_step_value_sequence_absolute(example_stream): result = minimalism.additive_process( example_stream, step_value=sequences.PRIMES, step_mode=minimalism.StepMode.ABSOLUTE, ) intended_result = converter.parse( """tinyNotation: C D C D E C D E F G C D E F G A B C D E F G A B c d e f C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_step_value_sequence_absolute_infinite_loop(example_stream): result = minimalism.additive_process( example_stream, step_value=[1, 2, 3], step_mode=minimalism.StepMode.ABSOLUTE ) intended_result = converter.parse( """tinyNotation: C C D C D E """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_repetitions_int(example_stream): result = minimalism.additive_process(example_stream, repetitions=2) intended_result = converter.parse( """tinyNotation: C C C D C D C D E C D E C D E F C D E F C D E F G C D E F G C D E F G A C D E F G A C D E F G A B C D E F G A B C D E F G A B c C D E F G A B c C D E F G A B c d C D E F G A B c d C D E F G A B c d e C D E F G A B c d e C D E F G A B c d e f C D E F G A B c d e f C D E F G A B c d e f g C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_repetitions_sequence(example_stream): result = minimalism.additive_process(example_stream, repetitions=[1, 2, 3]) intended_result = converter.parse( """tinyNotation: C C D C D C D E C D E C D E C D E F C D E F G C D E F G C D E F G A C D E F G A C D E F G A C D E F G A B C D E F G A B c C D E F G A B c C D E F G A B c d C D E F G A B c d C D E F G A B c d C D E F G A B c d e C D E F G A B c d e f C D E F G A B c d e f C D E F G A B c d e f g C D E F G A B c d e f g C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_iterations_start(example_stream): result = minimalism.additive_process(example_stream, iterations_start=3) intended_result = converter.parse( """tinyNotation: C D E C D E F C D E F G C D E F G A C D E F G A B C D E F G A B c C D E F G A B c d C D E F G A B c d e C D E F G A B c d e f C D E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_iterations_end(example_stream): result = minimalism.additive_process(example_stream, iterations_end=8) intended_result = converter.parse( """tinyNotation: C C D C D E C D E F C D E F G C D E F G A C D E F G A B C D E F G A B c """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_additive_process_nonlinear(example_stream): result = minimalism.additive_process( example_stream, step_value=sequences.kolakoski(), step_mode=minimalism.StepMode.ABSOLUTE, iterations_end=8, ) intended_result = converter.parse( """tinyNotation: C C D C D C C C D C C D """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) # Subtractive Process Tests def test_subtractive_process(example_stream): result = minimalism.subtractive_process(example_stream) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g F G A B c d e f g G A B c d e f g A B c d e f g B c d e f g c d e f g d e f g e f g f g g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) @pytest.mark.parametrize( "direction,intended_result", [ ( minimalism.Direction.BACKWARD, converter.parse( """tinyNotation: C D E F G A B c d e f g C D E F G A B c d e f C D E F G A B c d e C D E F G A B c d C D E F G A B c C D E F G A B C D E F G A C D E F G C D E F C D E C D C """ ), ), ( minimalism.Direction.INWARD, converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f E F G A B c d e F G A B c d G A B c A B """ ), ), ( minimalism.Direction.OUTWARD, converter.parse( """tinyNotation: C D E F G A B c d e f g C D E F G c d e f g C D E F d e f g C D E e f g C D f g C g """ ), ), ], ) def test_subtractive_process_direction(example_stream, direction, intended_result): result = minimalism.subtractive_process(example_stream, direction=direction) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_step_value_int(example_stream): result = minimalism.subtractive_process(example_stream, step_value=2) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g E F G A B c d e f g G A B c d e f g B c d e f g d e f g f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_step_value_sequence(example_stream): result = minimalism.subtractive_process(example_stream, step_value=[1, 2, 3]) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g F G A B c d e f g B c d e f g c d e f g e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_step_value_sequence_absolute(example_stream): result = minimalism.subtractive_process( example_stream, step_value=sequences.PRIMES, step_mode=minimalism.StepMode.ABSOLUTE, ) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g E F G A B c d e f g F G A B c d e f g A B c d e f g c d e f g g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_step_value_sequence_absolute_infinite_loop(example_stream): result = minimalism.subtractive_process( example_stream, step_value=[1, 2, 3], step_mode=minimalism.StepMode.ABSOLUTE ) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_repetitions_int(example_stream): result = minimalism.subtractive_process(example_stream, repetitions=2) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g C D E F G A B c d e f g D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g E F G A B c d e f g F G A B c d e f g F G A B c d e f g G A B c d e f g G A B c d e f g A B c d e f g A B c d e f g B c d e f g B c d e f g c d e f g c d e f g d e f g d e f g e f g e f g f g f g g g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_repetitions_sequence(example_stream): result = minimalism.subtractive_process(example_stream, repetitions=[1, 2, 3]) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g E F G A B c d e f g E F G A B c d e f g F G A B c d e f g G A B c d e f g G A B c d e f g A B c d e f g A B c d e f g A B c d e f g B c d e f g c d e f g c d e f g d e f g d e f g d e f g e f g f g f g g g g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_iterations_start(example_stream): result = minimalism.subtractive_process(example_stream, iterations_start=3) intended_result = converter.parse( """tinyNotation: F G A B c d e f g G A B c d e f g A B c d e f g B c d e f g c d e f g d e f g e f g f g g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_iterations_end(example_stream): result = minimalism.subtractive_process(example_stream, iterations_end=8) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g F G A B c d e f g G A B c d e f g A B c d e f g B c d e f g c d e f g d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes) def test_subtractive_process_nonlinear(example_stream): result = minimalism.subtractive_process( example_stream, step_value=sequences.kolakoski(), step_mode=minimalism.StepMode.ABSOLUTE, iterations_end=8, ) intended_result = converter.parse( """tinyNotation: C D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g E F G A B c d e f g D E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g D E F G A B c d e f g E F G A B c d e f g """ ) assert list(result.flat.notes) == list(intended_result.flat.notes)

25.93211

88

0.51355

92ddd9021e3080cae79d3a084d3079595f9d0f28

2,236

py

Python

drf_spectacular/types.py

Mopsan/drf-spectacular

5d1280c29994793be517b443fd5d902525c6ab3e

[ "BSD-3-Clause" ]

null

drf_spectacular/types.py

Mopsan/drf-spectacular

5d1280c29994793be517b443fd5d902525c6ab3e

[ "BSD-3-Clause" ]

null

drf_spectacular/types.py

Mopsan/drf-spectacular

5d1280c29994793be517b443fd5d902525c6ab3e

[ "BSD-3-Clause" ]

null

import enum from datetime import datetime, date from decimal import Decimal from uuid import UUID class OpenApiTypes(enum.Enum): """ Basic types known to the OpenApi specification or at least common format extension of it. - Use BYTE for base64 encoded data wrapped in a string - Use BINARY for raw binary data - Use OBJECT for arbitrary free-form object (usually a dict) """ FLOAT = enum.auto() BOOL = enum.auto() STR = enum.auto() BYTE = enum.auto() # base64 encoded BINARY = enum.auto() INT = enum.auto() UUID = enum.auto() URI = enum.auto() IP4 = enum.auto() IP6 = enum.auto() HOSTNAME = enum.auto() DECIMAL = enum.auto() DATETIME = enum.auto() DATE = enum.auto() EMAIL = enum.auto() OBJECT = enum.auto() NONE = enum.auto() # make a copy with dict() before modifying returned dict OPENAPI_TYPE_MAPPING = { OpenApiTypes.FLOAT: {'type': 'number', 'format': 'float'}, OpenApiTypes.BOOL: {'type': 'boolean'}, OpenApiTypes.STR: {'type': 'string'}, OpenApiTypes.BYTE: {'type': 'string', 'format': 'byte'}, OpenApiTypes.BINARY: {'type': 'string', 'format': 'binary'}, OpenApiTypes.INT: {'type': 'integer'}, OpenApiTypes.UUID: {'type': 'string', 'format': 'uuid'}, OpenApiTypes.URI: {'type': 'string', 'format': 'uri'}, OpenApiTypes.IP4: {'type': 'string', 'format': 'ipv4'}, OpenApiTypes.IP6: {'type': 'string', 'format': 'ipv6'}, OpenApiTypes.HOSTNAME: {'type': 'string', 'format': 'hostname'}, OpenApiTypes.DECIMAL: {'type': 'number', 'format': 'double'}, OpenApiTypes.DATETIME: {'type': 'string', 'format': 'date-time'}, OpenApiTypes.DATE: {'type': 'string', 'format': 'date'}, OpenApiTypes.EMAIL: {'type': 'string', 'format': 'email'}, OpenApiTypes.OBJECT: {'type': 'object', 'additionalProperties': {}}, OpenApiTypes.NONE: {}, } PYTHON_TYPE_MAPPING = { str: OpenApiTypes.STR, float: OpenApiTypes.FLOAT, bool: OpenApiTypes.BOOL, bytes: OpenApiTypes.BINARY, int: OpenApiTypes.INT, UUID: OpenApiTypes.UUID, Decimal: OpenApiTypes.DECIMAL, datetime: OpenApiTypes.DATETIME, date: OpenApiTypes.DATE, dict: OpenApiTypes.OBJECT, }

32.405797

72

0.636852

d34f9f5fc37012d32625baa62f9b895061d2172d

2,696

py

Python

tbx/core/migrations/0015_auto_20160218_1201.py

elviva404/wagtail-torchbox

718d9e2c4337073f010296932d369c726a01dbd3

[ "MIT" ]

103

2015-02-24T17:58:21.000Z

2022-03-23T08:08:58.000Z

tbx/core/migrations/0015_auto_20160218_1201.py

elviva404/wagtail-torchbox

718d9e2c4337073f010296932d369c726a01dbd3

[ "MIT" ]

145

2015-01-13T17:13:43.000Z

2022-03-29T12:56:20.000Z

tbx/core/migrations/0015_auto_20160218_1201.py

elviva404/wagtail-torchbox

718d9e2c4337073f010296932d369c726a01dbd3

[ "MIT" ]

57

2015-01-03T12:00:37.000Z

2022-02-09T13:11:30.000Z

# -*- coding: utf-8 -*- from django.db import models, migrations import wagtail.images.models import django.db.models.deletion from django.conf import settings import taggit.managers class Migration(migrations.Migration): dependencies = [ ("torchbox", "0014_workpage_show_in_play_menu"), ] operations = [ migrations.AddField( model_name="torchboximage", name="file_size", field=models.PositiveIntegerField(null=True, editable=False), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="created_at", field=models.DateTimeField( auto_now_add=True, verbose_name="created at", db_index=True ), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="file", field=models.ImageField( height_field="height", upload_to=wagtail.images.models.get_upload_to, width_field="width", verbose_name="file", ), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="height", field=models.IntegerField(verbose_name="height", editable=False), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="tags", field=taggit.managers.TaggableManager( to="taggit.Tag", through="taggit.TaggedItem", blank=True, help_text=None, verbose_name="tags", ), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="title", field=models.CharField(max_length=255, verbose_name="title"), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="uploaded_by_user", field=models.ForeignKey( on_delete=django.db.models.deletion.SET_NULL, blank=True, editable=False, to=settings.AUTH_USER_MODEL, null=True, verbose_name="uploaded by user", ), preserve_default=True, ), migrations.AlterField( model_name="torchboximage", name="width", field=models.IntegerField(verbose_name="width", editable=False), preserve_default=True, ), ]

30.988506

77

0.546365

c7c86252b8187032ffab0bbff534cb8f55761934

12,250

py

Python

thirdweb/core/classes/marketplace_direct.py

nftlabs/nftlabs-sdk-python

ea533142dc0881872b347cd8ce635dc0bfff3153

[ "Apache-2.0" ]

30

2021-10-31T13:17:58.000Z

2022-02-04T13:41:13.000Z

thirdweb/core/classes/marketplace_direct.py

nftlabs/nftlabs-sdk-python

ea533142dc0881872b347cd8ce635dc0bfff3153

[ "Apache-2.0" ]

36

2021-11-03T20:30:38.000Z

2022-02-14T10:15:40.000Z

thirdweb/core/classes/marketplace_direct.py

nftlabs/nftlabs-sdk-python

ea533142dc0881872b347cd8ce635dc0bfff3153

[ "Apache-2.0" ]

10

2021-11-10T19:59:41.000Z

2022-01-21T21:26:55.000Z

from re import M from typing import Any, Dict, Optional, cast from eth_utils import is_address from thirdweb.abi import Marketplace from thirdweb.abi import IERC165 from thirdweb.abi.ierc1155 import IERC1155 from thirdweb.abi.ierc721 import IERC721 from thirdweb.abi.marketplace import IMarketplaceListingParameters from thirdweb.common.currency import ( fetch_currency_value, is_native_token, normalize_price_value, set_erc20_allowance, ) from web3.constants import MAX_INT from thirdweb.common.error import ListingNotFoundException, WrongListingTypeException from thirdweb.common.marketplace import ( handle_token_approval, is_token_approved_for_transfer, map_offer, validate_new_listing_param, ) from thirdweb.common.nft import fetch_token_metadata_for_contract from thirdweb.constants.contract import INTERFACE_ID_IERC1155, INTERFACE_ID_IERC721 from thirdweb.constants.currency import ZERO_ADDRESS from thirdweb.core.classes.base_contract import BaseContract from thirdweb.core.classes.contract_wrapper import ContractWrapper from thirdweb.core.classes.ipfs_storage import IpfsStorage from thirdweb.types.currency import Price from thirdweb.types.marketplace import ( ContractListing, ContractOffer, DirectListing, ListingType, NewDirectListing, Offer, ) from web3.eth import TxReceipt class MarketplaceDirect(BaseContract[Marketplace]): _storage: IpfsStorage def __init__(self, contract_wrapper: ContractWrapper, storage: IpfsStorage): super().__init__(contract_wrapper) self._storage = storage """ READ FUNCTIONS """ def get_listing(self, listing_id: int) -> DirectListing: """ Get a direct listing from the marketplace by ID :param listing_id: The ID of the listing to get :return: The listing """ raw_listing = self._contract_wrapper._contract_abi.listings.call(listing_id) listing = ContractListing(*raw_listing) if listing.asset_contract == ZERO_ADDRESS: raise ListingNotFoundException(listing_id) if ListingType(listing.listing_type) != ListingType.DIRECT: raise WrongListingTypeException( listing_id, "Auction", "Direct", ) return self._map_listing(listing) def get_active_offer(self, listing_id: int, address: str) -> Optional[Offer]: """ Get an active offer for a direct listing :param listing_id: The ID of the listing to get the offer for :param address: The address of the user to get the offer for :return: The offer """ self._validate_listing(listing_id) if not is_address(address): raise Exception("Address must be a valid address") raw_ofers = self._contract_wrapper._contract_abi.offers.call( listing_id, address ) offers = ContractOffer(*raw_ofers) if offers.offeror == ZERO_ADDRESS: return None return map_offer(self._contract_wrapper.get_provider(), listing_id, offers) """ WRITE FUNCTIONS """ def create_listing(self, listing: NewDirectListing) -> int: """ Create a new direct listing :param listing: The listing to create :return: The ID of the listing """ validate_new_listing_param(listing) handle_token_approval( self._contract_wrapper.get_provider(), self._contract_wrapper.get_signer(), self.get_address(), listing.asset_contract_address, listing.token_id, self._contract_wrapper.get_signer_address(), ) normalized_price_per_token = normalize_price_value( self._contract_wrapper.get_provider(), listing.buyout_price_per_token, listing.currency_contract_address, ) receipt = self._contract_wrapper.send_transaction( "create_listing", [ IMarketplaceListingParameters( assetContract=listing.asset_contract_address, tokenId=listing.token_id, startTime=listing.start_time_in_seconds, secondsUntilEndTime=listing.listing_duration_in_seconds, quantityToList=listing.quantity, currencyToAccept=listing.currency_contract_address, reservePricePerToken=normalized_price_per_token, buyoutPricePerToken=normalized_price_per_token, listingType=0, ), ], ) events = self._contract_wrapper.get_events("ListingAdded", receipt) return cast(Any, events[0].get("args")).get("listingId") def make_offer( self, listing_id: int, quantity_desired: int, currency_contract_address: str, price_per_token: Price, expiration_date: int = int(MAX_INT, 0), ) -> TxReceipt: """ Make an offer on a direct listing :param listing_id: The ID of the listing to make the offer on :param quantity_desired: The quantity desired :param currency_contract_address: The address of the currency contract :param price_per_token: The price per token :return: The transaction receipt """ if is_native_token(currency_contract_address): raise Exception( "You must used the wrapped native token address when making an offer" ) normalized_price = normalize_price_value( self._contract_wrapper.get_provider(), price_per_token, currency_contract_address, ) try: self.get_listing(listing_id) except: raise ListingNotFoundException(listing_id) overrides: Dict[Any, Any] = {} set_erc20_allowance( self._contract_wrapper, quantity_desired * normalized_price, currency_contract_address, overrides, ) print( "PARAMS: ", [ listing_id, quantity_desired, currency_contract_address, normalized_price, expiration_date, ], ) # TODO: Add OVERRIDES return self._contract_wrapper.send_transaction( "offer", [ listing_id, quantity_desired, currency_contract_address, normalized_price, expiration_date, ], ) def accept_offer(self, listing_id: int, address_or_offerror: str) -> TxReceipt: """ Accept a direct listing offer :param listing_id: The ID of the listing to accept the offer on :param address_or_offerror: The address of the user to accept the offer for :return: The transaction receipt """ self._validate_listing(listing_id) raw_offer = self._contract_wrapper._contract_abi.offers.call( listing_id, address_or_offerror ) offer = ContractOffer(*raw_offer) return self._contract_wrapper.send_transaction( "accept_offer", [listing_id, address_or_offerror, offer.currency, offer.price_per_token], ) def buyout_listing( self, listing_id: int, quantity_desired: int, receiver: Optional[str] = None ) -> TxReceipt: """ Buyout a direct listing by ID :param listing_id: The ID of the listing to buyout :param quantity_desired: The quantity desired :param receiver: The address of the user to receive the tokens :return: The transaction receipt """ listing = self._validate_listing(listing_id) valid = self._is_still_valid_listing(listing, quantity_desired) if not valid: raise Exception( "The asset on this listing has been moved from the lister's wallet, this listing is now invalid" ) buy_for = receiver if receiver else self._contract_wrapper.get_signer_address() overrides: Dict[Any, Any] = {} set_erc20_allowance( self._contract_wrapper, listing.buyout_price * quantity_desired, listing.currency_contract_address, overrides, ) # TODO: Add OVERRIDES return self._contract_wrapper.send_transaction( "buy", [ listing_id, buy_for, quantity_desired, listing.currency_contract_address, listing.buyout_price * quantity_desired, ], ) def update_listing(self, listing: DirectListing) -> TxReceipt: """ Update a direct listing :param listing: The listing to update :return: The transaction receipt """ return self._contract_wrapper.send_transaction( "update_listing", [ listing.id, listing.quantity, listing.buyout_price, listing.buyout_price, listing.currency_contract_address, listing.start_time_in_seconds, listing.seconds_until_end, ], ) def cancel_listing(self, listing_id: int) -> TxReceipt: """ Cancel a direct listing :param listing_id: The ID of the listing to cancel :return: The transaction receipt """ return self._contract_wrapper.send_transaction( "cancel_direct_listing", [listing_id] ) """ INTERNAL FUNCTIONS """ def _validate_listing(self, listing_id: int) -> DirectListing: try: return self.get_listing(listing_id) except: raise ListingNotFoundException(listing_id) def _map_listing(self, listing: ContractListing) -> DirectListing: return DirectListing( id=listing.listing_id, asset_contract_address=listing.asset_contract, token_id=listing.token_id, buyout_price=listing.buyout_price_per_token, currency_contract_address=listing.currency, buyout_currency_value_per_token=fetch_currency_value( self._contract_wrapper.get_provider(), listing.currency, listing.buyout_price_per_token, ), quantity=listing.quantity, start_time_in_seconds=listing.start_time, asset=fetch_token_metadata_for_contract( listing.asset_contract, self._contract_wrapper.get_provider(), listing.token_id, self._storage, ), seconds_until_end=listing.end_time, seller_address=listing.token_owner, ) def _is_still_valid_listing( self, listing: DirectListing, quantity: Optional[int] = None ) -> bool: approved = is_token_approved_for_transfer( self._contract_wrapper.get_provider(), self.get_address(), listing.asset_contract_address, listing.token_id, listing.seller_address, ) if not approved: return False provider = self._contract_wrapper.get_provider() erc165 = IERC165(provider, listing.asset_contract_address) is_erc721 = erc165.supports_interface.call(INTERFACE_ID_IERC721) is_erc1155 = erc165.supports_interface.call(INTERFACE_ID_IERC1155) if is_erc721: ierc721 = IERC721(provider, listing.asset_contract_address) return ( ierc721.owner_of.call(listing.token_id).lower() == listing.seller_address.lower() ) elif is_erc1155: ierc1155 = IERC1155(provider, listing.asset_contract_address) balance = ierc1155.balance_of.call(listing.seller_address, listing.token_id) return balance > (quantity if quantity is not None else listing.quantity) else: return False

33.378747

112

0.627347

174f5f45696778188a3c2256a0cf562a5968cb49

18

py

Python

.venv/lib/python3.8/site-packages/nbclient/_version.py

RivtLib/replit01

ce1ae18b446a9c844f40e88a51c71fbc45ab3ad7

[ "MIT" ]

6

2019-01-23T03:51:31.000Z

2021-02-15T07:54:39.000Z

.venv/lib/python3.8/site-packages/nbclient/_version.py

RivtLib/replit01

ce1ae18b446a9c844f40e88a51c71fbc45ab3ad7

[ "MIT" ]

52

2019-01-23T10:10:30.000Z

2021-06-27T10:23:10.000Z

.venv/lib/python3.8/site-packages/nbclient/_version.py

RivtLib/replit01

ce1ae18b446a9c844f40e88a51c71fbc45ab3ad7

[ "MIT" ]

7

2019-01-23T10:06:03.000Z

2020-10-29T18:38:37.000Z

version = '0.5.4'

9

17

0.555556

01176a04b19fd37b8ba708957728e250140875bd

2,971

py

Python

src/bioregistry/schema/constants.py

kkaris/bioregistry

e8cdaf8e8c5670873ce10a5a67d7850b76e5eff7

[ "MIT" ]

null

src/bioregistry/schema/constants.py

kkaris/bioregistry

e8cdaf8e8c5670873ce10a5a67d7850b76e5eff7

[ "MIT" ]

null

src/bioregistry/schema/constants.py

kkaris/bioregistry

e8cdaf8e8c5670873ce10a5a67d7850b76e5eff7

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Schema constants.""" import rdflib.namespace __all__ = [ 'bioregistry_schema_terms', # Namespaces 'bioregistry_collection', 'bioregistry_resource', 'bioregistry_metaresource', 'bioregistry_schema', 'orcid', ] bioregistry_schema_terms = { 'Resource': "A type for entries in the Bioregistry's registry.", 'Registry': "A type for entries in the Bioregistry's metaregistry.", 'Collection': "A type for entries in the Bioregistry's collections", 'Mapping': "A type, typically instantiated as a blank node, that connects a given resource to a metaresource" " and a metaidentifier using the hasMetaresource and hasMetaidentifier relations.", 'hasExample': 'An identifier for a resource or metaresource.', 'isProvider': 'Denotes whether a metaresource is capable of acting as a provider. If so, should be accompanied' ' by a "provider_formatter" relation as well.', 'isResolver': 'Denotes whether a metaresource is capable of acting as a resolver. If so, should be accompanied' ' by a "resolver_formatter" relation as well.', 'hasProviderFormatter': "The URL format for a provider that contains $1 for the identifier (or metaidentifier)" " that should be resolved.", 'hasResolverFormatter': "The URL format for a resolver that contains $1 for the prefix and $2 for the identifier" " that should be resolved.", 'hasPattern': "The pattern for identifiers in the given resource", 'hasContactEmail': "The email of the contact person for the given resource", 'hasDownloadURL': "A download link for the given resource", 'providesFor': "For resources that do not create their own controlled vocabulary, this relation should be used" " to point to a different resource that it uses. For example, CTD's gene resource provides for" " the NCBI Entres Gene resource.", 'isDeprecated': "A property whose subject is a resource that denotes if it is still available and usable?" " Currently this is a blanket term for decomissioned, unable to locate, abandoned, etc.", 'hasMapping': "A property whose subject is a resource and object is a mapping", 'hasRegistry': "A property whose subject is a mapping and object is a metaresource.", 'hasMetaidentifier': "A property whose subject is a mapping and object is an identifier string.", } bioregistry_collection = rdflib.namespace.Namespace('https://bioregistry.io/collection/') bioregistry_resource = rdflib.namespace.Namespace('https://bioregistry.io/registry/') bioregistry_metaresource = rdflib.namespace.Namespace('https://bioregistry.io/metaregistry/') bioregistry_schema = rdflib.namespace.ClosedNamespace( 'https://bioregistry.io/schema/#', terms=sorted(bioregistry_schema_terms), ) orcid = rdflib.namespace.Namespace('https://orcid.org/')

57.134615

117

0.705486

711581c102a78a817dafd2e61356974155dab7f3

1,712

py

Python

app/core/migrations/0001_initial.py

mxuanvan02/recipe-app-api

f037e5650cbf97a583d6654e035baa518328176e

[ "MIT" ]

null

app/core/migrations/0001_initial.py

mxuanvan02/recipe-app-api

f037e5650cbf97a583d6654e035baa518328176e

[ "MIT" ]

null

app/core/migrations/0001_initial.py

mxuanvan02/recipe-app-api

f037e5650cbf97a583d6654e035baa518328176e

[ "MIT" ]

null

# Generated by Django 3.2.6 on 2021-08-23 16:29 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0012_alter_user_first_name_max_length'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

50.352941

266

0.639603

cc75956a5994bcbc5e2a64fda1299084eca1951b

20,297

py

Python

bop_toolkit_lib/renderer_py.py

Alex-B5/bop_toolkit_ab

a9fc94c68a84a0866b88169321048478763900ca

[ "MIT" ]

null

bop_toolkit_lib/renderer_py.py

Alex-B5/bop_toolkit_ab

a9fc94c68a84a0866b88169321048478763900ca

[ "MIT" ]

null

bop_toolkit_lib/renderer_py.py

Alex-B5/bop_toolkit_ab

a9fc94c68a84a0866b88169321048478763900ca

[ "MIT" ]

null

# Author: Tomas Hodan (hodantom@cmp.felk.cvut.cz) # Center for Machine Perception, Czech Technical University in Prague """A Python based renderer.""" import os import numpy as np from glumpy import app, gloo, gl from bop_toolkit_lib import inout from bop_toolkit_lib import misc from bop_toolkit_lib import renderer # Set glumpy logging level. from glumpy.log import log import logging log.setLevel(logging.WARNING) # Options: ERROR, WARNING, DEBUG, INFO. # Set backend (http://glumpy.readthedocs.io/en/latest/api/app-backends.html). # app.use('glfw') # Options: 'glfw', 'qt5', 'pyside', 'pyglet'. # RGB vertex shader. _rgb_vertex_code = """ uniform mat4 u_mv; uniform mat4 u_nm; uniform mat4 u_mvp; uniform vec3 u_light_eye_pos; attribute vec3 a_position; attribute vec3 a_normal; attribute vec3 a_color; attribute vec2 a_texcoord; varying vec3 v_color; varying vec2 v_texcoord; varying vec3 v_eye_pos; varying vec3 v_L; varying vec3 v_normal; void main() { gl_Position = u_mvp * vec4(a_position, 1.0); v_color = a_color; v_texcoord = a_texcoord; // The following points/vectors are expressed in the eye coordinates. v_eye_pos = (u_mv * vec4(a_position, 1.0)).xyz; // Vertex. v_L = normalize(u_light_eye_pos - v_eye_pos); // Vector to the light. v_normal = normalize(u_nm * vec4(a_normal, 1.0)).xyz; // Normal vector. } """ # RGB fragment shader - flat shading. _rgb_fragment_flat_code = """ uniform float u_light_ambient_w; uniform sampler2D u_texture; uniform int u_use_texture; varying vec3 v_color; varying vec2 v_texcoord; varying vec3 v_eye_pos; varying vec3 v_L; void main() { // Face normal in eye coords. vec3 f_normal = normalize(cross(dFdx(v_eye_pos), dFdy(v_eye_pos))); float light_diffuse_w = max(dot(normalize(v_L), normalize(f_normal)), 0.0); float light_w = u_light_ambient_w + light_diffuse_w; if(light_w > 1.0) light_w = 1.0; if(bool(u_use_texture)) { gl_FragColor = vec4(light_w * texture2D(u_texture, v_texcoord)); } else { gl_FragColor = vec4(light_w * v_color, 1.0); } } """ # RGB fragment shader - Phong shading. _rgb_fragment_phong_code = """ uniform float u_light_ambient_w; uniform sampler2D u_texture; uniform int u_use_texture; varying vec3 v_color; varying vec2 v_texcoord; varying vec3 v_eye_pos; varying vec3 v_L; varying vec3 v_normal; void main() { float light_diffuse_w = max(dot(normalize(v_L), normalize(v_normal)), 0.0); float light_w = u_light_ambient_w + light_diffuse_w; if(light_w > 1.0) light_w = 1.0; if(bool(u_use_texture)) { gl_FragColor = vec4(light_w * texture2D(u_texture, v_texcoord)); } else { gl_FragColor = vec4(light_w * v_color, 1.0); } } """ _rgb_fragment_no_light_code = """ uniform float u_light_ambient_w; uniform sampler2D u_texture; uniform int u_use_texture; varying vec3 v_color; varying vec2 v_texcoord; varying vec3 v_eye_pos; varying vec3 v_L; void main() { // Face normal in eye coords. vec3 f_normal = normalize(cross(dFdx(v_eye_pos), dFdy(v_eye_pos))); float light_diffuse_w = max(dot(normalize(v_L), normalize(f_normal)), 0.0); float light_w = u_light_ambient_w + light_diffuse_w; if(light_w > 1.0) light_w = 1.0; if(light_w < 1.0) light_w = 1.0; //line added. light is now always 1.0. Could have handled smarter if(bool(u_use_texture)) { gl_FragColor = vec4(light_w * texture2D(u_texture, v_texcoord)); } else { gl_FragColor = vec4(light_w * v_color, 1.0); } } """ # Depth vertex shader. # Ref: https://github.com/julienr/vertex_visibility/blob/master/depth.py # # Getting the depth from the depth buffer in OpenGL is doable, see here: # http://web.archive.org/web/20130416194336/http://olivers.posterous.com/linear-depth-in-glsl-for-real # http://web.archive.org/web/20130426093607/http://www.songho.ca/opengl/gl_projectionmatrix.html # http://stackoverflow.com/a/6657284/116067 # but it is difficult to achieve high precision, as explained in this article: # http://dev.theomader.com/depth-precision/ # # Once the vertex is in the view coordinates (view * model * v), its depth is # simply the Z axis. Hence, instead of reading from the depth buffer and undoing # the projection matrix, we store the Z coord of each vertex in the color # buffer. OpenGL allows for float32 color buffer components. _depth_vertex_code = """ uniform mat4 u_mv; uniform mat4 u_mvp; attribute vec3 a_position; attribute vec3 a_color; varying float v_eye_depth; void main() { gl_Position = u_mvp * vec4(a_position, 1.0); vec3 v_eye_pos = (u_mv * vec4(a_position, 1.0)).xyz; // In eye coords. // OpenGL Z axis goes out of the screen, so depths are negative v_eye_depth = -v_eye_pos.z; } """ # Depth fragment shader. _depth_fragment_code = """ varying float v_eye_depth; void main() { gl_FragColor = vec4(v_eye_depth, 0.0, 0.0, 1.0); } """ # Functions to calculate transformation matrices. # Note that OpenGL expects the matrices to be saved column-wise. # (Ref: http://www.songho.ca/opengl/gl_transform.html) def _calc_model_view(model, view): """Calculates the model-view matrix. :param model: 4x4 ndarray with the model matrix. :param view: 4x4 ndarray with the view matrix. :return: 4x4 ndarray with the model-view matrix. """ return np.dot(model, view) def _calc_model_view_proj(model, view, proj): """Calculates the model-view-projection matrix. :param model: 4x4 ndarray with the model matrix. :param view: 4x4 ndarray with the view matrix. :param proj: 4x4 ndarray with the projection matrix. :return: 4x4 ndarray with the model-view-projection matrix. """ return np.dot(np.dot(model, view), proj) def _calc_normal_matrix(model, view): """Calculates the normal matrix. Ref: http://www.songho.ca/opengl/gl_normaltransform.html :param model: 4x4 ndarray with the model matrix. :param view: 4x4 ndarray with the view matrix. :return: 4x4 ndarray with the normal matrix. """ return np.linalg.inv(np.dot(model, view)).T def _calc_calib_proj(K, x0, y0, w, h, nc, fc, window_coords='y_down'): """Conversion of Hartley-Zisserman intrinsic matrix to OpenGL proj. matrix. Ref: 1) https://strawlab.org/2011/11/05/augmented-reality-with-OpenGL 2) https://github.com/strawlab/opengl-hz/blob/master/src/calib_test_utils.py :param K: 3x3 ndarray with the intrinsic camera matrix. :param x0 The X coordinate of the camera image origin (typically 0). :param y0: The Y coordinate of the camera image origin (typically 0). :param w: Image width. :param h: Image height. :param nc: Near clipping plane. :param fc: Far clipping plane. :param window_coords: 'y_up' or 'y_down'. :return: 4x4 ndarray with the OpenGL projection matrix. """ depth = float(fc - nc) q = -(fc + nc) / depth qn = -2 * (fc * nc) / depth # Draw our images upside down, so that all the pixel-based coordinate # systems are the same. if window_coords == 'y_up': proj = np.array([ [2 * K[0, 0] / w, -2 * K[0, 1] / w, (-2 * K[0, 2] + w + 2 * x0) / w, 0], [0, -2 * K[1, 1] / h, (-2 * K[1, 2] + h + 2 * y0) / h, 0], [0, 0, q, qn], # Sets near and far planes (glPerspective). [0, 0, -1, 0] ]) # Draw the images upright and modify the projection matrix so that OpenGL # will generate window coords that compensate for the flipped image coords. else: assert window_coords == 'y_down' proj = np.array([ [2 * K[0, 0] / w, -2 * K[0, 1] / w, (-2 * K[0, 2] + w + 2 * x0) / w, 0], [0, 2 * K[1, 1] / h, (2 * K[1, 2] - h + 2 * y0) / h, 0], [0, 0, q, qn], # Sets near and far planes (glPerspective). [0, 0, -1, 0] ]) return proj.T class RendererPython(renderer.Renderer): """A Python based renderer.""" def __init__(self, width, height, mode='rgb+depth', shading='phong', bg_color=(0.0, 0.0, 0.0, 0.0)): """Constructor. :param width: Width of the rendered image. :param height: Height of the rendered image. :param mode: Rendering mode ('rgb+depth', 'rgb', 'depth'). :param shading: Type of shading ('flat', 'phong', 'no_light'). :param bg_color: Color of the background (R, G, B, A). """ super(RendererPython, self).__init__(width, height) self.mode = mode self.shading = shading self.bg_color = bg_color # Indicators whether to render RGB and/or depth image. self.render_rgb = self.mode in ['rgb', 'rgb+depth'] self.render_depth = self.mode in ['depth', 'rgb+depth'] # Structures to store object models and related info. self.models = {} self.model_bbox_corners = {} self.model_textures = {} # Rendered images. self.rgb = None self.depth = None # Window for rendering. self.window = app.Window(visible=False) # Per-object vertex and index buffer. self.vertex_buffers = {} self.index_buffers = {} # Per-object OpenGL programs for rendering of RGB and depth images. self.rgb_programs = {} self.depth_programs = {} # The frame buffer object. rgb_buf = np.zeros( (self.height, self.width, 4), np.float32).view(gloo.TextureFloat2D) depth_buf = np.zeros( (self.height, self.width), np.float32).view(gloo.DepthTexture) self.fbo = gloo.FrameBuffer(color=rgb_buf, depth=depth_buf) # Activate the created frame buffer object. self.fbo.activate() def add_object(self, obj_id, model_path, **kwargs): """See base class.""" # Color of the object model (the original color saved with the object model # will be used if None). surf_color = None if 'surf_color' in kwargs: surf_color = kwargs['surf_color'] # Load the object model. model = inout.load_ply(model_path) self.models[obj_id] = model # Calculate the 3D bounding box of the model (will be used to set the near # and far clipping plane). bb = misc.calc_3d_bbox( model['pts'][:, 0], model['pts'][:, 1], model['pts'][:, 2]) self.model_bbox_corners[obj_id] = np.array([ [bb[0], bb[1], bb[2]], [bb[0], bb[1], bb[2] + bb[5]], [bb[0], bb[1] + bb[4], bb[2]], [bb[0], bb[1] + bb[4], bb[2] + bb[5]], [bb[0] + bb[3], bb[1], bb[2]], [bb[0] + bb[3], bb[1], bb[2] + bb[5]], [bb[0] + bb[3], bb[1] + bb[4], bb[2]], [bb[0] + bb[3], bb[1] + bb[4], bb[2] + bb[5]], ]) # Set texture/color of vertices. self.model_textures[obj_id] = None # Use the specified uniform surface color. if surf_color is not None: colors = np.tile(list(surf_color) + [1.0], [model['pts'].shape[0], 1]) # Set UV texture coordinates to dummy values. texture_uv = np.zeros((model['pts'].shape[0], 2), np.float32) # Use the model texture. elif 'texture_file' in self.models[obj_id].keys(): model_texture_path = os.path.join( os.path.dirname(model_path), self.models[obj_id]['texture_file']) model_texture = inout.load_im(model_texture_path) # Normalize the texture image. if model_texture.max() > 1.0: model_texture = model_texture.astype(np.float32) / 255.0 model_texture = np.flipud(model_texture) self.model_textures[obj_id] = model_texture # UV texture coordinates. texture_uv = model['texture_uv'] # Set the per-vertex color to dummy values. colors = np.zeros((model['pts'].shape[0], 3), np.float32) # Use the original model color. elif 'colors' in model.keys(): assert (model['pts'].shape[0] == model['colors'].shape[0]) colors = model['colors'] if colors.max() > 1.0: colors /= 255.0 # Color values are expected in range [0, 1]. # Set UV texture coordinates to dummy values. texture_uv = np.zeros((model['pts'].shape[0], 2), np.float32) # Set the model color to gray. else: colors = np.ones((model['pts'].shape[0], 3), np.float32) * 0.5 # Set UV texture coordinates to dummy values. texture_uv = np.zeros((model['pts'].shape[0], 2), np.float32) # Set the vertex data. if self.mode == 'depth': vertices_type = [ ('a_position', np.float32, 3), ('a_color', np.float32, colors.shape[1]) ] vertices = np.array(list(zip(model['pts'], colors)), vertices_type) else: if self.shading == 'flat': vertices_type = [ ('a_position', np.float32, 3), ('a_color', np.float32, colors.shape[1]), ('a_texcoord', np.float32, 2) ] vertices = np.array(list(zip(model['pts'], colors, texture_uv)), vertices_type) elif self.shading == 'phong': vertices_type = [ ('a_position', np.float32, 3), ('a_normal', np.float32, 3), ('a_color', np.float32, colors.shape[1]), ('a_texcoord', np.float32, 2) ] vertices = np.array(list(zip(model['pts'], model['normals'], colors, texture_uv)), vertices_type) elif self.shading == 'no_light': vertices_type = [ ('a_position', np.float32, 3), ('a_color', np.float32, colors.shape[1]), ('a_texcoord', np.float32, 2) ] vertices = np.array(list(zip(model['pts'], colors, texture_uv)), vertices_type) else: raise ValueError('Unknown shading type.') # Create vertex and index buffer for the loaded object model. self.vertex_buffers[obj_id] = vertices.view(gloo.VertexBuffer) self.index_buffers[obj_id] = \ model['faces'].flatten().astype(np.uint32).view(gloo.IndexBuffer) # Set shader for the selected shading. if self.shading == 'flat': rgb_fragment_code = _rgb_fragment_flat_code elif self.shading == 'phong': rgb_fragment_code = _rgb_fragment_phong_code elif self.shading == 'no_light': rgb_fragment_code = _rgb_fragment_no_light_code else: raise ValueError('Unknown shading type.') # Prepare the RGB OpenGL program. rgb_program = gloo.Program(_rgb_vertex_code, rgb_fragment_code) rgb_program.bind(self.vertex_buffers[obj_id]) if self.model_textures[obj_id] is not None: rgb_program['u_use_texture'] = int(True) rgb_program['u_texture'] = self.model_textures[obj_id] else: rgb_program['u_use_texture'] = int(False) rgb_program['u_texture'] = np.zeros((1, 1, 4), np.float32) self.rgb_programs[obj_id] = rgb_program # Prepare the depth OpenGL program. depth_program = gloo.Program(_depth_vertex_code,_depth_fragment_code) depth_program.bind(self.vertex_buffers[obj_id]) self.depth_programs[obj_id] = depth_program def remove_object(self, obj_id): """See base class.""" del self.models[obj_id] del self.model_bbox_corners[obj_id] if obj_id in self.model_textures: del self.model_textures[obj_id] del self.vertex_buffers[obj_id] del self.index_buffers[obj_id] del self.rgb_programs[obj_id] del self.depth_programs[obj_id] def render_object(self, obj_id, R, t, fx, fy, cx, cy): """See base class.""" # Define the following variables as global so their latest values are always # seen in function on_draw below. global curr_obj_id, mat_model, mat_view, mat_proj curr_obj_id = obj_id # Model matrix (from object space to world space). mat_model = np.eye(4, dtype=np.float32) # View matrix (from world space to eye space; transforms also the coordinate # system from OpenCV to OpenGL camera space). mat_view_cv = np.eye(4, dtype=np.float32) mat_view_cv[:3, :3], mat_view_cv[:3, 3] = R, t.squeeze() yz_flip = np.eye(4, dtype=np.float32) yz_flip[1, 1], yz_flip[2, 2] = -1, -1 mat_view = yz_flip.dot(mat_view_cv) # OpenCV to OpenGL camera system. mat_view = mat_view.T # OpenGL expects column-wise matrix format. # Calculate the near and far clipping plane from the 3D bounding box. bbox_corners = self.model_bbox_corners[obj_id] bbox_corners_ht = np.concatenate( (bbox_corners, np.ones((bbox_corners.shape[0], 1))), axis=1).transpose() bbox_corners_eye_z = mat_view_cv[2, :].reshape((1, 4)).dot(bbox_corners_ht) clip_near = bbox_corners_eye_z.min() clip_far = bbox_corners_eye_z.max() # Projection matrix. K = np.array([[fx, 0.0, cx], [0.0, fy, cy], [0.0, 0.0, 1.0]]) mat_proj = _calc_calib_proj( K, 0, 0, self.width, self.height, clip_near, clip_far) @self.window.event def on_draw(dt): self.window.clear() global curr_obj_id, mat_model, mat_view, mat_proj # Render the RGB image. if self.render_rgb: self.rgb = self._draw_rgb( curr_obj_id, mat_model, mat_view, mat_proj) # Render the depth image. if self.render_depth: self.depth = self._draw_depth( curr_obj_id, mat_model, mat_view, mat_proj) # The on_draw function is called framecount+1 times. app.run(framecount=0) if self.mode == 'rgb': return {'rgb': self.rgb} elif self.mode == 'depth': return {'depth': self.depth} elif self.mode == 'rgb+depth': return {'rgb': self.rgb, 'depth': self.depth} def _draw_rgb(self, obj_id, mat_model, mat_view, mat_proj): """Renders an RGB image. :param obj_id: ID of the object model to render. :param mat_model: 4x4 ndarray with the model matrix. :param mat_view: 4x4 ndarray with the view matrix. :param mat_proj: 4x4 ndarray with the projection matrix. :return: HxWx3 ndarray with the rendered RGB image. """ # Update the OpenGL program. program = self.rgb_programs[obj_id] program['u_light_eye_pos'] = list(self.light_cam_pos) program['u_light_ambient_w'] = self.light_ambient_weight program['u_mv'] = _calc_model_view(mat_model, mat_view) program['u_nm'] = _calc_normal_matrix(mat_model, mat_view) program['u_mvp'] = _calc_model_view_proj(mat_model, mat_view, mat_proj) # OpenGL setup. gl.glEnable(gl.GL_DEPTH_TEST) gl.glClearColor( self.bg_color[0], self.bg_color[1], self.bg_color[2], self.bg_color[3]) gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT) gl.glViewport(0, 0, self.width, self.height) # Keep the back-face culling disabled because of objects which do not have # well-defined surface (e.g. the lamp from the lm dataset). gl.glDisable(gl.GL_CULL_FACE) # Rendering. program.draw(gl.GL_TRIANGLES, self.index_buffers[obj_id]) # Get the content of the FBO texture. rgb = np.zeros((self.height, self.width, 4), dtype=np.float32) gl.glReadPixels(0, 0, self.width, self.height, gl.GL_RGBA, gl.GL_FLOAT, rgb) rgb.shape = (self.height, self.width, 4) rgb = rgb[::-1, :] rgb = np.round(rgb[:, :, :3] * 255).astype(np.uint8) # Convert to [0, 255]. return rgb def _draw_depth(self, obj_id, mat_model, mat_view, mat_proj): """Renders a depth image. :param obj_id: ID of the object model to render. :param mat_model: 4x4 ndarray with the model matrix. :param mat_view: 4x4 ndarray with the view matrix. :param mat_proj: 4x4 ndarray with the projection matrix. :return: HxW ndarray with the rendered depth image. """ # Update the OpenGL program. program = self.depth_programs[obj_id] program['u_mv'] = _calc_model_view(mat_model, mat_view) program['u_mvp'] = _calc_model_view_proj(mat_model, mat_view, mat_proj) # OpenGL setup. gl.glEnable(gl.GL_DEPTH_TEST) gl.glClearColor(0.0, 0.0, 0.0, 0.0) gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT) gl.glViewport(0, 0, self.width, self.height) # Keep the back-face culling disabled because of objects which do not have # well-defined surface (e.g. the lamp from the lm dataset). gl.glDisable(gl.GL_CULL_FACE) # Rendering. program.draw(gl.GL_TRIANGLES, self.index_buffers[obj_id]) # Get the content of the FBO texture. depth = np.zeros((self.height, self.width, 4), dtype=np.float32) gl.glReadPixels( 0, 0, self.width, self.height, gl.GL_RGBA, gl.GL_FLOAT, depth) depth.shape = (self.height, self.width, 4) depth = depth[::-1, :] depth = depth[:, :, 0] # Depth is saved in the first channel return depth

34.227656

104

0.663793

f84aae3e00c7ffcc0d05092565deb86e680e3987

7,552

py

Python

rplugin/python3/lfx/main.py

dcampos/nvim-ulf

9b70096bf04133a33f09691b1ef1ac89f8f29a85

[ "MIT" ]

2

2020-10-15T09:34:53.000Z

2020-10-26T10:31:48.000Z

rplugin/python3/lfx/main.py

dcampos/nvim-ulf

9b70096bf04133a33f09691b1ef1ac89f8f29a85

[ "MIT" ]

null

rplugin/python3/lfx/main.py

dcampos/nvim-ulf

9b70096bf04133a33f09691b1ef1ac89f8f29a85

[ "MIT" ]

1

2020-10-15T09:35:05.000Z

#!/usr/bin/env python3 from core.types import ClientConfig, Settings from core.sessions import create_session from core.protocol import WorkspaceFolder from core.sessions import Session from core.protocol import Request, Notification from core.url import filename_to_uri from core.logging import set_debug_logging, set_exception_logging, setup_log import signal import sys import time import shutil import json import argparse import os INTELEPHENSE = ['node', '/home/dpc/.npm-global/lib/node_modules/intelephense/lib/intelephense.js', '--stdio'] PYLS = [shutil.which('pyls')] WORKING_FILE = '/home/dpc/sandbox/lfx/rplugin/python3/lfx/core/diff.py' # WORKING_FILE='/home/dpc/Dev/eproc1g/web/modulos/sandbox/tools/TesteDatas.php' # WORKING_FILE='/home/dpc/Dev/godot/my-circle-jump/Main.gd' initialized = False position = [1, 1] configs = { '.php': ClientConfig( name='intelephense', binary_args=INTELEPHENSE, # binary_args=[shutil.which('pyls')], tcp_port=None, languageId='python', settings=dict(), init_options={ 'clearCache': False, 'storagePath': '/tmp/foo', }), '.py': ClientConfig( name='pyls', binary_args=PYLS, tcp_port=None, languageId='python', settings={}, init_options={}), '.gd': ClientConfig( name='godot', binary_args=[], tcp_port=6008, languageId='gdscript3', settings={}, init_options={}), } def on_pre_initialize(session): print('Pre-initialize called') def on_post_initialize(session: Session): global initialized print('Post-initialize called') initialized = True print(session.capabilities) session.client.send_notification( Notification.initialized()) did_open(working_file) session.client.on_notification( 'indexingStarted', lambda params: print(params)) session.client.on_notification( 'indexingEnded', lambda params: print(params)) def find_root(file): head, tail = os.path.split(file) found = head while tail != '': for lookup in ['.gitmodules', '.git']: lookup = os.path.join(head, lookup) if os.path.exists(lookup): found = head break head, tail = os.path.split(head) return found parser = argparse.ArgumentParser() parser.add_argument('file', nargs='?', type=argparse.FileType('r'), default=WORKING_FILE) args = parser.parse_args() working_file = os.path.abspath(args.file.name) file_ext = os.path.splitext(working_file)[1] root_path = find_root(working_file) root_name = os.path.dirname(root_path) for key in configs.keys(): if key == file_ext: config = configs[key] break else: raise KeyError('No config found for this file') workspace = WorkspaceFolder(root_name, root_path) # workspace = WorkspaceFolder('eproc1g', '/home/dpc/sandbox/sublime-lsp/') settings = Settings() settings.log_stderr = True settings.log_payloads = True set_debug_logging(True) set_exception_logging(True) setup_log() current_file = None session = create_session( config=config, workspace_folders=[workspace], env=dict(), settings=settings, on_post_initialize=on_post_initialize, on_pre_initialize=on_pre_initialize, on_stderr_log=lambda what: print(what)) def signal_handler(sig, frame): session.end() signal.signal(signal.SIGINT, signal_handler) # Handlers def request_handler(res): print(res) def error_handler(error): print(error) # Params def text_document_position_params(): return { 'textDocument': { 'uri': filename_to_uri(working_file) }, 'position': { 'line': position[0] - 1, 'character': position[1] - 1 } } # Util def print_position(): with open(working_file, 'r', encoding='latin1') as file: lines = file.readlines() print(lines[position[0] - 1].rstrip()) print('{}^'.format(' ' * (position[1] - 1))) # Requests/notifications def did_close(path): session.client.send_notification( Notification.didClose({ 'textDocument': { 'uri': filename_to_uri(path) } }) ) def did_open(path): global current_file if current_file: did_close(current_file) with open(path, 'r', encoding='latin1') as file: text = file.read() session.client.send_notification( Notification.didOpen({ 'textDocument': { 'uri': filename_to_uri(path), 'languageId': 'php', 'version': 1, 'text': text }} )) current_file = path def workspace_symbol(): query = input('Symbol: ') session.client.execute_request( Request.workspaceSymbol({'query': query}), request_handler, error_handler, 300) def document_symbol(): session.client.execute_request( Request.documentSymbols({ 'textDocument': { 'uri': filename_to_uri(working_file) } }), request_handler, error_handler, 300) def hover(): session.client.execute_request( Request.hover(text_document_position_params()), request_handler, error_handler, 300) def completion(): session.client.execute_request( Request.complete(text_document_position_params()), lambda res: print(json.dumps(res, indent=4)), error_handler, 300) def references(): params = text_document_position_params() params['context'] = {'includeDeclaration' : False} session.client.execute_request( Request.references(params), lambda res: print(json.dumps(res, indent=4)), error_handler, 10) def goto_definition(): session.client.execute_request( Request.definition(text_document_position_params()), lambda res: print(json.dumps(res, indent=4)), error_handler, 300) def signature_help(): session.client.execute_request( Request.signatureHelp(text_document_position_params()), lambda res: print(json.dumps(res, indent=4)), error_handler, 10) def set_position(): line = input('Line: ') col = input('Col: ') position[0] = int(line) position[1] = int(col) print('Position set to ', position) print_position() def open_file(): file = input('File: ') print('Opening file {}...'.format(file)) did_open(file) def end(): session.end() sys.exit(0) menu_items=[ ['c', 'Completion', completion], ['h', 'Hover', hover], ['s', 'Document symbols', document_symbol], ['w', 'Workspace symbols', workspace_symbol], ['d', 'Go to definition', goto_definition], ['r', 'Find referenes', references], ['S', 'Signature help', signature_help], ['p', 'Set position', set_position], ['o', 'Open file', open_file], ['q', 'Exit', end]] def show_menu(): print('-' * 70) print('Choose an option:') for i, item in enumerate(menu_items): print('({}) {}'.format(item[0], item[1])) while True: if not initialized: time.sleep(1.0) continue show_menu() res = input('---> ') for item in menu_items: if item[0] == res: cmd = item[2] cmd() break else: print('Invalid choice!')

25.687075

90

0.620895

a1d0245a8cca7577ca292fa843f885bd44bba2c6

4,437

py

Python

gdmtl/datasets/assembler.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

null

gdmtl/datasets/assembler.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

null

gdmtl/datasets/assembler.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

1

2022-02-26T00:49:03.000Z

from __future__ import annotations from itertools import zip_longest from typing import Dict, Generic, List, Optional, Sequence, TypeVar import torch from irtools.pad import pad_batch from transformers import PreTrainedTokenizer T = TypeVar("T", str, List[int]) class Assembler(Generic[T]): """Assemble pre-tokenized ids into shapes accepted by transformer models. This assembler should only be used for fine-tuning as it disregards different tokenizers' assembling logic and enforce a unified assembling approach. Using it for pre-trained model inference would result in undefined behavior. Single: [bos] + [prefix] + ids + [eos] + [optional pad] Paired: [bos] + [prefix] + first_ids + [sep] + second_ids [eos] + [optional pad] The `bos`, `sep`, `eos`, and `pad` tokens are self-explained and must be single integer. The `prefix` is used as task identifier and can be a list of integers. The same model may be used for ranking, summarization, or other tasks. """ def __init__( self, *, # Enforcing keyword arguments for clarity tokenizer: PreTrainedTokenizer, max_length: Optional[int] = None, prefix_token_ids: Optional[T] = None, suffix_token_ids: Optional[T] = None, pad_to_max_length: bool = True, add_special_tokens: bool = True, return_special_tokens_mask: bool = False, return_token_type_ids: Optional[bool] = True, ): if pad_to_max_length: assert isinstance(max_length, int) self._tokenizer = tokenizer self._max_length = max_length self._prefix: Optional[T] = prefix_token_ids if isinstance(self._prefix, str): self._prefix = self._prefix.strip() if not self._prefix.endswith(">"): self._prefix += " " self._suffix: Optional[T] = suffix_token_ids if isinstance(self._suffix, str) and not self._suffix.startswith(" "): self._suffix = self._suffix.strip() if not self._suffix.startswith("<"): self._suffix = " " + self._suffix self._pad_to_max_length = pad_to_max_length self._add_special_tokens = add_special_tokens self._return_special_tokens_mask = return_special_tokens_mask self._return_token_type_ids = return_token_type_ids self._pad_values = { "input_ids": self._tokenizer.pad_token_id, "attention_mask": 0, "token_type_ids": self._tokenizer.pad_token_type_id, "special_tokens_mask": 1, } def batch_assemble( self, first_ids_seq: Sequence[T], second_ids_seq: Optional[Sequence[T]] = None, ) -> Dict[str, torch.Tensor]: if second_ids_seq is None: if self._prefix is not None: first_ids_seq = [self._prefix + x for x in first_ids_seq] if self._suffix is not None: first_ids_seq = [x + self._suffix for x in first_ids_seq] second_ids_seq = [] else: if self._prefix is not None: first_ids_seq = [self._prefix + x for x in first_ids_seq] if self._suffix is not None: second_ids_seq = [x + self._suffix for x in second_ids_seq] outputs: List[Dict[str, torch.Tensor]] = [] for first_ids, second_ids in zip_longest(first_ids_seq, second_ids_seq): output = self._tokenizer.encode_plus( first_ids, second_ids, add_special_tokens=self._add_special_tokens, max_length=self._max_length, pad_to_max_length=self._pad_to_max_length, return_tensors="pt", return_token_type_ids=self._return_token_type_ids, return_attention_mask=True, truncation="longest_first", return_special_tokens_mask=self._return_special_tokens_mask, ) assert all(v.size(0) == 1 for v in output.values()) outputs.append({k: v[0] for k, v in output.items()}) encoded_output: Dict[str, torch.Tensor] = {} for key in outputs[0]: padded = pad_batch( [one[key] for one in outputs], value=self._pad_values[key] ) tensor = torch.stack(padded) encoded_output[key] = tensor return encoded_output

38.921053

92

0.626549

21b62ee9ac048a021f53d66d14707f3bf6649f00

38,120

py

Python

api/data/constants/essence.py

XIIIsiren/CommunityAPI

e665638d2800b71b3d32d49c6897901f4c49a9c5

[ "Apache-2.0" ]

1

2021-06-15T07:31:13.000Z

api/data/constants/essence.py

XIIIsiren/CommunityAPI

e665638d2800b71b3d32d49c6897901f4c49a9c5

[ "Apache-2.0" ]

1

2021-06-01T10:14:32.000Z

2021-06-02T10:54:12.000Z

api/data/constants/essence.py

XIIIsiren/CommunityAPI

e665638d2800b71b3d32d49c6897901f4c49a9c5

[ "Apache-2.0" ]

2

2021-06-01T10:59:15.000Z

2021-06-03T18:29:36.000Z

ESSENCE_DICT = { "SOUL_WHIP": { "type": "Wither", "dungeonize": 300, "1": 200, "2": 300, "3": 400, "4": 550, "5": 750 }, "JERRY_STAFF": { "type": "Wither", "dungeonize": 0, "1": 5, "2": 10, "3": 25, "4": 50, "5": 100 }, "CRYPT_WITHERLORD_SWORD": { "type": "Wither", "1": 15, "2": 25, "3": 40, "4": 60, "5": 100 }, "BONZO_STAFF": { "type": "Wither", "1": 5, "2": 10, "3": 25, "4": 50, "5": 100 }, "BONZO_MASK": { "type": "Wither", "1": 5, "2": 10, "3": 25, "4": 50, "5": 100 }, "STONE_BLADE": { "type": "Wither", "1": 15, "2": 25, "3": 40, "4": 60, "5": 100 }, "ADAPTIVE_HELMET": { "type": "Wither", "1": 10, "2": 25, "3": 50, "4": 100, "5": 150 }, "ADAPTIVE_CHESTPLATE": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "ADAPTIVE_LEGGINGS": { "type": "Wither", "1": 10, "2": 25, "3": 50, "4": 100, "5": 200 }, "ADAPTIVE_BOOTS": { "type": "Wither", "1": 10, "2": 25, "3": 50, "4": 100, "5": 150 }, "SILENT_DEATH": { "type": "Wither", "1": 5, "2": 10, "3": 25, "4": 50, "5": 100 }, "CONJURING_SWORD": { "type": "Wither", "1": 5, "2": 10, "3": 25, "4": 50, "5": 100 }, "SPIRIT_SWORD": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "ITEM_SPIRIT_BOW": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "THORNS_BOOTS": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "SPIRIT_MASK": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "BONE_BOOMERANG": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "BAT_WAND": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "LAST_BREATH": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "SHADOW_ASSASSIN_HELMET": { "type": "Wither", "1": 25, "2": 50, "3": 100, "4": 200, "5": 300 }, "SHADOW_ASSASSIN_CHESTPLATE": { "type": "Wither", "1": 35, "2": 75, "3": 150, "4": 300, "5": 500 }, "SHADOW_ASSASSIN_LEGGINGS": { "type": "Wither", "1": 30, "2": 60, "3": 120, "4": 250, "5": 400 }, "SHADOW_ASSASSIN_BOOTS": { "type": "Wither", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 }, "SHADOW_FURY": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 400, "5": 800 }, "LIVID_DAGGER": { "type": "Wither", "1": 30, "2": 60, "3": 120, "4": 250, "5": 400 }, "FLOWER_OF_TRUTH": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 400, "5": 800 }, "FEL_SWORD": { "type": "Wither", "1": 15, "2": 25, "3": 40, "4": 60, "5": 100 }, "WITHER_CLOAK": { "type": "Wither", "1": 75, "2": 150, "3": 300, "4": 600, "5": 1200 }, "PRECURSOR_EYE": { "type": "Wither", "1": 75, "2": 150, "3": 300, "4": 600, "5": 1200 }, "GIANTS_SWORD": { "type": "Wither", "1": 75, "2": 150, "3": 300, "4": 600, "5": 1200 }, "NECROMANCER_LORD_HELMET": { "type": "Wither", "1": 30, "2": 60, "3": 120, "4": 250, "5": 400 }, "NECROMANCER_LORD_CHESTPLATE": { "type": "Wither", "1": 70, "2": 150, "3": 250, "4": 450, "5": 750 }, "NECROMANCER_LORD_LEGGINGS": { "type": "Wither", "1": 35, "2": 75, "3": 150, "4": 300, "5": 500 }, "NECROMANCER_LORD_BOOTS": { "type": "Wither", "1": 30, "2": 60, "3": 120, "4": 250, "5": 400 }, "NECROMANCER_SWORD": { "type": "Wither", "1": 70, "2": 150, "3": 250, "4": 450, "5": 750 }, "NECRON_BLADE": { "type": "Wither", "1": 150, "2": 300, "3": 500, "4": 900, "5": 1500 }, "HYPERION": { "type": "Wither", "1": 150, "2": 300, "3": 500, "4": 900, "5": 1500 }, "VALKYRIE": { "type": "Wither", "1": 150, "2": 300, "3": 500, "4": 900, "5": 1500 }, "SCYLLA": { "type": "Wither", "1": 150, "2": 300, "3": 500, "4": 900, "5": 1500 }, "ASTRAEA": { "type": "Wither", "1": 150, "2": 300, "3": 500, "4": 900, "5": 1500 }, "WITHER_HELMET": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "WITHER_CHESTPLATE": { "type": "Wither", "1": 100, "2": 200, "3": 350, "4": 600, "5": 1000 }, "WITHER_LEGGINGS": { "type": "Wither", "1": 75, "2": 150, "3": 250, "4": 400, "5": 700 }, "WITHER_BOOTS": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "TANK_WITHER_HELMET": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "TANK_WITHER_CHESTPLATE": { "type": "Wither", "1": 100, "2": 200, "3": 350, "4": 600, "5": 1000 }, "TANK_WITHER_LEGGINGS": { "type": "Wither", "1": 75, "2": 150, "3": 250, "4": 400, "5": 700 }, "TANK_WITHER_BOOTS": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "SPEED_WITHER_HELMET": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "SPEED_WITHER_CHESTPLATE": { "type": "Wither", "1": 100, "2": 200, "3": 350, "4": 600, "5": 1000 }, "SPEED_WITHER_LEGGINGS": { "type": "Wither", "1": 75, "2": 150, "3": 250, "4": 400, "5": 700 }, "SPEED_WITHER_BOOTS": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "WISE_WITHER_HELMET": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "WISE_WITHER_CHESTPLATE": { "type": "Wither", "1": 100, "2": 200, "3": 350, "4": 600, "5": 1000 }, "WISE_WITHER_LEGGINGS": { "type": "Wither", "1": 75, "2": 150, "3": 250, "4": 400, "5": 700 }, "WISE_WITHER_BOOTS": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "POWER_WITHER_HELMET": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "POWER_WITHER_CHESTPLATE": { "type": "Wither", "1": 100, "2": 200, "3": 350, "4": 600, "5": 1000 }, "POWER_WITHER_LEGGINGS": { "type": "Wither", "1": 75, "2": 150, "3": 250, "4": 400, "5": 700 }, "POWER_WITHER_BOOTS": { "type": "Wither", "1": 50, "2": 100, "3": 200, "4": 350, "5": 500 }, "RUNAANS_BOW": { "type": "Spider", "dungeonize": 500, "1": 50, "2": 75, "3": 100, "4": 125, "5": 150 }, "LEAPING_SWORD": { "type": "Spider", "dungeonize": 20, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "SILK_EDGE_SWORD": { "type": "Spider", "dungeonize": 20, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "SPIDER_HAT": { "type": "Spider", "dungeonize": 20, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "SPIDER_BOOTS": { "type": "Spider", "dungeonize": 20, "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "MOSQUITO_BOW": { "type": "Spider", "dungeonize": 30, "1": 15, "2": 25, "3": 35, "4": 45, "5": 65 }, "TARANTULA_HELMET": { "type": "Spider", "dungeonize": 30, "1": 12, "2": 25, "3": 35, "4": 45, "5": 65 }, "TARANTULA_CHESTPLATE": { "type": "Spider", "dungeonize": 30, "1": 12, "2": 25, "3": 35, "4": 45, "5": 65 }, "TARANTULA_LEGGINGS": { "type": "Spider", "dungeonize": 20, "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "TARANTULA_BOOTS": { "type": "Spider", "dungeonize": 20, "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "SCORPION_BOW": { "type": "Spider", "dungeonize": 20, "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "PHANTOM_ROD": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 300, "3": 400, "4": 500, "5": 600 }, "WEREWOLF_HELMET": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 300, "3": 400, "4": 500, "5": 600 }, "WEREWOLF_CHESTPLATE": { "type": "Undead", "dungeonize": 1000, "1": 400, "2": 600, "3": 800, "4": 1000, "5": 1200 }, "WEREWOLF_LEGGINGS": { "type": "Undead", "dungeonize": 750, "1": 300, "2": 450, "3": 600, "4": 750, "5": 900 }, "WEREWOLF_BOOTS": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 300, "3": 400, "4": 500, "5": 600 }, "PIGMAN_SWORD": { "type": "Undead", "dungeonize": 200, "1": 100, "2": 200, "3": 300, "4": 400, "5": 500 }, "UNDEAD_SWORD": { "type": "Undead", "dungeonize": 10, "1": 20, "2": 30, "3": 40, "4": 50, "5": 60 }, "ZOMBIE_SWORD": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "ORNATE_ZOMBIE_SWORD": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "FLORID_ZOMBIE_SWORD": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "SKELETON_HAT": { "type": "Undead", "dungeonize": 40, "1": 20, "2": 30, "3": 40, "4": 50, "5": 60 }, "ZOMBIE_HAT": { "type": "Undead", "dungeonize": 40, "1": 20, "2": 30, "3": 40, "4": 50, "5": 60 }, "ZOMBIE_HEART": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "ZOMBIE_CHESTPLATE": { "type": "Undead", "dungeonize": 200, "1": 40, "2": 60, "3": 80, "4": 100, "5": 120 }, "ZOMBIE_LEGGINGS": { "type": "Undead", "dungeonize": 150, "1": 40, "2": 60, "3": 80, "4": 100, "5": 120 }, "ZOMBIE_BOOTS": { "type": "Undead", "dungeonize": 120, "1": 35, "2": 50, "3": 65, "4": 80, "5": 100 }, "SKELETON_HELMET": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "MACHINE_GUN_BOW": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "CRYPT_BOW": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "CRYPT_DREADLORD_SWORD": { "type": "Undead", "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "CRYPT_WITHERLORD_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "CRYPT_WITHERLORD_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "CRYPT_WITHERLORD_LEGGINGS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "CRYPT_WITHERLORD_BOOTS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SKELETON_GRUNT_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SKELETON_GRUNT_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SKELETON_GRUNT_LEGGINGS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SKELETON_GRUNT_BOOTS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SNIPER_BOW": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SNIPER_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "ROTTEN_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "ROTTEN_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "ROTTEN_LEGGINGS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "ROTTEN_BOOTS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "UNDEAD_BOW": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "STONE_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "MENDER_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "DARK_GOGGLES": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "HEAVY_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "HEAVY_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "HEAVY_LEGGINGS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "HEAVY_BOOTS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SUPER_HEAVY_HELMET": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SUPER_HEAVY_CHESTPLATE": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SUPER_HEAVY_LEGGINGS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SUPER_HEAVY_BOOTS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "STINGER_BOW": { "type": "Undead", "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "BOUNCY_HELMET": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "BOUNCY_CHESTPLATE": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "BOUNCY_LEGGINGS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "BOUNCY_BOOTS": { "type": "Undead", "1": 10, "2": 20, "3": 40, "4": 80, "5": 160 }, "SKELETON_MASTER_HELMET": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_MASTER_CHESTPLATE": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_MASTER_LEGGINGS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_MASTER_BOOTS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_SOLDIER_HELMET": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_SOLDIER_CHESTPLATE": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_SOLDIER_LEGGINGS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETON_SOLDIER_BOOTS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_SOLDIER_HELMET": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_SOLDIER_CHESTPLATE": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_SOLDIER_LEGGINGS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_SOLDIER_BOOTS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_KNIGHT_HELMET": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 160, "5": 320 }, "ZOMBIE_KNIGHT_CHESTPLATE": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 160, "5": 320 }, "ZOMBIE_KNIGHT_LEGGINGS": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 160, "5": 320 }, "ZOMBIE_KNIGHT_BOOTS": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 160, "5": 320 }, "ZOMBIE_KNIGHT_SWORD": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 160, "5": 320 }, "ZOMBIE_COMMANDER_HELMET": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_COMMANDER_CHESTPLATE": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_COMMANDER_LEGGINGS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_COMMANDER_BOOTS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_COMMANDER_WHIP": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_LORD_HELMET": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_LORD_CHESTPLATE": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_LORD_LEGGINGS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "ZOMBIE_LORD_BOOTS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SKELETON_LORD_HELMET": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SKELETON_LORD_CHESTPLATE": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SKELETON_LORD_LEGGINGS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SKELETON_LORD_BOOTS": { "type": "Undead", "1": 25, "2": 50, "3": 100, "4": 200, "5": 400 }, "SKELETOR_HELMET": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETOR_CHESTPLATE": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETOR_LEGGINGS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "SKELETOR_BOOTS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "ZOMBIE_SOLDIER_CUTLASS": { "type": "Undead", "1": 15, "2": 30, "3": 60, "4": 120, "5": 240 }, "METAL_CHESTPLATE": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 150, "5": 300 }, "MENDER_FEDORA": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 150, "5": 300 }, "SHADOW_GOGGLES": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 150, "5": 300 }, "SUPER_UNDEAD_BOW": { "type": "Undead", "1": 20, "2": 40, "3": 80, "4": 150, "5": 300 }, "EARTH_SHARD": { "type": "Undead", "1": 50, "2": 100, "3": 200, "4": 400, "5": 600 }, "STEEL_CHESTPLATE": { "type": "Undead", "1": 40, "2": 80, "3": 150, "4": 300, "5": 600 }, "MENDER_CROWN": { "type": "Undead", "1": 40, "2": 80, "3": 150, "4": 300, "5": 600 }, "WITHER_GOGGLES": { "type": "Undead", "1": 40, "2": 80, "3": 150, "4": 300, "5": 600 }, "DEATH_BOW": { "type": "Undead", "1": 40, "2": 80, "3": 150, "4": 300, "5": 600 }, "CRYSTALLIZED_HEART": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "REVIVED_HEART": { "type": "Undead", "dungeonize": 100, "1": 30, "2": 45, "3": 60, "4": 75, "5": 90 }, "REAPER_MASK": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 400, "3": 600, "4": 850, "5": 1200 }, "REAPER_SCYTHE": { "type": "Undead", "dungeonize": 200, "1": 300, "2": 450, "3": 600, "4": 700, "5": 900 }, "REVENANT_SWORD": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 400, "3": 600, "4": 800, "5": 1000 }, "REAPER_SWORD": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 400, "3": 600, "4": 800, "5": 1000 }, "AXE_OF_THE_SHREDDED": { "type": "Undead", "dungeonize": 500, "1": 200, "2": 400, "3": 600, "4": 800, "5": 1000 }, "ASPECT_OF_THE_DRAGON": { "type": "Dragon", "dungeonize": 150, "1": 150, "2": 300, "3": 450, "4": 600, "5": 750 }, "YOUNG_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "YOUNG_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "YOUNG_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "YOUNG_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "OLD_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "OLD_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "OLD_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "OLD_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "STRONG_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "STRONG_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "STRONG_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "STRONG_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "PROTECTOR_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "PROTECTOR_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "PROTECTOR_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "PROTECTOR_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "WISE_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "WISE_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "WISE_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "WISE_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "UNSTABLE_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "UNSTABLE_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "UNSTABLE_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "UNSTABLE_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "SUPERIOR_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 70, "1": 40, "2": 60, "3": 100, "4": 200, "5": 300 }, "SUPERIOR_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 100, "1": 60, "2": 100, "3": 160, "4": 240, "5": 360 }, "SUPERIOR_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 85, "1": 50, "2": 80, "3": 130, "4": 220, "5": 320 }, "SUPERIOR_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 60, "1": 40, "2": 60, "3": 80, "4": 180, "5": 280 }, "HOLY_DRAGON_HELMET": { "type": "Dragon", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "HOLY_DRAGON_CHESTPLATE": { "type": "Dragon", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "HOLY_DRAGON_LEGGINGS": { "type": "Dragon", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "HOLY_DRAGON_BOOTS": { "type": "Dragon", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "TERMINATOR": { "type": "Dragon", "dungeonize": 300, "1": 100, "2": 200, "3": 300, "4": 500, "5": 750 }, "SINSEEKER_SCYTHE": { "type": "Dragon", "dungeonize": 250, "1": 70, "2": 140, "3": 210, "4": 350, "5": 600 }, "JUJU_SHORTBOW": { "type": "Dragon", "dungeonize": 150, "1": 30, "2": 60, "3": 120, "4": 200, "5": 300 }, "MIDAS_STAFF": { "type": "Gold", "dungeonize": 100, "1": 50, "2": 100, "3": 150, "4": 200, "5": 250 }, "ROGUE_SWORD": { "type": "Gold", "dungeonize": 5, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "MIDAS_SWORD": { "type": "Gold", "dungeonize": 100, "1": 25, "2": 50, "3": 75, "4": 125, "5": 200 }, "SUPER_CLEAVER": { "type": "Gold", "1": 1, "2": 1, "3": 2, "4": 2, "5": 3 }, "HYPER_CLEAVER": { "type": "Gold", "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "GIANT_CLEAVER": { "type": "Gold", "1": 10, "2": 20, "3": 30, "4": 40, "5": 50 }, "GOLD_BONZO_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_SCARF_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_PROFESSOR_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_THORN_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_LIVID_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_SADAN_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "GOLD_NECRON_HEAD": { "type": "Gold", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "HARDENED_DIAMOND_HELMET": { "type": "Diamond", "dungeonize": 10, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "HARDENED_DIAMOND_CHESTPLATE": { "type": "Diamond", "dungeonize": 10, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "HARDENED_DIAMOND_LEGGINGS": { "type": "Diamond", "dungeonize": 10, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "HARDENED_DIAMOND_BOOTS": { "type": "Diamond", "dungeonize": 10, "1": 5, "2": 10, "3": 15, "4": 20, "5": 25 }, "PERFECT_HELMET_1": { "type": "Diamond", "dungeonize": 40, "1": 20, "2": 30, "3": 50, "4": 100, "5": 150 }, "PERFECT_CHESTPLATE_1": { "type": "Diamond", "dungeonize": 70, "1": 30, "2": 50, "3": 80, "4": 120, "5": 180 }, "PERFECT_LEGGINGS_1": { "type": "Diamond", "dungeonize": 60, "1": 25, "2": 40, "3": 65, "4": 110, "5": 160 }, "PERFECT_BOOTS_1": { "type": "Diamond", "dungeonize": 35, "1": 20, "2": 30, "3": 40, "4": 90, "5": 140 }, "DIAMOND_BONZO_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_SCARF_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_PROFESSOR_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_THORN_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_LIVID_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_SADAN_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "DIAMOND_NECRON_HEAD": { "type": "Diamond", "1": 50, "2": 75, "3": 125, "4": 250, "5": 500 }, "YETI_SWORD": { "type": "Ice", "dungeonize": 100, "1": 100, "2": 150, "3": 200, "4": 250, "5": 300 }, "FROZEN_BLAZE_HELMET": { "type": "Ice", "dungeonize": 400, "1": 100, "2": 125, "3": 150, "4": 175, "5": 200 }, "FROZEN_BLAZE_CHESTPLATE": { "type": "Ice", "dungeonize": 40, "1": 10, "2": 15, "3": 25, "4": 35, "5": 60 }, "FROZEN_BLAZE_LEGGINGS": { "type": "Ice", "dungeonize": 60, "1": 15, "2": 20, "3": 30, "4": 40, "5": 65 }, "FROZEN_BLAZE_BOOTS": { "type": "Ice", "dungeonize": 30, "1": 5, "2": 10, "3": 20, "4": 30, "5": 55 }, "FROZEN_SCYTHE": { "type": "Ice", "dungeonize": 50, "1": 10, "2": 15, "3": 20, "4": 25, "5": 30 }, "ICE_SPRAY_WAND": { "type": "Ice", "1": 20, "2": 50, "3": 100, "4": 200, "5": 300 } }

18.649706

36

0.31393

fe2c29bc84b8d2fcc529fc29593ac37f259fbed7

13

py

Python

deplytils/__init__.py

joshuahaertel/putils

cd467560b4e97b1caee9ee2111a0748021744b07

[ "MIT" ]

null

deplytils/__init__.py

joshuahaertel/putils

cd467560b4e97b1caee9ee2111a0748021744b07

[ "MIT" ]

null

deplytils/__init__.py

joshuahaertel/putils

cd467560b4e97b1caee9ee2111a0748021744b07

[ "MIT" ]

null

"""putils"""

6.5

12

0.461538

e23183ddba3187ac893b4f19cac86853f913ef79

929

py

Python

run/wordsub_task3.py

osmanbaskaya/semeval14-task3

2d440cadd701de3cbff3a6be630157d3ade05176

[ "MIT" ]

1

2015-02-03T20:22:33.000Z

run/wordsub_task3.py

osmanbaskaya/semeval14-task3

2d440cadd701de3cbff3a6be630157d3ade05176

[ "MIT" ]

null

run/wordsub_task3.py

osmanbaskaya/semeval14-task3

2d440cadd701de3cbff3a6be630157d3ade05176

[ "MIT" ]

null

#! /usr/bin/python # -*- coding: utf-8 -*- __author__ = "Osman Baskaya" import sys import random from nltk.corpus import stopwords initial = sys.argv[1] # L or R n = int(sys.argv[2]) # number of substitutes s = int(sys.argv[3]) # seed random.seed(s) stop = set(stopwords.words('english')) map(stop.add, "lrb rrb rsb . ... , .. ? ` ' ! lsb lcb rcb -LRB- -RRB- -RSB- -LSB- -LCB- \ -RCB- @card@ n't 's".split()) c = 1 for line in sys.stdin: tokens = line.split() i = 0 word = tokens[0] if word == '</s>': c += 1 continue if word not in stop: while i < n: total = 0 for j in range(1, len(tokens), 2): tok, p = tokens[j], 10 ** float(tokens[j+1]) total += p if total * random.random() <= p: sub = tok print "{}{}\t{}".format(initial, c, sub) i += 1

22.658537

89

0.488698

8f7cba65e894c39ecbec2f6660cb8c6f83b646b2

4,814

py

Python

src/main.py

DickKemp/Blockus

e720ce5a50e001c9f0316adc16c30b603ba92c9b

[ "MIT" ]

null

src/main.py

DickKemp/Blockus

e720ce5a50e001c9f0316adc16c30b603ba92c9b

[ "MIT" ]

1

2022-02-08T03:29:52.000Z

src/main.py

DickKemp/Blockus

e720ce5a50e001c9f0316adc16c30b603ba92c9b

[ "MIT" ]

null

from __future__ import annotations from typing import Set from blokus.blokus import Blok, OverlapException, UnequalEdgesException from blokus.canvas import Canvas, CompositeShape, PolygonShape, Style, Box from blokus.point import Point import math import time def gen_next_level(levelset, b1, is_symmetric=False) -> Set[Blok]: unique_blocks = set() cntr = 0 # if the piece we are adding is symmetric, namely all edges are indistinguiable from one another, # then we only need to attach one edge, there is no need to try all the edges as it would be the same if is_symmetric: b1_edges = [0] else: b1_edges = range(Blok.num_edges(b1)) for b0 in iter(levelset): for b0e in range(Blok.num_edges(b0)): for b1e in b1_edges: try: (b,bm) = Blok.align_blocks_on_edge(b0, b0e, b1, b1e) newb = Blok.merge(b, bm) flip_edge = Blok.find_flip_edge(newb) flipped_newb = Blok.flip(newb, flip_edge) cntr = cntr + 1 newb_not_in = newb not in unique_blocks flipped_newb_not_in = flipped_newb not in unique_blocks if newb_not_in and flipped_newb_not_in: # print(f"adding newb: {newb}: id:{newb.gen_id()}, flipped_newb_id: {flipped_newb.gen_id()}") # print(f"newb: {newb}") unique_blocks.add(newb) except OverlapException: continue except UnequalEdgesException: continue return unique_blocks levels_cache = {} def runn(name, basic, num_levels, svg_file, is_symmetric = False): start_time = time.time() b0 = Blok(basic) b1 = Blok.rotate(b0, Blok.get_edge(b0,0).start_pt, math.pi/4) prev_level = set() prev_level.add(b0) all = [(1,b0)] for lev in range(num_levels-1): cache = levels_cache.get((name,lev), None) if cache: curr_level = cache else: curr_level = gen_next_level(prev_level, b1, is_symmetric) levels_cache[(name,lev)] = curr_level for s in iter(curr_level): all.append((lev+2,s)) prev_level = curr_level normalized_bloks = [(n, Blok.normalize(b)) for n,b in all] bigbox = Box.bounding_box_of_boxex([PolygonShape(b.points).bounding_box() for _,b in normalized_bloks]) d = int(math.sqrt(len(all))) cv = Canvas(width=20, height=20, nrows=d+2, ncols=d+2) i = 0 for n,b in normalized_bloks: c = int(i / d) r = int(i % d) box = cv.get_box_for_cell(r, c) sh = PolygonShape(b.points, style=Style(color='black', size='0.005')) if b.component_blocks: component_polygons = [PolygonShape(bp.points, style=Style(color='red')) for bp in b.component_blocks] + [sh] sh = CompositeShape(component_polygons) cv = Canvas.add_shape3(cv, sh, box, bigbox, margin_percent=0.3, label=f"{str(n)}") i = i + 1 with open(svg_file, 'w') as fd: Canvas.render_as_svg(cv, file=fd) print(f"{name}: Num shapes: {len(all)} --- Time: {(time.time() - start_time)} seconds ---") def pentagon(r): sum_interior_angles = (5-2)*math.pi pent_angle = ((sum_interior_angles)/5)/2 ND = r * math.cos(pent_angle) ON = r * math.sin(pent_angle) BC = 2 * ND FC = BC * math.sin(pent_angle) BF = BC * math.cos(pent_angle) FO = r - BF A = (-FC,FO) B = (0.0, r) C = (FC, FO) D = (ND, -ON) E = (-ND, -ON) return [A,B,C,D,E] if __name__ == '__main__': SQUARE = [Point(0.0,0.0), Point(1.0,0.0), Point(1.0,1.0), Point(0.0,1.0)] TRIANGLE = [Point(0.0, 0.0), Point(0.5,(1/2)*math.sqrt(3)),Point(1.0,0.0)] ISOTRIANGLE = [Point(0.0, 0.0), Point(1.0, 1.0), Point(1.0,0.0)] PENTAGON = [Point(x,y) for (x,y) in pentagon(1.0)] h = math.sin(math.pi/3.0) hex = [ (-0.5,-h), (-1,0), (-0.5,h), (0.5,h), (1,0), (0.5,-h) ] HEXAGON = [Point(x,y) for (x,y) in hex] is_symmetric = True start_time = time.time() for i in range(1,8): loop_time = time.time() print(f"\tStart LOOP {i}") runn("\t\tTRIANGLE", TRIANGLE, i, f"svg/triangle{i}.svg", is_symmetric) runn("\t\tSQUARE", SQUARE, i, f"svg/square{i}.svg", is_symmetric) runn("\t\tPENTAGON", PENTAGON, i, f"svg/pentagon{i}.svg", is_symmetric) runn("\t\tHEXAGON", HEXAGON, i, f"svg/hexagon{i}.svg", is_symmetric) # runn("ISOTRIANGLE", ISOTRIANGLE, i, f"svg/iso_triangle{i}.svg") print(f"\tEnd LOOP {i} --- Time: {(time.time() - loop_time)} seconds ---") pass print(f"TOTAL --- Time: {(time.time() - start_time)} seconds ---")

38.822581

120

0.582052

eab18eb9b090b2f98580b0991568c0cffa072c0a

2,366

py

Python

tightai/utils.py

tightai/tightai

3a440ad780f5c7ff84a54ad9dc6b342ce3b420b6

[ "Apache-2.0" ]

1

2020-09-13T08:10:59.000Z

tightai/utils.py

tightai/tightai

3a440ad780f5c7ff84a54ad9dc6b342ce3b420b6

[ "Apache-2.0" ]

1

2022-02-26T08:32:58.000Z

tightai/utils.py

tightai/tightai

3a440ad780f5c7ff84a54ad9dc6b342ce3b420b6

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 10_utils.ipynb (unless otherwise specified). __all__ = ['resolve_path', 'print_dict', 'sort_by_key', 'sec_to_hours', 'to_datetime', 'format_date'] # Cell import pathlib # Cell def resolve_path(path): return pathlib.Path(path).resolve() # Cell def print_dict(item_dict): for k, v in item_dict.items(): if isinstance(v, dict): print(f"{k}:") print_dict(v) elif isinstance(v, list): print(f"{k}:") for v1 in v: print(f"\t- {v1}") else: print(f"{k}: {v}") # Cell def sort_by_key(items=[], key=None): reverse = False if key.startswith("-"): key = key[1:] reverse = True if key == None: return items if len(items) ==0: return [] _item = items[0] valid_keys = [] if isinstance(_item, dict): print("This method is for sorting lists of objects and not `dict`.") return items if isinstance(_item, set): print("This method is for sorting lists of objects and not `set`.") return items if not isinstance(_item, object): print("This method is for sorting lists of objects.") return items try: valid_keys += list(set(_item.__dict__.keys())) except: pass if key not in list(valid_keys): valid_str = "" msg = f"{key} is not a valid sorting option." if len(valid_keys) > 0: valid_str = ", ".join(valid_keys) msg = f"{msg} Choices are: {valid_str}" else: msg = f"{msg} Are you sure that list of items with the Python class `{str(_item.__class__.__name__)}` can be sorted?" raise Exception(msg) items = sorted(items, key=lambda x: getattr(x, key), reverse=reverse) return items # Cell from datetime import datetime, timedelta def sec_to_hours(seconds): a=str(int(seconds//3600)) b=str(int(seconds%3600)//60) c=str(int((seconds%3600)%60)) d= f"{a} hours {b} mins {c} seconds ago." return d def to_datetime(datetime_str): try: datetime_str = datetime.strptime(datetime_str, "%Y-%m-%dT%H:%M:%S.%fz") valid = True except: valid = False return datetime_str, valid def format_date(datetime_obj): return datetime_obj.strftime('%d %b %Y')

27.195402

129

0.591293

e18594963a3614f54e2a9e22384bd35c698602e6

8,741

py

Python

expandseq/__main__.py

jrowellfx/expandSeq

c5f5677d684fa8ba7a4e10b1346aaaa9a32e0cdc

[ "BSD-3-Clause" ]

null

expandseq/__main__.py

jrowellfx/expandSeq

c5f5677d684fa8ba7a4e10b1346aaaa9a32e0cdc

[ "BSD-3-Clause" ]

null

expandseq/__main__.py

jrowellfx/expandSeq

c5f5677d684fa8ba7a4e10b1346aaaa9a32e0cdc

[ "BSD-3-Clause" ]

null

# 3-Clause BSD License # # Copyright (c) 2008-2021, James Philip Rowell, # Alpha Eleven Incorporated # www.alpha-eleven.com # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materials provided with the # distribution. # # 3. Neither the name of the copyright holder, "Alpha Eleven, Inc.", # nor the names of its contributors may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # expandseq/condenseseq - two command line utilities that expose the basic # functionality of the python-module "seqLister.py" functions "expandSeq()" # and "condenseSeq()". These functions translate back and forth between a # condensed form for listing sequences of integers and plain lists of integers. # Lists of integers in this condensed format are commonly used by various # computer programs dealing with sequences of images such as render farm # management tools (like smedge), image sequence viewers (like rv) or "ls" # commands (like lsseq) to list frames from CG-animation or video footage # which has been saved as a sequence of individually numbered frames. # # The "expandseq" and "condenseseq" commands enhance the simple behavior of # the "expandSeq()" and "condenseSeq()" python functions by adding the ability # to print out the lists in various forms. eg.; comma, space or newline # separators as well as sorting the lists, reversing order, and mixing and # matching expanded and condensed formats as arguments on the command line. import argparse import os import sys import subprocess import textwrap from operator import itemgetter import seqLister EXPAND_MODE = True VERSION = "2.3.0" def indexNegNumber(argList) : i = 0 argLen = len(argList) while i < argLen : if len(argList[i]) >= 2 : if argList[i][0] == '-' and argList[i][1].isdigit() : return i i += 1 return -1 def main(): # Redefine the exception handling routine so that it does NOT # do a trace dump if the user types ^C while expandseq or # condenseseq are running. # old_excepthook = sys.excepthook def new_hook(exceptionType, value, traceback): if exceptionType != KeyboardInterrupt and exceptionType != IOError: old_excepthook(exceptionType, value, traceback) else: pass sys.excepthook = new_hook global EXPAND_MODE if os.path.basename(sys.argv[0]) == "expandseq" : EXPAND_MODE = True elif os.path.basename(sys.argv[0]) == "condenseseq" : EXPAND_MODE = False else : print(os.path.basename(sys.argv[0]), ": must be named either expandseq or condenseseq", sep='', file=sys.stderr) sys.exit(1) if EXPAND_MODE : p = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent('''\ Expands a list of integers and integer sequences of the form 'A-B' or 'A-BxN' into a list of integers. A-BxN means list every Nth integer starting at A ending at the highest integer less than or equal to B. Numbers will only be listed once each. That is; '2-4 1-6' yeilds the list '2 3 4 1 5 6'. Helpful hint: To pass negative numbers as an argument enclose them with quotes but include a leading space. For example: " -12" or " -99-86" Allows you to pass a minus-twelve, or minus-ninety-nine through eighty-six. (Also see condenseseq). '''), usage="%(prog)s [OPTION]... [INTEGER SEQUENCE]...") else : p = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent('''\ Condenses a list of integers and/or integer sequences of the form 'A-B' or 'A-BxN' into the most minimal sequence format possible to represent the full list of numbers. Helpful hint: To pass negative numbers as an argument enclose them with quotes but include a leading space. For example: " -12" or " -99-86" Allows you to pass a minus-twelve, or minus-ninety-nine through eighty-six. (Also see expandseq). '''), usage="%(prog)s [OPTION]... [INTEGER SEQUENCE]...") p.add_argument("--version", action="version", version=VERSION) p.add_argument("--delimiter", "-d", action="store", type=str, choices=("comma", "space", "newline"), dest="seqDelimiter", metavar="DELIMITER", default="comma", help="List successive numbers delimited by a 'comma' (default) or a 'space' or a 'newline'.") if not EXPAND_MODE : # i.e.; condense p.add_argument("--onlyOnes", action="store_true", dest="onlyOnes", default=False, help="only condense sucessive frames, that is, do not list sequences on 2's, 3's, ... N's") p.add_argument("--pad", action="store", type=int, dest="pad", default=1, metavar="PAD", help="set the padding of the frame numbers to be <PAD> digits. [default: 1]") p.add_argument("--reverse", "-r", action="store_true", dest="reverseList", default=False, help="reverse the order of the list") if EXPAND_MODE : p.add_argument("--sort", "-s", action="store_true", dest="sortList", default=False, help="sort the resulting list") p.add_argument("numSequences", metavar="INTEGER SEQUENCE", nargs="*", help="is a single integer such as 'A', or a range \ of integers such as 'A-B' (A or B can be negative,\ and A may be greater than B to count backwards), or \ a range on N's such as 'A-BxN' where N is a positive integer.") # Copy the command line args (except prog name) and convert # commas into spaces thus making more args. # args = p.parse_args() separateArgs = [] for a in args.numSequences : for b in a.split(',') : separateArgs.append(b) remainingArgs = [] result = seqLister.expandSeq(separateArgs, remainingArgs) if EXPAND_MODE : if args.sortList : result.sort() # Pad list of integers and turn them into strings before printing. # ### jpr debug ### formatStr = "{0:0=-" + str(4) + "d}" formatStr = "{0:0=-" + str(args.pad) + "d}" paddedFrames = [] for frame in result: paddedFrames.append(formatStr.format(frame)) result = paddedFrames else : if args.onlyOnes : result = seqLister.condenseSeqOnes(result, args.pad) else : result = seqLister.condenseSeq(result, args.pad) if args.reverseList : result.reverse() isFirst = True for s in result : if args.seqDelimiter == 'space' : if not isFirst : sys.stdout.write(' ') sys.stdout.write(str(s)) isFirst = False elif args.seqDelimiter == 'comma' : if not isFirst : sys.stdout.write(',') sys.stdout.write(str(s)) isFirst = False else : # newline print(s) if (args.seqDelimiter == 'comma' or args.seqDelimiter == 'space') and not isFirst : print() if __name__ == '__main__': main()

39.731818

120

0.642604

2172ebd6a9e6e15fb223e8b00db7841c94c9b137

12,631

py

Python

CIS/command.py

charlesfoster/covid-illumina-snakemake

fa70f169259c8a0037e83343a3b9afcd1c2f8285

[ "MIT" ]

1

2022-03-08T00:41:20.000Z

CIS/command.py

charlesfoster/covid-illumina-snakemake

fa70f169259c8a0037e83343a3b9afcd1c2f8285

[ "MIT" ]

null

CIS/command.py

charlesfoster/covid-illumina-snakemake

fa70f169259c8a0037e83343a3b9afcd1c2f8285

[ "MIT" ]

1

2021-11-11T04:50:24.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Jun 3 15:57:54 2021 @author: cfos """ from CIS import __version__ import os import snakemake import argparse import sys import shutil import psutil from datetime import date from psutil import virtual_memory from psutil._common import bytes2human thisdir = os.path.abspath(os.path.dirname(__file__)) TODAY = date.today().strftime("%Y-%m-%d") def bytesto(bytes, to, bsize=1024): """convert bytes to megabytes, etc. sample code: print('mb= ' + str(bytesto(314575262000000, 'm'))) sample output: mb= 300002347.946 """ a = {"k": 1, "m": 2, "g": 3, "t": 4, "p": 5, "e": 6} r = float(bytes) for i in range(a[to]): r = r / bsize return r def main(sysargs=sys.argv[1:]): print( """\033[92m /^\/^\\ COVID _|__| O| Illumina \/ /~ \_/ \\ Pipeline \____|__________/ \\ Snakemake edition v{} \_______ \\ `\ \ \\ | | \\ / / \\ / / \\\\ / / \ \\ / / \ \\ / / _----_ \ \\ / / _-~ ~-_ | | ( ( _-~ _--_ ~-_ _/ | \ ~-____-~ _-~ ~-_ ~-_-~ / ~-_ _-~ ~-_ _-~ ~--______-~ ~-___-~ \033[0m """.format( __version__ ) ) max_mem = round(bytesto(virtual_memory().available, "m")) try: default_kraken = os.environ["KRAKEN2_DEFAULT_DB"] except: default_kraken = None parser = argparse.ArgumentParser( description="covid-illumina-snakemake: a pipeline for analysis SARS-CoV-2 samples", usage="""CIS [options] <query_directory> """, ) parser.add_argument( "query_directory", nargs="*", help="Path to directory with reads to process." ) parser.add_argument( "-c", "--consensus_freq", action="store", required=False, help="Variant allele frequency threshold for a variant to be incorporated into consensus genome. Default: {}".format( float(0.90) ), metavar="<int>", ) parser.add_argument( "-i", "--isolates", action="store", required=False, help="List of isolates to assemble (Default: all isolates in query_directory)", ) parser.add_argument( "-f", "--force", action="store_true", default=False, required=False, help="Force overwriting of completed files (Default: files not overwritten)", ) parser.add_argument( "-k", "--kraken2_db", action="store", help="kraken2 database. Default: {}".format(default_kraken), ) parser.add_argument( "-n", "--dry_run", action="store_true", default=False, help="Dry run only" ) parser.add_argument( "-o", "--outdir", action="store", help="Output directory. Default: {}".format( os.path.join(os.getcwd(), "results", TODAY) ), ) parser.add_argument( "-p", "--print_dag", action="store_true", default=False, help="Save directed acyclic graph (DAG) of workflow", ) parser.add_argument( "-r", "--reference", action="store", required=False, default=os.path.join(thisdir, "bin", "references", "NC_045512.fasta"), help="Reference genome to use (Default: {})".format( os.path.join(thisdir, "bin", "NC_045512.fasta") ), ) parser.add_argument( "-s", "--scheme", action="store", required=False, default="midnight", help="Primer scheme to use: built-in opts are 'midnight', 'swift', 'eden', 'articv4.1', and 'articv3', but if using your own scheme provide the full path to the bed file here (Default: {})".format( "midnight" ), ) parser.add_argument( "-t", "--threads", action="store", help="Number of threads to use", default=psutil.cpu_count(logical=True), metavar="<int>", ) parser.add_argument( "-v", "--variant_caller", action="store", help="Variant caller to use. Choices: 'lofreq' or 'ivar' Default: 'lofreq'", default="lofreq", ) parser.add_argument( "--keep_reads", action="store_true", help="Keep trimmed reads", default=False ) parser.add_argument( "--version", action="version", version=f"covid illumina pipeline snakemake edition: {__version__}", ) parser.add_argument( "--suffix", action="store", help="Suffix used to identify samples from reads. Default: {}".format( "_L001_R1_001.fastq.gz" ), metavar="<str>", ) parser.add_argument( "--max_memory", action="store", help="Maximum memory (in MB) that you would like to provide to snakemake. Default: {}MB".format( max_mem ), metavar="<int>", ) parser.add_argument("--verbose", action="store_true", help="Print junk to screen.") parser.add_argument("--report", action="store_true", help="Generate report.") if len(sysargs) < 1: parser.print_help() sys.exit(-1) else: args = parser.parse_args(sysargs) args = parser.parse_args() if not os.path.exists("".join(args.query_directory)): print( "#####\n\033[91mError\033[0m: Data directory does not appear to exist\n#####\n" ) print("Please check your input and try again\n") print("For help, run: CIS --help\n\n") sys.exit(-1) if default_kraken is None and not args.kraken2_db: print( "#####\n\033[91mError\033[0m: No kraken2 database supplied, and default database cannot be detected\n#####\n" ) parser.print_help() sys.exit(-1) elif args.kraken2_db: default_kraken = args.kraken2_db if args.outdir: outdir = args.outdir else: outdir = os.path.join(os.getcwd(), "results", TODAY) if not os.path.exists(outdir): os.makedirs(outdir) if args.max_memory: max_mem = {"mem_mb": int(args.max_memory)} else: max_mem = {"mem_mb": int(max_mem)} scheme = os.path.join(thisdir, "bin", "primer_schemes", "midnight.bed") if args.scheme and os.path.isfile( os.path.join(thisdir, "bin", "primer_schemes", "{}.bed".format(args.scheme)) ): scheme = os.path.join( thisdir, "bin", "primer_schemes", "{}.bed".format(args.scheme) ) else: scheme = args.scheme coverage_script = os.path.join(thisdir, "bin", "scripts", "plot_coverage.R") converter_script = os.path.join( thisdir, "bin", "scripts", "ivar_variants_to_vcf.py" ) vcf_script = os.path.join(thisdir, "bin", "scripts", "add_fake_genotype.sh") suffix = "_L001_R1_001.fastq.gz" if args.suffix: suffix = args.suffix # check input suffix check_dir = [ x for x in os.listdir("".join(args.query_directory)) if x.endswith(suffix) ] if len(check_dir) == 0: print( "#####\n\033[91mError\033[0m: Specified suffix does not appear to match the reads in your query_directory\n#####\n" ) print("Please check your input and try again\n") print("For help, run: CIS --help\n\n") sys.exit(-1) if args.isolates: with open(args.isolates) as f: isolates = [line.rstrip("\n") for line in f] else: isolates = False if args.variant_caller: if args.variant_caller not in ["lofreq", "ivar"]: print( '#####\n\033[91mError\033[0m: Variant caller must be either "lofreq" or "ivar"\n#####\n' ) sys.exit(1) variant_caller = args.variant_caller else: variant_caller = "lofreq" # variant_caller = shutil.which(variant_caller) # ivar is still necessary for trimming # if shutil.which('ivar') is None: # print('#####\n\033[91mError\033[0m: "ivar" is necessary for amplicon trimming but is not in your path\n#####\n') # sys.exit(1) if variant_caller is None: print( "#####\n\033[91mError\033[0m: {} could not be found in your path\n#####\n".format( variant_caller ) ) sys.exit(1) consensus_freq = 0.9 if args.consensus_freq: consensus_freq = float(args.consensus_freq) if consensus_freq > 1 or consensus_freq < 0: print( "#####\n\033[91mError\033[0m: The consensus_freq option must be a float number between 0 and 1\n#####\n" ) sys.exit(1) if not os.path.isfile(args.reference + ".fai"): print("Indexing {} with samtools".format(args.reference)) os.system("samtools faidx {} 2> /dev/null".format(args.reference)) if not os.path.isfile(args.reference + ".bwt"): print("Indexing {} with bwa".format(args.reference)) os.system("bwa index {} 2> /dev/null".format(args.reference)) snakefile = os.path.join(thisdir, "bin", "Snakefile") config = { "reads_dir": os.path.join(os.getcwd(), args.query_directory[0]), "kraken2_db": default_kraken, "outdir": outdir, "reference": args.reference, "isolates": isolates, "coverage_script": coverage_script, "converter_script": converter_script, "vcf_script": vcf_script, "variant_program": variant_caller, "scheme": scheme, "isolates": isolates, "suffix": suffix, "threads": args.threads, "consensus_freq": consensus_freq, "keep_reads": args.keep_reads, "verbose": args.verbose, } if args.print_dag: flat_config = [] for key in config: flat_config.append(key + "=" + str(config[key])) flat_config = " ".join(flat_config) cmd = 'snakemake -j1 -s {0} --config {1} --dag | grep -v "No negative control samples detected" | dot -Tpdf > dag.pdf'.format( os.path.join(thisdir, "bin", "Snakefile"), flat_config ) os.system(cmd) status = True elif args.report: status = snakemake.snakemake( snakefile, report=os.path.join(outdir, "pipeline_report.html"), use_conda=True, conda_frontend="mamba", dryrun=args.dry_run, printshellcmds=True, forceall=args.force, force_incomplete=True, resources=max_mem, config=config, quiet=False, cores=args.threads, lock=False, ) elif config["verbose"]: print("\n**** CONFIG ****") for k in config: print(k + ": ", config[k]) status = snakemake.snakemake( snakefile, use_conda=True, conda_frontend="mamba", dryrun=args.dry_run, printshellcmds=True, forceall=args.force, force_incomplete=True, resources=max_mem, config=config, quiet=False, cores=args.threads, lock=False, ) else: status = snakemake.snakemake( snakefile, use_conda=True, conda_frontend="mamba", dryrun=args.dry_run, printshellcmds=False, forceall=args.force, force_incomplete=True, resources=max_mem, config=config, quiet=True, cores=args.threads, lock=False, ) if status and args.print_dag: # translate "success" into shell exit code of 0 print( "\033[92m\nResults\033[0m: {}\n\n\033[92mPipeline complete!\033[0m\n".format( os.path.join(os.getcwd(), "dag.pdf") ) ) return 0 else: print( "\033[92m\nResults\033[0m: {}\n\n\033[92mPipeline complete!\033[0m\n".format( config["outdir"] ) ) return 0 return 1 if __name__ == "__main__": main()

31.187654

206

0.534399

69dfc38cc3c167d88b7cc364be4d540176eff86c

1,735

py

Python

scripts/create_agent.py

c-keil/SE2-equivariant-grasp-learning

f32230e45c6a7bc1693df9b27d1084d98347477e

[ "MIT" ]

null

scripts/create_agent.py

c-keil/SE2-equivariant-grasp-learning

f32230e45c6a7bc1693df9b27d1084d98347477e

[ "MIT" ]

null

scripts/create_agent.py

c-keil/SE2-equivariant-grasp-learning

f32230e45c6a7bc1693df9b27d1084d98347477e

[ "MIT" ]

1

2022-03-14T20:08:18.000Z

from agents.agents_3d.dqn_3d_asr import DQN3DASR from utils.parameters import * from networks.equivariant_models_refactor import EquResUReg, EquShiftQ2ResN def createAgent(): """ Creates the DQN agent with equivariant q1, q2 networks """ if half_rotation: rz_range = (0, (num_rotations - 1) * np.pi / num_rotations) else: rz_range = (0, (num_rotations - 1) * 2 * np.pi / num_rotations) diag_length = float(heightmap_size) * np.sqrt(2) diag_length = int(np.ceil(diag_length / 32) * 32) initialize = initialize_net if load_sub is not None or load_model_pre is not None: initialize = False assert args.use_depth + args.use_rgb > 0 q1_input_channel = 1 * args.use_depth + 3 * args.use_rgb q2_patch_channel = q1_input_channel patch_shape = (q2_patch_channel, patch_size, patch_size) q2_input_shape = (q2_patch_channel + 1, patch_size, patch_size) is_fcn_si = False fcn_out = num_primitives q1 = EquResUReg(q1_input_channel, fcn_out, domain_shape=(1, diag_length, diag_length), patch_shape=patch_shape, N=equi_n, flip=True, initialize=initialize, is_fcn_si=is_fcn_si, last_activation_softmax=True).to(device) q2 = EquShiftQ2ResN(q2_input_shape, num_rotations, num_primitives, kernel_size=7, quotient=False, last_quotient=True, initialize=initialize).to(device) agent = DQN3DASR(workspace, heightmap_size, device, lr=lr, num_primitives=num_primitives, patch_size=patch_size, num_rz=num_rotations, rz_range=rz_range, network=model) agent.initNetwork(q1, q2) agent.detach_es = detach_es agent.aug = aug return agent

41.309524

109

0.692795

bdbf53c48b90b3b7aee15ced3764bbf92ac1e2a1

8,849

py

Python

restoredb.py

cecton/restoredb.py

fbd97293112b47ecc39204a7845664c9a0981fd1

[ "MIT" ]

null

restoredb.py

cecton/restoredb.py

fbd97293112b47ecc39204a7845664c9a0981fd1

[ "MIT" ]

null

restoredb.py

cecton/restoredb.py

fbd97293112b47ecc39204a7845664c9a0981fd1

[ "MIT" ]

null

#!/usr/bin/env python2 # # restoredb # # This is a small example on how to use StreamDecompressor to make a # generic pg_restore-like command. # # TL;DR # # ~/$ restoredb -d dbname my_dump.pgdump.xz # # will restore a pgdump compressed in xz # # ~/$ restoredb -d dbname my_dump.sql.bz2 # # will restore an SQL dump compressed in bz2 # # ~/$ restoredb -d dbname my_dump.tar.gz # # will restore an tar dump compressed in gzip # # ~/$ restoredb -d dbname my_dump.7z # # will restore any dump if the 7z contains only one file # # and is a pgdump, SQL dump or tar dump # # ~/$ ssh toto@foo.bar "cat remote_dump.zip.xz" | restoredb -d dbname # # will restore any dump if the remote zip file over-compressed in xz # # contains only one file and is a pgdump, SQL dump or tar dump # import io import os import sys import errno import argparse import subprocess import tarfile import StreamDecompressor try: import pgheader import time except ImportError: pgheader = None __all__ = """ PostgreSQLDump PostgreSQLTarDump PlainSQL pgdump_guesser open parser run """.split() class PostgreSQLDump(StreamDecompressor.ExternalPipe): """ Stream custom dump to pg_restore in order to get SQL """ __command__ = ['pg_restore'] __compression__ = 'pgdmp_custom' __mimes__ = [ 'application/octet-stream', 'binary', ] __extensions__ = ['dump', 'dmp', 'pgdmp', 'pgdump'] def __init__(self, name, fileobj, toc_pos=0): if pgheader: self.header = pgheader.ArchiveHandle( io.BytesIO(fileobj.peek(16000)[toc_pos:])) self.header.ReadHead() else: self.header = None self.__command__[0] = self.find_pg_restore() super(PostgreSQLDump, self).__init__(name, fileobj) def find_pg_restore(self): return self.__command__[0] @classmethod def __guess__(cls, mime, name, fileobj, toc_pos=0): realname = super(PostgreSQLDump, cls).__guess__(mime, name, fileobj) magic = fileobj.peek(5)[toc_pos:] if not magic[:5] == 'PGDMP': raise ValueError("not a postgres custom dump") return realname class PostgreSQLTarDump(PostgreSQLDump): """ Inherit of PostgreSQLDump to add some specific behaviors related to tar. Note: this decompressor must run prior to the Untar decompressor """ __compression__ = 'pgdmp_tar' __mimes__ = [ 'application/x-tar', ] __extensions__ = ['tar'] def __init__(self, name, fileobj): super(PostgreSQLTarDump, self)\ .__init__(name, fileobj, toc_pos=tarfile.BLOCKSIZE) @classmethod def __guess__(cls, mime, name, fileobj): if mime not in cls.__mimes__: raise ValueError("not a tar file") tarinfo = tarfile.TarInfo.frombuf( fileobj.peek(tarfile.BLOCKSIZE)[:tarfile.BLOCKSIZE]) if not tarinfo.name == 'toc.dat': raise ValueError("does not look like a tar dump") return super(PostgreSQLTarDump, cls).__guess__( mime, name, fileobj, toc_pos=tarfile.BLOCKSIZE) class PlainSQL(StreamDecompressor.Archive): """ This class only make sure we have text/plain here """ __compression__ = 'sql' __mimes__ = [ 'text/plain', ] __extensions__ = ['dump', 'dmp'] __uniqueinstance__ = True def __init__(self, name, fileobj): if isinstance(fileobj, PostgreSQLDump): self.header = fileobj.header else: self.header = None super(PlainSQL, self).__init__(name, fileobj, fileobj) pgdump_guesser = StreamDecompressor.Guesser( extra_decompressors=[ (-10, PostgreSQLTarDump), ( 0, PostgreSQLDump), ( 0, PlainSQL), ], ) def open(name=None, fileobj=None): archive = pgdump_guesser.open(name=name, fileobj=fileobj) if not archive.compressions or archive.compressions[-1] != 'sql': raise IOError(errno.EPIPE, "Not a PostgreSQL dump") return archive parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--help', action='store_true') parser.add_argument('--dbname', '-d', dest='dbname', type=str) parser.add_argument('--host', '-h', help="Specifies the TCP port or the local Unix-domain socket file.") parser.add_argument('--username', '-U', help="Connect to the database as the user username instead " "of the default.") parser.add_argument('--port', '-p', type=int) parser.add_argument('--no-owner', '-O', action='store_true', help="Do not output commands to set ownership of objects to match the " "original database.") parser.add_argument('--no-privileges', '--no-acl', '-x', action='store_true', help="Prevent restoration of access privileges (grant/revoke commands).") parser.add_argument('--clean', '-c', action='store_true', help="Clean (drop) database objects before recreating them.") parser.add_argument('--create', '-C', action='store_true', help="Create the database before restoring into it. If --clean is also " "specified, drop and recreate the target database before connecting " "to it.") parser.add_argument('--no-header', action='store_true', help="Do not print header (when available)") parser.add_argument('--debug', action='store_true', default=bool(os.environ.get('DEBUG', False))) parser.add_argument('dump', nargs='?') def warn(*messages): sys.stderr.write(" ".join(map(unicode, messages))+"\n") def die(*messages): warn(*messages) sys.exit(1) def debug(*messages): if args.debug: warn("debug:", *messages) def run(args): if args.help: parser.print_help() sys.exit(0) if args.no_owner: PostgreSQLDump.__command__.append('--no-owner') if args.no_privileges: PostgreSQLDump.__command__.append('--no-privileges') if args.clean: PostgreSQLDump.__command__.append('--clean') if args.create: PostgreSQLDump.__command__.append('--create') if args.dump: try: archive = open(name=args.dump) except IOError, exc: die(args.dump+':', exc.strerror) else: try: archive = open(fileobj=sys.stdin) except IOError, exc: die('-:', exc.strerror) debug("real name:", archive.realname) debug("compressions:", *archive.compressions) if archive.header and not args.no_header: header = dict(archive.header.__dict__, createDate=time.ctime(archive.header.createDate), format={ 0 : 'UNKNOWN', 1 : 'CUSTOM', 3 : 'TAR', 4 : 'NULL', 5 : 'DIRECTORY', }[archive.header.format]) sys.stderr.write( ";\n" "; Archive created at %(createDate)s\n" "; dbname: %(archdbname)s\n" "; TOC Entries:\n" "; Compression: %(compression)s\n" "; Dump Version: %(vmaj)d.%(vmin)d-%(vrev)d\n" "; Format: %(format)s\n" "; Integer: %(intSize)d bytes\n" "; Offset: %(offSize)d bytes\n" "; Dumped from database version: %(archiveDumpVersion)s\n" "; Dumped by pg_dump version: %(archiveRemoteVersion)s\n" ";\n" % header ) if 'pgdump' in archive.compressions: debug("pg_restore arguments:", PostgreSQLDump.__command__) if (not args.dbname and not os.isatty(sys.stdout.fileno())) or \ args.dbname == '-': try: sys.stdout.writelines(archive) sys.exit(0) except IOError, e: archive.close() die(e.args[1]) else: command_args = ['psql', '-X', (args.dbname or archive.realname)] # psql arguments if args.host: command_args += ['--host', args.host] if args.port: command_args += ['--port', str(args.port)] if args.username: command_args += ['--username', args.username] debug("command arguments:", command_args) # NOTE: can't use stdin=archive because it doesn't flush line-by-line psql = subprocess.Popen(command_args, stdin=subprocess.PIPE, stdout=io.open(os.devnull)) try: psql.stdin.writelines(archive) debug("psql: finished writing lines, closing...") psql.stdin.close() except IOError, e: archive.close() die(e.args[1]) retcode = psql.wait() debug("psql exit status:", retcode) sys.exit(retcode) if __name__ == '__main__': args = parser.parse_args() run(args)

31.491103

78

0.607413

da809a59ca07e1ca2608390106867d362000110b

7,637

py

Python

lm_pickers/maxcover.py

dulek/alt-tester

d3925e28b2b01ca25706546646b921de90fcd5c8

[ "MIT" ]

null

lm_pickers/maxcover.py

dulek/alt-tester

d3925e28b2b01ca25706546646b921de90fcd5c8

[ "MIT" ]

null

lm_pickers/maxcover.py

dulek/alt-tester

d3925e28b2b01ca25706546646b921de90fcd5c8

[ "MIT" ]

null

from collections import defaultdict import math import random from numpy import random as random_np from colorama import Fore from lib import logger from lib.utils import all_dijkstra_tree, get_lower_bound, get_lm_distances from lm_picker import LMPicker LOG = logger.getLogger() class MaxcoverPicker(LMPicker): def get_avoid_landmarks(self, lms=None, lm_dists=None, lm_dists_rev=None, lm_num=10): lms = lms or [] lm_dists = lm_dists or {} lm_dists_rev = lm_dists_rev or {} # And now real picking begins... for i in range(len(lms), lm_num): LOG.info('Calculating landmark ' + Fore.RED + '%d' + Fore.RESET + '...', i) r = random.choice(self.G.keys()) LOG.debug(' Choosen r=%d.', r) r_dists, r_tree, r_order = all_dijkstra_tree(r, self.G) # First calculate "weights". LOG.info(' Calculating weights...') weights = {} for v in self.G.keys(): weights[v] = r_dists[v] - get_lower_bound(lm_dists, lm_dists_rev, r, v) LOG.info(' Calculating sizes...') # Then "sizes" dependent on "weights" sizes = defaultdict(lambda: 0) w = None # That's the node of max size # We're processing the vertices in reversed order of Dijkstra # algorithm. Basically we're starting on the leaves and going up. for v in reversed(r_order): # Traverse subtree of r_tree rooted at v using DFS Q = [v] while Q: u = Q.pop() # It's possible we already have size of the subtree if u in sizes: if sizes[u] == 0: sizes[v] = 0 break else: sizes[v] += sizes[u] continue # If subtree has landmark then size is 0 if u in lms: sizes[v] = 0 break else: sizes[v] += weights[u] for x in r_tree[u]: Q.append(x) if w is None or sizes[w] < sizes[v]: w = v LOG.info(' Calculating landmark...') # We have all the sizes calculated, and max one (w). Now we travese # subtree of r_tree rooted in w. We always choose branch of highest # size. while r_tree[w]: w = max(r_tree[w], key=lambda x: sizes[x]) # Adding leaf as a new landmark LOG.info(' Calculated node ' + Fore.RED + '%d' + Fore.RESET + ' as landmark.', w) lms.append(w) # Calculate distances for new landmark LOG.info(' Calculating distances for this landmark...') lm_dists[w] = get_lm_distances(self.G, [w])[w] lm_dists_rev[w] = get_lm_distances(self.G_reversed, [w])[w] LOG.info(Fore.RED + 'Choosen landmarks: %s' + Fore.RESET, str(lms)) return lms, lm_dists, lm_dists_rev def calculate_cost(self, lms, lm_dists): cost = 0 # Iterate over all edges. for v in self.G.keys(): for w in self.G[v].keys(): # Iterate over all landmarks for lm in lms: if self.G[v][w] - lm_dists[lm][w] + lm_dists[lm][v]: cost += 1 break LOG.info('Cost of current solution: %d', cost) return cost def get_landmarks(self, lm_num=10): k = lm_num # For compatibility with description in Goldberg's article. C = set() C_dists = {} C_dists_rev = {} # Start with getting k landmarks by avoid, add all as candidates lms, lm_dists, lm_dists_rev = self.get_avoid_landmarks(lm_num=k) C.update(lms) C_dists.update(lm_dists) C_dists_rev.update(lm_dists_rev) # Repeat until avoid is called 5k times or we have 4k landmarks i = 0 # Number of calls to Avoid. # TODO: Too much candidates are generated (4 landmarks - 21 candidates) # I should probably split that to use avoid one-by-one. while len(C) < (4 * k) and i < (5 * k): # With probability 1/2 remove each landmark from the solution for lm in lms: if random.randint(0, 1): lms.remove(lm) del lm_dists[lm] del lm_dists_rev[lm] i += k - len(lms) # Avoid will be called that many times. # Generate new landmarks lms, lm_dists, lm_dists_rev = self.get_avoid_landmarks(lms, lm_dists, lm_dists_rev, k) # Add new ones to C and repeat everything C.update(lms) C_dists.update(lm_dists) C_dists_rev.update(lm_dists_rev) LOG.info('Got %d candidates in C.', len(C)) # Multistart heuristic with local search - swapping solutions = [] costs = [] for i in xrange(int(math.log(k + 1, 2))): LOG.info('Calculating %d for %d sets', i + 1, int(math.log(k + 1, 2))) S = set(random.sample(C, k)) # Get k lms from C at random T = C - S # Rest of candidates current_cost = self.calculate_cost(S, C_dists) while True: profits = {} for s in S: for t in T: LOG.info('Trying out swap %d-%d.', s, t) new_S = S.copy() new_S.remove(s) new_S.add(t) new_cost = self.calculate_cost(new_S, C_dists) profit = new_cost - current_cost if profit > 0: LOG.info('Swap %d-%d profitable with %d profit.', s, t, profit) profits['%d-%d' % (s, t)] = profit # If no improvement can be made stop. if not profits: break # Otherwise choose swap at random with profit weights p = profits.values() p_sum = float(sum(p)) p = [x / p_sum for x in p] # Normalize weigths to sum to 1.0 swap = random_np.choice(profits.keys(), p=p) LOG.info('Swap %s chosen.', swap) s, t = swap.split('-') s, t = int(s), int(t) S.remove(s) S.add(t) current_cost += profits[swap] LOG.info('No improvements could be made - solution found: %s (%d).', str(S), current_cost) solutions.append(S) costs.append(current_cost) solution = solutions[max(xrange(len(costs)), key=costs.__getitem__)] LOG.info('Best solution chosen: %s.', str(solution)) return (list(solution), {k: v for k, v in C_dists.items() if k in solution}, {k: v for k, v in C_dists_rev.items() if k in solution})

38.376884

80

0.47846

b5f1deaa87bc7a0aea8c3b5ed3c4fd851cedd627

1,554

py

Python

people/migrations/0001_initial.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

null

people/migrations/0001_initial.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

null

people/migrations/0001_initial.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import django.db.models.deletion import wagtail.wagtailcore.blocks from django.db import migrations, models import people.fields class Migration(migrations.Migration): dependencies = [ ('wagtailimages', '0006_add_verbose_names'), ('wagtailcore', '0001_squashed_0016_change_page_url_path_to_text_field'), ] operations = [ migrations.CreateModel( name='ContributorPage', fields=[ ('page_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('first_name', models.CharField(default='', max_length=255, blank=True)), ('last_name', models.CharField(default='', max_length=255, blank=True)), ('nickname', models.CharField(default='', max_length=1024, blank=True)), ('email', models.EmailField(default='', max_length=254, blank=True)), ('twitter_handle', models.CharField(default='', max_length=16, blank=True)), ('short_bio', models.TextField(default='', blank=True)), ('long_bio', models.TextField(default='', blank=True)), ('headshot', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='wagtailimages.Image', null=True)), ], options={ 'abstract': False, }, bases=('wagtailcore.page',), ), ]

40.894737

161

0.616474

c29b5951c1d572fdf7110582de9e146296eb2433

8,477

py

Python

tensorflow/compiler/plugin/poplar/tests/topk_onehot_test.py

DebeshJha/tensorflow-1

2b5a225c49d25273532d11c424d37ce394d7579a

[ "Apache-2.0" ]

2

2021-03-08T23:32:06.000Z

2022-01-13T03:43:49.000Z

tensorflow/compiler/plugin/poplar/tests/topk_onehot_test.py

DebeshJha/tensorflow-1

2b5a225c49d25273532d11c424d37ce394d7579a

[ "Apache-2.0" ]

null

tensorflow/compiler/plugin/poplar/tests/topk_onehot_test.py

DebeshJha/tensorflow-1

2b5a225c49d25273532d11c424d37ce394d7579a

[ "Apache-2.0" ]

null

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np import test_utils as tu from tensorflow.compiler.tests import xla_test from tensorflow.python.platform import googletest from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn class OneHotTopK(xla_test.XLATestCase): def testOneHot(self): def executeModel(inputs, expected): with self.session() as sess: # Decide what the output type should be. data_type = inputs["on"].dtype # The actual model function which perfoms the one-hot operation based on the inputs given to executeModel. def model(a): return array_ops.one_hot(a, inputs["n_classes"], dtype=data_type, on_value=inputs["on"], off_value=inputs["off"], axis=inputs["axis"]) # We run once on the CPU to get the expected result, then on the IPU to compare the two. cpuRun = expected is None with ops.device('cpu'): pa = array_ops.placeholder(np.int32, inputs["shape"], name="a") # Check if we should be running on IPU or cpu. device = "cpu:0" if cpuRun else "/device:IPU:0" with ops.device(device): out = model(pa) tu.ReportJSON(self, sess) in_data = np.array(inputs["in_values"]) fd = {pa: in_data} result = sess.run(out, fd) if cpuRun: return result self.assertAllClose(result, expected) # Generate a multi dimensional matrix. largish_matrix_size = [4, 3, 4, 2, 2] largish_matrix_data = np.random.randint(1, np.prod(largish_matrix_size), largish_matrix_size) # Generate a vector as well, as using just the matrix will increase test times unnecessarily vector_size = [4, 3, 4, 2, 2] vector_data = np.random.randint(1, np.prod(largish_matrix_size), largish_matrix_size) inputs = [ # Test different dimensions. { "n_classes": 10, "shape": [4], "in_values": [1, 2, 3, 4], "on": np.float32(2.0), "off": np.float32(0.0), "axis": -1 }, { "n_classes": 1200, "shape": [4, 2], "in_values": [[1, 1], [2, 5], [4, 3], [4, 6]], "on": np.float32(1.0), "off": np.float32(0.0), "axis": -1 }, { "n_classes": 1200, "shape": largish_matrix_size, "in_values": largish_matrix_data, "on": np.float32(1.0), "off": np.float32(0.0), "axis": -1 }, # Test different depths { "n_classes": 1, "shape": [4], "in_values": [1, 2, 3, 4], "on": np.float32(1.0), "off": np.float32(0.0), "axis": -1 }, { "n_classes": 12000, "shape": [4, 2], "in_values": [[1, 1], [2, 5], [4, 3], [4, 6]], "on": np.float32(1.0), "off": np.float32(0.0), "axis": -1 }, # Test different axes. { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.float32(1.0), "off": np.float32(0.0), "axis": 0 }, { "n_classes": 1200, "shape": largish_matrix_size, "in_values": largish_matrix_data, "on": np.float32(1.0), "off": np.float32(0.0), "axis": 3 }, { "n_classes": 100, "shape": largish_matrix_size, "in_values": largish_matrix_data, "on": np.float32(1.0), "off": np.float32(0.0), "axis": 2 }, # Test different on/off. { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.float32(0.25), "off": np.float32(0.1), "axis": 0 }, { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.float32(20.0), "off": np.float32(-1.0), "axis": 0 }, # Float16 is the only data type we will run on assembly so we have specific cases for that. { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.float16(1.0), "off": np.float16(0.0), "axis": 0 }, { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.float16(2.0), "off": np.float16(3.0), "axis": 1 }, # Check int32 works as well { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.int32(4.0), "off": np.int32(2.0), "axis": 0 }, { "n_classes": 100, "shape": vector_size, "in_values": vector_data, "on": np.int32(4.0), "off": np.int32(2.0), "axis": 1 }, ] for test_case in inputs: # Run on CPU first result = executeModel(test_case, None) # Run on IPU and test against CPU out. executeModel(test_case, result) def testTopK(self): def doTestTopK(self, dtype): with self.session() as sess: n_categories = 1200 topn = 24 def model(a): _, indices = nn.top_k(a, topn) return indices with ops.device('cpu'): pa = array_ops.placeholder(dtype, [n_categories], name="a") with ops.device("/device:IPU:0"): out = model(pa) report = tu.ReportJSON(self, sess) report.reset() # Shuffled set of values of specified dtype in [0:n_categories). # This ensures there is a single unique sort result. pa_input = np.arange(n_categories, dtype=dtype) np.random.shuffle(pa_input) expected = (-pa_input).argsort()[:topn] fd = {pa: pa_input} result = sess.run(out, fd) self.assertAllClose(result, expected) report.parse_log(assert_len=4) testTypes = [np.float16, np.float32, np.int32] for dtype in testTypes: doTestTopK(self, dtype) def testInTopK(self): with self.session() as sess: batchsize = 4 n_categories = 1200 topn = 8 def model(a, b): return nn.in_top_k(a, b, topn) with ops.device('cpu'): pa = array_ops.placeholder(np.float32, [batchsize, n_categories]) pb = array_ops.placeholder(np.int32, [batchsize]) with ops.device("/device:IPU:0"): out = model(pa, pb) report = tu.ReportJSON(self, sess) report.reset() input = np.random.rand(batchsize, n_categories) input = input / np.sqrt(np.sum(input**2)) ref = (-input).argsort(axis=1)[:, :1] ref = ref.reshape([batchsize]) fd = {pa: input, pb: ref} result = sess.run(out, fd) self.assertAllClose(result, [True, True, True, True]) report.parse_log(assert_len=4) if __name__ == "__main__": os.environ['TF_XLA_FLAGS'] = ('--tf_xla_min_cluster_size=1 ' + os.environ.get('TF_XLA_FLAGS', '')) googletest.main()

29.848592

114

0.518226

402ad993dfdf629ab07b3402f88defdf78811af5

8,801

py

Python

third_party/gstreamer/scripts/gst-plot-timeline.py

isabella232/aistreams

209f4385425405676a581a749bb915e257dbc1c1

[ "Apache-2.0" ]

6

2020-09-22T18:07:15.000Z

2021-10-21T01:34:04.000Z

third_party/gstreamer/scripts/gst-plot-timeline.py

isabella232/aistreams

209f4385425405676a581a749bb915e257dbc1c1

[ "Apache-2.0" ]

2

2020-11-10T13:17:39.000Z

2022-03-30T11:22:14.000Z

third_party/gstreamer/scripts/gst-plot-timeline.py

isabella232/aistreams

209f4385425405676a581a749bb915e257dbc1c1

[ "Apache-2.0" ]

3

2020-09-26T08:40:35.000Z

2021-10-21T01:33:56.000Z

#!/usr/bin/env python # # based on plot-timeline.py by Federico Mena-Quintero <federico at ximian dotcom> # example: # GST_DEBUG_COLOR_MODE=off GST_DEBUG="*:3" gst-launch-1.0 2>debug.log audiotestsrc num-buffers=10 ! audioconvert ! alsasink # gst-plot-timeline.py debug.log --output=debug.png import math import optparse import os import re import sys import cairo FONT_NAME = "Bitstream Vera Sans" FONT_SIZE = 8 # how many pixels for a second on the timeline PIXELS_PER_SECOND = 300 # how many pixels for one line of log PIXELS_PER_LINE = 10 PLOT_WIDTH = 1400 TIME_SCALE_WIDTH = 20 SYSCALL_MARKER_WIDTH = 20 LOG_TEXT_XPOS = 400 LOG_MARKER_WIDTH = 20 BACKGROUND_COLOR = (0, 0, 0) # assumes GST_DEBUG_LOG_COLOR=1 # timestamp pid thread level category,file,line,msg mark_regex = re.compile (r'^(\d+:\d+:\d+\.\d+) +\d+ +0?x?[0-9a-f]+ [A-Z]+ +([-a-zA-Z0-9_]+ )(.*)') mark_timestamp_group = 1 mark_program_group = 2 mark_log_group = 3 success_result = "0" skip_lines = 0 max_lines = 500 filter_regex = re.compile ('') skip_regex = re.compile('') class BaseMark: colors = 0, 0, 0 def __init__(self, timestamp, log): self.timestamp = timestamp self.log = log self.timestamp_ypos = 0 self.log_ypos = 0 class AccessMark(BaseMark): pass class LastMark(BaseMark): colors = 1.0, 0, 0 class FirstMark(BaseMark): colors = 1.0, 0, 0 class ExecMark(BaseMark): # colors = 0.75, 0.33, 0.33 colors = (1.0, 0.0, 0.0) def __init__(self, timestamp, log): BaseMark.__init__(self, timestamp, 'execve: ' + os.path.basename(log)) class Metrics: def __init__(self): self.width = 0 self.height = 0 # don't use black or red palette = [ (0.12, 0.29, 0.49), (0.36, 0.51, 0.71), (0.75, 0.31, 0.30), (0.62, 0.73, 0.38), (0.50, 0.40, 0.63), (0.29, 0.67, 0.78), (0.96, 0.62, 0.34) ] class SyscallParser: def __init__ (self): self.syscalls = [] def add_line (self, str): m = mark_regex.search (str) if m: timestr = m.group (mark_timestamp_group).split(':') timestamp = float (timestr[2]) + (float (timestr[1]) * 60.0) + (float (timestr[0]) * 3600.0) program = m.group (mark_program_group) text = program + m.group (mark_log_group) if text == 'last': self.syscalls.append (LastMark (timestamp, text)) elif text == 'first': self.syscalls.append (FirstMark (timestamp, text)) else: s = AccessMark (timestamp, text) program_hash = program.__hash__ () s.colors = palette[program_hash % len (palette)] self.syscalls.append (s) else: print 'No log in %s' % str return def parse_strace(filename): parser = SyscallParser () global skip_lines global max_lines global skip_regex skip_found = False for line in file(filename, "r").readlines(): if line == "": break if not skip_found: if skip_regex.search(line): skip_found = True else: continue if skip_lines > 0: skip_lines -= 1 continue if len(parser.syscalls) >= max_lines: break if filter_regex.search(line): parser.add_line (line) return parser.syscalls def normalize_timestamps(syscalls): first_timestamp = syscalls[0].timestamp for syscall in syscalls: syscall.timestamp -= first_timestamp def compute_syscall_metrics(syscalls): global PIXELS_PER_SECOND global PIXELS_PER_LINE num_syscalls = len(syscalls) metrics = Metrics() metrics.width = PLOT_WIDTH last_timestamp = syscalls[num_syscalls - 1].timestamp time_height = int(math.ceil(last_timestamp * PIXELS_PER_SECOND)) line_height = num_syscalls * PIXELS_PER_LINE if time_height > line_height: metrics.height = time_height print "Adjusting PIXELS_PER_LINE = %d" % PIXELS_PER_LINE PIXELS_PER_LINE = metrics.height / num_syscalls print " PIXELS_PER_LINE = %d" % PIXELS_PER_LINE else: metrics.height = line_height print "Adjusting PIXELS_PER_SECOND %d" % PIXELS_PER_SECOND PIXELS_PER_SECOND = int(math.ceil(metrics.height / last_timestamp)) print " PIXELS_PER_SECOND %d" % PIXELS_PER_SECOND text_ypos = 0 for syscall in syscalls: syscall.timestamp_ypos = syscall.timestamp * PIXELS_PER_SECOND syscall.log_ypos = text_ypos + FONT_SIZE text_ypos += PIXELS_PER_LINE return metrics def plot_time_scale(surface, ctx, metrics): num_seconds = (metrics.height + PIXELS_PER_SECOND - 1) / PIXELS_PER_SECOND ctx.set_source_rgb(0.5, 0.5, 0.5) ctx.set_line_width(1.0) for i in range(num_seconds): ypos = i * PIXELS_PER_SECOND ctx.move_to(0, ypos + 0.5) ctx.line_to(TIME_SCALE_WIDTH, ypos + 0.5) ctx.stroke() ctx.move_to(0, ypos + 2 + FONT_SIZE) ctx.show_text("%d s" % i) def plot_syscall(surface, ctx, syscall): ctx.set_source_rgb(*syscall.colors) # Line ctx.move_to(TIME_SCALE_WIDTH, syscall.timestamp_ypos) ctx.line_to(TIME_SCALE_WIDTH + SYSCALL_MARKER_WIDTH, syscall.timestamp_ypos) ctx.line_to(LOG_TEXT_XPOS - LOG_MARKER_WIDTH, syscall.log_ypos - FONT_SIZE / 2 + 0.5) ctx.line_to(LOG_TEXT_XPOS, syscall.log_ypos - FONT_SIZE / 2 + 0.5) ctx.stroke() # Log text ctx.move_to(LOG_TEXT_XPOS, syscall.log_ypos) ctx.show_text("%8.5f: %s" % (syscall.timestamp, syscall.log)) def plot_syscalls_to_surface(syscalls, metrics): num_syscalls = len(syscalls) print 'picture size: %d x %d' % (metrics.width, metrics.height); surface = cairo.ImageSurface(cairo.FORMAT_RGB24, metrics.width, metrics.height) ctx = cairo.Context(surface) ctx.select_font_face(FONT_NAME) ctx.set_font_size(FONT_SIZE) # Background ctx.set_source_rgb (*BACKGROUND_COLOR) ctx.rectangle(0, 0, metrics.width, metrics.height) ctx.fill() # Time scale plot_time_scale(surface, ctx, metrics) # Contents ctx.set_line_width(1.0) for syscall in syscalls: plot_syscall(surface, ctx, syscall) return surface def main(args): global skip_lines global max_lines global filter_regex global skip_regex option_parser = optparse.OptionParser( usage="usage: %prog -o output.png <debug.log>") option_parser.add_option("-o", "--output", dest="output", metavar="FILE", help="Name of output file (output is a PNG file)") option_parser.add_option("-s", "--skip", dest="skip", metavar="LINES", help="Skip a number of loglines at the beginning of the file or wait till a regular expression happens") option_parser.add_option("-m", "--max-lines", dest="max", help="max lines that need to be plotted") option_parser.add_option("-f", "--filter", dest="filter", help="filter the log lines on a regular expression") options, args = option_parser.parse_args() if not options.output: print 'Please specify an output filename with "-o file.png" or "--output=file.png".' return 1 if len(args) != 1: print 'Please specify only one input filename, which is an debug log taken with "GST_DEBUG_COLOR_MODE=off GST_DEBUG=XXX <application>"' return 1 in_filename = args[0] out_filename = options.output if options.skip: try: skip_lines = int(options.skip) except: skip_regex = re.compile(options.skip) skip_lines = 0 if options.max: max_lines = int(options.max) if options.filter: filter_regex = re.compile(options.filter) syscalls = [] for syscall in parse_strace(in_filename): syscalls.append(syscall) if isinstance(syscall, FirstMark): syscalls = [] elif isinstance(syscall, LastMark): break if not syscalls: print 'No logs in %s' % in_filename return 1 normalize_timestamps(syscalls) metrics = compute_syscall_metrics(syscalls) surface = plot_syscalls_to_surface(syscalls, metrics) surface.write_to_png(out_filename) return 0 if __name__ == "__main__": sys.exit(main(sys.argv))

27.939683

143

0.614021

cee8f76ff69a5353235bde241610ac6aaff7ca27

868

py

Python

src/terra/api_lcd.py

terra-dashboard/staketaxcsv

5793105488bf799c61aee64a45f44e9ae8fef397

[ "MIT" ]

null

src/terra/api_lcd.py

terra-dashboard/staketaxcsv

5793105488bf799c61aee64a45f44e9ae8fef397

[ "MIT" ]

null

src/terra/api_lcd.py

terra-dashboard/staketaxcsv

5793105488bf799c61aee64a45f44e9ae8fef397

[ "MIT" ]

null

""" LCD documentation: * https://lcd.terra.dev/swagger/#/ * https://github.com/terra-money/terra.py/tree/main/terra_sdk/client/lcd/api """ import logging import time from urllib.parse import urlencode import requests from settings_csv import TERRA_LCD_NODE class LcdAPI: session = requests.Session() @classmethod def contract_info(cls, contract): uri = "/wasm/contracts/{}".format(contract) logging.info("Querying lcd for contract = %s ...", contract) data = cls._query(uri, {}) return data @classmethod def _query(cls, uri_path, query_params, sleep_seconds=1): url = f"{TERRA_LCD_NODE}{uri_path}" logging.info("Requesting url %s?%s", url, urlencode(query_params)) response = cls.session.get(url, params=query_params) time.sleep(sleep_seconds) return response.json()

26.30303

77

0.675115

8332b308a368d8b09433ba45a6b56b0e439dcbbc

4,985

py

Python

funtool_ipro_processes/adaptors/sequence_import.py

ActiveLearningLab/funtool-ipro-processes

35547048282d6f39868223fda2da5d3af07af0cf

[ "MIT" ]

null

funtool_ipro_processes/adaptors/sequence_import.py

ActiveLearningLab/funtool-ipro-processes

35547048282d6f39868223fda2da5d3af07af0cf

[ "MIT" ]

null

funtool_ipro_processes/adaptors/sequence_import.py

ActiveLearningLab/funtool-ipro-processes

35547048282d6f39868223fda2da5d3af07af0cf

[ "MIT" ]

null

import funtool.adaptor import funtool.state import funtool.state_collection import pymysql import pymysql.cursors import yaml import os import datetime import math def sequence_import(adaptor, state_collection, overriding_parameters=None, logging=None): adaptor_parameters= funtool.adaptor.get_adaptor_parameters(adaptor,overriding_parameters) mysql_query= _prepare_query(adaptor_parameters) connection= _open_connection(_connection_values(adaptor_parameters.get('db_connection'))) user_revisions={} start_time= datetime.datetime.strptime( adaptor_parameters.get('start_time'), "%Y-%m-%d %H:%M:%S" ) stop_time= datetime.datetime.strptime( adaptor_parameters.get('stop_time'), "%Y-%m-%d %H:%M:%S" ) time_step= int(adaptor_parameters.get('time_step')) time_slices= math.ceil( (stop_time - start_time).total_seconds()/time_step ) all_rows=[] try: reader= _mysql_row(connection, mysql_query) for row in reader: all_rows.append(row) user= row.get(adaptor_parameters.get('user_column','username')) characterization= row.get(adaptor_parameters.get('characterization_column','characterization')) time_slice= int(( row.get(adaptor_parameters.get('time_column'),'created_at') - start_time ).total_seconds())//time_step # Initialize for new user, time_slice user_revisions[user]= user_revisions.get(user,{}) user_revisions[user][time_slice]= user_revisions[user].get(time_slice,[]) user_revisions[user][time_slice].append(characterization) finally: connection.close() user_sequences= _create_sequences(user_revisions,time_slices,adaptor_parameters.get('trim_leading_none',False)) return funtool.state_collection.StateCollection(states=_create_states_from_sequences(user_sequences), groupings={}) def _open_connection(connection_values): return pymysql.connect( **connection_values) def _connection_values(adaptor_db_connection_parameters): conn_parameters= adaptor_db_connection_parameters.copy() config_file= conn_parameters.pop('config_file',None) yaml_config= None if not config_file is None: with open(config_file) as f: yaml_config= yaml.load(f) if yaml_config is None: yaml_config= {} yaml_config.update(conn_parameters) conn_parameters= yaml_config conn_parameters['cursorclass']= pymysql.cursors.DictCursor # Forces the cursor to return Dicts return conn_parameters def _mysql_row(connection, sql): with connection.cursor() as cur: cur.execute(sql) results= cur.fetchall() for result in results: yield result def _prepare_query(adaptor_parameters): mysql_query= adaptor_parameters.get('SQL') if mysql_query is not None: where_clauses=[] order_by_clause= None if adaptor_parameters.get('teams',False): where_clauses.append( '\nOR '.join([ adaptor_parameters.get('user_column','username') + ' LIKE \'' +team+'%\'' for team in adaptor_parameters.get('teams',[])])) if adaptor_parameters.get('start_time',False): where_clauses.append(adaptor_parameters.get('full_time_column')+' > \'' + adaptor_parameters.get('start_time')+'\'') if adaptor_parameters.get('stop_time',False): where_clauses.append(adaptor_parameters.get('full_time_column')+' < \'' + adaptor_parameters.get('stop_time') +'\'') if adaptor_parameters.get('order_by'): order_by_clause ='ORDER BY ' + ', '.join(adaptor_parameters.get('order_by')) if len(where_clauses) > 0: mysql_query+= '\nWHERE ' mysql_query+= 'AND '.join( [ '(' + clause + ')\n' for clause in where_clauses ] ) if not(order_by_clause is None): mysql_query+= order_by_clause return mysql_query def _create_sequences(user_timeslice_characterization, time_slices, trim_common_none=False): user_sequences= {} for user in user_timeslice_characterization.keys(): user_sequences[user]= [ user_timeslice_characterization[user].get(0,[None])[-1] ] for time_slice in range(1,time_slices): user_sequences[user]= user_sequences[user] + [ user_timeslice_characterization[user].get(time_slice,user_sequences[user])[-1] ] if trim_common_none: print('trimming') first_not_none= min([ next( i for i,c in enumerate(sequence) if not( c is None) ) for sequence in user_sequences.values()]) user_sequences= { user:sequence[first_not_none:] for user,sequence in user_sequences.items()} return user_sequences def _create_states_from_sequences(user_sequences): return [ funtool.state.State( id=None, data={'sequence':sequence,'username':user}, measures={}, meta={}, groupings={}) for user,sequence in user_sequences.items() ]

45.318182

139

0.693079

49280a106f4820635fa50743e1dfa0b1cd09eac3

4,388

py

Python

rax/_src/types.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

19

2022-01-25T12:37:51.000Z

2022-03-30T17:12:45.000Z

rax/_src/types.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

1

2022-02-08T23:02:42.000Z

rax/_src/types.py

google/rax

d6370d574246db9fb0566317f7cac8cd331526d7

[ "Apache-2.0" ]

null

# Copyright 2022 Google LLC. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Rax-specific types and protocols. .. note:: Types and protocols are provided for **type-checking** convenience only. You do **not** need to instantiate, subclass or extend them. """ from typing import Optional, Tuple, Union import jax # Protocol is a python 3.8+ feature. For older versions, we can use # typing_extensions, which provides the same functionality. try: from typing import Protocol # pylint: disable=g-import-not-at-top except ImportError: from typing_extensions import Protocol # pylint: disable=g-import-not-at-top # Type alias for a JAX array. Array = jax.numpy.ndarray class RankFn(Protocol): """:class:`typing.Protocol` for rank functions.""" def __call__(self, scores: Array, where: Optional[Array], key: Optional[Array]) -> Array: """Computes 1-based ranks based on the given scores. Args: scores: The scores to compute the 1-based ranks for. where: An optional :class:`jax.numpy.ndarray` of the same shape as ``a`` that indicates which elements to rank. Other elements will be ranked last. key: An optional :func:`~jax.random.PRNGKey` used for random operations. Returns: A :class:`jax.numpy.ndarray` of the same shape as ``scores`` that represents the 1-based ranks. """ pass class CutoffFn(Protocol): """:class:`typing.Protocol` for cutoff functions.""" def __call__(self, a: Array, n: Optional[int]) -> Array: """Computes cutoffs based on the given array. Args: a: The array for which to compute the cutoffs. n: The position of the cutoff. Returns: A binary :class:`jax.numpy.ndarray` of the same shape as ``a`` that represents which elements of ``a`` should be selected for the topn cutoff. """ pass class ReduceFn(Protocol): """:class:`typing.Protocol` for reduce functions.""" def __call__(self, a: Array, where: Optional[Array], axis: Optional[Union[int, Tuple[int, ...]]]) -> Array: """Reduces an array across one or more dimensions. Args: a: The array to reduce. where: An optional :class:`jax.numpy.ndarray` of the same shape as ``a`` that indicates which elements to include in the reduction. axis: One or more axes to use for the reduction. If ``None`` this reduces across all available axes. Returns: A :class:`jax.numpy.ndarray` that represents the reduced result of ``a`` over given ``axis``. """ pass class LossFn(Protocol): """:class:`typing.Protocol` for loss functions.""" def __call__(self, scores: Array, labels: Array, *, where: Optional[Array], **kwargs) -> Array: """Computes a loss. Args: scores: The score of each item. labels: The label of each item. where: An optional :class:`jax.numpy.ndarray` of the same shape as ``scores`` that indicates which elements to include in the loss. **kwargs: Optional loss-specific keyword arguments. Returns: A :class:`jax.numpy.ndarray` that represents the loss computed on the given ``scores`` and ``labels``. """ pass class MetricFn(Protocol): """:class:`typing.Protocol` for metric functions.""" def __call__(self, scores: Array, labels: Array, *, where: Optional[Array], **kwargs) -> Array: """Computes a metric. Args: scores: The score of each item. labels: The label of each item. where: An optional :class:`jax.numpy.ndarray` of the same shape as ``scores`` that indicates which elements to include in the metric. **kwargs: Optional metric-specific keyword arguments. Returns: A :class:`jax.numpy.ndarray` that represents the metric computed on the given ``scores`` and ``labels``. """ pass

31.797101

80

0.670693

4a13c6a1a7547673d9cd98ad4bb31e9ead66b1ac

4,126

py

Python

benchmark/startQiskit_noisy1054.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

benchmark/startQiskit_noisy1054.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

benchmark/startQiskit_noisy1054.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=5 # total number=47 import cirq import qiskit from qiskit.providers.aer import QasmSimulator from qiskit.test.mock import FakeVigo from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute, transpile from pprint import pprint from qiskit.test.mock import FakeVigo from math import log2,floor, sqrt, pi import numpy as np import networkx as nx def build_oracle(n: int, f) -> QuantumCircuit: # implement the oracle O_f^\pm # NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate # or multi_control_Z_gate (issue #127) controls = QuantumRegister(n, "ofc") oracle = QuantumCircuit(controls, name="Zf") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.h(controls[n]) if n >= 2: oracle.mcu1(pi, controls[1:], controls[0]) for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() return oracle def make_circuit(n:int,f) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n,"qc") classical = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classical) prog.h(input_qubit[0]) # number=3 prog.h(input_qubit[1]) # number=4 prog.h(input_qubit[2]) # number=5 prog.h(input_qubit[3]) # number=6 prog.h(input_qubit[0]) # number=41 prog.cz(input_qubit[1],input_qubit[0]) # number=42 prog.h(input_qubit[0]) # number=43 prog.z(input_qubit[1]) # number=37 prog.cx(input_qubit[1],input_qubit[0]) # number=38 prog.h(input_qubit[4]) # number=21 prog.cx(input_qubit[0],input_qubit[2]) # number=44 prog.x(input_qubit[2]) # number=45 prog.cx(input_qubit[0],input_qubit[2]) # number=46 Zf = build_oracle(n, f) repeat = floor(sqrt(2 ** n) * pi / 4) for i in range(repeat): prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)]) prog.h(input_qubit[0]) # number=1 prog.h(input_qubit[1]) # number=2 prog.h(input_qubit[2]) # number=7 prog.h(input_qubit[3]) # number=8 prog.cx(input_qubit[3],input_qubit[0]) # number=33 prog.z(input_qubit[3]) # number=34 prog.x(input_qubit[4]) # number=40 prog.cx(input_qubit[3],input_qubit[0]) # number=35 prog.x(input_qubit[0]) # number=9 prog.cx(input_qubit[0],input_qubit[1]) # number=29 prog.x(input_qubit[1]) # number=30 prog.cx(input_qubit[0],input_qubit[1]) # number=31 prog.x(input_qubit[2]) # number=11 prog.x(input_qubit[3]) # number=12 if n>=2: prog.mcu1(pi,input_qubit[1:],input_qubit[0]) prog.cx(input_qubit[1],input_qubit[0]) # number=24 prog.x(input_qubit[0]) # number=25 prog.cx(input_qubit[1],input_qubit[0]) # number=26 prog.x(input_qubit[1]) # number=14 prog.x(input_qubit[2]) # number=15 prog.x(input_qubit[3]) # number=16 prog.h(input_qubit[0]) # number=17 prog.h(input_qubit[1]) # number=18 prog.h(input_qubit[2]) # number=19 prog.h(input_qubit[3]) # number=20 prog.x(input_qubit[1]) # number=22 prog.y(input_qubit[1]) # number=32 prog.x(input_qubit[1]) # number=23 # circuit end for i in range(n): prog.measure(input_qubit[i], classical[i]) return prog if __name__ == '__main__': key = "00000" f = lambda rep: str(int(rep == key)) prog = make_circuit(5,f) backend = FakeVigo() sample_shot =7924 info = execute(prog, backend=backend, shots=sample_shot).result().get_counts() backend = FakeVigo() circuit1 = transpile(prog,backend,optimization_level=2) writefile = open("../data/startQiskit_noisy1054.csv","w") print(info,file=writefile) print("results end", file=writefile) print(circuit1.depth(),file=writefile) print(circuit1,file=writefile) writefile.close()

31.257576

82

0.618032

ab4d33eb50262c8bb65e58d3388be65b6e89f82b

2,221

py

Python

fiesta/fiesta/nuFlux.py

rodrigruiz/super-duper-fiesta

f09de41f6016fdef0790b783fba9111961e5e33c

[ "MIT" ]

null

fiesta/fiesta/nuFlux.py

rodrigruiz/super-duper-fiesta

f09de41f6016fdef0790b783fba9111961e5e33c

[ "MIT" ]

null

fiesta/fiesta/nuFlux.py

rodrigruiz/super-duper-fiesta

f09de41f6016fdef0790b783fba9111961e5e33c

[ "MIT" ]

null

import numpy as np def read_flux_file(f): """ Read atmospheric neutrino fluxes from an asscii file. The file is organised in 5 columns as follows: column1 -- log10(E/GeV) column2 -- F(nu_e)/(s^-1*m^-2) column3 -- F(nu_mu)/(s^-1*m^-2) column4 -- F(nu_e_bar)/(s^-1*m^-2) column5 -- F(nu_mu_bar)/(s^-1*m^-2) Keyword arguments: f -- the path to the file Returns a dictionary with the following keys (a conversion factor is applied so that the flux units are s^-1*cm^-2*sr^-1): d["log_E"]=np.array(log_e) log10(GeV) d["E"]=np.power(10, np.array(log_e)) GeV d["nu_e"]=np.array(nue) d["nu_mu"]=np.array(numu) d["anu_e"]=np.array(nuebar) d["anu_mu"]=np.array(numubar) """ d={} units_factor = 1.e-4/(4*np.pi) #from m^-2 to cm^-2, and from full solid angle to sr^-1 log_e, nue, numu, nuebar, numubar = ([] for i in range (5)) File = open(f,"r") lines = File.readlines() for line in lines: columns = line.split(' ') log_e.append(float(columns[0])) nue.append(float(columns[1])*units_factor) numu.append(float(columns[2])*units_factor) nuebar.append(float(columns[3])*units_factor) numubar.append(float(columns[4])*units_factor) d["log_E"]=np.array(log_e) d["E"]=np.power(10, np.array(log_e)) d["nu_e"]=np.array(nue) d["nu_mu"]=np.array(numu) d["anu_e"]=np.array(nuebar) d["anu_mu"]=np.array(numubar) File.close() return d def astro_flux(e, phi, gamma, e0, c0): """ Returns the astrophysical neutrino flux in units of s^-1*cm^-2*sr^-1. This follows the parameterisation described in https://arxiv.org/pdf/2001.09520.pdf. Keyword arguments: e -- energy phi -- flux normalisation gamma -- spectral index e0 -- reference energy c0 -- reference normalisation """ return c0 * phi * np.power((e/e0), -gamma) def atmospheric_flux(e, phi, gamma): """ Returns the atmospheric neutrino flux parametrised according to a simple power law, in units of s^-1*cm^-2*sr^-1. Keyword arguments: e -- energy phi -- flux normalisation gamma -- spectral index """ return phi * np.power((e), -gamma)

32.188406

158

0.620891

c3c86d37e2301b330cfa9c55a6dc58ca4716592f

74

py

Python

samples/run_bbands.py

tibkiss/pyalgotrade

4979315281c362dcba2e6d53da27dc4a7377ebec

[ "Apache-2.0" ]

2

2015-04-03T10:29:14.000Z

2017-01-21T05:55:00.000Z

samples/run_bbands.py

tibkiss/pyalgotrade

4979315281c362dcba2e6d53da27dc4a7377ebec

[ "Apache-2.0" ]

null

samples/run_bbands.py

tibkiss/pyalgotrade

4979315281c362dcba2e6d53da27dc4a7377ebec

[ "Apache-2.0" ]

null

import sys sys.path.append('samples') import bbands bbands.main(False)

9.25

26

0.756757

de36df902fc4d98c4ba9fbbcd0cb485250ed432e

1,616

py

Python

mindspore/ops/_op_impl/tbe/div.py

GuoSuiming/mindspore

48afc4cfa53d970c0b20eedfb46e039db2a133d5

[ "Apache-2.0" ]

55

2020-12-17T10:26:06.000Z

2022-03-28T07:18:26.000Z

mindspore/ops/_op_impl/tbe/div.py

forwhat461/mindspore

59a277756eb4faad9ac9afcc7fd526e8277d4994

[ "Apache-2.0" ]

1

2020-12-29T06:46:38.000Z

mindspore/ops/_op_impl/tbe/div.py

forwhat461/mindspore

59a277756eb4faad9ac9afcc7fd526e8277d4994

[ "Apache-2.0" ]

14

2021-01-29T02:39:47.000Z

2022-03-23T05:00:26.000Z

# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Div op""" from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType div_op_info = TBERegOp("Div") \ .fusion_type("ELEMWISE") \ .async_flag(False) \ .binfile_name("div.so") \ .compute_cost(10) \ .kernel_name("div") \ .partial_flag(True) \ .input(0, "x1", False, "required", "all") \ .input(1, "x2", False, "required", "all") \ .output(0, "y", False, "required", "all") \ .op_pattern("broadcast") \ .dtype_format(DataType.I8_None, DataType.I8_None, DataType.I8_None) \ .dtype_format(DataType.U8_None, DataType.U8_None, DataType.U8_None) \ .dtype_format(DataType.I32_None, DataType.I32_None, DataType.I32_None) \ .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None) \ .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None) \ .get_op_info() @op_info_register(div_op_info) def _div_tbe(): """Div TBE register""" return

38.47619

79

0.679455

c410285b244864f2308d26b162b393ab2a29a959

359

py

Python

pythonClass.py

AlisonGurgel/pythonNotes

963f6c9a7d24471b37ec7f27c4337a8d7540ca1e

[ "CC0-1.0" ]

null

pythonClass.py

AlisonGurgel/pythonNotes

963f6c9a7d24471b37ec7f27c4337a8d7540ca1e

[ "CC0-1.0" ]

null

pythonClass.py

AlisonGurgel/pythonNotes

963f6c9a7d24471b37ec7f27c4337a8d7540ca1e

[ "CC0-1.0" ]

null

class Car: def __init__(this, n, y): this.carName = n this.carYear = y this.attFunc = print(this.carName, "\_/", this.carYear) def getYear(this): print(this.carYear) def start(that, km): print(f"{that.carName} start moving at {km}kmh") c1 = Car("BMW", 1990) c1.start(10) c1.getYear() c1.attFunc

19.944444

63

0.573816

241d9ded8a5c7ad9e75d56166fa6eb1fa3243de0

5,302

py

Python

scripts/testing/test_model_load.py

latika-bhurani/fk-visual-search

6061d75b00163774135cf9297f44b4bbcc6b62df

[ "Apache-2.0" ]

1

2018-12-19T17:46:58.000Z

scripts/testing/test_model_load.py

latika-bhurani/fk-visual-search

6061d75b00163774135cf9297f44b4bbcc6b62df

[ "Apache-2.0" ]

null

scripts/testing/test_model_load.py

latika-bhurani/fk-visual-search

6061d75b00163774135cf9297f44b4bbcc6b62df

[ "Apache-2.0" ]

null

import base64 # import h import numpy as np import io from PIL import Image import keras from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.layers.core import Dense from keras.layers import BatchNormalization from keras.applications.vgg16 import VGG16 from keras.preprocessing import image from keras.models import Model # from keras import backend as K from flask import jsonify from flask import Flask from flask import request import tensorflow as tf import keras.backend as K from keras.layers import concatenate, Flatten, Input, Lambda from keras.applications.vgg16 import preprocess_input # from keras.applications import ResNet50 from keras.preprocessing.image import img_to_array # from keras.applications import imagenet_utils from keras.models import load_model # from PIL import Image # import numpy as np import flask # import io import h5py def triplet_loss(_, y_pred): margin = K.constant(1) return K.mean(K.maximum(K.constant(0), K.square(y_pred[:,0,0]) - K.square(y_pred[:,1,0]) + margin)) def accuracy(_, y_pred): return K.mean(y_pred[:,0,0] < y_pred[:,1,0]) def l2Norm(x): return K.l2_normalize(x, axis=-1) def euclidean_distance(vects): x, y = vects return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon())) print('Loading Model') my_model = load_model('fashion_lens_triplet_model.h5', custom_objects={'l2Norm': l2Norm, 'triplet_loss':triplet_loss, 'accuracy':accuracy, 'euclidean_distance':euclidean_distance}) print(" * Model Loaded! ") query_img_path = '.\\structured_images\\wtbi_skirts_query_crop_256\\3.jpg' query_img = image.load_img(query_img_path, target_size=(224, 224)) query_img_data = image.img_to_array(query_img) query_img_data = np.expand_dims(query_img_data, axis=0) query_img_data = preprocess_input(query_img_data) print(query_img_data.shape) dummy_pos = np.random.randn(*query_img_data.shape) dummy_neg = np.random.randn(*query_img_data.shape) # b = np.random.randn(*a.shape) # print(b.shape) # print(my_model.predict([query_img_data, dummy_pos, dummy_neg])) # [<tf.Tensor 'query_input:0' shape=(?, 224, 224, 3) dtype=float32>, <tf.Tensor 'positive_input:0' shape=(?, 224, 224, 3) dtype=float32>, <tf.Tensor 'negative_input:0' shape=(?, 224, 224, 3) dtype=float32>] print(my_model.get_input_at(0)[0]) # vizNet_model.get_input_at(0) ## extract the feature vector model_name = 'vizNet_model' layer_name = 'viznet_embedding' # extract_feature_model = Model(inputs=my_model.get_input_at(0)[0], # outputs=my_model.get_layer(model_name).get_layer(layer_name).output) extract_feature_model = Model(inputs=my_model.get_layer(model_name).get_input_at(0), outputs=my_model.get_layer(model_name).get_layer(layer_name).output) # feature_vector = my_model.predict([query_img_data, dummy_pos, dummy_neg]) feature_vector = extract_feature_model.predict(query_img_data) print(feature_vector) print(feature_vector.shape) print(feature_vector.tolist()) # Layer (type) Output Shape Param # Connected to # ================================================================================================== # query_input (InputLayer) (None, 224, 224, 3) 0 # __________________________________________________________________________________________________ # positive_input (InputLayer) (None, 224, 224, 3) 0 # __________________________________________________________________________________________________ # negative_input (InputLayer) (None, 224, 224, 3) 0 # __________________________________________________________________________________________________ # vizNet_model (Model) (None, 4096) 164846038 query_input[0][0] # positive_input[0][0] # negative_input[0][0] # __________________________________________________________________________________________________ # pos_dist (Lambda) (None, 1) 0 vizNet_model[1][0] # vizNet_model[2][0] # __________________________________________________________________________________________________ # neg_dist (Lambda) (None, 1) 0 vizNet_model[1][0] # vizNet_model[3][0] # __________________________________________________________________________________________________ # stacked_dists (Lambda) (None, 2, 1) 0 pos_dist[0][0] # neg_dist[0][0] # ===============================================================================================

47.339286

207

0.620709

d91fca0120368a2cb04a9679818504a34b2207b2

2,101

py

Python

examples/dice.py

jvanstraten/openql-ci-test

a8ff9c8c30991ff4ac81551a698a7e1c40471910

[ "Apache-2.0" ]

1

2021-11-10T11:45:57.000Z

examples/dice.py

jvanstraten/openql-ci-test

a8ff9c8c30991ff4ac81551a698a7e1c40471910

[ "Apache-2.0" ]

6

2020-03-26T16:59:23.000Z

2021-04-30T07:51:12.000Z

examples/dice.py

jvanstraten/openql-ci-test

a8ff9c8c30991ff4ac81551a698a7e1c40471910

[ "Apache-2.0" ]

1

2022-02-21T22:07:04.000Z

from openql import openql as ql import qxelarator from functools import reduce import os import matplotlib.pyplot as plt curdir = os.path.dirname(__file__) output_dir = os.path.join(curdir, 'test_output') ql.set_option('output_dir', output_dir) ql.set_option('write_qasm_files', 'yes') ql.set_option('scheduler', 'ASAP') ql.set_option('log_level', 'LOG_INFO') nqubits = 3 def dice_compile(): print('compiling 8-face dice program by openql') config = os.path.join(curdir, '../tests/hardware_config_qx.json') platform = ql.Platform("myPlatform", config) p = ql.Program('dice', platform, nqubits) k = ql.Kernel('aKernel', platform, nqubits) for q in range(nqubits): k.gate('h', [q]) for q in range(nqubits): k.gate('measure', [q]) p.add_kernel(k) p.compile() def plot_histogram(dice_faces): plt.hist(dice_faces, bins=8, color='#0504aa',alpha=0.7, rwidth=0.85) plt.grid(axis='y', alpha=0.75) plt.xlabel('Dice Face',fontsize=15) plt.ylabel('Frequency',fontsize=15) plt.xticks(fontsize=15) plt.yticks(fontsize=15) plt.ylabel('Frequency',fontsize=15) plt.title('Histogram',fontsize=15) plt.show() plt.savefig('hist.png') def dice_execute_singleshot(): print('executing 8-face dice program on qxelarator') qx = qxelarator.QX() qx.set('test_output/dice.qasm') qx.execute() res = [int(qx.get_measurement_outcome(q)) for q in range(nqubits)] dice_face = reduce(lambda x, y: 2*x+y, res, 0) + 1 print('Dice face : {}'.format(dice_face)) def dice_execute_multishot(): print('executing 8-face dice program on qxelarator') qx = qxelarator.QX() qx.set('test_output/dice.qasm') dice_faces = [] ntests = 100 for i in range(ntests): qx.execute() res = [int(qx.get_measurement_outcome(q)) for q in range(nqubits)] dice_face = reduce(lambda x, y: 2*x+y, res, 0) +1 dice_faces.append(dice_face) plot_histogram(dice_faces) if __name__ == '__main__': dice_compile() dice_execute_singleshot() # dice_execute_multishot()

28.391892

74

0.671109

21686540e82d4ecf18dc58d9ec797496b29cc5d7

539,374

py

Python

google/ads/google_ads/v3/services/enums.py

xflesym/google-ads-python

11abbea977345d3352b0c1efcea6f4312b25df30

[ "Apache-2.0" ]

null

google/ads/google_ads/v3/services/enums.py

xflesym/google-ads-python

11abbea977345d3352b0c1efcea6f4312b25df30

[ "Apache-2.0" ]

null

google/ads/google_ads/v3/services/enums.py

xflesym/google-ads-python

11abbea977345d3352b0c1efcea6f4312b25df30

[ "Apache-2.0" ]

1

2020-03-13T00:14:31.000Z

# -*- coding: utf-8 -*- # # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Wrappers for protocol buffer enum types.""" import enum class _CreateEnumTypeUponFirstAccess(object): r"""A lazy container class for a single capitalized Enum attribute. It will exec code to define the Enum type within the instance dict upon first access. Reducing import time for a module full of Enum definitions. Example: >>> class ThingHolder(_CreateEnumTypeUponFirstAccess): ... Thing = '''\ ... Thing = '''\ class Thing(enum.IntEnum): ... GOOD = 0 ... BAD = 1 ... UNKNOWN = 2 ... ''' ... >>> ThingHolder = ThingHolder() # The instance enables the magic. This avoids executing the slow Python enum.Enum metaclass construction code at import time. Instead, the first time ThingHolder.Thing is accessed, the code to define the enum type will be run and replace the Thing attribute on ThingHolder with the instantiated Thing type. >>> ThingHolder <ThingHolder object at 0x...> >>> dir(ThingHolder) ['Thing'] >>> ThingHolder.Thing.UNKNOWN # Triggers the actual creation of Thing. <Thing.UNKNOWN: 2> >>> ThingHolder.Thing.GOOD <Thing.GOOD: 0> >>> ThingHolder <class 'ThingHolder'> The import time enum type creation cost is deferred. The runtime cost upon first access will be a bit higher. Under the assumption that most applications never access the majority of the enum types, the reduction in startup time makes for a nicer experience. """ def __getattribute__(self, name): # We use type(self) instead of self.__class__ to avoid recursing. names = [k for k in type(self).__dict__ if k[0].isupper()] assert len(names) <= 1, names if not names: # Race condition, another thread finished executing this before us. return getattr(self, enum_type_name) enum_type_name = names[0] if name == enum_type_name: try: class_def_src = getattr(type(self), enum_type_name) except AttributeError: # The only way this should happen is if another thread was also # executing this method at the same time and finished defining # the enum class on our instance and deleting its source from # the class before our own call got this far. # Assume our method is gone and revert to a regular instance # attribute lookup to find our class. assert not hasattr(type(self), '__getattribute__') return getattr(self, enum_type_name) if not isinstance(class_def_src, str): # If the class attribute has been replaced with something else, # another must have already executed it and created our type. return class_def_src assert class_def_src.startswith('class %s(enum.' % enum_type_name), 'Expected ' + enum_type_name # It is possible for multiple threads to wind up doing this exec at # the same time. That'll create multiple identical types and assign # them into the instance dict under the same name. One of them will # "win" the final spot in class dict. BUT each simultaneous # "first" call may wind up returning an "obsolete" type... Not # great, but as they're IntEnum classes, nobody should be using # the class type in type checks. Add a lock if this bothers you. six.exec_(class_def_src, {'enum': enum}, object.__getattribute__(self, '__dict__')) # We've done our job, get out of the way forever. type(self).__getattribute__ = object.__getattribute__ enum_type = getattr(self, enum_type_name) # No need for the enum class source anymore. ###delattr(type(self), enum_type_name) # ... # These two statements are overkill and need rewriting if this base # class were moved to a utility library or used from outside this # file as globals() refers to the scope in the current file. If # you get rid of these, uncomment the delattr above to still # release some memory. setattr(type(self), enum_type_name, enum_type) # Get rid of our instance singleton now that its job is done. # (obsoletes the reassigning of __getattribute__ above) globals()[type(self).__name__] = type(self) # ... return enum_type else: raise AttributeError('%r has no attribute %r' % (type(self).__name__, name)) class AccessInvitationErrorEnum(_CreateEnumTypeUponFirstAccess): AccessInvitationErrorEnum = '''\ class AccessInvitationError(enum.IntEnum): """ Enum describing possible AccessInvitation errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_EMAIL_ADDRESS (int): The email address is invalid for sending an invitation. EMAIL_ADDRESS_ALREADY_HAS_ACCESS (int): Email address already has access to this customer. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_EMAIL_ADDRESS = 2 EMAIL_ADDRESS_ALREADY_HAS_ACCESS = 3 ''' AccessInvitationErrorEnum = AccessInvitationErrorEnum() # For __getattribute__ class AccessReasonEnum(_CreateEnumTypeUponFirstAccess): AccessReasonEnum = '''\ class AccessReason(enum.IntEnum): """ Enum describing possible access reasons. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. OWNED (int): The resource is owned by the user. SHARED (int): The resource is shared to the user. LICENSED (int): The resource is licensed to the user. SUBSCRIBED (int): The user subscribed to the resource. AFFILIATED (int): The resource is accessible to the user. """ UNSPECIFIED = 0 UNKNOWN = 1 OWNED = 2 SHARED = 3 LICENSED = 4 SUBSCRIBED = 5 AFFILIATED = 6 ''' AccessReasonEnum = AccessReasonEnum() # For __getattribute__ class AccessRoleEnum(_CreateEnumTypeUponFirstAccess): AccessRoleEnum = '''\ class AccessRole(enum.IntEnum): """ Possible access role of a user. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADMIN (int): Owns its account and can control the addition of other users. STANDARD (int): Can modify campaigns, but can't affect other users. READ_ONLY (int): Can view campaigns and account changes, but cannot make edits. """ UNSPECIFIED = 0 UNKNOWN = 1 ADMIN = 2 STANDARD = 3 READ_ONLY = 4 ''' AccessRoleEnum = AccessRoleEnum() # For __getattribute__ class AccountBudgetProposalErrorEnum(_CreateEnumTypeUponFirstAccess): AccountBudgetProposalErrorEnum = '''\ class AccountBudgetProposalError(enum.IntEnum): """ Enum describing possible account budget proposal errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FIELD_MASK_NOT_ALLOWED (int): The field mask must be empty for create/end/remove proposals. IMMUTABLE_FIELD (int): The field cannot be set because of the proposal type. REQUIRED_FIELD_MISSING (int): The field is required because of the proposal type. CANNOT_CANCEL_APPROVED_PROPOSAL (int): Proposals that have been approved cannot be cancelled. CANNOT_REMOVE_UNAPPROVED_BUDGET (int): Budgets that haven't been approved cannot be removed. CANNOT_REMOVE_RUNNING_BUDGET (int): Budgets that are currently running cannot be removed. CANNOT_END_UNAPPROVED_BUDGET (int): Budgets that haven't been approved cannot be truncated. CANNOT_END_INACTIVE_BUDGET (int): Only budgets that are currently running can be truncated. BUDGET_NAME_REQUIRED (int): All budgets must have names. CANNOT_UPDATE_OLD_BUDGET (int): Expired budgets cannot be edited after a sufficient amount of time has passed. CANNOT_END_IN_PAST (int): It is not permissible a propose a new budget that ends in the past. CANNOT_EXTEND_END_TIME (int): An expired budget cannot be extended to overlap with the running budget. PURCHASE_ORDER_NUMBER_REQUIRED (int): A purchase order number is required. PENDING_UPDATE_PROPOSAL_EXISTS (int): Budgets that have a pending update cannot be updated. MULTIPLE_BUDGETS_NOT_ALLOWED_FOR_UNAPPROVED_BILLING_SETUP (int): Cannot propose more than one budget when the corresponding billing setup hasn't been approved. CANNOT_UPDATE_START_TIME_FOR_STARTED_BUDGET (int): Cannot update the start time of a budget that has already started. SPENDING_LIMIT_LOWER_THAN_ACCRUED_COST_NOT_ALLOWED (int): Cannot update the spending limit of a budget with an amount lower than what has already been spent. UPDATE_IS_NO_OP (int): Cannot propose a budget update without actually changing any fields. END_TIME_MUST_FOLLOW_START_TIME (int): The end time must come after the start time. BUDGET_DATE_RANGE_INCOMPATIBLE_WITH_BILLING_SETUP (int): The budget's date range must fall within the date range of its billing setup. NOT_AUTHORIZED (int): The user is not authorized to mutate budgets for the given billing setup. INVALID_BILLING_SETUP (int): Mutates are not allowed for the given billing setup. """ UNSPECIFIED = 0 UNKNOWN = 1 FIELD_MASK_NOT_ALLOWED = 2 IMMUTABLE_FIELD = 3 REQUIRED_FIELD_MISSING = 4 CANNOT_CANCEL_APPROVED_PROPOSAL = 5 CANNOT_REMOVE_UNAPPROVED_BUDGET = 6 CANNOT_REMOVE_RUNNING_BUDGET = 7 CANNOT_END_UNAPPROVED_BUDGET = 8 CANNOT_END_INACTIVE_BUDGET = 9 BUDGET_NAME_REQUIRED = 10 CANNOT_UPDATE_OLD_BUDGET = 11 CANNOT_END_IN_PAST = 12 CANNOT_EXTEND_END_TIME = 13 PURCHASE_ORDER_NUMBER_REQUIRED = 14 PENDING_UPDATE_PROPOSAL_EXISTS = 15 MULTIPLE_BUDGETS_NOT_ALLOWED_FOR_UNAPPROVED_BILLING_SETUP = 16 CANNOT_UPDATE_START_TIME_FOR_STARTED_BUDGET = 17 SPENDING_LIMIT_LOWER_THAN_ACCRUED_COST_NOT_ALLOWED = 18 UPDATE_IS_NO_OP = 19 END_TIME_MUST_FOLLOW_START_TIME = 20 BUDGET_DATE_RANGE_INCOMPATIBLE_WITH_BILLING_SETUP = 21 NOT_AUTHORIZED = 22 INVALID_BILLING_SETUP = 23 ''' AccountBudgetProposalErrorEnum = AccountBudgetProposalErrorEnum() # For __getattribute__ class AccountBudgetProposalStatusEnum(_CreateEnumTypeUponFirstAccess): AccountBudgetProposalStatusEnum = '''\ class AccountBudgetProposalStatus(enum.IntEnum): """ The possible statuses of an AccountBudgetProposal. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): The proposal is pending approval. APPROVED_HELD (int): The proposal has been approved but the corresponding billing setup has not. This can occur for proposals that set up the first budget when signing up for billing or when performing a change of bill-to operation. APPROVED (int): The proposal has been approved. CANCELLED (int): The proposal has been cancelled by the user. REJECTED (int): The proposal has been rejected by the user, e.g. by rejecting an acceptance email. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 APPROVED_HELD = 3 APPROVED = 4 CANCELLED = 5 REJECTED = 6 ''' AccountBudgetProposalStatusEnum = AccountBudgetProposalStatusEnum() # For __getattribute__ class AccountBudgetProposalTypeEnum(_CreateEnumTypeUponFirstAccess): AccountBudgetProposalTypeEnum = '''\ class AccountBudgetProposalType(enum.IntEnum): """ The possible types of an AccountBudgetProposal. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CREATE (int): Identifies a request to create a new budget. UPDATE (int): Identifies a request to edit an existing budget. END (int): Identifies a request to end a budget that has already started. REMOVE (int): Identifies a request to remove a budget that hasn't started yet. """ UNSPECIFIED = 0 UNKNOWN = 1 CREATE = 2 UPDATE = 3 END = 4 REMOVE = 5 ''' AccountBudgetProposalTypeEnum = AccountBudgetProposalTypeEnum() # For __getattribute__ class AccountBudgetStatusEnum(_CreateEnumTypeUponFirstAccess): AccountBudgetStatusEnum = '''\ class AccountBudgetStatus(enum.IntEnum): """ The possible statuses of an AccountBudget. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): The account budget is pending approval. APPROVED (int): The account budget has been approved. CANCELLED (int): The account budget has been cancelled by the user. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 APPROVED = 3 CANCELLED = 4 ''' AccountBudgetStatusEnum = AccountBudgetStatusEnum() # For __getattribute__ class AdCustomizerErrorEnum(_CreateEnumTypeUponFirstAccess): AdCustomizerErrorEnum = '''\ class AdCustomizerError(enum.IntEnum): """ Enum describing possible ad customizer errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. COUNTDOWN_INVALID_DATE_FORMAT (int): Invalid date argument in countdown function. COUNTDOWN_DATE_IN_PAST (int): Countdown end date is in the past. COUNTDOWN_INVALID_LOCALE (int): Invalid locale string in countdown function. COUNTDOWN_INVALID_START_DAYS_BEFORE (int): Days-before argument to countdown function is not positive. UNKNOWN_USER_LIST (int): A user list referenced in an IF function does not exist. """ UNSPECIFIED = 0 UNKNOWN = 1 COUNTDOWN_INVALID_DATE_FORMAT = 2 COUNTDOWN_DATE_IN_PAST = 3 COUNTDOWN_INVALID_LOCALE = 4 COUNTDOWN_INVALID_START_DAYS_BEFORE = 5 UNKNOWN_USER_LIST = 6 ''' AdCustomizerErrorEnum = AdCustomizerErrorEnum() # For __getattribute__ class AdCustomizerPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): AdCustomizerPlaceholderFieldEnum = '''\ class AdCustomizerPlaceholderField(enum.IntEnum): """ Possible values for Ad Customizers placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INTEGER (int): Data Type: INT64. Integer value to be inserted. PRICE (int): Data Type: STRING. Price value to be inserted. DATE (int): Data Type: DATE\_TIME. Date value to be inserted. STRING (int): Data Type: STRING. String value to be inserted. """ UNSPECIFIED = 0 UNKNOWN = 1 INTEGER = 2 PRICE = 3 DATE = 4 STRING = 5 ''' AdCustomizerPlaceholderFieldEnum = AdCustomizerPlaceholderFieldEnum() # For __getattribute__ class AdErrorEnum(_CreateEnumTypeUponFirstAccess): AdErrorEnum = '''\ class AdError(enum.IntEnum): """ Enum describing possible ad errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AD_CUSTOMIZERS_NOT_SUPPORTED_FOR_AD_TYPE (int): Ad customizers are not supported for ad type. APPROXIMATELY_TOO_LONG (int): Estimating character sizes the string is too long. APPROXIMATELY_TOO_SHORT (int): Estimating character sizes the string is too short. BAD_SNIPPET (int): There is a problem with the snippet. CANNOT_MODIFY_AD (int): Cannot modify an ad. CANNOT_SET_BUSINESS_NAME_IF_URL_SET (int): business name and url cannot be set at the same time CANNOT_SET_FIELD (int): The specified field is incompatible with this ad's type or settings. CANNOT_SET_FIELD_WITH_ORIGIN_AD_ID_SET (int): Cannot set field when originAdId is set. CANNOT_SET_FIELD_WITH_AD_ID_SET_FOR_SHARING (int): Cannot set field when an existing ad id is set for sharing. CANNOT_SET_ALLOW_FLEXIBLE_COLOR_FALSE (int): Cannot set allowFlexibleColor false if no color is provided by user. CANNOT_SET_COLOR_CONTROL_WHEN_NATIVE_FORMAT_SETTING (int): When user select native, no color control is allowed because we will always respect publisher color for native format serving. CANNOT_SET_URL (int): Cannot specify a url for the ad type CANNOT_SET_WITHOUT_FINAL_URLS (int): Cannot specify a tracking or mobile url without also setting final urls CANNOT_SET_WITH_FINAL_URLS (int): Cannot specify a legacy url and a final url simultaneously CANNOT_SET_WITH_URL_DATA (int): Cannot specify a urls in UrlData and in template fields simultaneously. CANNOT_USE_AD_SUBCLASS_FOR_OPERATOR (int): This operator cannot be used with a subclass of Ad. CUSTOMER_NOT_APPROVED_MOBILEADS (int): Customer is not approved for mobile ads. CUSTOMER_NOT_APPROVED_THIRDPARTY_ADS (int): Customer is not approved for 3PAS richmedia ads. CUSTOMER_NOT_APPROVED_THIRDPARTY_REDIRECT_ADS (int): Customer is not approved for 3PAS redirect richmedia (Ad Exchange) ads. CUSTOMER_NOT_ELIGIBLE (int): Not an eligible customer CUSTOMER_NOT_ELIGIBLE_FOR_UPDATING_BEACON_URL (int): Customer is not eligible for updating beacon url DIMENSION_ALREADY_IN_UNION (int): There already exists an ad with the same dimensions in the union. DIMENSION_MUST_BE_SET (int): Ad's dimension must be set before setting union dimension. DIMENSION_NOT_IN_UNION (int): Ad's dimension must be included in the union dimensions. DISPLAY_URL_CANNOT_BE_SPECIFIED (int): Display Url cannot be specified (applies to Ad Exchange Ads) DOMESTIC_PHONE_NUMBER_FORMAT (int): Telephone number contains invalid characters or invalid format. Please re-enter your number using digits (0-9), dashes (-), and parentheses only. EMERGENCY_PHONE_NUMBER (int): Emergency telephone numbers are not allowed. Please enter a valid domestic phone number to connect customers to your business. EMPTY_FIELD (int): A required field was not specified or is an empty string. FEED_ATTRIBUTE_MUST_HAVE_MAPPING_FOR_TYPE_ID (int): A feed attribute referenced in an ad customizer tag is not in the ad customizer mapping for the feed. FEED_ATTRIBUTE_MAPPING_TYPE_MISMATCH (int): The ad customizer field mapping for the feed attribute does not match the expected field type. ILLEGAL_AD_CUSTOMIZER_TAG_USE (int): The use of ad customizer tags in the ad text is disallowed. Details in trigger. ILLEGAL_TAG_USE (int): Tags of the form {PH\_x}, where x is a number, are disallowed in ad text. INCONSISTENT_DIMENSIONS (int): The dimensions of the ad are specified or derived in multiple ways and are not consistent. INCONSISTENT_STATUS_IN_TEMPLATE_UNION (int): The status cannot differ among template ads of the same union. INCORRECT_LENGTH (int): The length of the string is not valid. INELIGIBLE_FOR_UPGRADE (int): The ad is ineligible for upgrade. INVALID_AD_ADDRESS_CAMPAIGN_TARGET (int): User cannot create mobile ad for countries targeted in specified campaign. INVALID_AD_TYPE (int): Invalid Ad type. A specific type of Ad is required. INVALID_ATTRIBUTES_FOR_MOBILE_IMAGE (int): Headline, description or phone cannot be present when creating mobile image ad. INVALID_ATTRIBUTES_FOR_MOBILE_TEXT (int): Image cannot be present when creating mobile text ad. INVALID_CALL_TO_ACTION_TEXT (int): Invalid call to action text. INVALID_CHARACTER_FOR_URL (int): Invalid character in URL. INVALID_COUNTRY_CODE (int): Creative's country code is not valid. INVALID_EXPANDED_DYNAMIC_SEARCH_AD_TAG (int): Invalid use of Expanded Dynamic Search Ads tags ({lpurl} etc.) INVALID_INPUT (int): An input error whose real reason was not properly mapped (should not happen). INVALID_MARKUP_LANGUAGE (int): An invalid markup language was entered. INVALID_MOBILE_CARRIER (int): An invalid mobile carrier was entered. INVALID_MOBILE_CARRIER_TARGET (int): Specified mobile carriers target a country not targeted by the campaign. INVALID_NUMBER_OF_ELEMENTS (int): Wrong number of elements for given element type INVALID_PHONE_NUMBER_FORMAT (int): The format of the telephone number is incorrect. Please re-enter the number using the correct format. INVALID_RICH_MEDIA_CERTIFIED_VENDOR_FORMAT_ID (int): The certified vendor format id is incorrect. INVALID_TEMPLATE_DATA (int): The template ad data contains validation errors. INVALID_TEMPLATE_ELEMENT_FIELD_TYPE (int): The template field doesn't have have the correct type. INVALID_TEMPLATE_ID (int): Invalid template id. LINE_TOO_WIDE (int): After substituting replacement strings, the line is too wide. MISSING_AD_CUSTOMIZER_MAPPING (int): The feed referenced must have ad customizer mapping to be used in a customizer tag. MISSING_ADDRESS_COMPONENT (int): Missing address component in template element address field. MISSING_ADVERTISEMENT_NAME (int): An ad name must be entered. MISSING_BUSINESS_NAME (int): Business name must be entered. MISSING_DESCRIPTION1 (int): Description (line 2) must be entered. MISSING_DESCRIPTION2 (int): Description (line 3) must be entered. MISSING_DESTINATION_URL_TAG (int): The destination url must contain at least one tag (e.g. {lpurl}) MISSING_LANDING_PAGE_URL_TAG (int): The tracking url template of ExpandedDynamicSearchAd must contain at least one tag. (e.g. {lpurl}) MISSING_DIMENSION (int): A valid dimension must be specified for this ad. MISSING_DISPLAY_URL (int): A display URL must be entered. MISSING_HEADLINE (int): Headline must be entered. MISSING_HEIGHT (int): A height must be entered. MISSING_IMAGE (int): An image must be entered. MISSING_MARKETING_IMAGE_OR_PRODUCT_VIDEOS (int): Marketing image or product videos are required. MISSING_MARKUP_LANGUAGES (int): The markup language in which your site is written must be entered. MISSING_MOBILE_CARRIER (int): A mobile carrier must be entered. MISSING_PHONE (int): Phone number must be entered. MISSING_REQUIRED_TEMPLATE_FIELDS (int): Missing required template fields MISSING_TEMPLATE_FIELD_VALUE (int): Missing a required field value MISSING_TEXT (int): The ad must have text. MISSING_VISIBLE_URL (int): A visible URL must be entered. MISSING_WIDTH (int): A width must be entered. MULTIPLE_DISTINCT_FEEDS_UNSUPPORTED (int): Only 1 feed can be used as the source of ad customizer substitutions in a single ad. MUST_USE_TEMP_AD_UNION_ID_ON_ADD (int): TempAdUnionId must be use when adding template ads. TOO_LONG (int): The string has too many characters. TOO_SHORT (int): The string has too few characters. UNION_DIMENSIONS_CANNOT_CHANGE (int): Ad union dimensions cannot change for saved ads. UNKNOWN_ADDRESS_COMPONENT (int): Address component is not {country, lat, lng}. UNKNOWN_FIELD_NAME (int): Unknown unique field name UNKNOWN_UNIQUE_NAME (int): Unknown unique name (template element type specifier) UNSUPPORTED_DIMENSIONS (int): Unsupported ad dimension URL_INVALID_SCHEME (int): URL starts with an invalid scheme. URL_INVALID_TOP_LEVEL_DOMAIN (int): URL ends with an invalid top-level domain name. URL_MALFORMED (int): URL contains illegal characters. URL_NO_HOST (int): URL must contain a host name. URL_NOT_EQUIVALENT (int): URL not equivalent during upgrade. URL_HOST_NAME_TOO_LONG (int): URL host name too long to be stored as visible URL (applies to Ad Exchange ads) URL_NO_SCHEME (int): URL must start with a scheme. URL_NO_TOP_LEVEL_DOMAIN (int): URL should end in a valid domain extension, such as .com or .net. URL_PATH_NOT_ALLOWED (int): URL must not end with a path. URL_PORT_NOT_ALLOWED (int): URL must not specify a port. URL_QUERY_NOT_ALLOWED (int): URL must not contain a query. URL_SCHEME_BEFORE_EXPANDED_DYNAMIC_SEARCH_AD_TAG (int): A url scheme is not allowed in front of tag in tracking url template (e.g. http://{lpurl}) USER_DOES_NOT_HAVE_ACCESS_TO_TEMPLATE (int): The user does not have permissions to create a template ad for the given template. INCONSISTENT_EXPANDABLE_SETTINGS (int): Expandable setting is inconsistent/wrong. For example, an AdX ad is invalid if it has a expandable vendor format but no expanding directions specified, or expanding directions is specified, but the vendor format is not expandable. INVALID_FORMAT (int): Format is invalid INVALID_FIELD_TEXT (int): The text of this field did not match a pattern of allowed values. ELEMENT_NOT_PRESENT (int): Template element is mising IMAGE_ERROR (int): Error occurred during image processing VALUE_NOT_IN_RANGE (int): The value is not within the valid range FIELD_NOT_PRESENT (int): Template element field is not present ADDRESS_NOT_COMPLETE (int): Address is incomplete ADDRESS_INVALID (int): Invalid address VIDEO_RETRIEVAL_ERROR (int): Error retrieving specified video AUDIO_ERROR (int): Error processing audio INVALID_YOUTUBE_DISPLAY_URL (int): Display URL is incorrect for YouTube PYV ads TOO_MANY_PRODUCT_IMAGES (int): Too many product Images in GmailAd TOO_MANY_PRODUCT_VIDEOS (int): Too many product Videos in GmailAd INCOMPATIBLE_AD_TYPE_AND_DEVICE_PREFERENCE (int): The device preference is not compatible with the ad type CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY (int): Call tracking is not supported for specified country. CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED (int): Carrier specific short number is not allowed. DISALLOWED_NUMBER_TYPE (int): Specified phone number type is disallowed. PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY (int): Phone number not supported for country. PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY (int): Phone number not supported with call tracking enabled for country. PREMIUM_RATE_NUMBER_NOT_ALLOWED (int): Premium rate phone number is not allowed. VANITY_PHONE_NUMBER_NOT_ALLOWED (int): Vanity phone number is not allowed. INVALID_CALL_CONVERSION_TYPE_ID (int): Invalid call conversion type id. CANNOT_DISABLE_CALL_CONVERSION_AND_SET_CONVERSION_TYPE_ID (int): Cannot disable call conversion and set conversion type id. CANNOT_SET_PATH2_WITHOUT_PATH1 (int): Cannot set path2 without path1. MISSING_DYNAMIC_SEARCH_ADS_SETTING_DOMAIN_NAME (int): Missing domain name in campaign setting when adding expanded dynamic search ad. INCOMPATIBLE_WITH_RESTRICTION_TYPE (int): The associated ad is not compatible with restriction type. CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED (int): Consent for call recording is required for creating/updating call only ads. Please see https://support.google.com/google-ads/answer/7412639. MISSING_IMAGE_OR_MEDIA_BUNDLE (int): Either an image or a media bundle is required in a display upload ad. PRODUCT_TYPE_NOT_SUPPORTED_IN_THIS_CAMPAIGN (int): The display upload product type is not supported in this campaign. PLACEHOLDER_CANNOT_HAVE_EMPTY_DEFAULT_VALUE (int): The default value of an ad placeholder can not be the empty string. PLACEHOLDER_COUNTDOWN_FUNCTION_CANNOT_HAVE_DEFAULT_VALUE (int): Ad placeholders with countdown functions must not have a default value. PLACEHOLDER_DEFAULT_VALUE_MISSING (int): A previous ad placeholder that had a default value was found which means that all (non-countdown) placeholders must have a default value. This ad placeholder does not have a default value. UNEXPECTED_PLACEHOLDER_DEFAULT_VALUE (int): A previous ad placeholder that did not have a default value was found which means that no placeholders may have a default value. This ad placeholder does have a default value. AD_CUSTOMIZERS_MAY_NOT_BE_ADJACENT (int): Two ad customizers may not be directly adjacent in an ad text. They must be separated by at least one character. UPDATING_AD_WITH_NO_ENABLED_ASSOCIATION (int): The ad is not associated with any enabled AdGroupAd, and cannot be updated. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_CUSTOMIZERS_NOT_SUPPORTED_FOR_AD_TYPE = 2 APPROXIMATELY_TOO_LONG = 3 APPROXIMATELY_TOO_SHORT = 4 BAD_SNIPPET = 5 CANNOT_MODIFY_AD = 6 CANNOT_SET_BUSINESS_NAME_IF_URL_SET = 7 CANNOT_SET_FIELD = 8 CANNOT_SET_FIELD_WITH_ORIGIN_AD_ID_SET = 9 CANNOT_SET_FIELD_WITH_AD_ID_SET_FOR_SHARING = 10 CANNOT_SET_ALLOW_FLEXIBLE_COLOR_FALSE = 11 CANNOT_SET_COLOR_CONTROL_WHEN_NATIVE_FORMAT_SETTING = 12 CANNOT_SET_URL = 13 CANNOT_SET_WITHOUT_FINAL_URLS = 14 CANNOT_SET_WITH_FINAL_URLS = 15 CANNOT_SET_WITH_URL_DATA = 17 CANNOT_USE_AD_SUBCLASS_FOR_OPERATOR = 18 CUSTOMER_NOT_APPROVED_MOBILEADS = 19 CUSTOMER_NOT_APPROVED_THIRDPARTY_ADS = 20 CUSTOMER_NOT_APPROVED_THIRDPARTY_REDIRECT_ADS = 21 CUSTOMER_NOT_ELIGIBLE = 22 CUSTOMER_NOT_ELIGIBLE_FOR_UPDATING_BEACON_URL = 23 DIMENSION_ALREADY_IN_UNION = 24 DIMENSION_MUST_BE_SET = 25 DIMENSION_NOT_IN_UNION = 26 DISPLAY_URL_CANNOT_BE_SPECIFIED = 27 DOMESTIC_PHONE_NUMBER_FORMAT = 28 EMERGENCY_PHONE_NUMBER = 29 EMPTY_FIELD = 30 FEED_ATTRIBUTE_MUST_HAVE_MAPPING_FOR_TYPE_ID = 31 FEED_ATTRIBUTE_MAPPING_TYPE_MISMATCH = 32 ILLEGAL_AD_CUSTOMIZER_TAG_USE = 33 ILLEGAL_TAG_USE = 34 INCONSISTENT_DIMENSIONS = 35 INCONSISTENT_STATUS_IN_TEMPLATE_UNION = 36 INCORRECT_LENGTH = 37 INELIGIBLE_FOR_UPGRADE = 38 INVALID_AD_ADDRESS_CAMPAIGN_TARGET = 39 INVALID_AD_TYPE = 40 INVALID_ATTRIBUTES_FOR_MOBILE_IMAGE = 41 INVALID_ATTRIBUTES_FOR_MOBILE_TEXT = 42 INVALID_CALL_TO_ACTION_TEXT = 43 INVALID_CHARACTER_FOR_URL = 44 INVALID_COUNTRY_CODE = 45 INVALID_EXPANDED_DYNAMIC_SEARCH_AD_TAG = 47 INVALID_INPUT = 48 INVALID_MARKUP_LANGUAGE = 49 INVALID_MOBILE_CARRIER = 50 INVALID_MOBILE_CARRIER_TARGET = 51 INVALID_NUMBER_OF_ELEMENTS = 52 INVALID_PHONE_NUMBER_FORMAT = 53 INVALID_RICH_MEDIA_CERTIFIED_VENDOR_FORMAT_ID = 54 INVALID_TEMPLATE_DATA = 55 INVALID_TEMPLATE_ELEMENT_FIELD_TYPE = 56 INVALID_TEMPLATE_ID = 57 LINE_TOO_WIDE = 58 MISSING_AD_CUSTOMIZER_MAPPING = 59 MISSING_ADDRESS_COMPONENT = 60 MISSING_ADVERTISEMENT_NAME = 61 MISSING_BUSINESS_NAME = 62 MISSING_DESCRIPTION1 = 63 MISSING_DESCRIPTION2 = 64 MISSING_DESTINATION_URL_TAG = 65 MISSING_LANDING_PAGE_URL_TAG = 66 MISSING_DIMENSION = 67 MISSING_DISPLAY_URL = 68 MISSING_HEADLINE = 69 MISSING_HEIGHT = 70 MISSING_IMAGE = 71 MISSING_MARKETING_IMAGE_OR_PRODUCT_VIDEOS = 72 MISSING_MARKUP_LANGUAGES = 73 MISSING_MOBILE_CARRIER = 74 MISSING_PHONE = 75 MISSING_REQUIRED_TEMPLATE_FIELDS = 76 MISSING_TEMPLATE_FIELD_VALUE = 77 MISSING_TEXT = 78 MISSING_VISIBLE_URL = 79 MISSING_WIDTH = 80 MULTIPLE_DISTINCT_FEEDS_UNSUPPORTED = 81 MUST_USE_TEMP_AD_UNION_ID_ON_ADD = 82 TOO_LONG = 83 TOO_SHORT = 84 UNION_DIMENSIONS_CANNOT_CHANGE = 85 UNKNOWN_ADDRESS_COMPONENT = 86 UNKNOWN_FIELD_NAME = 87 UNKNOWN_UNIQUE_NAME = 88 UNSUPPORTED_DIMENSIONS = 89 URL_INVALID_SCHEME = 90 URL_INVALID_TOP_LEVEL_DOMAIN = 91 URL_MALFORMED = 92 URL_NO_HOST = 93 URL_NOT_EQUIVALENT = 94 URL_HOST_NAME_TOO_LONG = 95 URL_NO_SCHEME = 96 URL_NO_TOP_LEVEL_DOMAIN = 97 URL_PATH_NOT_ALLOWED = 98 URL_PORT_NOT_ALLOWED = 99 URL_QUERY_NOT_ALLOWED = 100 URL_SCHEME_BEFORE_EXPANDED_DYNAMIC_SEARCH_AD_TAG = 102 USER_DOES_NOT_HAVE_ACCESS_TO_TEMPLATE = 103 INCONSISTENT_EXPANDABLE_SETTINGS = 104 INVALID_FORMAT = 105 INVALID_FIELD_TEXT = 106 ELEMENT_NOT_PRESENT = 107 IMAGE_ERROR = 108 VALUE_NOT_IN_RANGE = 109 FIELD_NOT_PRESENT = 110 ADDRESS_NOT_COMPLETE = 111 ADDRESS_INVALID = 112 VIDEO_RETRIEVAL_ERROR = 113 AUDIO_ERROR = 114 INVALID_YOUTUBE_DISPLAY_URL = 115 TOO_MANY_PRODUCT_IMAGES = 116 TOO_MANY_PRODUCT_VIDEOS = 117 INCOMPATIBLE_AD_TYPE_AND_DEVICE_PREFERENCE = 118 CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY = 119 CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED = 120 DISALLOWED_NUMBER_TYPE = 121 PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY = 122 PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY = 123 PREMIUM_RATE_NUMBER_NOT_ALLOWED = 124 VANITY_PHONE_NUMBER_NOT_ALLOWED = 125 INVALID_CALL_CONVERSION_TYPE_ID = 126 CANNOT_DISABLE_CALL_CONVERSION_AND_SET_CONVERSION_TYPE_ID = 127 CANNOT_SET_PATH2_WITHOUT_PATH1 = 128 MISSING_DYNAMIC_SEARCH_ADS_SETTING_DOMAIN_NAME = 129 INCOMPATIBLE_WITH_RESTRICTION_TYPE = 130 CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED = 131 MISSING_IMAGE_OR_MEDIA_BUNDLE = 132 PRODUCT_TYPE_NOT_SUPPORTED_IN_THIS_CAMPAIGN = 133 PLACEHOLDER_CANNOT_HAVE_EMPTY_DEFAULT_VALUE = 134 PLACEHOLDER_COUNTDOWN_FUNCTION_CANNOT_HAVE_DEFAULT_VALUE = 135 PLACEHOLDER_DEFAULT_VALUE_MISSING = 136 UNEXPECTED_PLACEHOLDER_DEFAULT_VALUE = 137 AD_CUSTOMIZERS_MAY_NOT_BE_ADJACENT = 138 UPDATING_AD_WITH_NO_ENABLED_ASSOCIATION = 139 ''' AdErrorEnum = AdErrorEnum() # For __getattribute__ class AdGroupAdErrorEnum(_CreateEnumTypeUponFirstAccess): AdGroupAdErrorEnum = '''\ class AdGroupAdError(enum.IntEnum): """ Enum describing possible ad group ad errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AD_GROUP_AD_LABEL_DOES_NOT_EXIST (int): No link found between the adgroup ad and the label. AD_GROUP_AD_LABEL_ALREADY_EXISTS (int): The label has already been attached to the adgroup ad. AD_NOT_UNDER_ADGROUP (int): The specified ad was not found in the adgroup CANNOT_OPERATE_ON_REMOVED_ADGROUPAD (int): Removed ads may not be modified CANNOT_CREATE_DEPRECATED_ADS (int): An ad of this type is deprecated and cannot be created. Only deletions are permitted. CANNOT_CREATE_TEXT_ADS (int): Text ads are deprecated and cannot be created. Use expanded text ads instead. EMPTY_FIELD (int): A required field was not specified or is an empty string. RESOURCE_REFERENCED_IN_MULTIPLE_OPS (int): An ad may only be modified once per call """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP_AD_LABEL_DOES_NOT_EXIST = 2 AD_GROUP_AD_LABEL_ALREADY_EXISTS = 3 AD_NOT_UNDER_ADGROUP = 4 CANNOT_OPERATE_ON_REMOVED_ADGROUPAD = 5 CANNOT_CREATE_DEPRECATED_ADS = 6 CANNOT_CREATE_TEXT_ADS = 7 EMPTY_FIELD = 8 RESOURCE_REFERENCED_IN_MULTIPLE_OPS = 9 ''' AdGroupAdErrorEnum = AdGroupAdErrorEnum() # For __getattribute__ class AdGroupAdRotationModeEnum(_CreateEnumTypeUponFirstAccess): AdGroupAdRotationModeEnum = '''\ class AdGroupAdRotationMode(enum.IntEnum): """ The possible ad rotation modes of an ad group. Attributes: UNSPECIFIED (int): The ad rotation mode has not been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. OPTIMIZE (int): Optimize ad group ads based on clicks or conversions. ROTATE_FOREVER (int): Rotate evenly forever. """ UNSPECIFIED = 0 UNKNOWN = 1 OPTIMIZE = 2 ROTATE_FOREVER = 3 ''' AdGroupAdRotationModeEnum = AdGroupAdRotationModeEnum() # For __getattribute__ class AdGroupAdStatusEnum(_CreateEnumTypeUponFirstAccess): AdGroupAdStatusEnum = '''\ class AdGroupAdStatus(enum.IntEnum): """ The possible statuses of an AdGroupAd. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The ad group ad is enabled. PAUSED (int): The ad group ad is paused. REMOVED (int): The ad group ad is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PAUSED = 3 REMOVED = 4 ''' AdGroupAdStatusEnum = AdGroupAdStatusEnum() # For __getattribute__ class AdGroupBidModifierErrorEnum(_CreateEnumTypeUponFirstAccess): AdGroupBidModifierErrorEnum = '''\ class AdGroupBidModifierError(enum.IntEnum): """ Enum describing possible ad group bid modifier errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CRITERION_ID_NOT_SUPPORTED (int): The criterion ID does not support bid modification. CANNOT_OVERRIDE_OPTED_OUT_CAMPAIGN_CRITERION_BID_MODIFIER (int): Cannot override the bid modifier for the given criterion ID if the parent campaign is opted out of the same criterion. """ UNSPECIFIED = 0 UNKNOWN = 1 CRITERION_ID_NOT_SUPPORTED = 2 CANNOT_OVERRIDE_OPTED_OUT_CAMPAIGN_CRITERION_BID_MODIFIER = 3 ''' AdGroupBidModifierErrorEnum = AdGroupBidModifierErrorEnum() # For __getattribute__ class AdGroupCriterionApprovalStatusEnum(_CreateEnumTypeUponFirstAccess): AdGroupCriterionApprovalStatusEnum = '''\ class AdGroupCriterionApprovalStatus(enum.IntEnum): """ Enumerates AdGroupCriterion approval statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. APPROVED (int): Approved. DISAPPROVED (int): Disapproved. PENDING_REVIEW (int): Pending Review. UNDER_REVIEW (int): Under review. """ UNSPECIFIED = 0 UNKNOWN = 1 APPROVED = 2 DISAPPROVED = 3 PENDING_REVIEW = 4 UNDER_REVIEW = 5 ''' AdGroupCriterionApprovalStatusEnum = AdGroupCriterionApprovalStatusEnum() # For __getattribute__ class AdGroupCriterionErrorEnum(_CreateEnumTypeUponFirstAccess): AdGroupCriterionErrorEnum = '''\ class AdGroupCriterionError(enum.IntEnum): """ Enum describing possible ad group criterion errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AD_GROUP_CRITERION_LABEL_DOES_NOT_EXIST (int): No link found between the AdGroupCriterion and the label. AD_GROUP_CRITERION_LABEL_ALREADY_EXISTS (int): The label has already been attached to the AdGroupCriterion. CANNOT_ADD_LABEL_TO_NEGATIVE_CRITERION (int): Negative AdGroupCriterion cannot have labels. TOO_MANY_OPERATIONS (int): Too many operations for a single call. CANT_UPDATE_NEGATIVE (int): Negative ad group criteria are not updateable. CONCRETE_TYPE_REQUIRED (int): Concrete type of criterion (keyword v.s. placement) is required for ADD and SET operations. BID_INCOMPATIBLE_WITH_ADGROUP (int): Bid is incompatible with ad group's bidding settings. CANNOT_TARGET_AND_EXCLUDE (int): Cannot target and exclude the same criterion at once. ILLEGAL_URL (int): The URL of a placement is invalid. INVALID_KEYWORD_TEXT (int): Keyword text was invalid. INVALID_DESTINATION_URL (int): Destination URL was invalid. MISSING_DESTINATION_URL_TAG (int): The destination url must contain at least one tag (e.g. {lpurl}) KEYWORD_LEVEL_BID_NOT_SUPPORTED_FOR_MANUALCPM (int): Keyword-level cpm bid is not supported INVALID_USER_STATUS (int): For example, cannot add a biddable ad group criterion that had been removed. CANNOT_ADD_CRITERIA_TYPE (int): Criteria type cannot be targeted for the ad group. Either the account is restricted to keywords only, the criteria type is incompatible with the campaign's bidding strategy, or the criteria type can only be applied to campaigns. CANNOT_EXCLUDE_CRITERIA_TYPE (int): Criteria type cannot be excluded for the ad group. Refer to the documentation for a specific criterion to check if it is excludable. CAMPAIGN_TYPE_NOT_COMPATIBLE_WITH_PARTIAL_FAILURE (int): Partial failure is not supported for shopping campaign mutate operations. OPERATIONS_FOR_TOO_MANY_SHOPPING_ADGROUPS (int): Operations in the mutate request changes too many shopping ad groups. Please split requests for multiple shopping ad groups across multiple requests. CANNOT_MODIFY_URL_FIELDS_WITH_DUPLICATE_ELEMENTS (int): Not allowed to modify url fields of an ad group criterion if there are duplicate elements for that ad group criterion in the request. CANNOT_SET_WITHOUT_FINAL_URLS (int): Cannot set url fields without also setting final urls. CANNOT_CLEAR_FINAL_URLS_IF_FINAL_MOBILE_URLS_EXIST (int): Cannot clear final urls if final mobile urls exist. CANNOT_CLEAR_FINAL_URLS_IF_FINAL_APP_URLS_EXIST (int): Cannot clear final urls if final app urls exist. CANNOT_CLEAR_FINAL_URLS_IF_TRACKING_URL_TEMPLATE_EXISTS (int): Cannot clear final urls if tracking url template exists. CANNOT_CLEAR_FINAL_URLS_IF_URL_CUSTOM_PARAMETERS_EXIST (int): Cannot clear final urls if url custom parameters exist. CANNOT_SET_BOTH_DESTINATION_URL_AND_FINAL_URLS (int): Cannot set both destination url and final urls. CANNOT_SET_BOTH_DESTINATION_URL_AND_TRACKING_URL_TEMPLATE (int): Cannot set both destination url and tracking url template. FINAL_URLS_NOT_SUPPORTED_FOR_CRITERION_TYPE (int): Final urls are not supported for this criterion type. FINAL_MOBILE_URLS_NOT_SUPPORTED_FOR_CRITERION_TYPE (int): Final mobile urls are not supported for this criterion type. INVALID_LISTING_GROUP_HIERARCHY (int): Ad group is invalid due to the listing groups it contains. LISTING_GROUP_UNIT_CANNOT_HAVE_CHILDREN (int): Listing group unit cannot have children. LISTING_GROUP_SUBDIVISION_REQUIRES_OTHERS_CASE (int): Subdivided listing groups must have an "others" case. LISTING_GROUP_REQUIRES_SAME_DIMENSION_TYPE_AS_SIBLINGS (int): Dimension type of listing group must be the same as that of its siblings. LISTING_GROUP_ALREADY_EXISTS (int): Listing group cannot be added to the ad group because it already exists. LISTING_GROUP_DOES_NOT_EXIST (int): Listing group referenced in the operation was not found in the ad group. LISTING_GROUP_CANNOT_BE_REMOVED (int): Recursive removal failed because listing group subdivision is being created or modified in this request. INVALID_LISTING_GROUP_TYPE (int): Listing group type is not allowed for specified ad group criterion type. LISTING_GROUP_ADD_MAY_ONLY_USE_TEMP_ID (int): Listing group in an ADD operation specifies a non temporary criterion id. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP_CRITERION_LABEL_DOES_NOT_EXIST = 2 AD_GROUP_CRITERION_LABEL_ALREADY_EXISTS = 3 CANNOT_ADD_LABEL_TO_NEGATIVE_CRITERION = 4 TOO_MANY_OPERATIONS = 5 CANT_UPDATE_NEGATIVE = 6 CONCRETE_TYPE_REQUIRED = 7 BID_INCOMPATIBLE_WITH_ADGROUP = 8 CANNOT_TARGET_AND_EXCLUDE = 9 ILLEGAL_URL = 10 INVALID_KEYWORD_TEXT = 11 INVALID_DESTINATION_URL = 12 MISSING_DESTINATION_URL_TAG = 13 KEYWORD_LEVEL_BID_NOT_SUPPORTED_FOR_MANUALCPM = 14 INVALID_USER_STATUS = 15 CANNOT_ADD_CRITERIA_TYPE = 16 CANNOT_EXCLUDE_CRITERIA_TYPE = 17 CAMPAIGN_TYPE_NOT_COMPATIBLE_WITH_PARTIAL_FAILURE = 27 OPERATIONS_FOR_TOO_MANY_SHOPPING_ADGROUPS = 28 CANNOT_MODIFY_URL_FIELDS_WITH_DUPLICATE_ELEMENTS = 29 CANNOT_SET_WITHOUT_FINAL_URLS = 30 CANNOT_CLEAR_FINAL_URLS_IF_FINAL_MOBILE_URLS_EXIST = 31 CANNOT_CLEAR_FINAL_URLS_IF_FINAL_APP_URLS_EXIST = 32 CANNOT_CLEAR_FINAL_URLS_IF_TRACKING_URL_TEMPLATE_EXISTS = 33 CANNOT_CLEAR_FINAL_URLS_IF_URL_CUSTOM_PARAMETERS_EXIST = 34 CANNOT_SET_BOTH_DESTINATION_URL_AND_FINAL_URLS = 35 CANNOT_SET_BOTH_DESTINATION_URL_AND_TRACKING_URL_TEMPLATE = 36 FINAL_URLS_NOT_SUPPORTED_FOR_CRITERION_TYPE = 37 FINAL_MOBILE_URLS_NOT_SUPPORTED_FOR_CRITERION_TYPE = 38 INVALID_LISTING_GROUP_HIERARCHY = 39 LISTING_GROUP_UNIT_CANNOT_HAVE_CHILDREN = 40 LISTING_GROUP_SUBDIVISION_REQUIRES_OTHERS_CASE = 41 LISTING_GROUP_REQUIRES_SAME_DIMENSION_TYPE_AS_SIBLINGS = 42 LISTING_GROUP_ALREADY_EXISTS = 43 LISTING_GROUP_DOES_NOT_EXIST = 44 LISTING_GROUP_CANNOT_BE_REMOVED = 45 INVALID_LISTING_GROUP_TYPE = 46 LISTING_GROUP_ADD_MAY_ONLY_USE_TEMP_ID = 47 ''' AdGroupCriterionErrorEnum = AdGroupCriterionErrorEnum() # For __getattribute__ class AdGroupCriterionStatusEnum(_CreateEnumTypeUponFirstAccess): AdGroupCriterionStatusEnum = '''\ class AdGroupCriterionStatus(enum.IntEnum): """ The possible statuses of an AdGroupCriterion. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The ad group criterion is enabled. PAUSED (int): The ad group criterion is paused. REMOVED (int): The ad group criterion is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PAUSED = 3 REMOVED = 4 ''' AdGroupCriterionStatusEnum = AdGroupCriterionStatusEnum() # For __getattribute__ class AdGroupErrorEnum(_CreateEnumTypeUponFirstAccess): AdGroupErrorEnum = '''\ class AdGroupError(enum.IntEnum): """ Enum describing possible ad group errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_ADGROUP_NAME (int): AdGroup with the same name already exists for the campaign. INVALID_ADGROUP_NAME (int): AdGroup name is not valid. ADVERTISER_NOT_ON_CONTENT_NETWORK (int): Advertiser is not allowed to target sites or set site bids that are not on the Google Search Network. BID_TOO_BIG (int): Bid amount is too big. BID_TYPE_AND_BIDDING_STRATEGY_MISMATCH (int): AdGroup bid does not match the campaign's bidding strategy. MISSING_ADGROUP_NAME (int): AdGroup name is required for Add. ADGROUP_LABEL_DOES_NOT_EXIST (int): No link found between the ad group and the label. ADGROUP_LABEL_ALREADY_EXISTS (int): The label has already been attached to the ad group. INVALID_CONTENT_BID_CRITERION_TYPE_GROUP (int): The CriterionTypeGroup is not supported for the content bid dimension. AD_GROUP_TYPE_NOT_VALID_FOR_ADVERTISING_CHANNEL_TYPE (int): The ad group type is not compatible with the campaign channel type. ADGROUP_TYPE_NOT_SUPPORTED_FOR_CAMPAIGN_SALES_COUNTRY (int): The ad group type is not supported in the country of sale of the campaign. CANNOT_ADD_ADGROUP_OF_TYPE_DSA_TO_CAMPAIGN_WITHOUT_DSA_SETTING (int): Ad groups of AdGroupType.SEARCH\_DYNAMIC\_ADS can only be added to campaigns that have DynamicSearchAdsSetting attached. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_ADGROUP_NAME = 2 INVALID_ADGROUP_NAME = 3 ADVERTISER_NOT_ON_CONTENT_NETWORK = 5 BID_TOO_BIG = 6 BID_TYPE_AND_BIDDING_STRATEGY_MISMATCH = 7 MISSING_ADGROUP_NAME = 8 ADGROUP_LABEL_DOES_NOT_EXIST = 9 ADGROUP_LABEL_ALREADY_EXISTS = 10 INVALID_CONTENT_BID_CRITERION_TYPE_GROUP = 11 AD_GROUP_TYPE_NOT_VALID_FOR_ADVERTISING_CHANNEL_TYPE = 12 ADGROUP_TYPE_NOT_SUPPORTED_FOR_CAMPAIGN_SALES_COUNTRY = 13 CANNOT_ADD_ADGROUP_OF_TYPE_DSA_TO_CAMPAIGN_WITHOUT_DSA_SETTING = 14 ''' AdGroupErrorEnum = AdGroupErrorEnum() # For __getattribute__ class AdGroupFeedErrorEnum(_CreateEnumTypeUponFirstAccess): AdGroupFeedErrorEnum = '''\ class AdGroupFeedError(enum.IntEnum): """ Enum describing possible ad group feed errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active feed already exists for this ad group and place holder type. CANNOT_CREATE_FOR_REMOVED_FEED (int): The specified feed is removed. ADGROUP_FEED_ALREADY_EXISTS (int): The AdGroupFeed already exists. UPDATE operation should be used to modify the existing AdGroupFeed. CANNOT_OPERATE_ON_REMOVED_ADGROUP_FEED (int): Cannot operate on removed AdGroupFeed. INVALID_PLACEHOLDER_TYPE (int): Invalid placeholder type. MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE (int): Feed mapping for this placeholder type does not exist. NO_EXISTING_LOCATION_CUSTOMER_FEED (int): Location AdGroupFeeds cannot be created unless there is a location CustomerFeed for the specified feed. """ UNSPECIFIED = 0 UNKNOWN = 1 FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 2 CANNOT_CREATE_FOR_REMOVED_FEED = 3 ADGROUP_FEED_ALREADY_EXISTS = 4 CANNOT_OPERATE_ON_REMOVED_ADGROUP_FEED = 5 INVALID_PLACEHOLDER_TYPE = 6 MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE = 7 NO_EXISTING_LOCATION_CUSTOMER_FEED = 8 ''' AdGroupFeedErrorEnum = AdGroupFeedErrorEnum() # For __getattribute__ class AdGroupStatusEnum(_CreateEnumTypeUponFirstAccess): AdGroupStatusEnum = '''\ class AdGroupStatus(enum.IntEnum): """ The possible statuses of an ad group. Attributes: UNSPECIFIED (int): The status has not been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The ad group is enabled. PAUSED (int): The ad group is paused. REMOVED (int): The ad group is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PAUSED = 3 REMOVED = 4 ''' AdGroupStatusEnum = AdGroupStatusEnum() # For __getattribute__ class AdGroupTypeEnum(_CreateEnumTypeUponFirstAccess): AdGroupTypeEnum = '''\ class AdGroupType(enum.IntEnum): """ Enum listing the possible types of an ad group. Attributes: UNSPECIFIED (int): The type has not been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. SEARCH_STANDARD (int): The default ad group type for Search campaigns. DISPLAY_STANDARD (int): The default ad group type for Display campaigns. SHOPPING_PRODUCT_ADS (int): The ad group type for Shopping campaigns serving standard product ads. HOTEL_ADS (int): The default ad group type for Hotel campaigns. SHOPPING_SMART_ADS (int): The type for ad groups in Smart Shopping campaigns. VIDEO_BUMPER (int): Short unskippable in-stream video ads. VIDEO_TRUE_VIEW_IN_STREAM (int): TrueView (skippable) in-stream video ads. VIDEO_TRUE_VIEW_IN_DISPLAY (int): TrueView in-display video ads. VIDEO_NON_SKIPPABLE_IN_STREAM (int): Unskippable in-stream video ads. VIDEO_OUTSTREAM (int): Outstream video ads. SEARCH_DYNAMIC_ADS (int): Ad group type for Dynamic Search Ads ad groups. SHOPPING_COMPARISON_LISTING_ADS (int): The type for ad groups in Shopping Comparison Listing campaigns. PROMOTED_HOTEL_ADS (int): The ad group type for Promoted Hotel ad groups. """ UNSPECIFIED = 0 UNKNOWN = 1 SEARCH_STANDARD = 2 DISPLAY_STANDARD = 3 SHOPPING_PRODUCT_ADS = 4 HOTEL_ADS = 6 SHOPPING_SMART_ADS = 7 VIDEO_BUMPER = 8 VIDEO_TRUE_VIEW_IN_STREAM = 9 VIDEO_TRUE_VIEW_IN_DISPLAY = 10 VIDEO_NON_SKIPPABLE_IN_STREAM = 11 VIDEO_OUTSTREAM = 12 SEARCH_DYNAMIC_ADS = 13 SHOPPING_COMPARISON_LISTING_ADS = 14 PROMOTED_HOTEL_ADS = 15 ''' AdGroupTypeEnum = AdGroupTypeEnum() # For __getattribute__ class AdNetworkTypeEnum(_CreateEnumTypeUponFirstAccess): AdNetworkTypeEnum = '''\ class AdNetworkType(enum.IntEnum): """ Enumerates Google Ads network types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. SEARCH (int): Google search. SEARCH_PARTNERS (int): Search partners. CONTENT (int): Display Network. YOUTUBE_SEARCH (int): YouTube Search. YOUTUBE_WATCH (int): YouTube Videos MIXED (int): Cross-network. """ UNSPECIFIED = 0 UNKNOWN = 1 SEARCH = 2 SEARCH_PARTNERS = 3 CONTENT = 4 YOUTUBE_SEARCH = 5 YOUTUBE_WATCH = 6 MIXED = 7 ''' AdNetworkTypeEnum = AdNetworkTypeEnum() # For __getattribute__ class AdParameterErrorEnum(_CreateEnumTypeUponFirstAccess): AdParameterErrorEnum = '''\ class AdParameterError(enum.IntEnum): """ Enum describing possible ad parameter errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AD_GROUP_CRITERION_MUST_BE_KEYWORD (int): The ad group criterion must be a keyword criterion. INVALID_INSERTION_TEXT_FORMAT (int): The insertion text is invalid. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP_CRITERION_MUST_BE_KEYWORD = 2 INVALID_INSERTION_TEXT_FORMAT = 3 ''' AdParameterErrorEnum = AdParameterErrorEnum() # For __getattribute__ class AdServingOptimizationStatusEnum(_CreateEnumTypeUponFirstAccess): AdServingOptimizationStatusEnum = '''\ class AdServingOptimizationStatus(enum.IntEnum): """ Enum describing possible serving statuses. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. OPTIMIZE (int): Ad serving is optimized based on CTR for the campaign. CONVERSION_OPTIMIZE (int): Ad serving is optimized based on CTR \* Conversion for the campaign. If the campaign is not in the conversion optimizer bidding strategy, it will default to OPTIMIZED. ROTATE (int): Ads are rotated evenly for 90 days, then optimized for clicks. ROTATE_INDEFINITELY (int): Show lower performing ads more evenly with higher performing ads, and do not optimize. UNAVAILABLE (int): Ad serving optimization status is not available. """ UNSPECIFIED = 0 UNKNOWN = 1 OPTIMIZE = 2 CONVERSION_OPTIMIZE = 3 ROTATE = 4 ROTATE_INDEFINITELY = 5 UNAVAILABLE = 6 ''' AdServingOptimizationStatusEnum = AdServingOptimizationStatusEnum() # For __getattribute__ class AdSharingErrorEnum(_CreateEnumTypeUponFirstAccess): AdSharingErrorEnum = '''\ class AdSharingError(enum.IntEnum): """ Enum describing possible ad sharing errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AD_GROUP_ALREADY_CONTAINS_AD (int): Error resulting in attempting to add an Ad to an AdGroup that already contains the Ad. INCOMPATIBLE_AD_UNDER_AD_GROUP (int): Ad is not compatible with the AdGroup it is being shared with. CANNOT_SHARE_INACTIVE_AD (int): Cannot add AdGroupAd on inactive Ad. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP_ALREADY_CONTAINS_AD = 2 INCOMPATIBLE_AD_UNDER_AD_GROUP = 3 CANNOT_SHARE_INACTIVE_AD = 4 ''' AdSharingErrorEnum = AdSharingErrorEnum() # For __getattribute__ class AdStrengthEnum(_CreateEnumTypeUponFirstAccess): AdStrengthEnum = '''\ class AdStrength(enum.IntEnum): """ Enum listing the possible ad strengths. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): The ad strength is currently pending. NO_ADS (int): No ads could be generated. POOR (int): Poor strength. AVERAGE (int): Average strength. GOOD (int): Good strength. EXCELLENT (int): Excellent strength. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 NO_ADS = 3 POOR = 4 AVERAGE = 5 GOOD = 6 EXCELLENT = 7 ''' AdStrengthEnum = AdStrengthEnum() # For __getattribute__ class AdTypeEnum(_CreateEnumTypeUponFirstAccess): AdTypeEnum = '''\ class AdType(enum.IntEnum): """ The possible types of an ad. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. TEXT_AD (int): The ad is a text ad. EXPANDED_TEXT_AD (int): The ad is an expanded text ad. CALL_ONLY_AD (int): The ad is a call only ad. EXPANDED_DYNAMIC_SEARCH_AD (int): The ad is an expanded dynamic search ad. HOTEL_AD (int): The ad is a hotel ad. SHOPPING_SMART_AD (int): The ad is a Smart Shopping ad. SHOPPING_PRODUCT_AD (int): The ad is a standard Shopping ad. VIDEO_AD (int): The ad is a video ad. GMAIL_AD (int): This ad is a Gmail ad. IMAGE_AD (int): This ad is an Image ad. RESPONSIVE_SEARCH_AD (int): The ad is a responsive search ad. LEGACY_RESPONSIVE_DISPLAY_AD (int): The ad is a legacy responsive display ad. APP_AD (int): The ad is an app ad. LEGACY_APP_INSTALL_AD (int): The ad is a legacy app install ad. RESPONSIVE_DISPLAY_AD (int): The ad is a responsive display ad. HTML5_UPLOAD_AD (int): The ad is a display upload ad with the HTML5\_UPLOAD\_AD product type. DYNAMIC_HTML5_AD (int): The ad is a display upload ad with one of the DYNAMIC\_HTML5\_\* product types. APP_ENGAGEMENT_AD (int): The ad is an app engagement ad. SHOPPING_COMPARISON_LISTING_AD (int): The ad is a Shopping Comparison Listing ad. """ UNSPECIFIED = 0 UNKNOWN = 1 TEXT_AD = 2 EXPANDED_TEXT_AD = 3 CALL_ONLY_AD = 6 EXPANDED_DYNAMIC_SEARCH_AD = 7 HOTEL_AD = 8 SHOPPING_SMART_AD = 9 SHOPPING_PRODUCT_AD = 10 VIDEO_AD = 12 GMAIL_AD = 13 IMAGE_AD = 14 RESPONSIVE_SEARCH_AD = 15 LEGACY_RESPONSIVE_DISPLAY_AD = 16 APP_AD = 17 LEGACY_APP_INSTALL_AD = 18 RESPONSIVE_DISPLAY_AD = 19 HTML5_UPLOAD_AD = 21 DYNAMIC_HTML5_AD = 22 APP_ENGAGEMENT_AD = 23 SHOPPING_COMPARISON_LISTING_AD = 24 ''' AdTypeEnum = AdTypeEnum() # For __getattribute__ class AdvertisingChannelSubTypeEnum(_CreateEnumTypeUponFirstAccess): AdvertisingChannelSubTypeEnum = '''\ class AdvertisingChannelSubType(enum.IntEnum): """ Enum describing the different channel subtypes. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used as a return value only. Represents value unknown in this version. SEARCH_MOBILE_APP (int): Mobile app campaigns for Search. DISPLAY_MOBILE_APP (int): Mobile app campaigns for Display. SEARCH_EXPRESS (int): AdWords express campaigns for search. DISPLAY_EXPRESS (int): AdWords Express campaigns for display. SHOPPING_SMART_ADS (int): Smart Shopping campaigns. DISPLAY_GMAIL_AD (int): Gmail Ad campaigns. DISPLAY_SMART_CAMPAIGN (int): Smart display campaigns. VIDEO_OUTSTREAM (int): Video Outstream campaigns. VIDEO_ACTION (int): Video TrueView for Action campaigns. VIDEO_NON_SKIPPABLE (int): Video campaigns with non-skippable video ads. APP_CAMPAIGN (int): App Campaign that allows you to easily promote your Android or iOS app across Google's top properties including Search, Play, YouTube, and the Google Display Network. APP_CAMPAIGN_FOR_ENGAGEMENT (int): App Campaign for engagement, focused on driving re-engagement with the app across several of Google’s top properties including Search, YouTube, and the Google Display Network. SHOPPING_COMPARISON_LISTING_ADS (int): Shopping Comparison Listing campaigns. """ UNSPECIFIED = 0 UNKNOWN = 1 SEARCH_MOBILE_APP = 2 DISPLAY_MOBILE_APP = 3 SEARCH_EXPRESS = 4 DISPLAY_EXPRESS = 5 SHOPPING_SMART_ADS = 6 DISPLAY_GMAIL_AD = 7 DISPLAY_SMART_CAMPAIGN = 8 VIDEO_OUTSTREAM = 9 VIDEO_ACTION = 10 VIDEO_NON_SKIPPABLE = 11 APP_CAMPAIGN = 12 APP_CAMPAIGN_FOR_ENGAGEMENT = 13 SHOPPING_COMPARISON_LISTING_ADS = 15 ''' AdvertisingChannelSubTypeEnum = AdvertisingChannelSubTypeEnum() # For __getattribute__ class AdvertisingChannelTypeEnum(_CreateEnumTypeUponFirstAccess): AdvertisingChannelTypeEnum = '''\ class AdvertisingChannelType(enum.IntEnum): """ Enum describing the various advertising channel types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SEARCH (int): Search Network. Includes display bundled, and Search+ campaigns. DISPLAY (int): Google Display Network only. SHOPPING (int): Shopping campaigns serve on the shopping property and on google.com search results. HOTEL (int): Hotel Ads campaigns. VIDEO (int): Video campaigns. MULTI_CHANNEL (int): App Campaigns, and App Campaigns for Engagement, that run across multiple channels. """ UNSPECIFIED = 0 UNKNOWN = 1 SEARCH = 2 DISPLAY = 3 SHOPPING = 4 HOTEL = 5 VIDEO = 6 MULTI_CHANNEL = 7 ''' AdvertisingChannelTypeEnum = AdvertisingChannelTypeEnum() # For __getattribute__ class AdxErrorEnum(_CreateEnumTypeUponFirstAccess): AdxErrorEnum = '''\ class AdxError(enum.IntEnum): """ Enum describing possible adx errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. UNSUPPORTED_FEATURE (int): Attempt to use non-AdX feature by AdX customer. """ UNSPECIFIED = 0 UNKNOWN = 1 UNSUPPORTED_FEATURE = 2 ''' AdxErrorEnum = AdxErrorEnum() # For __getattribute__ class AffiliateLocationFeedRelationshipTypeEnum(_CreateEnumTypeUponFirstAccess): AffiliateLocationFeedRelationshipTypeEnum = '''\ class AffiliateLocationFeedRelationshipType(enum.IntEnum): """ Possible values for a relationship type for an affiliate location feed. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. GENERAL_RETAILER (int): General retailer relationship. """ UNSPECIFIED = 0 UNKNOWN = 1 GENERAL_RETAILER = 2 ''' AffiliateLocationFeedRelationshipTypeEnum = AffiliateLocationFeedRelationshipTypeEnum() # For __getattribute__ class AffiliateLocationPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): AffiliateLocationPlaceholderFieldEnum = '''\ class AffiliateLocationPlaceholderField(enum.IntEnum): """ Possible values for Affiliate Location placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BUSINESS_NAME (int): Data Type: STRING. The name of the business. ADDRESS_LINE_1 (int): Data Type: STRING. Line 1 of the business address. ADDRESS_LINE_2 (int): Data Type: STRING. Line 2 of the business address. CITY (int): Data Type: STRING. City of the business address. PROVINCE (int): Data Type: STRING. Province of the business address. POSTAL_CODE (int): Data Type: STRING. Postal code of the business address. COUNTRY_CODE (int): Data Type: STRING. Country code of the business address. PHONE_NUMBER (int): Data Type: STRING. Phone number of the business. LANGUAGE_CODE (int): Data Type: STRING. Language code of the business. CHAIN_ID (int): Data Type: INT64. ID of the chain. CHAIN_NAME (int): Data Type: STRING. Name of the chain. """ UNSPECIFIED = 0 UNKNOWN = 1 BUSINESS_NAME = 2 ADDRESS_LINE_1 = 3 ADDRESS_LINE_2 = 4 CITY = 5 PROVINCE = 6 POSTAL_CODE = 7 COUNTRY_CODE = 8 PHONE_NUMBER = 9 LANGUAGE_CODE = 10 CHAIN_ID = 11 CHAIN_NAME = 12 ''' AffiliateLocationPlaceholderFieldEnum = AffiliateLocationPlaceholderFieldEnum() # For __getattribute__ class AgeRangeTypeEnum(_CreateEnumTypeUponFirstAccess): AgeRangeTypeEnum = '''\ class AgeRangeType(enum.IntEnum): """ The type of demographic age ranges (e.g. between 18 and 24 years old). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AGE_RANGE_18_24 (int): Between 18 and 24 years old. AGE_RANGE_25_34 (int): Between 25 and 34 years old. AGE_RANGE_35_44 (int): Between 35 and 44 years old. AGE_RANGE_45_54 (int): Between 45 and 54 years old. AGE_RANGE_55_64 (int): Between 55 and 64 years old. AGE_RANGE_65_UP (int): 65 years old and beyond. AGE_RANGE_UNDETERMINED (int): Undetermined age range. """ UNSPECIFIED = 0 UNKNOWN = 1 AGE_RANGE_18_24 = 503001 AGE_RANGE_25_34 = 503002 AGE_RANGE_35_44 = 503003 AGE_RANGE_45_54 = 503004 AGE_RANGE_55_64 = 503005 AGE_RANGE_65_UP = 503006 AGE_RANGE_UNDETERMINED = 503999 ''' AgeRangeTypeEnum = AgeRangeTypeEnum() # For __getattribute__ class AppCampaignAppStoreEnum(_CreateEnumTypeUponFirstAccess): AppCampaignAppStoreEnum = '''\ class AppCampaignAppStore(enum.IntEnum): """ Enum describing app campaign app store. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. APPLE_APP_STORE (int): Apple app store. GOOGLE_APP_STORE (int): Google play. """ UNSPECIFIED = 0 UNKNOWN = 1 APPLE_APP_STORE = 2 GOOGLE_APP_STORE = 3 ''' AppCampaignAppStoreEnum = AppCampaignAppStoreEnum() # For __getattribute__ class AppCampaignBiddingStrategyGoalTypeEnum(_CreateEnumTypeUponFirstAccess): AppCampaignBiddingStrategyGoalTypeEnum = '''\ class AppCampaignBiddingStrategyGoalType(enum.IntEnum): """ Goal type of App campaign BiddingStrategy. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. OPTIMIZE_INSTALLS_TARGET_INSTALL_COST (int): Aim to maximize the number of app installs. The cpa bid is the target cost per install. OPTIMIZE_IN_APP_CONVERSIONS_TARGET_INSTALL_COST (int): Aim to maximize the long term number of selected in-app conversions from app installs. The cpa bid is the target cost per install. OPTIMIZE_IN_APP_CONVERSIONS_TARGET_CONVERSION_COST (int): Aim to maximize the long term number of selected in-app conversions from app installs. The cpa bid is the target cost per in-app conversion. Note that the actual cpa may seem higher than the target cpa at first, since the long term conversions haven’t happened yet. OPTIMIZE_RETURN_ON_ADVERTISING_SPEND (int): Aim to maximize all conversions' value, i.e. install + selected in-app conversions while achieving or exceeding target return on advertising spend. """ UNSPECIFIED = 0 UNKNOWN = 1 OPTIMIZE_INSTALLS_TARGET_INSTALL_COST = 2 OPTIMIZE_IN_APP_CONVERSIONS_TARGET_INSTALL_COST = 3 OPTIMIZE_IN_APP_CONVERSIONS_TARGET_CONVERSION_COST = 4 OPTIMIZE_RETURN_ON_ADVERTISING_SPEND = 5 ''' AppCampaignBiddingStrategyGoalTypeEnum = AppCampaignBiddingStrategyGoalTypeEnum() # For __getattribute__ class AppPaymentModelTypeEnum(_CreateEnumTypeUponFirstAccess): AppPaymentModelTypeEnum = '''\ class AppPaymentModelType(enum.IntEnum): """ Enum describing possible app payment models. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PAID (int): Represents paid-for apps. """ UNSPECIFIED = 0 UNKNOWN = 1 PAID = 30 ''' AppPaymentModelTypeEnum = AppPaymentModelTypeEnum() # For __getattribute__ class AppPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): AppPlaceholderFieldEnum = '''\ class AppPlaceholderField(enum.IntEnum): """ Possible values for App placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. STORE (int): Data Type: INT64. The application store that the target application belongs to. Valid values are: 1 = Apple iTunes Store; 2 = Google Play Store. ID (int): Data Type: STRING. The store-specific ID for the target application. LINK_TEXT (int): Data Type: STRING. The visible text displayed when the link is rendered in an ad. URL (int): Data Type: STRING. The destination URL of the in-app link. FINAL_URLS (int): Data Type: URL\_LIST. Final URLs for the in-app link when using Upgraded URLs. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final Mobile URLs for the in-app link when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the in-app link when using Upgraded URLs. FINAL_URL_SUFFIX (int): Data Type: STRING. Final URL suffix for the in-app link when using parallel tracking. """ UNSPECIFIED = 0 UNKNOWN = 1 STORE = 2 ID = 3 LINK_TEXT = 4 URL = 5 FINAL_URLS = 6 FINAL_MOBILE_URLS = 7 TRACKING_URL = 8 FINAL_URL_SUFFIX = 9 ''' AppPlaceholderFieldEnum = AppPlaceholderFieldEnum() # For __getattribute__ class AppStoreEnum(_CreateEnumTypeUponFirstAccess): AppStoreEnum = '''\ class AppStore(enum.IntEnum): """ App store type in an app extension. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. APPLE_ITUNES (int): Apple iTunes. GOOGLE_PLAY (int): Google Play. """ UNSPECIFIED = 0 UNKNOWN = 1 APPLE_ITUNES = 2 GOOGLE_PLAY = 3 ''' AppStoreEnum = AppStoreEnum() # For __getattribute__ class AppUrlOperatingSystemTypeEnum(_CreateEnumTypeUponFirstAccess): AppUrlOperatingSystemTypeEnum = '''\ class AppUrlOperatingSystemType(enum.IntEnum): """ Operating System Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. IOS (int): The Apple IOS operating system. ANDROID (int): The Android operating system. """ UNSPECIFIED = 0 UNKNOWN = 1 IOS = 2 ANDROID = 3 ''' AppUrlOperatingSystemTypeEnum = AppUrlOperatingSystemTypeEnum() # For __getattribute__ class AssetErrorEnum(_CreateEnumTypeUponFirstAccess): AssetErrorEnum = '''\ class AssetError(enum.IntEnum): """ Enum describing possible asset errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CUSTOMER_NOT_WHITELISTED_FOR_ASSET_TYPE (int): The customer is not whitelisted for this asset type. DUPLICATE_ASSET (int): Assets are duplicated across operations. DUPLICATE_ASSET_NAME (int): The asset name is duplicated, either across operations or with an existing asset. ASSET_DATA_IS_MISSING (int): The Asset.asset\_data oneof is empty. CANNOT_MODIFY_ASSET_NAME (int): The asset has a name which is different from an existing duplicate that represents the same content. """ UNSPECIFIED = 0 UNKNOWN = 1 CUSTOMER_NOT_WHITELISTED_FOR_ASSET_TYPE = 2 DUPLICATE_ASSET = 3 DUPLICATE_ASSET_NAME = 4 ASSET_DATA_IS_MISSING = 5 CANNOT_MODIFY_ASSET_NAME = 6 ''' AssetErrorEnum = AssetErrorEnum() # For __getattribute__ class AssetFieldTypeEnum(_CreateEnumTypeUponFirstAccess): AssetFieldTypeEnum = '''\ class AssetFieldType(enum.IntEnum): """ Enum describing the possible placements of an asset. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. HEADLINE (int): The asset is linked for use as a headline. DESCRIPTION (int): The asset is linked for use as a description. MANDATORY_AD_TEXT (int): The asset is linked for use as mandatory ad text. MARKETING_IMAGE (int): The asset is linked for use as a marketing image. MEDIA_BUNDLE (int): The asset is linked for use as a media bundle. YOUTUBE_VIDEO (int): The asset is linked for use as a YouTube video. """ UNSPECIFIED = 0 UNKNOWN = 1 HEADLINE = 2 DESCRIPTION = 3 MANDATORY_AD_TEXT = 4 MARKETING_IMAGE = 5 MEDIA_BUNDLE = 6 YOUTUBE_VIDEO = 7 ''' AssetFieldTypeEnum = AssetFieldTypeEnum() # For __getattribute__ class AssetPerformanceLabelEnum(_CreateEnumTypeUponFirstAccess): AssetPerformanceLabelEnum = '''\ class AssetPerformanceLabel(enum.IntEnum): """ Enum describing the possible performance labels of an asset, usually computed in the context of a linkage. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): This asset does not yet have any performance informantion. This may be because it is still under review. LEARNING (int): The asset has started getting impressions but the stats are not statistically significant enough to get an asset performance label. LOW (int): Worst performing assets. GOOD (int): Good performing assets. BEST (int): Best performing assets. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 LEARNING = 3 LOW = 4 GOOD = 5 BEST = 6 ''' AssetPerformanceLabelEnum = AssetPerformanceLabelEnum() # For __getattribute__ class AssetTypeEnum(_CreateEnumTypeUponFirstAccess): AssetTypeEnum = '''\ class AssetType(enum.IntEnum): """ Enum describing possible types of asset. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. YOUTUBE_VIDEO (int): YouTube video asset. MEDIA_BUNDLE (int): Media bundle asset. IMAGE (int): Image asset. TEXT (int): Text asset. """ UNSPECIFIED = 0 UNKNOWN = 1 YOUTUBE_VIDEO = 2 MEDIA_BUNDLE = 3 IMAGE = 4 TEXT = 5 ''' AssetTypeEnum = AssetTypeEnum() # For __getattribute__ class AttributionModelEnum(_CreateEnumTypeUponFirstAccess): AttributionModelEnum = '''\ class AttributionModel(enum.IntEnum): """ The attribution model that describes how to distribute credit for a particular conversion across potentially many prior interactions. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EXTERNAL (int): Uses external attribution. GOOGLE_ADS_LAST_CLICK (int): Attributes all credit for a conversion to its last click. GOOGLE_SEARCH_ATTRIBUTION_FIRST_CLICK (int): Attributes all credit for a conversion to its first click using Google Search attribution. GOOGLE_SEARCH_ATTRIBUTION_LINEAR (int): Attributes credit for a conversion equally across all of its clicks using Google Search attribution. GOOGLE_SEARCH_ATTRIBUTION_TIME_DECAY (int): Attributes exponentially more credit for a conversion to its more recent clicks using Google Search attribution (half-life is 1 week). GOOGLE_SEARCH_ATTRIBUTION_POSITION_BASED (int): Attributes 40% of the credit for a conversion to its first and last clicks. Remaining 20% is evenly distributed across all other clicks. This uses Google Search attribution. GOOGLE_SEARCH_ATTRIBUTION_DATA_DRIVEN (int): Flexible model that uses machine learning to determine the appropriate distribution of credit among clicks using Google Search attribution. """ UNSPECIFIED = 0 UNKNOWN = 1 EXTERNAL = 100 GOOGLE_ADS_LAST_CLICK = 101 GOOGLE_SEARCH_ATTRIBUTION_FIRST_CLICK = 102 GOOGLE_SEARCH_ATTRIBUTION_LINEAR = 103 GOOGLE_SEARCH_ATTRIBUTION_TIME_DECAY = 104 GOOGLE_SEARCH_ATTRIBUTION_POSITION_BASED = 105 GOOGLE_SEARCH_ATTRIBUTION_DATA_DRIVEN = 106 ''' AttributionModelEnum = AttributionModelEnum() # For __getattribute__ class AuthenticationErrorEnum(_CreateEnumTypeUponFirstAccess): AuthenticationErrorEnum = '''\ class AuthenticationError(enum.IntEnum): """ Enum describing possible authentication errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. AUTHENTICATION_ERROR (int): Authentication of the request failed. CLIENT_CUSTOMER_ID_INVALID (int): Client Customer Id is not a number. CUSTOMER_NOT_FOUND (int): No customer found for the provided customer id. GOOGLE_ACCOUNT_DELETED (int): Client's Google Account is deleted. GOOGLE_ACCOUNT_COOKIE_INVALID (int): Google account login token in the cookie is invalid. GOOGLE_ACCOUNT_AUTHENTICATION_FAILED (int): A problem occurred during Google account authentication. GOOGLE_ACCOUNT_USER_AND_ADS_USER_MISMATCH (int): The user in the google account login token does not match the UserId in the cookie. LOGIN_COOKIE_REQUIRED (int): Login cookie is required for authentication. NOT_ADS_USER (int): User in the cookie is not a valid Ads user. OAUTH_TOKEN_INVALID (int): Oauth token in the header is not valid. OAUTH_TOKEN_EXPIRED (int): Oauth token in the header has expired. OAUTH_TOKEN_DISABLED (int): Oauth token in the header has been disabled. OAUTH_TOKEN_REVOKED (int): Oauth token in the header has been revoked. OAUTH_TOKEN_HEADER_INVALID (int): Oauth token HTTP header is malformed. LOGIN_COOKIE_INVALID (int): Login cookie is not valid. USER_ID_INVALID (int): User Id in the header is not a valid id. TWO_STEP_VERIFICATION_NOT_ENROLLED (int): An account administrator changed this account's authentication settings. To access this Google Ads account, enable 2-Step Verification in your Google account at https://www.google.com/landing/2step. ADVANCED_PROTECTION_NOT_ENROLLED (int): An account administrator changed this account's authentication settings. To access this Google Ads account, enable Advanced Protection in your Google account at https://landing.google.com/advancedprotection. """ UNSPECIFIED = 0 UNKNOWN = 1 AUTHENTICATION_ERROR = 2 CLIENT_CUSTOMER_ID_INVALID = 5 CUSTOMER_NOT_FOUND = 8 GOOGLE_ACCOUNT_DELETED = 9 GOOGLE_ACCOUNT_COOKIE_INVALID = 10 GOOGLE_ACCOUNT_AUTHENTICATION_FAILED = 25 GOOGLE_ACCOUNT_USER_AND_ADS_USER_MISMATCH = 12 LOGIN_COOKIE_REQUIRED = 13 NOT_ADS_USER = 14 OAUTH_TOKEN_INVALID = 15 OAUTH_TOKEN_EXPIRED = 16 OAUTH_TOKEN_DISABLED = 17 OAUTH_TOKEN_REVOKED = 18 OAUTH_TOKEN_HEADER_INVALID = 19 LOGIN_COOKIE_INVALID = 20 USER_ID_INVALID = 22 TWO_STEP_VERIFICATION_NOT_ENROLLED = 23 ADVANCED_PROTECTION_NOT_ENROLLED = 24 ''' AuthenticationErrorEnum = AuthenticationErrorEnum() # For __getattribute__ class AuthorizationErrorEnum(_CreateEnumTypeUponFirstAccess): AuthorizationErrorEnum = '''\ class AuthorizationError(enum.IntEnum): """ Enum describing possible authorization errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. USER_PERMISSION_DENIED (int): User doesn't have permission to access customer. Note: If you're accessing a client customer, the manager's customer ID must be set in the ``login-customer-id`` header. Learn more at https://developers.google.com/google-ads/api/docs/concepts/call-structure#cid DEVELOPER_TOKEN_NOT_WHITELISTED (int): The developer token is not whitelisted. DEVELOPER_TOKEN_PROHIBITED (int): The developer token is not allowed with the project sent in the request. PROJECT_DISABLED (int): The Google Cloud project sent in the request does not have permission to access the api. AUTHORIZATION_ERROR (int): Authorization of the client failed. ACTION_NOT_PERMITTED (int): The user does not have permission to perform this action (e.g., ADD, UPDATE, REMOVE) on the resource or call a method. INCOMPLETE_SIGNUP (int): Signup not complete. CUSTOMER_NOT_ENABLED (int): The customer can't be used because it isn't enabled. MISSING_TOS (int): The developer must sign the terms of service. They can be found here: ads.google.com/aw/apicenter DEVELOPER_TOKEN_NOT_APPROVED (int): The developer token is not approved. Non-approved developer tokens can only be used with test accounts. INVALID_LOGIN_CUSTOMER_ID_SERVING_CUSTOMER_ID_COMBINATION (int): The login customer specified does not have access to the account specified, so the request is invalid. SERVICE_ACCESS_DENIED (int): The developer specified does not have access to the service. """ UNSPECIFIED = 0 UNKNOWN = 1 USER_PERMISSION_DENIED = 2 DEVELOPER_TOKEN_NOT_WHITELISTED = 3 DEVELOPER_TOKEN_PROHIBITED = 4 PROJECT_DISABLED = 5 AUTHORIZATION_ERROR = 6 ACTION_NOT_PERMITTED = 7 INCOMPLETE_SIGNUP = 8 CUSTOMER_NOT_ENABLED = 24 MISSING_TOS = 9 DEVELOPER_TOKEN_NOT_APPROVED = 10 INVALID_LOGIN_CUSTOMER_ID_SERVING_CUSTOMER_ID_COMBINATION = 11 SERVICE_ACCESS_DENIED = 12 ''' AuthorizationErrorEnum = AuthorizationErrorEnum() # For __getattribute__ class BidModifierSourceEnum(_CreateEnumTypeUponFirstAccess): BidModifierSourceEnum = '''\ class BidModifierSource(enum.IntEnum): """ Enum describing possible bid modifier sources. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CAMPAIGN (int): The bid modifier is specified at the campaign level, on the campaign level criterion. AD_GROUP (int): The bid modifier is specified (overridden) at the ad group level. """ UNSPECIFIED = 0 UNKNOWN = 1 CAMPAIGN = 2 AD_GROUP = 3 ''' BidModifierSourceEnum = BidModifierSourceEnum() # For __getattribute__ class BiddingErrorEnum(_CreateEnumTypeUponFirstAccess): BiddingErrorEnum = '''\ class BiddingError(enum.IntEnum): """ Enum describing possible bidding errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. BIDDING_STRATEGY_TRANSITION_NOT_ALLOWED (int): Cannot transition to new bidding strategy. CANNOT_ATTACH_BIDDING_STRATEGY_TO_CAMPAIGN (int): Cannot attach bidding strategy to campaign. INVALID_ANONYMOUS_BIDDING_STRATEGY_TYPE (int): Bidding strategy is not supported or cannot be used as anonymous. INVALID_BIDDING_STRATEGY_TYPE (int): The type does not match the named strategy's type. INVALID_BID (int): The bid is invalid. BIDDING_STRATEGY_NOT_AVAILABLE_FOR_ACCOUNT_TYPE (int): Bidding strategy is not available for the account type. CONVERSION_TRACKING_NOT_ENABLED (int): Conversion tracking is not enabled for the campaign for VBB transition. NOT_ENOUGH_CONVERSIONS (int): Not enough conversions tracked for VBB transitions. CANNOT_CREATE_CAMPAIGN_WITH_BIDDING_STRATEGY (int): Campaign can not be created with given bidding strategy. It can be transitioned to the strategy, once eligible. CANNOT_TARGET_CONTENT_NETWORK_ONLY_WITH_CAMPAIGN_LEVEL_POP_BIDDING_STRATEGY (int): Cannot target content network only as campaign uses Page One Promoted bidding strategy. BIDDING_STRATEGY_NOT_SUPPORTED_WITH_AD_SCHEDULE (int): Budget Optimizer and Target Spend bidding strategies are not supported for campaigns with AdSchedule targeting. PAY_PER_CONVERSION_NOT_AVAILABLE_FOR_CUSTOMER (int): Pay per conversion is not available to all the customer, only few whitelisted customers can use this. PAY_PER_CONVERSION_NOT_ALLOWED_WITH_TARGET_CPA (int): Pay per conversion is not allowed with Target CPA. BIDDING_STRATEGY_NOT_ALLOWED_FOR_SEARCH_ONLY_CAMPAIGNS (int): Cannot set bidding strategy to Manual CPM for search network only campaigns. BIDDING_STRATEGY_NOT_SUPPORTED_IN_DRAFTS_OR_EXPERIMENTS (int): The bidding strategy is not supported for use in drafts or experiments. BIDDING_STRATEGY_TYPE_DOES_NOT_SUPPORT_PRODUCT_TYPE_ADGROUP_CRITERION (int): Bidding strategy type does not support product type ad group criterion. BID_TOO_SMALL (int): Bid amount is too small. BID_TOO_BIG (int): Bid amount is too big. BID_TOO_MANY_FRACTIONAL_DIGITS (int): Bid has too many fractional digit precision. INVALID_DOMAIN_NAME (int): Invalid domain name specified. NOT_COMPATIBLE_WITH_PAYMENT_MODE (int): The field is not compatible with the payment mode. NOT_COMPATIBLE_WITH_BUDGET_TYPE (int): The field is not compatible with the budget type. NOT_COMPATIBLE_WITH_BIDDING_STRATEGY_TYPE (int): The field is not compatible with the bidding strategy type. BIDDING_STRATEGY_TYPE_INCOMPATIBLE_WITH_SHARED_BUDGET (int): Bidding strategy type is incompatible with shared budget. """ UNSPECIFIED = 0 UNKNOWN = 1 BIDDING_STRATEGY_TRANSITION_NOT_ALLOWED = 2 CANNOT_ATTACH_BIDDING_STRATEGY_TO_CAMPAIGN = 7 INVALID_ANONYMOUS_BIDDING_STRATEGY_TYPE = 10 INVALID_BIDDING_STRATEGY_TYPE = 14 INVALID_BID = 17 BIDDING_STRATEGY_NOT_AVAILABLE_FOR_ACCOUNT_TYPE = 18 CONVERSION_TRACKING_NOT_ENABLED = 19 NOT_ENOUGH_CONVERSIONS = 20 CANNOT_CREATE_CAMPAIGN_WITH_BIDDING_STRATEGY = 21 CANNOT_TARGET_CONTENT_NETWORK_ONLY_WITH_CAMPAIGN_LEVEL_POP_BIDDING_STRATEGY = 23 BIDDING_STRATEGY_NOT_SUPPORTED_WITH_AD_SCHEDULE = 24 PAY_PER_CONVERSION_NOT_AVAILABLE_FOR_CUSTOMER = 25 PAY_PER_CONVERSION_NOT_ALLOWED_WITH_TARGET_CPA = 26 BIDDING_STRATEGY_NOT_ALLOWED_FOR_SEARCH_ONLY_CAMPAIGNS = 27 BIDDING_STRATEGY_NOT_SUPPORTED_IN_DRAFTS_OR_EXPERIMENTS = 28 BIDDING_STRATEGY_TYPE_DOES_NOT_SUPPORT_PRODUCT_TYPE_ADGROUP_CRITERION = 29 BID_TOO_SMALL = 30 BID_TOO_BIG = 31 BID_TOO_MANY_FRACTIONAL_DIGITS = 32 INVALID_DOMAIN_NAME = 33 NOT_COMPATIBLE_WITH_PAYMENT_MODE = 34 NOT_COMPATIBLE_WITH_BUDGET_TYPE = 35 NOT_COMPATIBLE_WITH_BIDDING_STRATEGY_TYPE = 36 BIDDING_STRATEGY_TYPE_INCOMPATIBLE_WITH_SHARED_BUDGET = 37 ''' BiddingErrorEnum = BiddingErrorEnum() # For __getattribute__ class BiddingSourceEnum(_CreateEnumTypeUponFirstAccess): BiddingSourceEnum = '''\ class BiddingSource(enum.IntEnum): """ Indicates where a bid or target is defined. For example, an ad group criterion may define a cpc bid directly, or it can inherit its cpc bid from the ad group. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CAMPAIGN_BIDDING_STRATEGY (int): Effective bid or target is inherited from campaign bidding strategy. AD_GROUP (int): The bid or target is defined on the ad group. AD_GROUP_CRITERION (int): The bid or target is defined on the ad group criterion. """ UNSPECIFIED = 0 UNKNOWN = 1 CAMPAIGN_BIDDING_STRATEGY = 5 AD_GROUP = 6 AD_GROUP_CRITERION = 7 ''' BiddingSourceEnum = BiddingSourceEnum() # For __getattribute__ class BiddingStrategyErrorEnum(_CreateEnumTypeUponFirstAccess): BiddingStrategyErrorEnum = '''\ class BiddingStrategyError(enum.IntEnum): """ Enum describing possible bidding strategy errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_NAME (int): Each bidding strategy must have a unique name. CANNOT_CHANGE_BIDDING_STRATEGY_TYPE (int): Bidding strategy type is immutable. CANNOT_REMOVE_ASSOCIATED_STRATEGY (int): Only bidding strategies not linked to campaigns, adgroups or adgroup criteria can be removed. BIDDING_STRATEGY_NOT_SUPPORTED (int): The specified bidding strategy is not supported. INCOMPATIBLE_BIDDING_STRATEGY_AND_BIDDING_STRATEGY_GOAL_TYPE (int): The bidding strategy is incompatible with the campaign's bidding strategy goal type. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_NAME = 2 CANNOT_CHANGE_BIDDING_STRATEGY_TYPE = 3 CANNOT_REMOVE_ASSOCIATED_STRATEGY = 4 BIDDING_STRATEGY_NOT_SUPPORTED = 5 INCOMPATIBLE_BIDDING_STRATEGY_AND_BIDDING_STRATEGY_GOAL_TYPE = 6 ''' BiddingStrategyErrorEnum = BiddingStrategyErrorEnum() # For __getattribute__ class BiddingStrategyStatusEnum(_CreateEnumTypeUponFirstAccess): BiddingStrategyStatusEnum = '''\ class BiddingStrategyStatus(enum.IntEnum): """ The possible statuses of a BiddingStrategy. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The bidding strategy is enabled. REMOVED (int): The bidding strategy is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 4 ''' BiddingStrategyStatusEnum = BiddingStrategyStatusEnum() # For __getattribute__ class BiddingStrategyTypeEnum(_CreateEnumTypeUponFirstAccess): BiddingStrategyTypeEnum = '''\ class BiddingStrategyType(enum.IntEnum): """ Enum describing possible bidding strategy types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. COMMISSION (int): Commission is an automatic bidding strategy in which the advertiser pays a certain portion of the conversion value. ENHANCED_CPC (int): Enhanced CPC is a bidding strategy that raises bids for clicks that seem more likely to lead to a conversion and lowers them for clicks where they seem less likely. MANUAL_CPC (int): Manual click based bidding where user pays per click. MANUAL_CPM (int): Manual impression based bidding where user pays per thousand impressions. MANUAL_CPV (int): A bidding strategy that pays a configurable amount per video view. MAXIMIZE_CONVERSIONS (int): A bidding strategy that automatically maximizes number of conversions given a daily budget. MAXIMIZE_CONVERSION_VALUE (int): An automated bidding strategy that automatically sets bids to maximize revenue while spending your budget. PAGE_ONE_PROMOTED (int): Page-One Promoted bidding scheme, which sets max cpc bids to target impressions on page one or page one promoted slots on google.com. This enum value is deprecated. PERCENT_CPC (int): Percent Cpc is bidding strategy where bids are a fraction of the advertised price for some good or service. TARGET_CPA (int): Target CPA is an automated bid strategy that sets bids to help get as many conversions as possible at the target cost-per-acquisition (CPA) you set. TARGET_CPM (int): Target CPM is an automated bid strategy that sets bids to help get as many impressions as possible at the target cost per one thousand impressions (CPM) you set. TARGET_IMPRESSION_SHARE (int): An automated bidding strategy that sets bids so that a certain percentage of search ads are shown at the top of the first page (or other targeted location). TARGET_OUTRANK_SHARE (int): Target Outrank Share is an automated bidding strategy that sets bids based on the target fraction of auctions where the advertiser should outrank a specific competitor. This enum value is deprecated. TARGET_ROAS (int): Target ROAS is an automated bidding strategy that helps you maximize revenue while averaging a specific target Return On Average Spend (ROAS). TARGET_SPEND (int): Target Spend is an automated bid strategy that sets your bids to help get as many clicks as possible within your budget. """ UNSPECIFIED = 0 UNKNOWN = 1 COMMISSION = 16 ENHANCED_CPC = 2 MANUAL_CPC = 3 MANUAL_CPM = 4 MANUAL_CPV = 13 MAXIMIZE_CONVERSIONS = 10 MAXIMIZE_CONVERSION_VALUE = 11 PAGE_ONE_PROMOTED = 5 PERCENT_CPC = 12 TARGET_CPA = 6 TARGET_CPM = 14 TARGET_IMPRESSION_SHARE = 15 TARGET_OUTRANK_SHARE = 7 TARGET_ROAS = 8 TARGET_SPEND = 9 ''' BiddingStrategyTypeEnum = BiddingStrategyTypeEnum() # For __getattribute__ class BillingSetupErrorEnum(_CreateEnumTypeUponFirstAccess): BillingSetupErrorEnum = '''\ class BillingSetupError(enum.IntEnum): """ Enum describing possible billing setup errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_USE_EXISTING_AND_NEW_ACCOUNT (int): Cannot specify both an existing payments account and a new payments account when setting up billing. CANNOT_REMOVE_STARTED_BILLING_SETUP (int): Cannot cancel an approved billing setup whose start time has passed. CANNOT_CHANGE_BILLING_TO_SAME_PAYMENTS_ACCOUNT (int): Cannot perform a Change of Bill-To (CBT) to the same payments account. BILLING_SETUP_NOT_PERMITTED_FOR_CUSTOMER_STATUS (int): Billing setups can only be used by customers with ENABLED or DRAFT status. INVALID_PAYMENTS_ACCOUNT (int): Billing setups must either include a correctly formatted existing payments account id, or a non-empty new payments account name. BILLING_SETUP_NOT_PERMITTED_FOR_CUSTOMER_CATEGORY (int): Only billable and third-party customers can create billing setups. INVALID_START_TIME_TYPE (int): Billing setup creations can only use NOW for start time type. THIRD_PARTY_ALREADY_HAS_BILLING (int): Billing setups can only be created for a third-party customer if they do not already have a setup. BILLING_SETUP_IN_PROGRESS (int): Billing setups cannot be created if there is already a pending billing in progress. NO_SIGNUP_PERMISSION (int): Billing setups can only be created by customers who have permission to setup billings. Users can contact a representative for help setting up permissions. CHANGE_OF_BILL_TO_IN_PROGRESS (int): Billing setups cannot be created if there is already a future-approved billing. PAYMENTS_PROFILE_NOT_FOUND (int): Requested payments profile not found. PAYMENTS_ACCOUNT_NOT_FOUND (int): Requested payments account not found. PAYMENTS_PROFILE_INELIGIBLE (int): Billing setup creation failed because the payments profile is ineligible. PAYMENTS_ACCOUNT_INELIGIBLE (int): Billing setup creation failed because the payments account is ineligible. CUSTOMER_NEEDS_INTERNAL_APPROVAL (int): Billing setup creation failed because the payments profile needs internal approval. PAYMENTS_ACCOUNT_INELIGIBLE_CURRENCY_CODE_MISMATCH (int): Payments account has different currency code than the current customer and hence cannot be used to setup billing. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_USE_EXISTING_AND_NEW_ACCOUNT = 2 CANNOT_REMOVE_STARTED_BILLING_SETUP = 3 CANNOT_CHANGE_BILLING_TO_SAME_PAYMENTS_ACCOUNT = 4 BILLING_SETUP_NOT_PERMITTED_FOR_CUSTOMER_STATUS = 5 INVALID_PAYMENTS_ACCOUNT = 6 BILLING_SETUP_NOT_PERMITTED_FOR_CUSTOMER_CATEGORY = 7 INVALID_START_TIME_TYPE = 8 THIRD_PARTY_ALREADY_HAS_BILLING = 9 BILLING_SETUP_IN_PROGRESS = 10 NO_SIGNUP_PERMISSION = 11 CHANGE_OF_BILL_TO_IN_PROGRESS = 12 PAYMENTS_PROFILE_NOT_FOUND = 13 PAYMENTS_ACCOUNT_NOT_FOUND = 14 PAYMENTS_PROFILE_INELIGIBLE = 15 PAYMENTS_ACCOUNT_INELIGIBLE = 16 CUSTOMER_NEEDS_INTERNAL_APPROVAL = 17 PAYMENTS_ACCOUNT_INELIGIBLE_CURRENCY_CODE_MISMATCH = 19 ''' BillingSetupErrorEnum = BillingSetupErrorEnum() # For __getattribute__ class BillingSetupStatusEnum(_CreateEnumTypeUponFirstAccess): BillingSetupStatusEnum = '''\ class BillingSetupStatus(enum.IntEnum): """ The possible statuses of a BillingSetup. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): The billing setup is pending approval. APPROVED_HELD (int): The billing setup has been approved but the corresponding first budget has not. This can only occur for billing setups configured for monthly invoicing. APPROVED (int): The billing setup has been approved. CANCELLED (int): The billing setup was cancelled by the user prior to approval. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 APPROVED_HELD = 3 APPROVED = 4 CANCELLED = 5 ''' BillingSetupStatusEnum = BillingSetupStatusEnum() # For __getattribute__ class BrandSafetySuitabilityEnum(_CreateEnumTypeUponFirstAccess): BrandSafetySuitabilityEnum = '''\ class BrandSafetySuitability(enum.IntEnum): """ 3-Tier brand safety suitability control. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EXPANDED_INVENTORY (int): This option lets you show ads across all inventory on YouTube and video partners that meet our standards for monetization. This option may be an appropriate choice for brands that want maximum access to the full breadth of videos eligible for ads, including, for example, videos that have strong profanity in the context of comedy or a documentary, or excessive violence as featured in video games. STANDARD_INVENTORY (int): This option lets you show ads across a wide range of content that's appropriate for most brands, such as popular music videos, documentaries, and movie trailers. The content you can show ads on is based on YouTube's advertiser-friendly content guidelines that take into account, for example, the strength or frequency of profanity, or the appropriateness of subject matter like sensitive events. Ads won't show, for example, on content with repeated strong profanity, strong sexual content, or graphic violence. LIMITED_INVENTORY (int): This option lets you show ads on a reduced range of content that's appropriate for brands with particularly strict guidelines around inappropriate language and sexual suggestiveness; above and beyond what YouTube's advertiser-friendly content guidelines address. The videos accessible in this sensitive category meet heightened requirements, especially for inappropriate language and sexual suggestiveness. For example, your ads will be excluded from showing on some of YouTube's most popular music videos and other pop culture content across YouTube and Google video partners. """ UNSPECIFIED = 0 UNKNOWN = 1 EXPANDED_INVENTORY = 2 STANDARD_INVENTORY = 3 LIMITED_INVENTORY = 4 ''' BrandSafetySuitabilityEnum = BrandSafetySuitabilityEnum() # For __getattribute__ class BudgetDeliveryMethodEnum(_CreateEnumTypeUponFirstAccess): BudgetDeliveryMethodEnum = '''\ class BudgetDeliveryMethod(enum.IntEnum): """ Possible delivery methods of a Budget. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. STANDARD (int): The budget server will throttle serving evenly across the entire time period. ACCELERATED (int): The budget server will not throttle serving, and ads will serve as fast as possible. """ UNSPECIFIED = 0 UNKNOWN = 1 STANDARD = 2 ACCELERATED = 3 ''' BudgetDeliveryMethodEnum = BudgetDeliveryMethodEnum() # For __getattribute__ class BudgetPeriodEnum(_CreateEnumTypeUponFirstAccess): BudgetPeriodEnum = '''\ class BudgetPeriod(enum.IntEnum): """ Possible period of a Budget. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DAILY (int): Daily budget. """ UNSPECIFIED = 0 UNKNOWN = 1 DAILY = 2 ''' BudgetPeriodEnum = BudgetPeriodEnum() # For __getattribute__ class BudgetStatusEnum(_CreateEnumTypeUponFirstAccess): BudgetStatusEnum = '''\ class BudgetStatus(enum.IntEnum): """ Possible statuses of a Budget. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Budget is enabled. REMOVED (int): Budget is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' BudgetStatusEnum = BudgetStatusEnum() # For __getattribute__ class BudgetTypeEnum(_CreateEnumTypeUponFirstAccess): BudgetTypeEnum = '''\ class BudgetType(enum.IntEnum): """ Possible Budget types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. STANDARD (int): Budget type for standard Google Ads usage. Caps daily spend at two times the specified budget amount. Full details: https://support.google.com/google-ads/answer/6385083 HOTEL_ADS_COMMISSION (int): Budget type for Hotels Ads commission program. Full details: https://support.google.com/google-ads/answer/9243945 This type is only supported by campaigns with AdvertisingChannelType.HOTEL, BiddingStrategyType.COMMISSION and PaymentMode.CONVERSION\_VALUE. FIXED_CPA (int): Budget type with a fixed cost-per-acquisition (conversion). Full details: https://support.google.com/google-ads/answer/7528254 This type is only supported by campaigns with AdvertisingChannelType.DISPLAY (excluding AdvertisingChannelSubType.DISPLAY\_GMAIL), BiddingStrategyType.TARGET\_CPA and PaymentMode.CONVERSIONS. """ UNSPECIFIED = 0 UNKNOWN = 1 STANDARD = 2 HOTEL_ADS_COMMISSION = 3 FIXED_CPA = 4 ''' BudgetTypeEnum = BudgetTypeEnum() # For __getattribute__ class CallConversionReportingStateEnum(_CreateEnumTypeUponFirstAccess): CallConversionReportingStateEnum = '''\ class CallConversionReportingState(enum.IntEnum): """ Possible data types for a call conversion action state. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DISABLED (int): Call conversion action is disabled. USE_ACCOUNT_LEVEL_CALL_CONVERSION_ACTION (int): Call conversion action will use call conversion type set at the account level. USE_RESOURCE_LEVEL_CALL_CONVERSION_ACTION (int): Call conversion action will use call conversion type set at the resource (call only ads/call extensions) level. """ UNSPECIFIED = 0 UNKNOWN = 1 DISABLED = 2 USE_ACCOUNT_LEVEL_CALL_CONVERSION_ACTION = 3 USE_RESOURCE_LEVEL_CALL_CONVERSION_ACTION = 4 ''' CallConversionReportingStateEnum = CallConversionReportingStateEnum() # For __getattribute__ class CallPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): CallPlaceholderFieldEnum = '''\ class CallPlaceholderField(enum.IntEnum): """ Possible values for Call placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PHONE_NUMBER (int): Data Type: STRING. The advertiser's phone number to append to the ad. COUNTRY_CODE (int): Data Type: STRING. Uppercase two-letter country code of the advertiser's phone number. TRACKED (int): Data Type: BOOLEAN. Indicates whether call tracking is enabled. Default: true. CONVERSION_TYPE_ID (int): Data Type: INT64. The ID of an AdCallMetricsConversion object. This object contains the phoneCallDurationfield which is the minimum duration (in seconds) of a call to be considered a conversion. CONVERSION_REPORTING_STATE (int): Data Type: STRING. Indicates whether this call extension uses its own call conversion setting or follows the account level setting. Valid values are: USE\_ACCOUNT\_LEVEL\_CALL\_CONVERSION\_ACTION and USE\_RESOURCE\_LEVEL\_CALL\_CONVERSION\_ACTION. """ UNSPECIFIED = 0 UNKNOWN = 1 PHONE_NUMBER = 2 COUNTRY_CODE = 3 TRACKED = 4 CONVERSION_TYPE_ID = 5 CONVERSION_REPORTING_STATE = 6 ''' CallPlaceholderFieldEnum = CallPlaceholderFieldEnum() # For __getattribute__ class CalloutPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): CalloutPlaceholderFieldEnum = '''\ class CalloutPlaceholderField(enum.IntEnum): """ Possible values for Callout placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CALLOUT_TEXT (int): Data Type: STRING. Callout text. """ UNSPECIFIED = 0 UNKNOWN = 1 CALLOUT_TEXT = 2 ''' CalloutPlaceholderFieldEnum = CalloutPlaceholderFieldEnum() # For __getattribute__ class CampaignBudgetErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignBudgetErrorEnum = '''\ class CampaignBudgetError(enum.IntEnum): """ Enum describing possible campaign budget errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CAMPAIGN_BUDGET_CANNOT_BE_SHARED (int): The campaign budget cannot be shared. CAMPAIGN_BUDGET_REMOVED (int): The requested campaign budget no longer exists. CAMPAIGN_BUDGET_IN_USE (int): The campaign budget is associated with at least one campaign, and so the campaign budget cannot be removed. CAMPAIGN_BUDGET_PERIOD_NOT_AVAILABLE (int): Customer is not whitelisted for this campaign budget period. CANNOT_MODIFY_FIELD_OF_IMPLICITLY_SHARED_CAMPAIGN_BUDGET (int): This field is not mutable on implicitly shared campaign budgets CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_IMPLICITLY_SHARED (int): Cannot change explicitly shared campaign budgets back to implicitly shared ones. CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_EXPLICITLY_SHARED_WITHOUT_NAME (int): An implicit campaign budget without a name cannot be changed to explicitly shared campaign budget. CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_EXPLICITLY_SHARED (int): Cannot change an implicitly shared campaign budget to an explicitly shared one. CANNOT_USE_IMPLICITLY_SHARED_CAMPAIGN_BUDGET_WITH_MULTIPLE_CAMPAIGNS (int): Only explicitly shared campaign budgets can be used with multiple campaigns. DUPLICATE_NAME (int): A campaign budget with this name already exists. MONEY_AMOUNT_IN_WRONG_CURRENCY (int): A money amount was not in the expected currency. MONEY_AMOUNT_LESS_THAN_CURRENCY_MINIMUM_CPC (int): A money amount was less than the minimum CPC for currency. MONEY_AMOUNT_TOO_LARGE (int): A money amount was greater than the maximum allowed. NEGATIVE_MONEY_AMOUNT (int): A money amount was negative. NON_MULTIPLE_OF_MINIMUM_CURRENCY_UNIT (int): A money amount was not a multiple of a minimum unit. """ UNSPECIFIED = 0 UNKNOWN = 1 CAMPAIGN_BUDGET_CANNOT_BE_SHARED = 17 CAMPAIGN_BUDGET_REMOVED = 2 CAMPAIGN_BUDGET_IN_USE = 3 CAMPAIGN_BUDGET_PERIOD_NOT_AVAILABLE = 4 CANNOT_MODIFY_FIELD_OF_IMPLICITLY_SHARED_CAMPAIGN_BUDGET = 6 CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_IMPLICITLY_SHARED = 7 CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_EXPLICITLY_SHARED_WITHOUT_NAME = 8 CANNOT_UPDATE_CAMPAIGN_BUDGET_TO_EXPLICITLY_SHARED = 9 CANNOT_USE_IMPLICITLY_SHARED_CAMPAIGN_BUDGET_WITH_MULTIPLE_CAMPAIGNS = 10 DUPLICATE_NAME = 11 MONEY_AMOUNT_IN_WRONG_CURRENCY = 12 MONEY_AMOUNT_LESS_THAN_CURRENCY_MINIMUM_CPC = 13 MONEY_AMOUNT_TOO_LARGE = 14 NEGATIVE_MONEY_AMOUNT = 15 NON_MULTIPLE_OF_MINIMUM_CURRENCY_UNIT = 16 ''' CampaignBudgetErrorEnum = CampaignBudgetErrorEnum() # For __getattribute__ class CampaignCriterionErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignCriterionErrorEnum = '''\ class CampaignCriterionError(enum.IntEnum): """ Enum describing possible campaign criterion errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CONCRETE_TYPE_REQUIRED (int): Concrete type of criterion (keyword v.s. placement) is required for CREATE and UPDATE operations. INVALID_PLACEMENT_URL (int): Invalid placement URL. CANNOT_EXCLUDE_CRITERIA_TYPE (int): Criteria type can not be excluded for the campaign by the customer. like AOL account type cannot target site type criteria CANNOT_SET_STATUS_FOR_CRITERIA_TYPE (int): Cannot set the campaign criterion status for this criteria type. CANNOT_SET_STATUS_FOR_EXCLUDED_CRITERIA (int): Cannot set the campaign criterion status for an excluded criteria. CANNOT_TARGET_AND_EXCLUDE (int): Cannot target and exclude the same criterion. TOO_MANY_OPERATIONS (int): The mutate contained too many operations. OPERATOR_NOT_SUPPORTED_FOR_CRITERION_TYPE (int): This operator cannot be applied to a criterion of this type. SHOPPING_CAMPAIGN_SALES_COUNTRY_NOT_SUPPORTED_FOR_SALES_CHANNEL (int): The Shopping campaign sales country is not supported for ProductSalesChannel targeting. CANNOT_ADD_EXISTING_FIELD (int): The existing field can't be updated with CREATE operation. It can be updated with UPDATE operation only. CANNOT_UPDATE_NEGATIVE_CRITERION (int): Negative criteria are immutable, so updates are not allowed. """ UNSPECIFIED = 0 UNKNOWN = 1 CONCRETE_TYPE_REQUIRED = 2 INVALID_PLACEMENT_URL = 3 CANNOT_EXCLUDE_CRITERIA_TYPE = 4 CANNOT_SET_STATUS_FOR_CRITERIA_TYPE = 5 CANNOT_SET_STATUS_FOR_EXCLUDED_CRITERIA = 6 CANNOT_TARGET_AND_EXCLUDE = 7 TOO_MANY_OPERATIONS = 8 OPERATOR_NOT_SUPPORTED_FOR_CRITERION_TYPE = 9 SHOPPING_CAMPAIGN_SALES_COUNTRY_NOT_SUPPORTED_FOR_SALES_CHANNEL = 10 CANNOT_ADD_EXISTING_FIELD = 11 CANNOT_UPDATE_NEGATIVE_CRITERION = 12 ''' CampaignCriterionErrorEnum = CampaignCriterionErrorEnum() # For __getattribute__ class CampaignCriterionStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignCriterionStatusEnum = '''\ class CampaignCriterionStatus(enum.IntEnum): """ The possible statuses of a CampaignCriterion. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The campaign criterion is enabled. PAUSED (int): The campaign criterion is paused. REMOVED (int): The campaign criterion is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PAUSED = 3 REMOVED = 4 ''' CampaignCriterionStatusEnum = CampaignCriterionStatusEnum() # For __getattribute__ class CampaignDraftErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignDraftErrorEnum = '''\ class CampaignDraftError(enum.IntEnum): """ Enum describing possible campaign draft errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_DRAFT_NAME (int): A draft with this name already exists for this campaign. INVALID_STATUS_TRANSITION_FROM_REMOVED (int): The draft is removed and cannot be transitioned to another status. INVALID_STATUS_TRANSITION_FROM_PROMOTED (int): The draft has been promoted and cannot be transitioned to the specified status. INVALID_STATUS_TRANSITION_FROM_PROMOTE_FAILED (int): The draft has failed to be promoted and cannot be transitioned to the specified status. CUSTOMER_CANNOT_CREATE_DRAFT (int): This customer is not allowed to create drafts. CAMPAIGN_CANNOT_CREATE_DRAFT (int): This campaign is not allowed to create drafts. INVALID_DRAFT_CHANGE (int): This modification cannot be made on a draft. INVALID_STATUS_TRANSITION (int): The draft cannot be transitioned to the specified status from its current status. MAX_NUMBER_OF_DRAFTS_PER_CAMPAIGN_REACHED (int): The campaign has reached the maximum number of drafts that can be created for a campaign throughout its lifetime. No additional drafts can be created for this campaign. Removed drafts also count towards this limit. LIST_ERRORS_FOR_PROMOTED_DRAFT_ONLY (int): ListAsyncErrors was called without first promoting the draft. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_DRAFT_NAME = 2 INVALID_STATUS_TRANSITION_FROM_REMOVED = 3 INVALID_STATUS_TRANSITION_FROM_PROMOTED = 4 INVALID_STATUS_TRANSITION_FROM_PROMOTE_FAILED = 5 CUSTOMER_CANNOT_CREATE_DRAFT = 6 CAMPAIGN_CANNOT_CREATE_DRAFT = 7 INVALID_DRAFT_CHANGE = 8 INVALID_STATUS_TRANSITION = 9 MAX_NUMBER_OF_DRAFTS_PER_CAMPAIGN_REACHED = 10 LIST_ERRORS_FOR_PROMOTED_DRAFT_ONLY = 11 ''' CampaignDraftErrorEnum = CampaignDraftErrorEnum() # For __getattribute__ class CampaignDraftStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignDraftStatusEnum = '''\ class CampaignDraftStatus(enum.IntEnum): """ Possible statuses of a campaign draft. Attributes: UNSPECIFIED (int): The status has not been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PROPOSED (int): Initial state of the draft, the advertiser can start adding changes with no effect on serving. REMOVED (int): The campaign draft is removed. PROMOTING (int): Advertiser requested to promote draft's changes back into the original campaign. Advertiser can poll the long running operation returned by the promote action to see the status of the promotion. PROMOTED (int): The process to merge changes in the draft back to the original campaign has completed successfully. PROMOTE_FAILED (int): The promotion failed after it was partially applied. Promote cannot be attempted again safely, so the issue must be corrected in the original campaign. """ UNSPECIFIED = 0 UNKNOWN = 1 PROPOSED = 2 REMOVED = 3 PROMOTING = 5 PROMOTED = 4 PROMOTE_FAILED = 6 ''' CampaignDraftStatusEnum = CampaignDraftStatusEnum() # For __getattribute__ class CampaignErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignErrorEnum = '''\ class CampaignError(enum.IntEnum): """ Enum describing possible campaign errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_TARGET_CONTENT_NETWORK (int): Cannot target content network. CANNOT_TARGET_SEARCH_NETWORK (int): Cannot target search network. CANNOT_TARGET_SEARCH_NETWORK_WITHOUT_GOOGLE_SEARCH (int): Cannot cover search network without google search network. CANNOT_TARGET_GOOGLE_SEARCH_FOR_CPM_CAMPAIGN (int): Cannot target Google Search network for a CPM campaign. CAMPAIGN_MUST_TARGET_AT_LEAST_ONE_NETWORK (int): Must target at least one network. CANNOT_TARGET_PARTNER_SEARCH_NETWORK (int): Only some Google partners are allowed to target partner search network. CANNOT_TARGET_CONTENT_NETWORK_ONLY_WITH_CRITERIA_LEVEL_BIDDING_STRATEGY (int): Cannot target content network only as campaign has criteria-level bidding strategy. CAMPAIGN_DURATION_MUST_CONTAIN_ALL_RUNNABLE_TRIALS (int): Cannot modify the start or end date such that the campaign duration would not contain the durations of all runnable trials. CANNOT_MODIFY_FOR_TRIAL_CAMPAIGN (int): Cannot modify dates, budget or campaign name of a trial campaign. DUPLICATE_CAMPAIGN_NAME (int): Trying to modify the name of an active or paused campaign, where the name is already assigned to another active or paused campaign. INCOMPATIBLE_CAMPAIGN_FIELD (int): Two fields are in conflicting modes. INVALID_CAMPAIGN_NAME (int): Campaign name cannot be used. INVALID_AD_SERVING_OPTIMIZATION_STATUS (int): Given status is invalid. INVALID_TRACKING_URL (int): Error in the campaign level tracking url. CANNOT_SET_BOTH_TRACKING_URL_TEMPLATE_AND_TRACKING_SETTING (int): Cannot set both tracking url template and tracking setting. An user has to clear legacy tracking setting in order to add tracking url template. MAX_IMPRESSIONS_NOT_IN_RANGE (int): The maximum number of impressions for Frequency Cap should be an integer greater than 0. TIME_UNIT_NOT_SUPPORTED (int): Only the Day, Week and Month time units are supported. INVALID_OPERATION_IF_SERVING_STATUS_HAS_ENDED (int): Operation not allowed on a campaign whose serving status has ended BUDGET_CANNOT_BE_SHARED (int): This budget is exclusively linked to a Campaign that is using experiments so it cannot be shared. CAMPAIGN_CANNOT_USE_SHARED_BUDGET (int): Campaigns using experiments cannot use a shared budget. CANNOT_CHANGE_BUDGET_ON_CAMPAIGN_WITH_TRIALS (int): A different budget cannot be assigned to a campaign when there are running or scheduled trials. CAMPAIGN_LABEL_DOES_NOT_EXIST (int): No link found between the campaign and the label. CAMPAIGN_LABEL_ALREADY_EXISTS (int): The label has already been attached to the campaign. MISSING_SHOPPING_SETTING (int): A ShoppingSetting was not found when creating a shopping campaign. INVALID_SHOPPING_SALES_COUNTRY (int): The country in shopping setting is not an allowed country. ADVERTISING_CHANNEL_TYPE_NOT_AVAILABLE_FOR_ACCOUNT_TYPE (int): The requested channel type is not available according to the customer's account setting. INVALID_ADVERTISING_CHANNEL_SUB_TYPE (int): The AdvertisingChannelSubType is not a valid subtype of the primary channel type. AT_LEAST_ONE_CONVERSION_MUST_BE_SELECTED (int): At least one conversion must be selected. CANNOT_SET_AD_ROTATION_MODE (int): Setting ad rotation mode for a campaign is not allowed. Ad rotation mode at campaign is deprecated. CANNOT_MODIFY_START_DATE_IF_ALREADY_STARTED (int): Trying to change start date on a campaign that has started. CANNOT_SET_DATE_TO_PAST (int): Trying to modify a date into the past. MISSING_HOTEL_CUSTOMER_LINK (int): Hotel center id in the hotel setting does not match any customer links. INVALID_HOTEL_CUSTOMER_LINK (int): Hotel center id in the hotel setting must match an active customer link. MISSING_HOTEL_SETTING (int): Hotel setting was not found when creating a hotel ads campaign. CANNOT_USE_SHARED_CAMPAIGN_BUDGET_WHILE_PART_OF_CAMPAIGN_GROUP (int): A Campaign cannot use shared campaign budgets and be part of a campaign group. APP_NOT_FOUND (int): The app ID was not found. SHOPPING_ENABLE_LOCAL_NOT_SUPPORTED_FOR_CAMPAIGN_TYPE (int): Campaign.shopping\_setting.enable\_local is not supported for the specified campaign type. MERCHANT_NOT_ALLOWED_FOR_COMPARISON_LISTING_ADS (int): The merchant does not support the creation of campaigns for Shopping Comparison Listing Ads. INSUFFICIENT_APP_INSTALLS_COUNT (int): The App campaign for engagement cannot be created because there aren't enough installs. SENSITIVE_CATEGORY_APP (int): The App campaign for engagement cannot be created because the app is sensitive. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_TARGET_CONTENT_NETWORK = 3 CANNOT_TARGET_SEARCH_NETWORK = 4 CANNOT_TARGET_SEARCH_NETWORK_WITHOUT_GOOGLE_SEARCH = 5 CANNOT_TARGET_GOOGLE_SEARCH_FOR_CPM_CAMPAIGN = 6 CAMPAIGN_MUST_TARGET_AT_LEAST_ONE_NETWORK = 7 CANNOT_TARGET_PARTNER_SEARCH_NETWORK = 8 CANNOT_TARGET_CONTENT_NETWORK_ONLY_WITH_CRITERIA_LEVEL_BIDDING_STRATEGY = 9 CAMPAIGN_DURATION_MUST_CONTAIN_ALL_RUNNABLE_TRIALS = 10 CANNOT_MODIFY_FOR_TRIAL_CAMPAIGN = 11 DUPLICATE_CAMPAIGN_NAME = 12 INCOMPATIBLE_CAMPAIGN_FIELD = 13 INVALID_CAMPAIGN_NAME = 14 INVALID_AD_SERVING_OPTIMIZATION_STATUS = 15 INVALID_TRACKING_URL = 16 CANNOT_SET_BOTH_TRACKING_URL_TEMPLATE_AND_TRACKING_SETTING = 17 MAX_IMPRESSIONS_NOT_IN_RANGE = 18 TIME_UNIT_NOT_SUPPORTED = 19 INVALID_OPERATION_IF_SERVING_STATUS_HAS_ENDED = 20 BUDGET_CANNOT_BE_SHARED = 21 CAMPAIGN_CANNOT_USE_SHARED_BUDGET = 22 CANNOT_CHANGE_BUDGET_ON_CAMPAIGN_WITH_TRIALS = 23 CAMPAIGN_LABEL_DOES_NOT_EXIST = 24 CAMPAIGN_LABEL_ALREADY_EXISTS = 25 MISSING_SHOPPING_SETTING = 26 INVALID_SHOPPING_SALES_COUNTRY = 27 ADVERTISING_CHANNEL_TYPE_NOT_AVAILABLE_FOR_ACCOUNT_TYPE = 31 INVALID_ADVERTISING_CHANNEL_SUB_TYPE = 32 AT_LEAST_ONE_CONVERSION_MUST_BE_SELECTED = 33 CANNOT_SET_AD_ROTATION_MODE = 34 CANNOT_MODIFY_START_DATE_IF_ALREADY_STARTED = 35 CANNOT_SET_DATE_TO_PAST = 36 MISSING_HOTEL_CUSTOMER_LINK = 37 INVALID_HOTEL_CUSTOMER_LINK = 38 MISSING_HOTEL_SETTING = 39 CANNOT_USE_SHARED_CAMPAIGN_BUDGET_WHILE_PART_OF_CAMPAIGN_GROUP = 40 APP_NOT_FOUND = 41 SHOPPING_ENABLE_LOCAL_NOT_SUPPORTED_FOR_CAMPAIGN_TYPE = 42 MERCHANT_NOT_ALLOWED_FOR_COMPARISON_LISTING_ADS = 43 INSUFFICIENT_APP_INSTALLS_COUNT = 44 SENSITIVE_CATEGORY_APP = 45 ''' CampaignErrorEnum = CampaignErrorEnum() # For __getattribute__ class CampaignExperimentErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignExperimentErrorEnum = '''\ class CampaignExperimentError(enum.IntEnum): """ Enum describing possible campaign experiment errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_NAME (int): An active campaign or experiment with this name already exists. INVALID_TRANSITION (int): Experiment cannot be updated from the current state to the requested target state. For example, an experiment can only graduate if its status is ENABLED. CANNOT_CREATE_EXPERIMENT_WITH_SHARED_BUDGET (int): Cannot create an experiment from a campaign using an explicitly shared budget. CANNOT_CREATE_EXPERIMENT_FOR_REMOVED_BASE_CAMPAIGN (int): Cannot create an experiment for a removed base campaign. CANNOT_CREATE_EXPERIMENT_FOR_NON_PROPOSED_DRAFT (int): Cannot create an experiment from a draft, which has a status other than proposed. CUSTOMER_CANNOT_CREATE_EXPERIMENT (int): This customer is not allowed to create an experiment. CAMPAIGN_CANNOT_CREATE_EXPERIMENT (int): This campaign is not allowed to create an experiment. EXPERIMENT_DURATIONS_MUST_NOT_OVERLAP (int): Trying to set an experiment duration which overlaps with another experiment. EXPERIMENT_DURATION_MUST_BE_WITHIN_CAMPAIGN_DURATION (int): All non-removed experiments must start and end within their campaign's duration. CANNOT_MUTATE_EXPERIMENT_DUE_TO_STATUS (int): The experiment cannot be modified because its status is in a terminal state, such as REMOVED. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_NAME = 2 INVALID_TRANSITION = 3 CANNOT_CREATE_EXPERIMENT_WITH_SHARED_BUDGET = 4 CANNOT_CREATE_EXPERIMENT_FOR_REMOVED_BASE_CAMPAIGN = 5 CANNOT_CREATE_EXPERIMENT_FOR_NON_PROPOSED_DRAFT = 6 CUSTOMER_CANNOT_CREATE_EXPERIMENT = 7 CAMPAIGN_CANNOT_CREATE_EXPERIMENT = 8 EXPERIMENT_DURATIONS_MUST_NOT_OVERLAP = 9 EXPERIMENT_DURATION_MUST_BE_WITHIN_CAMPAIGN_DURATION = 10 CANNOT_MUTATE_EXPERIMENT_DUE_TO_STATUS = 11 ''' CampaignExperimentErrorEnum = CampaignExperimentErrorEnum() # For __getattribute__ class CampaignExperimentStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignExperimentStatusEnum = '''\ class CampaignExperimentStatus(enum.IntEnum): """ Possible statuses of a campaign experiment. Attributes: UNSPECIFIED (int): The status has not been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INITIALIZING (int): The experiment campaign is being initialized. INITIALIZATION_FAILED (int): Initialization of the experiment campaign failed. ENABLED (int): The experiment campaign is fully initialized. The experiment is currently running, scheduled to run in the future or has ended based on its end date. An experiment with the status INITIALIZING will be updated to ENABLED when it is fully created. GRADUATED (int): The experiment campaign was graduated to a stand-alone campaign, existing independently of the experiment. REMOVED (int): The experiment is removed. PROMOTING (int): The experiment's changes are being applied to the original campaign. The long running operation returned by the promote method can be polled to see the status of the promotion. PROMOTION_FAILED (int): Promote of the experiment campaign failed. PROMOTED (int): The changes of the experiment are promoted to their original campaign. ENDED_MANUALLY (int): The experiment was ended manually. It did not end based on its end date. """ UNSPECIFIED = 0 UNKNOWN = 1 INITIALIZING = 2 INITIALIZATION_FAILED = 8 ENABLED = 3 GRADUATED = 4 REMOVED = 5 PROMOTING = 6 PROMOTION_FAILED = 9 PROMOTED = 7 ENDED_MANUALLY = 10 ''' CampaignExperimentStatusEnum = CampaignExperimentStatusEnum() # For __getattribute__ class CampaignExperimentTrafficSplitTypeEnum(_CreateEnumTypeUponFirstAccess): CampaignExperimentTrafficSplitTypeEnum = '''\ class CampaignExperimentTrafficSplitType(enum.IntEnum): """ Enum of strategies for splitting traffic between base and experiment campaigns in campaign experiment. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. RANDOM_QUERY (int): Traffic is randomly assigned to the base or experiment arm for each query, independent of previous assignments for the same user. COOKIE (int): Traffic is split using cookies to keep users in the same arm (base or experiment) of the experiment. """ UNSPECIFIED = 0 UNKNOWN = 1 RANDOM_QUERY = 2 COOKIE = 3 ''' CampaignExperimentTrafficSplitTypeEnum = CampaignExperimentTrafficSplitTypeEnum() # For __getattribute__ class CampaignExperimentTypeEnum(_CreateEnumTypeUponFirstAccess): CampaignExperimentTypeEnum = '''\ class CampaignExperimentType(enum.IntEnum): """ Indicates if this campaign is a normal campaign, a draft campaign, or an experiment campaign. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BASE (int): This is a regular campaign. DRAFT (int): This is a draft version of a campaign. It has some modifications from a base campaign, but it does not serve or accrue metrics. EXPERIMENT (int): This is an experiment version of a campaign. It has some modifications from a base campaign, and a percentage of traffic is being diverted from the BASE campaign to this experiment campaign. """ UNSPECIFIED = 0 UNKNOWN = 1 BASE = 2 DRAFT = 3 EXPERIMENT = 4 ''' CampaignExperimentTypeEnum = CampaignExperimentTypeEnum() # For __getattribute__ class CampaignFeedErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignFeedErrorEnum = '''\ class CampaignFeedError(enum.IntEnum): """ Enum describing possible campaign feed errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active feed already exists for this campaign and placeholder type. CANNOT_CREATE_FOR_REMOVED_FEED (int): The specified feed is removed. CANNOT_CREATE_ALREADY_EXISTING_CAMPAIGN_FEED (int): The CampaignFeed already exists. UPDATE should be used to modify the existing CampaignFeed. CANNOT_MODIFY_REMOVED_CAMPAIGN_FEED (int): Cannot update removed campaign feed. INVALID_PLACEHOLDER_TYPE (int): Invalid placeholder type. MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE (int): Feed mapping for this placeholder type does not exist. NO_EXISTING_LOCATION_CUSTOMER_FEED (int): Location CampaignFeeds cannot be created unless there is a location CustomerFeed for the specified feed. """ UNSPECIFIED = 0 UNKNOWN = 1 FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 2 CANNOT_CREATE_FOR_REMOVED_FEED = 4 CANNOT_CREATE_ALREADY_EXISTING_CAMPAIGN_FEED = 5 CANNOT_MODIFY_REMOVED_CAMPAIGN_FEED = 6 INVALID_PLACEHOLDER_TYPE = 7 MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE = 8 NO_EXISTING_LOCATION_CUSTOMER_FEED = 9 ''' CampaignFeedErrorEnum = CampaignFeedErrorEnum() # For __getattribute__ class CampaignServingStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignServingStatusEnum = '''\ class CampaignServingStatus(enum.IntEnum): """ Possible serving statuses of a campaign. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. SERVING (int): Serving. NONE (int): None. ENDED (int): Ended. PENDING (int): Pending. SUSPENDED (int): Suspended. """ UNSPECIFIED = 0 UNKNOWN = 1 SERVING = 2 NONE = 3 ENDED = 4 PENDING = 5 SUSPENDED = 6 ''' CampaignServingStatusEnum = CampaignServingStatusEnum() # For __getattribute__ class CampaignSharedSetErrorEnum(_CreateEnumTypeUponFirstAccess): CampaignSharedSetErrorEnum = '''\ class CampaignSharedSetError(enum.IntEnum): """ Enum describing possible campaign shared set errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. SHARED_SET_ACCESS_DENIED (int): The shared set belongs to another customer and permission isn't granted. """ UNSPECIFIED = 0 UNKNOWN = 1 SHARED_SET_ACCESS_DENIED = 2 ''' CampaignSharedSetErrorEnum = CampaignSharedSetErrorEnum() # For __getattribute__ class CampaignSharedSetStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignSharedSetStatusEnum = '''\ class CampaignSharedSetStatus(enum.IntEnum): """ Enum listing the possible campaign shared set statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): The campaign shared set is enabled. REMOVED (int): The campaign shared set is removed and can no longer be used. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' CampaignSharedSetStatusEnum = CampaignSharedSetStatusEnum() # For __getattribute__ class CampaignStatusEnum(_CreateEnumTypeUponFirstAccess): CampaignStatusEnum = '''\ class CampaignStatus(enum.IntEnum): """ Possible statuses of a campaign. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Campaign is currently serving ads depending on budget information. PAUSED (int): Campaign has been paused by the user. REMOVED (int): Campaign has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PAUSED = 3 REMOVED = 4 ''' CampaignStatusEnum = CampaignStatusEnum() # For __getattribute__ class ChangeStatusErrorEnum(_CreateEnumTypeUponFirstAccess): ChangeStatusErrorEnum = '''\ class ChangeStatusError(enum.IntEnum): """ Enum describing possible change status errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. START_DATE_TOO_OLD (int): The requested start date is too old. """ UNSPECIFIED = 0 UNKNOWN = 1 START_DATE_TOO_OLD = 3 ''' ChangeStatusErrorEnum = ChangeStatusErrorEnum() # For __getattribute__ class ChangeStatusOperationEnum(_CreateEnumTypeUponFirstAccess): ChangeStatusOperationEnum = '''\ class ChangeStatusOperation(enum.IntEnum): """ Status of the changed resource Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents an unclassified resource unknown in this version. ADDED (int): The resource was created. CHANGED (int): The resource was modified. REMOVED (int): The resource was removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ADDED = 2 CHANGED = 3 REMOVED = 4 ''' ChangeStatusOperationEnum = ChangeStatusOperationEnum() # For __getattribute__ class ChangeStatusResourceTypeEnum(_CreateEnumTypeUponFirstAccess): ChangeStatusResourceTypeEnum = '''\ class ChangeStatusResourceType(enum.IntEnum): """ Enum listing the resource types support by the ChangeStatus resource. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents an unclassified resource unknown in this version. AD_GROUP (int): An AdGroup resource change. AD_GROUP_AD (int): An AdGroupAd resource change. AD_GROUP_CRITERION (int): An AdGroupCriterion resource change. CAMPAIGN (int): A Campaign resource change. CAMPAIGN_CRITERION (int): A CampaignCriterion resource change. FEED (int): A Feed resource change. FEED_ITEM (int): A FeedItem resource change. AD_GROUP_FEED (int): An AdGroupFeed resource change. CAMPAIGN_FEED (int): A CampaignFeed resource change. AD_GROUP_BID_MODIFIER (int): An AdGroupBidModifier resource change. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP = 3 AD_GROUP_AD = 4 AD_GROUP_CRITERION = 5 CAMPAIGN = 6 CAMPAIGN_CRITERION = 7 FEED = 9 FEED_ITEM = 10 AD_GROUP_FEED = 11 CAMPAIGN_FEED = 12 AD_GROUP_BID_MODIFIER = 13 ''' ChangeStatusResourceTypeEnum = ChangeStatusResourceTypeEnum() # For __getattribute__ class ClickTypeEnum(_CreateEnumTypeUponFirstAccess): ClickTypeEnum = '''\ class ClickType(enum.IntEnum): """ Enumerates Google Ads click types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. APP_DEEPLINK (int): App engagement ad deep link. BREADCRUMBS (int): Breadcrumbs. BROADBAND_PLAN (int): Broadband Plan. CALL_TRACKING (int): Manually dialed phone calls. CALLS (int): Phone calls. CLICK_ON_ENGAGEMENT_AD (int): Click on engagement ad. GET_DIRECTIONS (int): Driving direction. LOCATION_EXPANSION (int): Get location details. LOCATION_FORMAT_CALL (int): Call. LOCATION_FORMAT_DIRECTIONS (int): Directions. LOCATION_FORMAT_IMAGE (int): Image(s). LOCATION_FORMAT_LANDING_PAGE (int): Go to landing page. LOCATION_FORMAT_MAP (int): Map. LOCATION_FORMAT_STORE_INFO (int): Go to store info. LOCATION_FORMAT_TEXT (int): Text. MOBILE_CALL_TRACKING (int): Mobile phone calls. OFFER_PRINTS (int): Print offer. OTHER (int): Other. PRODUCT_EXTENSION_CLICKS (int): Product plusbox offer. PRODUCT_LISTING_AD_CLICKS (int): Shopping - Product - Online. SITELINKS (int): Sitelink. STORE_LOCATOR (int): Show nearby locations. URL_CLICKS (int): Headline. VIDEO_APP_STORE_CLICKS (int): App store. VIDEO_CALL_TO_ACTION_CLICKS (int): Call-to-Action overlay. VIDEO_CARD_ACTION_HEADLINE_CLICKS (int): Cards. VIDEO_END_CAP_CLICKS (int): End cap. VIDEO_WEBSITE_CLICKS (int): Website. VISUAL_SITELINKS (int): Visual Sitelinks. WIRELESS_PLAN (int): Wireless Plan. PRODUCT_LISTING_AD_LOCAL (int): Shopping - Product - Local. PRODUCT_LISTING_AD_MULTICHANNEL_LOCAL (int): Shopping - Product - MultiChannel Local. PRODUCT_LISTING_AD_MULTICHANNEL_ONLINE (int): Shopping - Product - MultiChannel Online. PRODUCT_LISTING_ADS_COUPON (int): Shopping - Product - Coupon. PRODUCT_LISTING_AD_TRANSACTABLE (int): Shopping - Product - Sell on Google. PRODUCT_AD_APP_DEEPLINK (int): Shopping - Product - App engagement ad deep link. SHOWCASE_AD_CATEGORY_LINK (int): Shopping - Showcase - Category. SHOWCASE_AD_LOCAL_STOREFRONT_LINK (int): Shopping - Showcase - Local storefront. SHOWCASE_AD_ONLINE_PRODUCT_LINK (int): Shopping - Showcase - Online product. SHOWCASE_AD_LOCAL_PRODUCT_LINK (int): Shopping - Showcase - Local product. PROMOTION_EXTENSION (int): Promotion Extension. SWIPEABLE_GALLERY_AD_HEADLINE (int): Ad Headline. SWIPEABLE_GALLERY_AD_SWIPES (int): Swipes. SWIPEABLE_GALLERY_AD_SEE_MORE (int): See More. SWIPEABLE_GALLERY_AD_SITELINK_ONE (int): Sitelink 1. SWIPEABLE_GALLERY_AD_SITELINK_TWO (int): Sitelink 2. SWIPEABLE_GALLERY_AD_SITELINK_THREE (int): Sitelink 3. SWIPEABLE_GALLERY_AD_SITELINK_FOUR (int): Sitelink 4. SWIPEABLE_GALLERY_AD_SITELINK_FIVE (int): Sitelink 5. HOTEL_PRICE (int): Hotel price. PRICE_EXTENSION (int): Price Extension. HOTEL_BOOK_ON_GOOGLE_ROOM_SELECTION (int): Book on Google hotel room selection. SHOPPING_COMPARISON_LISTING (int): Shopping - Comparison Listing. """ UNSPECIFIED = 0 UNKNOWN = 1 APP_DEEPLINK = 2 BREADCRUMBS = 3 BROADBAND_PLAN = 4 CALL_TRACKING = 5 CALLS = 6 CLICK_ON_ENGAGEMENT_AD = 7 GET_DIRECTIONS = 8 LOCATION_EXPANSION = 9 LOCATION_FORMAT_CALL = 10 LOCATION_FORMAT_DIRECTIONS = 11 LOCATION_FORMAT_IMAGE = 12 LOCATION_FORMAT_LANDING_PAGE = 13 LOCATION_FORMAT_MAP = 14 LOCATION_FORMAT_STORE_INFO = 15 LOCATION_FORMAT_TEXT = 16 MOBILE_CALL_TRACKING = 17 OFFER_PRINTS = 18 OTHER = 19 PRODUCT_EXTENSION_CLICKS = 20 PRODUCT_LISTING_AD_CLICKS = 21 SITELINKS = 22 STORE_LOCATOR = 23 URL_CLICKS = 25 VIDEO_APP_STORE_CLICKS = 26 VIDEO_CALL_TO_ACTION_CLICKS = 27 VIDEO_CARD_ACTION_HEADLINE_CLICKS = 28 VIDEO_END_CAP_CLICKS = 29 VIDEO_WEBSITE_CLICKS = 30 VISUAL_SITELINKS = 31 WIRELESS_PLAN = 32 PRODUCT_LISTING_AD_LOCAL = 33 PRODUCT_LISTING_AD_MULTICHANNEL_LOCAL = 34 PRODUCT_LISTING_AD_MULTICHANNEL_ONLINE = 35 PRODUCT_LISTING_ADS_COUPON = 36 PRODUCT_LISTING_AD_TRANSACTABLE = 37 PRODUCT_AD_APP_DEEPLINK = 38 SHOWCASE_AD_CATEGORY_LINK = 39 SHOWCASE_AD_LOCAL_STOREFRONT_LINK = 40 SHOWCASE_AD_ONLINE_PRODUCT_LINK = 42 SHOWCASE_AD_LOCAL_PRODUCT_LINK = 43 PROMOTION_EXTENSION = 44 SWIPEABLE_GALLERY_AD_HEADLINE = 45 SWIPEABLE_GALLERY_AD_SWIPES = 46 SWIPEABLE_GALLERY_AD_SEE_MORE = 47 SWIPEABLE_GALLERY_AD_SITELINK_ONE = 48 SWIPEABLE_GALLERY_AD_SITELINK_TWO = 49 SWIPEABLE_GALLERY_AD_SITELINK_THREE = 50 SWIPEABLE_GALLERY_AD_SITELINK_FOUR = 51 SWIPEABLE_GALLERY_AD_SITELINK_FIVE = 52 HOTEL_PRICE = 53 PRICE_EXTENSION = 54 HOTEL_BOOK_ON_GOOGLE_ROOM_SELECTION = 55 SHOPPING_COMPARISON_LISTING = 56 ''' ClickTypeEnum = ClickTypeEnum() # For __getattribute__ class CollectionSizeErrorEnum(_CreateEnumTypeUponFirstAccess): CollectionSizeErrorEnum = '''\ class CollectionSizeError(enum.IntEnum): """ Enum describing possible collection size errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. TOO_FEW (int): Too few. TOO_MANY (int): Too many. """ UNSPECIFIED = 0 UNKNOWN = 1 TOO_FEW = 2 TOO_MANY = 3 ''' CollectionSizeErrorEnum = CollectionSizeErrorEnum() # For __getattribute__ class ContentLabelTypeEnum(_CreateEnumTypeUponFirstAccess): ContentLabelTypeEnum = '''\ class ContentLabelType(enum.IntEnum): """ Enum listing the content label types supported by ContentLabel criterion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SEXUALLY_SUGGESTIVE (int): Sexually suggestive content. BELOW_THE_FOLD (int): Below the fold placement. PARKED_DOMAIN (int): Parked domain. JUVENILE (int): Juvenile, gross & bizarre content. PROFANITY (int): Profanity & rough language. TRAGEDY (int): Death & tragedy. VIDEO (int): Video. VIDEO_RATING_DV_G (int): Content rating: G. VIDEO_RATING_DV_PG (int): Content rating: PG. VIDEO_RATING_DV_T (int): Content rating: T. VIDEO_RATING_DV_MA (int): Content rating: MA. VIDEO_NOT_YET_RATED (int): Content rating: not yet rated. EMBEDDED_VIDEO (int): Embedded video. LIVE_STREAMING_VIDEO (int): Live streaming video. SOCIAL_ISSUES (int): Sensitive social issues. """ UNSPECIFIED = 0 UNKNOWN = 1 SEXUALLY_SUGGESTIVE = 2 BELOW_THE_FOLD = 3 PARKED_DOMAIN = 4 JUVENILE = 6 PROFANITY = 7 TRAGEDY = 8 VIDEO = 9 VIDEO_RATING_DV_G = 10 VIDEO_RATING_DV_PG = 11 VIDEO_RATING_DV_T = 12 VIDEO_RATING_DV_MA = 13 VIDEO_NOT_YET_RATED = 14 EMBEDDED_VIDEO = 15 LIVE_STREAMING_VIDEO = 16 SOCIAL_ISSUES = 17 ''' ContentLabelTypeEnum = ContentLabelTypeEnum() # For __getattribute__ class ContextErrorEnum(_CreateEnumTypeUponFirstAccess): ContextErrorEnum = '''\ class ContextError(enum.IntEnum): """ Enum describing possible context errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. OPERATION_NOT_PERMITTED_FOR_CONTEXT (int): The operation is not allowed for the given context. OPERATION_NOT_PERMITTED_FOR_REMOVED_RESOURCE (int): The operation is not allowed for removed resources. """ UNSPECIFIED = 0 UNKNOWN = 1 OPERATION_NOT_PERMITTED_FOR_CONTEXT = 2 OPERATION_NOT_PERMITTED_FOR_REMOVED_RESOURCE = 3 ''' ContextErrorEnum = ContextErrorEnum() # For __getattribute__ class ConversionActionCategoryEnum(_CreateEnumTypeUponFirstAccess): ConversionActionCategoryEnum = '''\ class ConversionActionCategory(enum.IntEnum): """ The category of conversions that are associated with a ConversionAction. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DEFAULT (int): Default category. PAGE_VIEW (int): User visiting a page. PURCHASE (int): Purchase, sales, or "order placed" event. SIGNUP (int): Signup user action. LEAD (int): Lead-generating action. DOWNLOAD (int): Software download action (as for an app). """ UNSPECIFIED = 0 UNKNOWN = 1 DEFAULT = 2 PAGE_VIEW = 3 PURCHASE = 4 SIGNUP = 5 LEAD = 6 DOWNLOAD = 7 ''' ConversionActionCategoryEnum = ConversionActionCategoryEnum() # For __getattribute__ class ConversionActionCountingTypeEnum(_CreateEnumTypeUponFirstAccess): ConversionActionCountingTypeEnum = '''\ class ConversionActionCountingType(enum.IntEnum): """ Indicates how conversions for this action will be counted. For more information, see https://support.google.com/google-ads/answer/3438531. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ONE_PER_CLICK (int): Count only one conversion per click. MANY_PER_CLICK (int): Count all conversions per click. """ UNSPECIFIED = 0 UNKNOWN = 1 ONE_PER_CLICK = 2 MANY_PER_CLICK = 3 ''' ConversionActionCountingTypeEnum = ConversionActionCountingTypeEnum() # For __getattribute__ class ConversionActionErrorEnum(_CreateEnumTypeUponFirstAccess): ConversionActionErrorEnum = '''\ class ConversionActionError(enum.IntEnum): """ Enum describing possible conversion action errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_NAME (int): The specified conversion action name already exists. DUPLICATE_APP_ID (int): Another conversion action with the specified app id already exists. TWO_CONVERSION_ACTIONS_BIDDING_ON_SAME_APP_DOWNLOAD (int): Android first open action conflicts with Google play codeless download action tracking the same app. BIDDING_ON_SAME_APP_DOWNLOAD_AS_GLOBAL_ACTION (int): Android first open action conflicts with Google play codeless download action tracking the same app. DATA_DRIVEN_MODEL_WAS_NEVER_GENERATED (int): The attribution model cannot be set to DATA\_DRIVEN because a data-driven model has never been generated. DATA_DRIVEN_MODEL_EXPIRED (int): The attribution model cannot be set to DATA\_DRIVEN because the data-driven model is expired. DATA_DRIVEN_MODEL_STALE (int): The attribution model cannot be set to DATA\_DRIVEN because the data-driven model is stale. DATA_DRIVEN_MODEL_UNKNOWN (int): The attribution model cannot be set to DATA\_DRIVEN because the data-driven model is unavailable or the conversion action was newly added. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_NAME = 2 DUPLICATE_APP_ID = 3 TWO_CONVERSION_ACTIONS_BIDDING_ON_SAME_APP_DOWNLOAD = 4 BIDDING_ON_SAME_APP_DOWNLOAD_AS_GLOBAL_ACTION = 5 DATA_DRIVEN_MODEL_WAS_NEVER_GENERATED = 6 DATA_DRIVEN_MODEL_EXPIRED = 7 DATA_DRIVEN_MODEL_STALE = 8 DATA_DRIVEN_MODEL_UNKNOWN = 9 ''' ConversionActionErrorEnum = ConversionActionErrorEnum() # For __getattribute__ class ConversionActionStatusEnum(_CreateEnumTypeUponFirstAccess): ConversionActionStatusEnum = '''\ class ConversionActionStatus(enum.IntEnum): """ Possible statuses of a conversion action. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Conversions will be recorded. REMOVED (int): Conversions will not be recorded. HIDDEN (int): Conversions will not be recorded and the conversion action will not appear in the UI. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 HIDDEN = 4 ''' ConversionActionStatusEnum = ConversionActionStatusEnum() # For __getattribute__ class ConversionActionTypeEnum(_CreateEnumTypeUponFirstAccess): ConversionActionTypeEnum = '''\ class ConversionActionType(enum.IntEnum): """ Possible types of a conversion action. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AD_CALL (int): Conversions that occur when a user clicks on an ad's call extension. CLICK_TO_CALL (int): Conversions that occur when a user on a mobile device clicks a phone number. GOOGLE_PLAY_DOWNLOAD (int): Conversions that occur when a user downloads a mobile app from the Google Play Store. GOOGLE_PLAY_IN_APP_PURCHASE (int): Conversions that occur when a user makes a purchase in an app through Android billing. UPLOAD_CALLS (int): Call conversions that are tracked by the advertiser and uploaded. UPLOAD_CLICKS (int): Conversions that are tracked by the advertiser and uploaded with attributed clicks. WEBPAGE (int): Conversions that occur on a webpage. WEBSITE_CALL (int): Conversions that occur when a user calls a dynamically-generated phone number from an advertiser's website. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_CALL = 2 CLICK_TO_CALL = 3 GOOGLE_PLAY_DOWNLOAD = 4 GOOGLE_PLAY_IN_APP_PURCHASE = 5 UPLOAD_CALLS = 6 UPLOAD_CLICKS = 7 WEBPAGE = 8 WEBSITE_CALL = 9 ''' ConversionActionTypeEnum = ConversionActionTypeEnum() # For __getattribute__ class ConversionAdjustmentTypeEnum(_CreateEnumTypeUponFirstAccess): ConversionAdjustmentTypeEnum = '''\ class ConversionAdjustmentType(enum.IntEnum): """ The different actions advertisers can take to adjust the conversions that they already reported. Retractions negate a conversion. Restatements change the value of a conversion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Represents value unknown in this version. RETRACTION (int): Negates a conversion so that its total value and count are both zero. RESTATEMENT (int): Changes the value of a conversion. """ UNSPECIFIED = 0 UNKNOWN = 1 RETRACTION = 2 RESTATEMENT = 3 ''' ConversionAdjustmentTypeEnum = ConversionAdjustmentTypeEnum() # For __getattribute__ class ConversionAdjustmentUploadErrorEnum(_CreateEnumTypeUponFirstAccess): ConversionAdjustmentUploadErrorEnum = '''\ class ConversionAdjustmentUploadError(enum.IntEnum): """ Enum describing possible conversion adjustment upload errors. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The received error code is not known in this version. TOO_RECENT_CONVERSION_ACTION (int): The specified conversion action was created too recently. Please try the upload again after 4-6 hours have passed since the conversion action was created. INVALID_CONVERSION_ACTION (int): No conversion action of a supported ConversionActionType that matches the provided information can be found for the customer. CONVERSION_ALREADY_RETRACTED (int): A retraction was already reported for this conversion. CONVERSION_NOT_FOUND (int): A conversion for the supplied combination of conversion action and conversion identifier could not be found. CONVERSION_EXPIRED (int): The specified conversion has already expired. Conversions expire after 55 days, after which adjustments cannot be reported against them. ADJUSTMENT_PRECEDES_CONVERSION (int): The supplied adjustment date time precedes that of the original conversion. MORE_RECENT_RESTATEMENT_FOUND (int): A restatement with a more recent adjustment date time was already reported for this conversion. TOO_RECENT_CONVERSION (int): The conversion was created too recently. CANNOT_RESTATE_CONVERSION_ACTION_THAT_ALWAYS_USES_DEFAULT_CONVERSION_VALUE (int): Restatements cannot be reported for a conversion action that always uses the default value. TOO_MANY_ADJUSTMENTS_IN_REQUEST (int): The request contained more than 2000 adjustments. TOO_MANY_ADJUSTMENTS (int): The conversion has been adjusted too many times. """ UNSPECIFIED = 0 UNKNOWN = 1 TOO_RECENT_CONVERSION_ACTION = 2 INVALID_CONVERSION_ACTION = 3 CONVERSION_ALREADY_RETRACTED = 4 CONVERSION_NOT_FOUND = 5 CONVERSION_EXPIRED = 6 ADJUSTMENT_PRECEDES_CONVERSION = 7 MORE_RECENT_RESTATEMENT_FOUND = 8 TOO_RECENT_CONVERSION = 9 CANNOT_RESTATE_CONVERSION_ACTION_THAT_ALWAYS_USES_DEFAULT_CONVERSION_VALUE = 10 TOO_MANY_ADJUSTMENTS_IN_REQUEST = 11 TOO_MANY_ADJUSTMENTS = 12 ''' ConversionAdjustmentUploadErrorEnum = ConversionAdjustmentUploadErrorEnum() # For __getattribute__ class ConversionAttributionEventTypeEnum(_CreateEnumTypeUponFirstAccess): ConversionAttributionEventTypeEnum = '''\ class ConversionAttributionEventType(enum.IntEnum): """ The event type of conversions that are attributed to. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Represents value unknown in this version. IMPRESSION (int): The conversion is attributed to an impression. INTERACTION (int): The conversion is attributed to an interaction. """ UNSPECIFIED = 0 UNKNOWN = 1 IMPRESSION = 2 INTERACTION = 3 ''' ConversionAttributionEventTypeEnum = ConversionAttributionEventTypeEnum() # For __getattribute__ class ConversionLagBucketEnum(_CreateEnumTypeUponFirstAccess): ConversionLagBucketEnum = '''\ class ConversionLagBucket(enum.IntEnum): """ Enum representing the number of days between impression and conversion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LESS_THAN_ONE_DAY (int): Conversion lag bucket from 0 to 1 day. 0 day is included, 1 day is not. ONE_TO_TWO_DAYS (int): Conversion lag bucket from 1 to 2 days. 1 day is included, 2 days is not. TWO_TO_THREE_DAYS (int): Conversion lag bucket from 2 to 3 days. 2 days is included, 3 days is not. THREE_TO_FOUR_DAYS (int): Conversion lag bucket from 3 to 4 days. 3 days is included, 4 days is not. FOUR_TO_FIVE_DAYS (int): Conversion lag bucket from 4 to 5 days. 4 days is included, 5 days is not. FIVE_TO_SIX_DAYS (int): Conversion lag bucket from 5 to 6 days. 5 days is included, 6 days is not. SIX_TO_SEVEN_DAYS (int): Conversion lag bucket from 6 to 7 days. 6 days is included, 7 days is not. SEVEN_TO_EIGHT_DAYS (int): Conversion lag bucket from 7 to 8 days. 7 days is included, 8 days is not. EIGHT_TO_NINE_DAYS (int): Conversion lag bucket from 8 to 9 days. 8 days is included, 9 days is not. NINE_TO_TEN_DAYS (int): Conversion lag bucket from 9 to 10 days. 9 days is included, 10 days is not. TEN_TO_ELEVEN_DAYS (int): Conversion lag bucket from 10 to 11 days. 10 days is included, 11 days is not. ELEVEN_TO_TWELVE_DAYS (int): Conversion lag bucket from 11 to 12 days. 11 days is included, 12 days is not. TWELVE_TO_THIRTEEN_DAYS (int): Conversion lag bucket from 12 to 13 days. 12 days is included, 13 days is not. THIRTEEN_TO_FOURTEEN_DAYS (int): Conversion lag bucket from 13 to 14 days. 13 days is included, 14 days is not. FOURTEEN_TO_TWENTY_ONE_DAYS (int): Conversion lag bucket from 14 to 21 days. 14 days is included, 21 days is not. TWENTY_ONE_TO_THIRTY_DAYS (int): Conversion lag bucket from 21 to 30 days. 21 days is included, 30 days is not. THIRTY_TO_FORTY_FIVE_DAYS (int): Conversion lag bucket from 30 to 45 days. 30 days is included, 45 days is not. FORTY_FIVE_TO_SIXTY_DAYS (int): Conversion lag bucket from 45 to 60 days. 45 days is included, 60 days is not. SIXTY_TO_NINETY_DAYS (int): Conversion lag bucket from 60 to 90 days. 60 days is included, 90 days is not. """ UNSPECIFIED = 0 UNKNOWN = 1 LESS_THAN_ONE_DAY = 2 ONE_TO_TWO_DAYS = 3 TWO_TO_THREE_DAYS = 4 THREE_TO_FOUR_DAYS = 5 FOUR_TO_FIVE_DAYS = 6 FIVE_TO_SIX_DAYS = 7 SIX_TO_SEVEN_DAYS = 8 SEVEN_TO_EIGHT_DAYS = 9 EIGHT_TO_NINE_DAYS = 10 NINE_TO_TEN_DAYS = 11 TEN_TO_ELEVEN_DAYS = 12 ELEVEN_TO_TWELVE_DAYS = 13 TWELVE_TO_THIRTEEN_DAYS = 14 THIRTEEN_TO_FOURTEEN_DAYS = 15 FOURTEEN_TO_TWENTY_ONE_DAYS = 16 TWENTY_ONE_TO_THIRTY_DAYS = 17 THIRTY_TO_FORTY_FIVE_DAYS = 18 FORTY_FIVE_TO_SIXTY_DAYS = 19 SIXTY_TO_NINETY_DAYS = 20 ''' ConversionLagBucketEnum = ConversionLagBucketEnum() # For __getattribute__ class ConversionOrAdjustmentLagBucketEnum(_CreateEnumTypeUponFirstAccess): ConversionOrAdjustmentLagBucketEnum = '''\ class ConversionOrAdjustmentLagBucket(enum.IntEnum): """ Enum representing the number of days between the impression and the conversion or between the impression and adjustments to the conversion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CONVERSION_LESS_THAN_ONE_DAY (int): Conversion lag bucket from 0 to 1 day. 0 day is included, 1 day is not. CONVERSION_ONE_TO_TWO_DAYS (int): Conversion lag bucket from 1 to 2 days. 1 day is included, 2 days is not. CONVERSION_TWO_TO_THREE_DAYS (int): Conversion lag bucket from 2 to 3 days. 2 days is included, 3 days is not. CONVERSION_THREE_TO_FOUR_DAYS (int): Conversion lag bucket from 3 to 4 days. 3 days is included, 4 days is not. CONVERSION_FOUR_TO_FIVE_DAYS (int): Conversion lag bucket from 4 to 5 days. 4 days is included, 5 days is not. CONVERSION_FIVE_TO_SIX_DAYS (int): Conversion lag bucket from 5 to 6 days. 5 days is included, 6 days is not. CONVERSION_SIX_TO_SEVEN_DAYS (int): Conversion lag bucket from 6 to 7 days. 6 days is included, 7 days is not. CONVERSION_SEVEN_TO_EIGHT_DAYS (int): Conversion lag bucket from 7 to 8 days. 7 days is included, 8 days is not. CONVERSION_EIGHT_TO_NINE_DAYS (int): Conversion lag bucket from 8 to 9 days. 8 days is included, 9 days is not. CONVERSION_NINE_TO_TEN_DAYS (int): Conversion lag bucket from 9 to 10 days. 9 days is included, 10 days is not. CONVERSION_TEN_TO_ELEVEN_DAYS (int): Conversion lag bucket from 10 to 11 days. 10 days is included, 11 days is not. CONVERSION_ELEVEN_TO_TWELVE_DAYS (int): Conversion lag bucket from 11 to 12 days. 11 days is included, 12 days is not. CONVERSION_TWELVE_TO_THIRTEEN_DAYS (int): Conversion lag bucket from 12 to 13 days. 12 days is included, 13 days is not. CONVERSION_THIRTEEN_TO_FOURTEEN_DAYS (int): Conversion lag bucket from 13 to 14 days. 13 days is included, 14 days is not. CONVERSION_FOURTEEN_TO_TWENTY_ONE_DAYS (int): Conversion lag bucket from 14 to 21 days. 14 days is included, 21 days is not. CONVERSION_TWENTY_ONE_TO_THIRTY_DAYS (int): Conversion lag bucket from 21 to 30 days. 21 days is included, 30 days is not. CONVERSION_THIRTY_TO_FORTY_FIVE_DAYS (int): Conversion lag bucket from 30 to 45 days. 30 days is included, 45 days is not. CONVERSION_FORTY_FIVE_TO_SIXTY_DAYS (int): Conversion lag bucket from 45 to 60 days. 45 days is included, 60 days is not. CONVERSION_SIXTY_TO_NINETY_DAYS (int): Conversion lag bucket from 60 to 90 days. 60 days is included, 90 days is not. ADJUSTMENT_LESS_THAN_ONE_DAY (int): Conversion adjustment lag bucket from 0 to 1 day. 0 day is included, 1 day is not. ADJUSTMENT_ONE_TO_TWO_DAYS (int): Conversion adjustment lag bucket from 1 to 2 days. 1 day is included, 2 days is not. ADJUSTMENT_TWO_TO_THREE_DAYS (int): Conversion adjustment lag bucket from 2 to 3 days. 2 days is included, 3 days is not. ADJUSTMENT_THREE_TO_FOUR_DAYS (int): Conversion adjustment lag bucket from 3 to 4 days. 3 days is included, 4 days is not. ADJUSTMENT_FOUR_TO_FIVE_DAYS (int): Conversion adjustment lag bucket from 4 to 5 days. 4 days is included, 5 days is not. ADJUSTMENT_FIVE_TO_SIX_DAYS (int): Conversion adjustment lag bucket from 5 to 6 days. 5 days is included, 6 days is not. ADJUSTMENT_SIX_TO_SEVEN_DAYS (int): Conversion adjustment lag bucket from 6 to 7 days. 6 days is included, 7 days is not. ADJUSTMENT_SEVEN_TO_EIGHT_DAYS (int): Conversion adjustment lag bucket from 7 to 8 days. 7 days is included, 8 days is not. ADJUSTMENT_EIGHT_TO_NINE_DAYS (int): Conversion adjustment lag bucket from 8 to 9 days. 8 days is included, 9 days is not. ADJUSTMENT_NINE_TO_TEN_DAYS (int): Conversion adjustment lag bucket from 9 to 10 days. 9 days is included, 10 days is not. ADJUSTMENT_TEN_TO_ELEVEN_DAYS (int): Conversion adjustment lag bucket from 10 to 11 days. 10 days is included, 11 days is not. ADJUSTMENT_ELEVEN_TO_TWELVE_DAYS (int): Conversion adjustment lag bucket from 11 to 12 days. 11 days is included, 12 days is not. ADJUSTMENT_TWELVE_TO_THIRTEEN_DAYS (int): Conversion adjustment lag bucket from 12 to 13 days. 12 days is included, 13 days is not. ADJUSTMENT_THIRTEEN_TO_FOURTEEN_DAYS (int): Conversion adjustment lag bucket from 13 to 14 days. 13 days is included, 14 days is not. ADJUSTMENT_FOURTEEN_TO_TWENTY_ONE_DAYS (int): Conversion adjustment lag bucket from 14 to 21 days. 14 days is included, 21 days is not. ADJUSTMENT_TWENTY_ONE_TO_THIRTY_DAYS (int): Conversion adjustment lag bucket from 21 to 30 days. 21 days is included, 30 days is not. ADJUSTMENT_THIRTY_TO_FORTY_FIVE_DAYS (int): Conversion adjustment lag bucket from 30 to 45 days. 30 days is included, 45 days is not. ADJUSTMENT_FORTY_FIVE_TO_SIXTY_DAYS (int): Conversion adjustment lag bucket from 45 to 60 days. 45 days is included, 60 days is not. ADJUSTMENT_SIXTY_TO_NINETY_DAYS (int): Conversion adjustment lag bucket from 60 to 90 days. 60 days is included, 90 days is not. ADJUSTMENT_NINETY_TO_ONE_HUNDRED_AND_FORTY_FIVE_DAYS (int): Conversion adjustment lag bucket from 90 to 145 days. 90 days is included, 145 days is not. CONVERSION_UNKNOWN (int): Conversion lag bucket UNKNOWN. This is for dates before conversion lag bucket was available in Google Ads. ADJUSTMENT_UNKNOWN (int): Conversion adjustment lag bucket UNKNOWN. This is for dates before conversion adjustment lag bucket was available in Google Ads. """ UNSPECIFIED = 0 UNKNOWN = 1 CONVERSION_LESS_THAN_ONE_DAY = 2 CONVERSION_ONE_TO_TWO_DAYS = 3 CONVERSION_TWO_TO_THREE_DAYS = 4 CONVERSION_THREE_TO_FOUR_DAYS = 5 CONVERSION_FOUR_TO_FIVE_DAYS = 6 CONVERSION_FIVE_TO_SIX_DAYS = 7 CONVERSION_SIX_TO_SEVEN_DAYS = 8 CONVERSION_SEVEN_TO_EIGHT_DAYS = 9 CONVERSION_EIGHT_TO_NINE_DAYS = 10 CONVERSION_NINE_TO_TEN_DAYS = 11 CONVERSION_TEN_TO_ELEVEN_DAYS = 12 CONVERSION_ELEVEN_TO_TWELVE_DAYS = 13 CONVERSION_TWELVE_TO_THIRTEEN_DAYS = 14 CONVERSION_THIRTEEN_TO_FOURTEEN_DAYS = 15 CONVERSION_FOURTEEN_TO_TWENTY_ONE_DAYS = 16 CONVERSION_TWENTY_ONE_TO_THIRTY_DAYS = 17 CONVERSION_THIRTY_TO_FORTY_FIVE_DAYS = 18 CONVERSION_FORTY_FIVE_TO_SIXTY_DAYS = 19 CONVERSION_SIXTY_TO_NINETY_DAYS = 20 ADJUSTMENT_LESS_THAN_ONE_DAY = 21 ADJUSTMENT_ONE_TO_TWO_DAYS = 22 ADJUSTMENT_TWO_TO_THREE_DAYS = 23 ADJUSTMENT_THREE_TO_FOUR_DAYS = 24 ADJUSTMENT_FOUR_TO_FIVE_DAYS = 25 ADJUSTMENT_FIVE_TO_SIX_DAYS = 26 ADJUSTMENT_SIX_TO_SEVEN_DAYS = 27 ADJUSTMENT_SEVEN_TO_EIGHT_DAYS = 28 ADJUSTMENT_EIGHT_TO_NINE_DAYS = 29 ADJUSTMENT_NINE_TO_TEN_DAYS = 30 ADJUSTMENT_TEN_TO_ELEVEN_DAYS = 31 ADJUSTMENT_ELEVEN_TO_TWELVE_DAYS = 32 ADJUSTMENT_TWELVE_TO_THIRTEEN_DAYS = 33 ADJUSTMENT_THIRTEEN_TO_FOURTEEN_DAYS = 34 ADJUSTMENT_FOURTEEN_TO_TWENTY_ONE_DAYS = 35 ADJUSTMENT_TWENTY_ONE_TO_THIRTY_DAYS = 36 ADJUSTMENT_THIRTY_TO_FORTY_FIVE_DAYS = 37 ADJUSTMENT_FORTY_FIVE_TO_SIXTY_DAYS = 38 ADJUSTMENT_SIXTY_TO_NINETY_DAYS = 39 ADJUSTMENT_NINETY_TO_ONE_HUNDRED_AND_FORTY_FIVE_DAYS = 40 CONVERSION_UNKNOWN = 41 ADJUSTMENT_UNKNOWN = 42 ''' ConversionOrAdjustmentLagBucketEnum = ConversionOrAdjustmentLagBucketEnum() # For __getattribute__ class ConversionUploadErrorEnum(_CreateEnumTypeUponFirstAccess): ConversionUploadErrorEnum = '''\ class ConversionUploadError(enum.IntEnum): """ Enum describing possible conversion upload errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. TOO_MANY_CONVERSIONS_IN_REQUEST (int): The request contained more than 2000 conversions. UNPARSEABLE_GCLID (int): The specified gclid could not be decoded. CONVERSION_PRECEDES_GCLID (int): The specified conversion\_date\_time is before the event time associated with the given gclid. EXPIRED_GCLID (int): The click associated with the given gclid is either too old to be imported or occurred outside of the click through lookback window for the specified conversion action. TOO_RECENT_GCLID (int): The click associated with the given gclid occurred too recently. Please try uploading again after 6 hours have passed since the click occurred. GCLID_NOT_FOUND (int): The click associated with the given gclid could not be found in the system. This can happen if Google Click IDs are collected for non Google Ads clicks. UNAUTHORIZED_CUSTOMER (int): The click associated with the given gclid is owned by a customer account that the uploading customer does not manage. INVALID_CONVERSION_ACTION (int): No upload eligible conversion action that matches the provided information can be found for the customer. TOO_RECENT_CONVERSION_ACTION (int): The specified conversion action was created too recently. Please try the upload again after 4-6 hours have passed since the conversion action was created. CONVERSION_TRACKING_NOT_ENABLED_AT_IMPRESSION_TIME (int): The click associated with the given gclid does not contain conversion tracking information. EXTERNAL_ATTRIBUTION_DATA_SET_FOR_NON_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION (int): The specified conversion action does not use an external attribution model, but external\_attribution\_data was set. EXTERNAL_ATTRIBUTION_DATA_NOT_SET_FOR_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION (int): The specified conversion action uses an external attribution model, but external\_attribution\_data or one of its contained fields was not set. Both external\_attribution\_credit and external\_attribution\_model must be set for externally attributed conversion actions. ORDER_ID_NOT_PERMITTED_FOR_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION (int): Order IDs are not supported for conversion actions which use an external attribution model. ORDER_ID_ALREADY_IN_USE (int): A conversion with the same order id and conversion action combination already exists in our system. DUPLICATE_ORDER_ID (int): The request contained two or more conversions with the same order id and conversion action combination. TOO_RECENT_CALL (int): The call occurred too recently. Please try uploading again after 6 hours have passed since the call occurred. EXPIRED_CALL (int): The click that initiated the call is too old for this conversion to be imported. CALL_NOT_FOUND (int): The call or the click leading to the call was not found. CONVERSION_PRECEDES_CALL (int): The specified conversion\_date\_time is before the call\_start\_date\_time. CONVERSION_TRACKING_NOT_ENABLED_AT_CALL_TIME (int): The click associated with the call does not contain conversion tracking information. UNPARSEABLE_CALLERS_PHONE_NUMBER (int): The caller’s phone number cannot be parsed. It should be formatted either as E.164 "+16502531234", International "+64 3-331 6005" or US national number "6502531234". """ UNSPECIFIED = 0 UNKNOWN = 1 TOO_MANY_CONVERSIONS_IN_REQUEST = 2 UNPARSEABLE_GCLID = 3 CONVERSION_PRECEDES_GCLID = 4 EXPIRED_GCLID = 5 TOO_RECENT_GCLID = 6 GCLID_NOT_FOUND = 7 UNAUTHORIZED_CUSTOMER = 8 INVALID_CONVERSION_ACTION = 9 TOO_RECENT_CONVERSION_ACTION = 10 CONVERSION_TRACKING_NOT_ENABLED_AT_IMPRESSION_TIME = 11 EXTERNAL_ATTRIBUTION_DATA_SET_FOR_NON_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION = 12 EXTERNAL_ATTRIBUTION_DATA_NOT_SET_FOR_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION = 13 ORDER_ID_NOT_PERMITTED_FOR_EXTERNALLY_ATTRIBUTED_CONVERSION_ACTION = 14 ORDER_ID_ALREADY_IN_USE = 15 DUPLICATE_ORDER_ID = 16 TOO_RECENT_CALL = 17 EXPIRED_CALL = 18 CALL_NOT_FOUND = 19 CONVERSION_PRECEDES_CALL = 20 CONVERSION_TRACKING_NOT_ENABLED_AT_CALL_TIME = 21 UNPARSEABLE_CALLERS_PHONE_NUMBER = 22 ''' ConversionUploadErrorEnum = ConversionUploadErrorEnum() # For __getattribute__ class CountryCodeErrorEnum(_CreateEnumTypeUponFirstAccess): CountryCodeErrorEnum = '''\ class CountryCodeError(enum.IntEnum): """ Enum describing country code errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_COUNTRY_CODE (int): The country code is invalid. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_COUNTRY_CODE = 2 ''' CountryCodeErrorEnum = CountryCodeErrorEnum() # For __getattribute__ class CriterionCategoryChannelAvailabilityModeEnum(_CreateEnumTypeUponFirstAccess): CriterionCategoryChannelAvailabilityModeEnum = '''\ class CriterionCategoryChannelAvailabilityMode(enum.IntEnum): """ Enum containing the possible CriterionCategoryChannelAvailabilityMode. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ALL_CHANNELS (int): The category is available to campaigns of all channel types and subtypes. CHANNEL_TYPE_AND_ALL_SUBTYPES (int): The category is available to campaigns of a specific channel type, including all subtypes under it. CHANNEL_TYPE_AND_SUBSET_SUBTYPES (int): The category is available to campaigns of a specific channel type and subtype(s). """ UNSPECIFIED = 0 UNKNOWN = 1 ALL_CHANNELS = 2 CHANNEL_TYPE_AND_ALL_SUBTYPES = 3 CHANNEL_TYPE_AND_SUBSET_SUBTYPES = 4 ''' CriterionCategoryChannelAvailabilityModeEnum = CriterionCategoryChannelAvailabilityModeEnum() # For __getattribute__ class CriterionCategoryLocaleAvailabilityModeEnum(_CreateEnumTypeUponFirstAccess): CriterionCategoryLocaleAvailabilityModeEnum = '''\ class CriterionCategoryLocaleAvailabilityMode(enum.IntEnum): """ Enum containing the possible CriterionCategoryLocaleAvailabilityMode. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ALL_LOCALES (int): The category is available to campaigns of all locales. COUNTRY_AND_ALL_LANGUAGES (int): The category is available to campaigns within a list of countries, regardless of language. LANGUAGE_AND_ALL_COUNTRIES (int): The category is available to campaigns within a list of languages, regardless of country. COUNTRY_AND_LANGUAGE (int): The category is available to campaigns within a list of country, language pairs. """ UNSPECIFIED = 0 UNKNOWN = 1 ALL_LOCALES = 2 COUNTRY_AND_ALL_LANGUAGES = 3 LANGUAGE_AND_ALL_COUNTRIES = 4 COUNTRY_AND_LANGUAGE = 5 ''' CriterionCategoryLocaleAvailabilityModeEnum = CriterionCategoryLocaleAvailabilityModeEnum() # For __getattribute__ class CriterionErrorEnum(_CreateEnumTypeUponFirstAccess): CriterionErrorEnum = '''\ class CriterionError(enum.IntEnum): """ Enum describing possible criterion errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CONCRETE_TYPE_REQUIRED (int): Concrete type of criterion is required for CREATE and UPDATE operations. INVALID_EXCLUDED_CATEGORY (int): The category requested for exclusion is invalid. INVALID_KEYWORD_TEXT (int): Invalid keyword criteria text. KEYWORD_TEXT_TOO_LONG (int): Keyword text should be less than 80 chars. KEYWORD_HAS_TOO_MANY_WORDS (int): Keyword text has too many words. KEYWORD_HAS_INVALID_CHARS (int): Keyword text has invalid characters or symbols. INVALID_PLACEMENT_URL (int): Invalid placement URL. INVALID_USER_LIST (int): Invalid user list criterion. INVALID_USER_INTEREST (int): Invalid user interest criterion. INVALID_FORMAT_FOR_PLACEMENT_URL (int): Placement URL has wrong format. PLACEMENT_URL_IS_TOO_LONG (int): Placement URL is too long. PLACEMENT_URL_HAS_ILLEGAL_CHAR (int): Indicates the URL contains an illegal character. PLACEMENT_URL_HAS_MULTIPLE_SITES_IN_LINE (int): Indicates the URL contains multiple comma separated URLs. PLACEMENT_IS_NOT_AVAILABLE_FOR_TARGETING_OR_EXCLUSION (int): Indicates the domain is blacklisted. INVALID_TOPIC_PATH (int): Invalid topic path. INVALID_YOUTUBE_CHANNEL_ID (int): The YouTube Channel Id is invalid. INVALID_YOUTUBE_VIDEO_ID (int): The YouTube Video Id is invalid. YOUTUBE_VERTICAL_CHANNEL_DEPRECATED (int): Indicates the placement is a YouTube vertical channel, which is no longer supported. YOUTUBE_DEMOGRAPHIC_CHANNEL_DEPRECATED (int): Indicates the placement is a YouTube demographic channel, which is no longer supported. YOUTUBE_URL_UNSUPPORTED (int): YouTube urls are not supported in Placement criterion. Use YouTubeChannel and YouTubeVideo criterion instead. CANNOT_EXCLUDE_CRITERIA_TYPE (int): Criteria type can not be excluded by the customer, like AOL account type cannot target site type criteria. CANNOT_ADD_CRITERIA_TYPE (int): Criteria type can not be targeted. INVALID_PRODUCT_FILTER (int): Product filter in the product criteria has invalid characters. Operand and the argument in the filter can not have "==" or "&+". PRODUCT_FILTER_TOO_LONG (int): Product filter in the product criteria is translated to a string as operand1==argument1&+operand2==argument2, maximum allowed length for the string is 255 chars. CANNOT_EXCLUDE_SIMILAR_USER_LIST (int): Not allowed to exclude similar user list. CANNOT_ADD_CLOSED_USER_LIST (int): Not allowed to target a closed user list. CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SEARCH_ONLY_CAMPAIGNS (int): Not allowed to add display only UserLists to search only campaigns. CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SEARCH_CAMPAIGNS (int): Not allowed to add display only UserLists to search plus campaigns. CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SHOPPING_CAMPAIGNS (int): Not allowed to add display only UserLists to shopping campaigns. CANNOT_ADD_USER_INTERESTS_TO_SEARCH_CAMPAIGNS (int): Not allowed to add User interests to search only campaigns. CANNOT_SET_BIDS_ON_CRITERION_TYPE_IN_SEARCH_CAMPAIGNS (int): Not allowed to set bids for this criterion type in search campaigns CANNOT_ADD_URLS_TO_CRITERION_TYPE_FOR_CAMPAIGN_TYPE (int): Final URLs, URL Templates and CustomParameters cannot be set for the criterion types of Gender, AgeRange, UserList, Placement, MobileApp, and MobileAppCategory in search campaigns and shopping campaigns. INVALID_CUSTOM_AFFINITY (int): Invalid custom affinity criterion. INVALID_CUSTOM_INTENT (int): Invalid custom intent criterion. INVALID_IP_ADDRESS (int): IP address is not valid. INVALID_IP_FORMAT (int): IP format is not valid. INVALID_MOBILE_APP (int): Mobile application is not valid. INVALID_MOBILE_APP_CATEGORY (int): Mobile application category is not valid. INVALID_CRITERION_ID (int): The CriterionId does not exist or is of the incorrect type. CANNOT_TARGET_CRITERION (int): The Criterion is not allowed to be targeted. CANNOT_TARGET_OBSOLETE_CRITERION (int): The criterion is not allowed to be targeted as it is deprecated. CRITERION_ID_AND_TYPE_MISMATCH (int): The CriterionId is not valid for the type. INVALID_PROXIMITY_RADIUS (int): Distance for the radius for the proximity criterion is invalid. INVALID_PROXIMITY_RADIUS_UNITS (int): Units for the distance for the radius for the proximity criterion is invalid. INVALID_STREETADDRESS_LENGTH (int): Street address in the address is not valid. INVALID_CITYNAME_LENGTH (int): City name in the address is not valid. INVALID_REGIONCODE_LENGTH (int): Region code in the address is not valid. INVALID_REGIONNAME_LENGTH (int): Region name in the address is not valid. INVALID_POSTALCODE_LENGTH (int): Postal code in the address is not valid. INVALID_COUNTRY_CODE (int): Country code in the address is not valid. INVALID_LATITUDE (int): Latitude for the GeoPoint is not valid. INVALID_LONGITUDE (int): Longitude for the GeoPoint is not valid. PROXIMITY_GEOPOINT_AND_ADDRESS_BOTH_CANNOT_BE_NULL (int): The Proximity input is not valid. Both address and geoPoint cannot be null. INVALID_PROXIMITY_ADDRESS (int): The Proximity address cannot be geocoded to a valid lat/long. INVALID_USER_DOMAIN_NAME (int): User domain name is not valid. CRITERION_PARAMETER_TOO_LONG (int): Length of serialized criterion parameter exceeded size limit. AD_SCHEDULE_TIME_INTERVALS_OVERLAP (int): Time interval in the AdSchedule overlaps with another AdSchedule. AD_SCHEDULE_INTERVAL_CANNOT_SPAN_MULTIPLE_DAYS (int): AdSchedule time interval cannot span multiple days. AD_SCHEDULE_INVALID_TIME_INTERVAL (int): AdSchedule time interval specified is invalid, endTime cannot be earlier than startTime. AD_SCHEDULE_EXCEEDED_INTERVALS_PER_DAY_LIMIT (int): The number of AdSchedule entries in a day exceeds the limit. AD_SCHEDULE_CRITERION_ID_MISMATCHING_FIELDS (int): CriteriaId does not match the interval of the AdSchedule specified. CANNOT_BID_MODIFY_CRITERION_TYPE (int): Cannot set bid modifier for this criterion type. CANNOT_BID_MODIFY_CRITERION_CAMPAIGN_OPTED_OUT (int): Cannot bid modify criterion, since it is opted out of the campaign. CANNOT_BID_MODIFY_NEGATIVE_CRITERION (int): Cannot set bid modifier for a negative criterion. BID_MODIFIER_ALREADY_EXISTS (int): Bid Modifier already exists. Use SET operation to update. FEED_ID_NOT_ALLOWED (int): Feed Id is not allowed in these Location Groups. ACCOUNT_INELIGIBLE_FOR_CRITERIA_TYPE (int): The account may not use the requested criteria type. For example, some accounts are restricted to keywords only. CRITERIA_TYPE_INVALID_FOR_BIDDING_STRATEGY (int): The requested criteria type cannot be used with campaign or ad group bidding strategy. CANNOT_EXCLUDE_CRITERION (int): The Criterion is not allowed to be excluded. CANNOT_REMOVE_CRITERION (int): The criterion is not allowed to be removed. For example, we cannot remove any of the device criterion. PRODUCT_SCOPE_TOO_LONG (int): The combined length of product dimension values of the product scope criterion is too long. PRODUCT_SCOPE_TOO_MANY_DIMENSIONS (int): Product scope contains too many dimensions. PRODUCT_PARTITION_TOO_LONG (int): The combined length of product dimension values of the product partition criterion is too long. PRODUCT_PARTITION_TOO_MANY_DIMENSIONS (int): Product partition contains too many dimensions. INVALID_PRODUCT_DIMENSION (int): The product dimension is invalid (e.g. dimension contains illegal value, dimension type is represented with wrong class, etc). Product dimension value can not contain "==" or "&+". INVALID_PRODUCT_DIMENSION_TYPE (int): Product dimension type is either invalid for campaigns of this type or cannot be used in the current context. BIDDING\_CATEGORY\_Lx and PRODUCT\_TYPE\_Lx product dimensions must be used in ascending order of their levels: L1, L2, L3, L4, L5... The levels must be specified sequentially and start from L1. Furthermore, an "others" product partition cannot be subdivided with a dimension of the same type but of a higher level ("others" BIDDING\_CATEGORY\_L3 can be subdivided with BRAND but not with BIDDING\_CATEGORY\_L4). INVALID_PRODUCT_BIDDING_CATEGORY (int): Bidding categories do not form a valid path in the Shopping bidding category taxonomy. MISSING_SHOPPING_SETTING (int): ShoppingSetting must be added to the campaign before ProductScope criteria can be added. INVALID_MATCHING_FUNCTION (int): Matching function is invalid. LOCATION_FILTER_NOT_ALLOWED (int): Filter parameters not allowed for location groups targeting. INVALID_FEED_FOR_LOCATION_FILTER (int): Feed not found, or the feed is not an enabled location feed. LOCATION_FILTER_INVALID (int): Given location filter parameter is invalid for location groups targeting. CANNOT_ATTACH_CRITERIA_AT_CAMPAIGN_AND_ADGROUP (int): Criteria type cannot be associated with a campaign and its ad group(s) simultaneously. HOTEL_LENGTH_OF_STAY_OVERLAPS_WITH_EXISTING_CRITERION (int): Range represented by hotel length of stay's min nights and max nights overlaps with an existing criterion. HOTEL_ADVANCE_BOOKING_WINDOW_OVERLAPS_WITH_EXISTING_CRITERION (int): Range represented by hotel advance booking window's min days and max days overlaps with an existing criterion. FIELD_INCOMPATIBLE_WITH_NEGATIVE_TARGETING (int): The field is not allowed to be set when the negative field is set to true, e.g. we don't allow bids in negative ad group or campaign criteria. INVALID_WEBPAGE_CONDITION (int): The combination of operand and operator in webpage condition is invalid. INVALID_WEBPAGE_CONDITION_URL (int): The URL of webpage condition is invalid. WEBPAGE_CONDITION_URL_CANNOT_BE_EMPTY (int): The URL of webpage condition cannot be empty or contain white space. WEBPAGE_CONDITION_URL_UNSUPPORTED_PROTOCOL (int): The URL of webpage condition contains an unsupported protocol. WEBPAGE_CONDITION_URL_CANNOT_BE_IP_ADDRESS (int): The URL of webpage condition cannot be an IP address. WEBPAGE_CONDITION_URL_DOMAIN_NOT_CONSISTENT_WITH_CAMPAIGN_SETTING (int): The domain of the URL is not consistent with the domain in campaign setting. WEBPAGE_CONDITION_URL_CANNOT_BE_PUBLIC_SUFFIX (int): The URL of webpage condition cannot be a public suffix itself. WEBPAGE_CONDITION_URL_INVALID_PUBLIC_SUFFIX (int): The URL of webpage condition has an invalid public suffix. WEBPAGE_CONDITION_URL_VALUE_TRACK_VALUE_NOT_SUPPORTED (int): Value track parameter is not supported in webpage condition URL. WEBPAGE_CRITERION_URL_EQUALS_CAN_HAVE_ONLY_ONE_CONDITION (int): Only one URL-EQUALS webpage condition is allowed in a webpage criterion and it cannot be combined with other conditions. WEBPAGE_CRITERION_NOT_SUPPORTED_ON_NON_DSA_AD_GROUP (int): A webpage criterion cannot be added to a non-DSA ad group. CANNOT_TARGET_USER_LIST_FOR_SMART_DISPLAY_CAMPAIGNS (int): Cannot add positive user list criteria in Smart Display campaigns. """ UNSPECIFIED = 0 UNKNOWN = 1 CONCRETE_TYPE_REQUIRED = 2 INVALID_EXCLUDED_CATEGORY = 3 INVALID_KEYWORD_TEXT = 4 KEYWORD_TEXT_TOO_LONG = 5 KEYWORD_HAS_TOO_MANY_WORDS = 6 KEYWORD_HAS_INVALID_CHARS = 7 INVALID_PLACEMENT_URL = 8 INVALID_USER_LIST = 9 INVALID_USER_INTEREST = 10 INVALID_FORMAT_FOR_PLACEMENT_URL = 11 PLACEMENT_URL_IS_TOO_LONG = 12 PLACEMENT_URL_HAS_ILLEGAL_CHAR = 13 PLACEMENT_URL_HAS_MULTIPLE_SITES_IN_LINE = 14 PLACEMENT_IS_NOT_AVAILABLE_FOR_TARGETING_OR_EXCLUSION = 15 INVALID_TOPIC_PATH = 16 INVALID_YOUTUBE_CHANNEL_ID = 17 INVALID_YOUTUBE_VIDEO_ID = 18 YOUTUBE_VERTICAL_CHANNEL_DEPRECATED = 19 YOUTUBE_DEMOGRAPHIC_CHANNEL_DEPRECATED = 20 YOUTUBE_URL_UNSUPPORTED = 21 CANNOT_EXCLUDE_CRITERIA_TYPE = 22 CANNOT_ADD_CRITERIA_TYPE = 23 INVALID_PRODUCT_FILTER = 24 PRODUCT_FILTER_TOO_LONG = 25 CANNOT_EXCLUDE_SIMILAR_USER_LIST = 26 CANNOT_ADD_CLOSED_USER_LIST = 27 CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SEARCH_ONLY_CAMPAIGNS = 28 CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SEARCH_CAMPAIGNS = 29 CANNOT_ADD_DISPLAY_ONLY_LISTS_TO_SHOPPING_CAMPAIGNS = 30 CANNOT_ADD_USER_INTERESTS_TO_SEARCH_CAMPAIGNS = 31 CANNOT_SET_BIDS_ON_CRITERION_TYPE_IN_SEARCH_CAMPAIGNS = 32 CANNOT_ADD_URLS_TO_CRITERION_TYPE_FOR_CAMPAIGN_TYPE = 33 INVALID_CUSTOM_AFFINITY = 96 INVALID_CUSTOM_INTENT = 97 INVALID_IP_ADDRESS = 34 INVALID_IP_FORMAT = 35 INVALID_MOBILE_APP = 36 INVALID_MOBILE_APP_CATEGORY = 37 INVALID_CRITERION_ID = 38 CANNOT_TARGET_CRITERION = 39 CANNOT_TARGET_OBSOLETE_CRITERION = 40 CRITERION_ID_AND_TYPE_MISMATCH = 41 INVALID_PROXIMITY_RADIUS = 42 INVALID_PROXIMITY_RADIUS_UNITS = 43 INVALID_STREETADDRESS_LENGTH = 44 INVALID_CITYNAME_LENGTH = 45 INVALID_REGIONCODE_LENGTH = 46 INVALID_REGIONNAME_LENGTH = 47 INVALID_POSTALCODE_LENGTH = 48 INVALID_COUNTRY_CODE = 49 INVALID_LATITUDE = 50 INVALID_LONGITUDE = 51 PROXIMITY_GEOPOINT_AND_ADDRESS_BOTH_CANNOT_BE_NULL = 52 INVALID_PROXIMITY_ADDRESS = 53 INVALID_USER_DOMAIN_NAME = 54 CRITERION_PARAMETER_TOO_LONG = 55 AD_SCHEDULE_TIME_INTERVALS_OVERLAP = 56 AD_SCHEDULE_INTERVAL_CANNOT_SPAN_MULTIPLE_DAYS = 57 AD_SCHEDULE_INVALID_TIME_INTERVAL = 58 AD_SCHEDULE_EXCEEDED_INTERVALS_PER_DAY_LIMIT = 59 AD_SCHEDULE_CRITERION_ID_MISMATCHING_FIELDS = 60 CANNOT_BID_MODIFY_CRITERION_TYPE = 61 CANNOT_BID_MODIFY_CRITERION_CAMPAIGN_OPTED_OUT = 62 CANNOT_BID_MODIFY_NEGATIVE_CRITERION = 63 BID_MODIFIER_ALREADY_EXISTS = 64 FEED_ID_NOT_ALLOWED = 65 ACCOUNT_INELIGIBLE_FOR_CRITERIA_TYPE = 66 CRITERIA_TYPE_INVALID_FOR_BIDDING_STRATEGY = 67 CANNOT_EXCLUDE_CRITERION = 68 CANNOT_REMOVE_CRITERION = 69 PRODUCT_SCOPE_TOO_LONG = 70 PRODUCT_SCOPE_TOO_MANY_DIMENSIONS = 71 PRODUCT_PARTITION_TOO_LONG = 72 PRODUCT_PARTITION_TOO_MANY_DIMENSIONS = 73 INVALID_PRODUCT_DIMENSION = 74 INVALID_PRODUCT_DIMENSION_TYPE = 75 INVALID_PRODUCT_BIDDING_CATEGORY = 76 MISSING_SHOPPING_SETTING = 77 INVALID_MATCHING_FUNCTION = 78 LOCATION_FILTER_NOT_ALLOWED = 79 INVALID_FEED_FOR_LOCATION_FILTER = 98 LOCATION_FILTER_INVALID = 80 CANNOT_ATTACH_CRITERIA_AT_CAMPAIGN_AND_ADGROUP = 81 HOTEL_LENGTH_OF_STAY_OVERLAPS_WITH_EXISTING_CRITERION = 82 HOTEL_ADVANCE_BOOKING_WINDOW_OVERLAPS_WITH_EXISTING_CRITERION = 83 FIELD_INCOMPATIBLE_WITH_NEGATIVE_TARGETING = 84 INVALID_WEBPAGE_CONDITION = 85 INVALID_WEBPAGE_CONDITION_URL = 86 WEBPAGE_CONDITION_URL_CANNOT_BE_EMPTY = 87 WEBPAGE_CONDITION_URL_UNSUPPORTED_PROTOCOL = 88 WEBPAGE_CONDITION_URL_CANNOT_BE_IP_ADDRESS = 89 WEBPAGE_CONDITION_URL_DOMAIN_NOT_CONSISTENT_WITH_CAMPAIGN_SETTING = 90 WEBPAGE_CONDITION_URL_CANNOT_BE_PUBLIC_SUFFIX = 91 WEBPAGE_CONDITION_URL_INVALID_PUBLIC_SUFFIX = 92 WEBPAGE_CONDITION_URL_VALUE_TRACK_VALUE_NOT_SUPPORTED = 93 WEBPAGE_CRITERION_URL_EQUALS_CAN_HAVE_ONLY_ONE_CONDITION = 94 WEBPAGE_CRITERION_NOT_SUPPORTED_ON_NON_DSA_AD_GROUP = 95 CANNOT_TARGET_USER_LIST_FOR_SMART_DISPLAY_CAMPAIGNS = 99 ''' CriterionErrorEnum = CriterionErrorEnum() # For __getattribute__ class CriterionSystemServingStatusEnum(_CreateEnumTypeUponFirstAccess): CriterionSystemServingStatusEnum = '''\ class CriterionSystemServingStatus(enum.IntEnum): """ Enumerates criterion system serving statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. ELIGIBLE (int): Eligible. RARELY_SERVED (int): Low search volume. """ UNSPECIFIED = 0 UNKNOWN = 1 ELIGIBLE = 2 RARELY_SERVED = 3 ''' CriterionSystemServingStatusEnum = CriterionSystemServingStatusEnum() # For __getattribute__ class CriterionTypeEnum(_CreateEnumTypeUponFirstAccess): CriterionTypeEnum = '''\ class CriterionType(enum.IntEnum): """ Enum describing possible criterion types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. KEYWORD (int): Keyword. e.g. 'mars cruise'. PLACEMENT (int): Placement, aka Website. e.g. 'www.flowers4sale.com' MOBILE_APP_CATEGORY (int): Mobile application categories to target. MOBILE_APPLICATION (int): Mobile applications to target. DEVICE (int): Devices to target. LOCATION (int): Locations to target. LISTING_GROUP (int): Listing groups to target. AD_SCHEDULE (int): Ad Schedule. AGE_RANGE (int): Age range. GENDER (int): Gender. INCOME_RANGE (int): Income Range. PARENTAL_STATUS (int): Parental status. YOUTUBE_VIDEO (int): YouTube Video. YOUTUBE_CHANNEL (int): YouTube Channel. USER_LIST (int): User list. PROXIMITY (int): Proximity. TOPIC (int): A topic target on the display network (e.g. "Pets & Animals"). LISTING_SCOPE (int): Listing scope to target. LANGUAGE (int): Language. IP_BLOCK (int): IpBlock. CONTENT_LABEL (int): Content Label for category exclusion. CARRIER (int): Carrier. USER_INTEREST (int): A category the user is interested in. WEBPAGE (int): Webpage criterion for dynamic search ads. OPERATING_SYSTEM_VERSION (int): Operating system version. APP_PAYMENT_MODEL (int): App payment model. MOBILE_DEVICE (int): Mobile device. CUSTOM_AFFINITY (int): Custom affinity. CUSTOM_INTENT (int): Custom intent. LOCATION_GROUP (int): Location group. """ UNSPECIFIED = 0 UNKNOWN = 1 KEYWORD = 2 PLACEMENT = 3 MOBILE_APP_CATEGORY = 4 MOBILE_APPLICATION = 5 DEVICE = 6 LOCATION = 7 LISTING_GROUP = 8 AD_SCHEDULE = 9 AGE_RANGE = 10 GENDER = 11 INCOME_RANGE = 12 PARENTAL_STATUS = 13 YOUTUBE_VIDEO = 14 YOUTUBE_CHANNEL = 15 USER_LIST = 16 PROXIMITY = 17 TOPIC = 18 LISTING_SCOPE = 19 LANGUAGE = 20 IP_BLOCK = 21 CONTENT_LABEL = 22 CARRIER = 23 USER_INTEREST = 24 WEBPAGE = 25 OPERATING_SYSTEM_VERSION = 26 APP_PAYMENT_MODEL = 27 MOBILE_DEVICE = 28 CUSTOM_AFFINITY = 29 CUSTOM_INTENT = 30 LOCATION_GROUP = 31 ''' CriterionTypeEnum = CriterionTypeEnum() # For __getattribute__ class CurrencyCodeErrorEnum(_CreateEnumTypeUponFirstAccess): CurrencyCodeErrorEnum = '''\ class CurrencyCodeError(enum.IntEnum): """ Enum describing possible currency code errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. UNSUPPORTED (int): The currency code is not supported. """ UNSPECIFIED = 0 UNKNOWN = 1 UNSUPPORTED = 2 ''' CurrencyCodeErrorEnum = CurrencyCodeErrorEnum() # For __getattribute__ class CustomInterestErrorEnum(_CreateEnumTypeUponFirstAccess): CustomInterestErrorEnum = '''\ class CustomInterestError(enum.IntEnum): """ Enum describing possible custom interest errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NAME_ALREADY_USED (int): Duplicate custom interest name ignoring case. CUSTOM_INTEREST_MEMBER_ID_AND_TYPE_PARAMETER_NOT_PRESENT_IN_REMOVE (int): In the remove custom interest member operation, both member ID and pair [type, parameter] are not present. TYPE_AND_PARAMETER_NOT_FOUND (int): The pair of [type, parameter] does not exist. TYPE_AND_PARAMETER_ALREADY_EXISTED (int): The pair of [type, parameter] already exists. INVALID_CUSTOM_INTEREST_MEMBER_TYPE (int): Unsupported custom interest member type. CANNOT_REMOVE_WHILE_IN_USE (int): Cannot remove a custom interest while it's still being targeted. CANNOT_CHANGE_TYPE (int): Cannot mutate custom interest type. """ UNSPECIFIED = 0 UNKNOWN = 1 NAME_ALREADY_USED = 2 CUSTOM_INTEREST_MEMBER_ID_AND_TYPE_PARAMETER_NOT_PRESENT_IN_REMOVE = 3 TYPE_AND_PARAMETER_NOT_FOUND = 4 TYPE_AND_PARAMETER_ALREADY_EXISTED = 5 INVALID_CUSTOM_INTEREST_MEMBER_TYPE = 6 CANNOT_REMOVE_WHILE_IN_USE = 7 CANNOT_CHANGE_TYPE = 8 ''' CustomInterestErrorEnum = CustomInterestErrorEnum() # For __getattribute__ class CustomInterestMemberTypeEnum(_CreateEnumTypeUponFirstAccess): CustomInterestMemberTypeEnum = '''\ class CustomInterestMemberType(enum.IntEnum): """ Enum containing possible custom interest member types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. KEYWORD (int): Custom interest member type KEYWORD. URL (int): Custom interest member type URL. """ UNSPECIFIED = 0 UNKNOWN = 1 KEYWORD = 2 URL = 3 ''' CustomInterestMemberTypeEnum = CustomInterestMemberTypeEnum() # For __getattribute__ class CustomInterestStatusEnum(_CreateEnumTypeUponFirstAccess): CustomInterestStatusEnum = '''\ class CustomInterestStatus(enum.IntEnum): """ Enum containing possible custom interest types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Enabled status - custom interest is enabled and can be targeted to. REMOVED (int): Removed status - custom interest is removed and cannot be used for targeting. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' CustomInterestStatusEnum = CustomInterestStatusEnum() # For __getattribute__ class CustomInterestTypeEnum(_CreateEnumTypeUponFirstAccess): CustomInterestTypeEnum = '''\ class CustomInterestType(enum.IntEnum): """ Enum containing possible custom interest types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CUSTOM_AFFINITY (int): Allows brand advertisers to define custom affinity audience lists. CUSTOM_INTENT (int): Allows advertisers to define custom intent audience lists. """ UNSPECIFIED = 0 UNKNOWN = 1 CUSTOM_AFFINITY = 2 CUSTOM_INTENT = 3 ''' CustomInterestTypeEnum = CustomInterestTypeEnum() # For __getattribute__ class CustomPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): CustomPlaceholderFieldEnum = '''\ class CustomPlaceholderField(enum.IntEnum): """ Possible values for Custom placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ID (int): Data Type: STRING. Required. Combination ID and ID2 must be unique per offer. ID2 (int): Data Type: STRING. Combination ID and ID2 must be unique per offer. ITEM_TITLE (int): Data Type: STRING. Required. Main headline with product name to be shown in dynamic ad. ITEM_SUBTITLE (int): Data Type: STRING. Optional text to be shown in the image ad. ITEM_DESCRIPTION (int): Data Type: STRING. Optional description of the product to be shown in the ad. ITEM_ADDRESS (int): Data Type: STRING. Full address of your offer or service, including postal code. This will be used to identify the closest product to the user when there are multiple offers in the feed that are relevant to the user. PRICE (int): Data Type: STRING. Price to be shown in the ad. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" SALE_PRICE (int): Data Type: STRING. Sale price to be shown in the ad. Example: "80.00 USD" FORMATTED_SALE_PRICE (int): Data Type: STRING. Formatted sale price to be shown in the ad. Example: "On sale for $80.00", "$60 - $80" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. Highly recommended for image ads. ITEM_CATEGORY (int): Data Type: STRING. Used as a recommendation engine signal to serve items in the same category. FINAL_URLS (int): Data Type: URL\_LIST. Final URLs for the ad when using Upgraded URLs. User will be redirected to these URLs when they click on an ad, or when they click on a specific product for ads that have multiple products. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_IDS (int): Data Type: STRING\_LIST. List of recommended IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 ID = 2 ID2 = 3 ITEM_TITLE = 4 ITEM_SUBTITLE = 5 ITEM_DESCRIPTION = 6 ITEM_ADDRESS = 7 PRICE = 8 FORMATTED_PRICE = 9 SALE_PRICE = 10 FORMATTED_SALE_PRICE = 11 IMAGE_URL = 12 ITEM_CATEGORY = 13 FINAL_URLS = 14 FINAL_MOBILE_URLS = 15 TRACKING_URL = 16 CONTEXTUAL_KEYWORDS = 17 ANDROID_APP_LINK = 18 SIMILAR_IDS = 19 IOS_APP_LINK = 20 IOS_APP_STORE_ID = 21 ''' CustomPlaceholderFieldEnum = CustomPlaceholderFieldEnum() # For __getattribute__ class CustomerClientLinkErrorEnum(_CreateEnumTypeUponFirstAccess): CustomerClientLinkErrorEnum = '''\ class CustomerClientLinkError(enum.IntEnum): """ Enum describing possible CustomerClientLink errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CLIENT_ALREADY_INVITED_BY_THIS_MANAGER (int): Trying to manage a client that already in being managed by customer. CLIENT_ALREADY_MANAGED_IN_HIERARCHY (int): Already managed by some other manager in the hierarchy. CYCLIC_LINK_NOT_ALLOWED (int): Attempt to create a cycle in the hierarchy. CUSTOMER_HAS_TOO_MANY_ACCOUNTS (int): Managed accounts has the maximum number of linked accounts. CLIENT_HAS_TOO_MANY_INVITATIONS (int): Invitor has the maximum pending invitations. CANNOT_HIDE_OR_UNHIDE_MANAGER_ACCOUNTS (int): Attempt to change hidden status of a link that is not active. CUSTOMER_HAS_TOO_MANY_ACCOUNTS_AT_MANAGER (int): Parent manager account has the maximum number of linked accounts. """ UNSPECIFIED = 0 UNKNOWN = 1 CLIENT_ALREADY_INVITED_BY_THIS_MANAGER = 2 CLIENT_ALREADY_MANAGED_IN_HIERARCHY = 3 CYCLIC_LINK_NOT_ALLOWED = 4 CUSTOMER_HAS_TOO_MANY_ACCOUNTS = 5 CLIENT_HAS_TOO_MANY_INVITATIONS = 6 CANNOT_HIDE_OR_UNHIDE_MANAGER_ACCOUNTS = 7 CUSTOMER_HAS_TOO_MANY_ACCOUNTS_AT_MANAGER = 8 ''' CustomerClientLinkErrorEnum = CustomerClientLinkErrorEnum() # For __getattribute__ class CustomerErrorEnum(_CreateEnumTypeUponFirstAccess): CustomerErrorEnum = '''\ class CustomerError(enum.IntEnum): """ Set of errors that are related to requests dealing with Customer. Next id: 26 Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. STATUS_CHANGE_DISALLOWED (int): Customer status is not allowed to be changed from DRAFT and CLOSED. Currency code and at least one of country code and time zone needs to be set when status is changed to ENABLED. ACCOUNT_NOT_SET_UP (int): CustomerService cannot get a customer that has not been fully set up. """ UNSPECIFIED = 0 UNKNOWN = 1 STATUS_CHANGE_DISALLOWED = 2 ACCOUNT_NOT_SET_UP = 3 ''' CustomerErrorEnum = CustomerErrorEnum() # For __getattribute__ class CustomerFeedErrorEnum(_CreateEnumTypeUponFirstAccess): CustomerFeedErrorEnum = '''\ class CustomerFeedError(enum.IntEnum): """ Enum describing possible customer feed errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active feed already exists for this customer and place holder type. CANNOT_CREATE_FOR_REMOVED_FEED (int): The specified feed is removed. CANNOT_CREATE_ALREADY_EXISTING_CUSTOMER_FEED (int): The CustomerFeed already exists. Update should be used to modify the existing CustomerFeed. CANNOT_MODIFY_REMOVED_CUSTOMER_FEED (int): Cannot update removed customer feed. INVALID_PLACEHOLDER_TYPE (int): Invalid placeholder type. MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE (int): Feed mapping for this placeholder type does not exist. PLACEHOLDER_TYPE_NOT_ALLOWED_ON_CUSTOMER_FEED (int): Placeholder not allowed at the account level. """ UNSPECIFIED = 0 UNKNOWN = 1 FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 2 CANNOT_CREATE_FOR_REMOVED_FEED = 3 CANNOT_CREATE_ALREADY_EXISTING_CUSTOMER_FEED = 4 CANNOT_MODIFY_REMOVED_CUSTOMER_FEED = 5 INVALID_PLACEHOLDER_TYPE = 6 MISSING_FEEDMAPPING_FOR_PLACEHOLDER_TYPE = 7 PLACEHOLDER_TYPE_NOT_ALLOWED_ON_CUSTOMER_FEED = 8 ''' CustomerFeedErrorEnum = CustomerFeedErrorEnum() # For __getattribute__ class CustomerManagerLinkErrorEnum(_CreateEnumTypeUponFirstAccess): CustomerManagerLinkErrorEnum = '''\ class CustomerManagerLinkError(enum.IntEnum): """ Enum describing possible CustomerManagerLink errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NO_PENDING_INVITE (int): No pending invitation. SAME_CLIENT_MORE_THAN_ONCE_PER_CALL (int): Attempt to operate on the same client more than once in the same call. MANAGER_HAS_MAX_NUMBER_OF_LINKED_ACCOUNTS (int): Manager account has the maximum number of linked accounts. CANNOT_UNLINK_ACCOUNT_WITHOUT_ACTIVE_USER (int): If no active user on account it cannot be unlinked from its manager. CANNOT_REMOVE_LAST_CLIENT_ACCOUNT_OWNER (int): Account should have at least one active owner on it before being unlinked. CANNOT_CHANGE_ROLE_BY_NON_ACCOUNT_OWNER (int): Only account owners may change their permission role. CANNOT_CHANGE_ROLE_FOR_NON_ACTIVE_LINK_ACCOUNT (int): When a client's link to its manager is not active, the link role cannot be changed. DUPLICATE_CHILD_FOUND (int): Attempt to link a child to a parent that contains or will contain duplicate children. TEST_ACCOUNT_LINKS_TOO_MANY_CHILD_ACCOUNTS (int): The authorized customer is a test account. It can add no more than the allowed number of accounts """ UNSPECIFIED = 0 UNKNOWN = 1 NO_PENDING_INVITE = 2 SAME_CLIENT_MORE_THAN_ONCE_PER_CALL = 3 MANAGER_HAS_MAX_NUMBER_OF_LINKED_ACCOUNTS = 4 CANNOT_UNLINK_ACCOUNT_WITHOUT_ACTIVE_USER = 5 CANNOT_REMOVE_LAST_CLIENT_ACCOUNT_OWNER = 6 CANNOT_CHANGE_ROLE_BY_NON_ACCOUNT_OWNER = 7 CANNOT_CHANGE_ROLE_FOR_NON_ACTIVE_LINK_ACCOUNT = 8 DUPLICATE_CHILD_FOUND = 9 TEST_ACCOUNT_LINKS_TOO_MANY_CHILD_ACCOUNTS = 10 ''' CustomerManagerLinkErrorEnum = CustomerManagerLinkErrorEnum() # For __getattribute__ class CustomerMatchUploadKeyTypeEnum(_CreateEnumTypeUponFirstAccess): CustomerMatchUploadKeyTypeEnum = '''\ class CustomerMatchUploadKeyType(enum.IntEnum): """ Enum describing possible customer match upload key types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CONTACT_INFO (int): Members are matched from customer info such as email address, phone number or physical address. CRM_ID (int): Members are matched from a user id generated and assigned by the advertiser. MOBILE_ADVERTISING_ID (int): Members are matched from mobile advertising ids. """ UNSPECIFIED = 0 UNKNOWN = 1 CONTACT_INFO = 2 CRM_ID = 3 MOBILE_ADVERTISING_ID = 4 ''' CustomerMatchUploadKeyTypeEnum = CustomerMatchUploadKeyTypeEnum() # For __getattribute__ class CustomerPayPerConversionEligibilityFailureReasonEnum(_CreateEnumTypeUponFirstAccess): CustomerPayPerConversionEligibilityFailureReasonEnum = '''\ class CustomerPayPerConversionEligibilityFailureReason(enum.IntEnum): """ Enum describing possible reasons a customer is not eligible to use PaymentMode.CONVERSIONS. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NOT_ENOUGH_CONVERSIONS (int): Customer does not have enough conversions. CONVERSION_LAG_TOO_HIGH (int): Customer's conversion lag is too high. HAS_CAMPAIGN_WITH_SHARED_BUDGET (int): Customer uses shared budgets. HAS_UPLOAD_CLICKS_CONVERSION (int): Customer has conversions with ConversionActionType.UPLOAD\_CLICKS. AVERAGE_DAILY_SPEND_TOO_HIGH (int): Customer's average daily spend is too high. ANALYSIS_NOT_COMPLETE (int): Customer's eligibility has not yet been calculated by the Google Ads backend. Check back soon. OTHER (int): Customer is not eligible due to other reasons. """ UNSPECIFIED = 0 UNKNOWN = 1 NOT_ENOUGH_CONVERSIONS = 2 CONVERSION_LAG_TOO_HIGH = 3 HAS_CAMPAIGN_WITH_SHARED_BUDGET = 4 HAS_UPLOAD_CLICKS_CONVERSION = 5 AVERAGE_DAILY_SPEND_TOO_HIGH = 6 ANALYSIS_NOT_COMPLETE = 7 OTHER = 8 ''' CustomerPayPerConversionEligibilityFailureReasonEnum = CustomerPayPerConversionEligibilityFailureReasonEnum() # For __getattribute__ class DataDrivenModelStatusEnum(_CreateEnumTypeUponFirstAccess): DataDrivenModelStatusEnum = '''\ class DataDrivenModelStatus(enum.IntEnum): """ Enumerates data driven model statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AVAILABLE (int): The data driven model is available. STALE (int): The data driven model is stale. It hasn't been updated for at least 7 days. It is still being used, but will become expired if it does not get updated for 30 days. EXPIRED (int): The data driven model expired. It hasn't been updated for at least 30 days and cannot be used. Most commonly this is because there hasn't been the required number of events in a recent 30-day period. NEVER_GENERATED (int): The data driven model has never been generated. Most commonly this is because there has never been the required number of events in any 30-day period. """ UNSPECIFIED = 0 UNKNOWN = 1 AVAILABLE = 2 STALE = 3 EXPIRED = 4 NEVER_GENERATED = 5 ''' DataDrivenModelStatusEnum = DataDrivenModelStatusEnum() # For __getattribute__ class DatabaseErrorEnum(_CreateEnumTypeUponFirstAccess): DatabaseErrorEnum = '''\ class DatabaseError(enum.IntEnum): """ Enum describing possible database errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CONCURRENT_MODIFICATION (int): Multiple requests were attempting to modify the same resource at once. Please retry the request. DATA_CONSTRAINT_VIOLATION (int): The request conflicted with existing data. This error will usually be replaced with a more specific error if the request is retried. REQUEST_TOO_LARGE (int): The data written is too large. Please split the request into smaller requests. """ UNSPECIFIED = 0 UNKNOWN = 1 CONCURRENT_MODIFICATION = 2 DATA_CONSTRAINT_VIOLATION = 3 REQUEST_TOO_LARGE = 4 ''' DatabaseErrorEnum = DatabaseErrorEnum() # For __getattribute__ class DateErrorEnum(_CreateEnumTypeUponFirstAccess): DateErrorEnum = '''\ class DateError(enum.IntEnum): """ Enum describing possible date errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_FIELD_VALUES_IN_DATE (int): Given field values do not correspond to a valid date. INVALID_FIELD_VALUES_IN_DATE_TIME (int): Given field values do not correspond to a valid date time. INVALID_STRING_DATE (int): The string date's format should be yyyy-mm-dd. INVALID_STRING_DATE_TIME_MICROS (int): The string date time's format should be yyyy-mm-dd hh:mm:ss.ssssss. INVALID_STRING_DATE_TIME_SECONDS (int): The string date time's format should be yyyy-mm-dd hh:mm:ss. INVALID_STRING_DATE_TIME_SECONDS_WITH_OFFSET (int): The string date time's format should be yyyy-mm-dd hh:mm:ss+|-hh:mm. EARLIER_THAN_MINIMUM_DATE (int): Date is before allowed minimum. LATER_THAN_MAXIMUM_DATE (int): Date is after allowed maximum. DATE_RANGE_MINIMUM_DATE_LATER_THAN_MAXIMUM_DATE (int): Date range bounds are not in order. DATE_RANGE_MINIMUM_AND_MAXIMUM_DATES_BOTH_NULL (int): Both dates in range are null. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_FIELD_VALUES_IN_DATE = 2 INVALID_FIELD_VALUES_IN_DATE_TIME = 3 INVALID_STRING_DATE = 4 INVALID_STRING_DATE_TIME_MICROS = 6 INVALID_STRING_DATE_TIME_SECONDS = 11 INVALID_STRING_DATE_TIME_SECONDS_WITH_OFFSET = 12 EARLIER_THAN_MINIMUM_DATE = 7 LATER_THAN_MAXIMUM_DATE = 8 DATE_RANGE_MINIMUM_DATE_LATER_THAN_MAXIMUM_DATE = 9 DATE_RANGE_MINIMUM_AND_MAXIMUM_DATES_BOTH_NULL = 10 ''' DateErrorEnum = DateErrorEnum() # For __getattribute__ class DateRangeErrorEnum(_CreateEnumTypeUponFirstAccess): DateRangeErrorEnum = '''\ class DateRangeError(enum.IntEnum): """ Enum describing possible date range errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_DATE (int): Invalid date. START_DATE_AFTER_END_DATE (int): The start date was after the end date. CANNOT_SET_DATE_TO_PAST (int): Cannot set date to past time AFTER_MAXIMUM_ALLOWABLE_DATE (int): A date was used that is past the system "last" date. CANNOT_MODIFY_START_DATE_IF_ALREADY_STARTED (int): Trying to change start date on a resource that has started. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_DATE = 2 START_DATE_AFTER_END_DATE = 3 CANNOT_SET_DATE_TO_PAST = 4 AFTER_MAXIMUM_ALLOWABLE_DATE = 5 CANNOT_MODIFY_START_DATE_IF_ALREADY_STARTED = 6 ''' DateRangeErrorEnum = DateRangeErrorEnum() # For __getattribute__ class DayOfWeekEnum(_CreateEnumTypeUponFirstAccess): DayOfWeekEnum = '''\ class DayOfWeek(enum.IntEnum): """ Enumerates days of the week, e.g., "Monday". Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. MONDAY (int): Monday. TUESDAY (int): Tuesday. WEDNESDAY (int): Wednesday. THURSDAY (int): Thursday. FRIDAY (int): Friday. SATURDAY (int): Saturday. SUNDAY (int): Sunday. """ UNSPECIFIED = 0 UNKNOWN = 1 MONDAY = 2 TUESDAY = 3 WEDNESDAY = 4 THURSDAY = 5 FRIDAY = 6 SATURDAY = 7 SUNDAY = 8 ''' DayOfWeekEnum = DayOfWeekEnum() # For __getattribute__ class DeviceEnum(_CreateEnumTypeUponFirstAccess): DeviceEnum = '''\ class Device(enum.IntEnum): """ Enumerates Google Ads devices available for targeting. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. MOBILE (int): Mobile devices with full browsers. TABLET (int): Tablets with full browsers. DESKTOP (int): Computers. CONNECTED_TV (int): Smart TVs and game consoles. OTHER (int): Other device types. """ UNSPECIFIED = 0 UNKNOWN = 1 MOBILE = 2 TABLET = 3 DESKTOP = 4 CONNECTED_TV = 6 OTHER = 5 ''' DeviceEnum = DeviceEnum() # For __getattribute__ class DisplayAdFormatSettingEnum(_CreateEnumTypeUponFirstAccess): DisplayAdFormatSettingEnum = '''\ class DisplayAdFormatSetting(enum.IntEnum): """ Enumerates display ad format settings. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. ALL_FORMATS (int): Text, image and native formats. NON_NATIVE (int): Text and image formats. NATIVE (int): Native format, i.e. the format rendering is controlled by the publisher and not by Google. """ UNSPECIFIED = 0 UNKNOWN = 1 ALL_FORMATS = 2 NON_NATIVE = 3 NATIVE = 4 ''' DisplayAdFormatSettingEnum = DisplayAdFormatSettingEnum() # For __getattribute__ class DisplayUploadProductTypeEnum(_CreateEnumTypeUponFirstAccess): DisplayUploadProductTypeEnum = '''\ class DisplayUploadProductType(enum.IntEnum): """ Enumerates display upload product types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. HTML5_UPLOAD_AD (int): HTML5 upload ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. DYNAMIC_HTML5_EDUCATION_AD (int): Dynamic HTML5 education ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in an education campaign. DYNAMIC_HTML5_FLIGHT_AD (int): Dynamic HTML5 flight ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a flight campaign. DYNAMIC_HTML5_HOTEL_RENTAL_AD (int): Dynamic HTML5 hotel and rental ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a hotel campaign. DYNAMIC_HTML5_JOB_AD (int): Dynamic HTML5 job ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a job campaign. DYNAMIC_HTML5_LOCAL_AD (int): Dynamic HTML5 local ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a local campaign. DYNAMIC_HTML5_REAL_ESTATE_AD (int): Dynamic HTML5 real estate ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a real estate campaign. DYNAMIC_HTML5_CUSTOM_AD (int): Dynamic HTML5 custom ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a custom campaign. DYNAMIC_HTML5_TRAVEL_AD (int): Dynamic HTML5 travel ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a travel campaign. DYNAMIC_HTML5_HOTEL_AD (int): Dynamic HTML5 hotel ad. This product type requires the upload\_media\_bundle field in DisplayUploadAdInfo to be set. Can only be used in a hotel campaign. """ UNSPECIFIED = 0 UNKNOWN = 1 HTML5_UPLOAD_AD = 2 DYNAMIC_HTML5_EDUCATION_AD = 3 DYNAMIC_HTML5_FLIGHT_AD = 4 DYNAMIC_HTML5_HOTEL_RENTAL_AD = 5 DYNAMIC_HTML5_JOB_AD = 6 DYNAMIC_HTML5_LOCAL_AD = 7 DYNAMIC_HTML5_REAL_ESTATE_AD = 8 DYNAMIC_HTML5_CUSTOM_AD = 9 DYNAMIC_HTML5_TRAVEL_AD = 10 DYNAMIC_HTML5_HOTEL_AD = 11 ''' DisplayUploadProductTypeEnum = DisplayUploadProductTypeEnum() # For __getattribute__ class DistanceBucketEnum(_CreateEnumTypeUponFirstAccess): DistanceBucketEnum = '''\ class DistanceBucket(enum.IntEnum): """ The distance bucket for a user’s distance from an advertiser’s location extension. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. WITHIN_700M (int): User was within 700m of the location. WITHIN_1KM (int): User was within 1KM of the location. WITHIN_5KM (int): User was within 5KM of the location. WITHIN_10KM (int): User was within 10KM of the location. WITHIN_15KM (int): User was within 15KM of the location. WITHIN_20KM (int): User was within 20KM of the location. WITHIN_25KM (int): User was within 25KM of the location. WITHIN_30KM (int): User was within 30KM of the location. WITHIN_35KM (int): User was within 35KM of the location. WITHIN_40KM (int): User was within 40KM of the location. WITHIN_45KM (int): User was within 45KM of the location. WITHIN_50KM (int): User was within 50KM of the location. WITHIN_55KM (int): User was within 55KM of the location. WITHIN_60KM (int): User was within 60KM of the location. WITHIN_65KM (int): User was within 65KM of the location. BEYOND_65KM (int): User was beyond 65KM of the location. WITHIN_0_7MILES (int): User was within 0.7 miles of the location. WITHIN_1MILE (int): User was within 1 mile of the location. WITHIN_5MILES (int): User was within 5 miles of the location. WITHIN_10MILES (int): User was within 10 miles of the location. WITHIN_15MILES (int): User was within 15 miles of the location. WITHIN_20MILES (int): User was within 20 miles of the location. WITHIN_25MILES (int): User was within 25 miles of the location. WITHIN_30MILES (int): User was within 30 miles of the location. WITHIN_35MILES (int): User was within 35 miles of the location. WITHIN_40MILES (int): User was within 40 miles of the location. BEYOND_40MILES (int): User was beyond 40 miles of the location. """ UNSPECIFIED = 0 UNKNOWN = 1 WITHIN_700M = 2 WITHIN_1KM = 3 WITHIN_5KM = 4 WITHIN_10KM = 5 WITHIN_15KM = 6 WITHIN_20KM = 7 WITHIN_25KM = 8 WITHIN_30KM = 9 WITHIN_35KM = 10 WITHIN_40KM = 11 WITHIN_45KM = 12 WITHIN_50KM = 13 WITHIN_55KM = 14 WITHIN_60KM = 15 WITHIN_65KM = 16 BEYOND_65KM = 17 WITHIN_0_7MILES = 18 WITHIN_1MILE = 19 WITHIN_5MILES = 20 WITHIN_10MILES = 21 WITHIN_15MILES = 22 WITHIN_20MILES = 23 WITHIN_25MILES = 24 WITHIN_30MILES = 25 WITHIN_35MILES = 26 WITHIN_40MILES = 27 BEYOND_40MILES = 28 ''' DistanceBucketEnum = DistanceBucketEnum() # For __getattribute__ class DistinctErrorEnum(_CreateEnumTypeUponFirstAccess): DistinctErrorEnum = '''\ class DistinctError(enum.IntEnum): """ Enum describing possible distinct errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. DUPLICATE_ELEMENT (int): Duplicate element. DUPLICATE_TYPE (int): Duplicate type. """ UNSPECIFIED = 0 UNKNOWN = 1 DUPLICATE_ELEMENT = 2 DUPLICATE_TYPE = 3 ''' DistinctErrorEnum = DistinctErrorEnum() # For __getattribute__ class DsaPageFeedCriterionFieldEnum(_CreateEnumTypeUponFirstAccess): DsaPageFeedCriterionFieldEnum = '''\ class DsaPageFeedCriterionField(enum.IntEnum): """ Possible values for Dynamic Search Ad Page Feed criterion fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PAGE_URL (int): Data Type: URL or URL\_LIST. URL of the web page you want to target. LABEL (int): Data Type: STRING\_LIST. The labels that will help you target ads within your page feed. """ UNSPECIFIED = 0 UNKNOWN = 1 PAGE_URL = 2 LABEL = 3 ''' DsaPageFeedCriterionFieldEnum = DsaPageFeedCriterionFieldEnum() # For __getattribute__ class EducationPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): EducationPlaceholderFieldEnum = '''\ class EducationPlaceholderField(enum.IntEnum): """ Possible values for Education placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PROGRAM_ID (int): Data Type: STRING. Required. Combination of PROGRAM ID and LOCATION ID must be unique per offer. LOCATION_ID (int): Data Type: STRING. Combination of PROGRAM ID and LOCATION ID must be unique per offer. PROGRAM_NAME (int): Data Type: STRING. Required. Main headline with program name to be shown in dynamic ad. AREA_OF_STUDY (int): Data Type: STRING. Area of study that can be shown in dynamic ad. PROGRAM_DESCRIPTION (int): Data Type: STRING. Description of program that can be shown in dynamic ad. SCHOOL_NAME (int): Data Type: STRING. Name of school that can be shown in dynamic ad. ADDRESS (int): Data Type: STRING. Complete school address, including postal code. THUMBNAIL_IMAGE_URL (int): Data Type: URL. Image to be displayed in ads. ALTERNATIVE_THUMBNAIL_IMAGE_URL (int): Data Type: URL. Alternative hosted file of image to be used in the ad. FINAL_URLS (int): Data Type: URL\_LIST. Required. Final URLs to be used in ad when using Upgraded URLs; the more specific the better (e.g. the individual URL of a specific program and its location). FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_PROGRAM_IDS (int): Data Type: STRING\_LIST. List of recommended program IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 PROGRAM_ID = 2 LOCATION_ID = 3 PROGRAM_NAME = 4 AREA_OF_STUDY = 5 PROGRAM_DESCRIPTION = 6 SCHOOL_NAME = 7 ADDRESS = 8 THUMBNAIL_IMAGE_URL = 9 ALTERNATIVE_THUMBNAIL_IMAGE_URL = 10 FINAL_URLS = 11 FINAL_MOBILE_URLS = 12 TRACKING_URL = 13 CONTEXTUAL_KEYWORDS = 14 ANDROID_APP_LINK = 15 SIMILAR_PROGRAM_IDS = 16 IOS_APP_LINK = 17 IOS_APP_STORE_ID = 18 ''' EducationPlaceholderFieldEnum = EducationPlaceholderFieldEnum() # For __getattribute__ class EnumErrorEnum(_CreateEnumTypeUponFirstAccess): EnumErrorEnum = '''\ class EnumError(enum.IntEnum): """ Enum describing possible enum errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ENUM_VALUE_NOT_PERMITTED (int): The enum value is not permitted. """ UNSPECIFIED = 0 UNKNOWN = 1 ENUM_VALUE_NOT_PERMITTED = 3 ''' EnumErrorEnum = EnumErrorEnum() # For __getattribute__ class ExtensionFeedItemErrorEnum(_CreateEnumTypeUponFirstAccess): ExtensionFeedItemErrorEnum = '''\ class ExtensionFeedItemError(enum.IntEnum): """ Enum describing possible extension feed item errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. VALUE_OUT_OF_RANGE (int): Value is not within the accepted range. URL_LIST_TOO_LONG (int): Url list is too long. CANNOT_HAVE_RESTRICTION_ON_EMPTY_GEO_TARGETING (int): Cannot have a geo targeting restriction without having geo targeting. CANNOT_SET_WITH_FINAL_URLS (int): Cannot simultaneously set sitelink field with final urls. CANNOT_SET_WITHOUT_FINAL_URLS (int): Must set field with final urls. INVALID_PHONE_NUMBER (int): Phone number for a call extension is invalid. PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY (int): Phone number for a call extension is not supported for the given country code. CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED (int): A carrier specific number in short format is not allowed for call extensions. PREMIUM_RATE_NUMBER_NOT_ALLOWED (int): Premium rate numbers are not allowed for call extensions. DISALLOWED_NUMBER_TYPE (int): Phone number type for a call extension is not allowed. For example, personal number is not allowed for a call extension in most regions. INVALID_DOMESTIC_PHONE_NUMBER_FORMAT (int): Phone number for a call extension does not meet domestic format requirements. VANITY_PHONE_NUMBER_NOT_ALLOWED (int): Vanity phone numbers (i.e. those including letters) are not allowed for call extensions. INVALID_CALL_CONVERSION_ACTION (int): Call conversion action provided for a call extension is invalid. CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING (int): For a call extension, the customer is not whitelisted for call tracking. CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY (int): Call tracking is not supported for the given country for a call extension. CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED (int): Customer hasn't consented for call recording, which is required for creating/updating call feed items. Please see https://support.google.com/google-ads/answer/7412639. INVALID_APP_ID (int): App id provided for an app extension is invalid. QUOTES_IN_REVIEW_EXTENSION_SNIPPET (int): Quotation marks present in the review text for a review extension. HYPHENS_IN_REVIEW_EXTENSION_SNIPPET (int): Hyphen character present in the review text for a review extension. REVIEW_EXTENSION_SOURCE_INELIGIBLE (int): A blacklisted review source name or url was provided for a review extension. SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT (int): Review source name should not be found in the review text. INCONSISTENT_CURRENCY_CODES (int): Inconsistent currency codes. PRICE_EXTENSION_HAS_DUPLICATED_HEADERS (int): Price extension cannot have duplicated headers. PRICE_ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION (int): Price item cannot have duplicated header and description. PRICE_EXTENSION_HAS_TOO_FEW_ITEMS (int): Price extension has too few items. PRICE_EXTENSION_HAS_TOO_MANY_ITEMS (int): Price extension has too many items. UNSUPPORTED_VALUE (int): The input value is not currently supported. UNSUPPORTED_VALUE_IN_SELECTED_LANGUAGE (int): The input value is not currently supported in the selected language of an extension. INVALID_DEVICE_PREFERENCE (int): Unknown or unsupported device preference. INVALID_SCHEDULE_END (int): Invalid feed item schedule end time (i.e., endHour = 24 and endMinute != 0). DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE (int): Date time zone does not match the account's time zone. INVALID_SNIPPETS_HEADER (int): Invalid structured snippet header. CANNOT_OPERATE_ON_REMOVED_FEED_ITEM (int): Cannot operate on removed feed item. PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY (int): Phone number not supported when call tracking enabled for country. CONFLICTING_CALL_CONVERSION_SETTINGS (int): Cannot set call\_conversion\_action while call\_conversion\_tracking\_enabled is set to true. EXTENSION_TYPE_MISMATCH (int): The type of the input extension feed item doesn't match the existing extension feed item. EXTENSION_SUBTYPE_REQUIRED (int): The oneof field extension i.e. subtype of extension feed item is required. EXTENSION_TYPE_UNSUPPORTED (int): The referenced feed item is not mapped to a supported extension type. CANNOT_OPERATE_ON_FEED_WITH_MULTIPLE_MAPPINGS (int): Cannot operate on a Feed with more than one active FeedMapping. CANNOT_OPERATE_ON_FEED_WITH_KEY_ATTRIBUTES (int): Cannot operate on a Feed that has key attributes. INVALID_PRICE_FORMAT (int): Input price is not in a valid format. PROMOTION_INVALID_TIME (int): The promotion time is invalid. TOO_MANY_DECIMAL_PLACES_SPECIFIED (int): This field has too many decimal places specified. CONCRETE_EXTENSION_TYPE_REQUIRED (int): Concrete sub type of ExtensionFeedItem is required for this operation. SCHEDULE_END_NOT_AFTER_START (int): Feed item schedule end time must be after start time. """ UNSPECIFIED = 0 UNKNOWN = 1 VALUE_OUT_OF_RANGE = 2 URL_LIST_TOO_LONG = 3 CANNOT_HAVE_RESTRICTION_ON_EMPTY_GEO_TARGETING = 4 CANNOT_SET_WITH_FINAL_URLS = 5 CANNOT_SET_WITHOUT_FINAL_URLS = 6 INVALID_PHONE_NUMBER = 7 PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY = 8 CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED = 9 PREMIUM_RATE_NUMBER_NOT_ALLOWED = 10 DISALLOWED_NUMBER_TYPE = 11 INVALID_DOMESTIC_PHONE_NUMBER_FORMAT = 12 VANITY_PHONE_NUMBER_NOT_ALLOWED = 13 INVALID_CALL_CONVERSION_ACTION = 14 CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING = 15 CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY = 16 CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED = 17 INVALID_APP_ID = 18 QUOTES_IN_REVIEW_EXTENSION_SNIPPET = 19 HYPHENS_IN_REVIEW_EXTENSION_SNIPPET = 20 REVIEW_EXTENSION_SOURCE_INELIGIBLE = 21 SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT = 22 INCONSISTENT_CURRENCY_CODES = 23 PRICE_EXTENSION_HAS_DUPLICATED_HEADERS = 24 PRICE_ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION = 25 PRICE_EXTENSION_HAS_TOO_FEW_ITEMS = 26 PRICE_EXTENSION_HAS_TOO_MANY_ITEMS = 27 UNSUPPORTED_VALUE = 28 UNSUPPORTED_VALUE_IN_SELECTED_LANGUAGE = 29 INVALID_DEVICE_PREFERENCE = 30 INVALID_SCHEDULE_END = 31 DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE = 32 INVALID_SNIPPETS_HEADER = 33 CANNOT_OPERATE_ON_REMOVED_FEED_ITEM = 34 PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY = 35 CONFLICTING_CALL_CONVERSION_SETTINGS = 36 EXTENSION_TYPE_MISMATCH = 37 EXTENSION_SUBTYPE_REQUIRED = 38 EXTENSION_TYPE_UNSUPPORTED = 39 CANNOT_OPERATE_ON_FEED_WITH_MULTIPLE_MAPPINGS = 40 CANNOT_OPERATE_ON_FEED_WITH_KEY_ATTRIBUTES = 41 INVALID_PRICE_FORMAT = 42 PROMOTION_INVALID_TIME = 43 TOO_MANY_DECIMAL_PLACES_SPECIFIED = 44 CONCRETE_EXTENSION_TYPE_REQUIRED = 45 SCHEDULE_END_NOT_AFTER_START = 46 ''' ExtensionFeedItemErrorEnum = ExtensionFeedItemErrorEnum() # For __getattribute__ class ExtensionSettingDeviceEnum(_CreateEnumTypeUponFirstAccess): ExtensionSettingDeviceEnum = '''\ class ExtensionSettingDevice(enum.IntEnum): """ Possbile device types for an extension setting. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. MOBILE (int): Mobile. The extensions in the extension setting will only serve on mobile devices. DESKTOP (int): Desktop. The extensions in the extension setting will only serve on desktop devices. """ UNSPECIFIED = 0 UNKNOWN = 1 MOBILE = 2 DESKTOP = 3 ''' ExtensionSettingDeviceEnum = ExtensionSettingDeviceEnum() # For __getattribute__ class ExtensionSettingErrorEnum(_CreateEnumTypeUponFirstAccess): ExtensionSettingErrorEnum = '''\ class ExtensionSettingError(enum.IntEnum): """ Enum describing possible extension setting errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. EXTENSIONS_REQUIRED (int): A platform restriction was provided without input extensions or existing extensions. FEED_TYPE_EXTENSION_TYPE_MISMATCH (int): The provided feed type does not correspond to the provided extensions. INVALID_FEED_TYPE (int): The provided feed type cannot be used. INVALID_FEED_TYPE_FOR_CUSTOMER_EXTENSION_SETTING (int): The provided feed type cannot be used at the customer level. CANNOT_CHANGE_FEED_ITEM_ON_CREATE (int): Cannot change a feed item field on a CREATE operation. CANNOT_UPDATE_NEWLY_CREATED_EXTENSION (int): Cannot update an extension that is not already in this setting. NO_EXISTING_AD_GROUP_EXTENSION_SETTING_FOR_TYPE (int): There is no existing AdGroupExtensionSetting for this type. NO_EXISTING_CAMPAIGN_EXTENSION_SETTING_FOR_TYPE (int): There is no existing CampaignExtensionSetting for this type. NO_EXISTING_CUSTOMER_EXTENSION_SETTING_FOR_TYPE (int): There is no existing CustomerExtensionSetting for this type. AD_GROUP_EXTENSION_SETTING_ALREADY_EXISTS (int): The AdGroupExtensionSetting already exists. UPDATE should be used to modify the existing AdGroupExtensionSetting. CAMPAIGN_EXTENSION_SETTING_ALREADY_EXISTS (int): The CampaignExtensionSetting already exists. UPDATE should be used to modify the existing CampaignExtensionSetting. CUSTOMER_EXTENSION_SETTING_ALREADY_EXISTS (int): The CustomerExtensionSetting already exists. UPDATE should be used to modify the existing CustomerExtensionSetting. AD_GROUP_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active ad group feed already exists for this place holder type. CAMPAIGN_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active campaign feed already exists for this place holder type. CUSTOMER_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE (int): An active customer feed already exists for this place holder type. VALUE_OUT_OF_RANGE (int): Value is not within the accepted range. CANNOT_SET_FIELD_WITH_FINAL_URLS (int): Cannot simultaneously set specified field with final urls. FINAL_URLS_NOT_SET (int): Must set field with final urls. INVALID_PHONE_NUMBER (int): Phone number for a call extension is invalid. PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY (int): Phone number for a call extension is not supported for the given country code. CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED (int): A carrier specific number in short format is not allowed for call extensions. PREMIUM_RATE_NUMBER_NOT_ALLOWED (int): Premium rate numbers are not allowed for call extensions. DISALLOWED_NUMBER_TYPE (int): Phone number type for a call extension is not allowed. INVALID_DOMESTIC_PHONE_NUMBER_FORMAT (int): Phone number for a call extension does not meet domestic format requirements. VANITY_PHONE_NUMBER_NOT_ALLOWED (int): Vanity phone numbers (i.e. those including letters) are not allowed for call extensions. INVALID_COUNTRY_CODE (int): Country code provided for a call extension is invalid. INVALID_CALL_CONVERSION_TYPE_ID (int): Call conversion type id provided for a call extension is invalid. CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING (int): For a call extension, the customer is not whitelisted for call tracking. CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY (int): Call tracking is not supported for the given country for a call extension. INVALID_APP_ID (int): App id provided for an app extension is invalid. QUOTES_IN_REVIEW_EXTENSION_SNIPPET (int): Quotation marks present in the review text for a review extension. HYPHENS_IN_REVIEW_EXTENSION_SNIPPET (int): Hyphen character present in the review text for a review extension. REVIEW_EXTENSION_SOURCE_NOT_ELIGIBLE (int): A blacklisted review source name or url was provided for a review extension. SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT (int): Review source name should not be found in the review text. MISSING_FIELD (int): Field must be set. INCONSISTENT_CURRENCY_CODES (int): Inconsistent currency codes. PRICE_EXTENSION_HAS_DUPLICATED_HEADERS (int): Price extension cannot have duplicated headers. PRICE_ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION (int): Price item cannot have duplicated header and description. PRICE_EXTENSION_HAS_TOO_FEW_ITEMS (int): Price extension has too few items PRICE_EXTENSION_HAS_TOO_MANY_ITEMS (int): Price extension has too many items UNSUPPORTED_VALUE (int): The input value is not currently supported. INVALID_DEVICE_PREFERENCE (int): Unknown or unsupported device preference. INVALID_SCHEDULE_END (int): Invalid feed item schedule end time (i.e., endHour = 24 and endMinute != 0). DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE (int): Date time zone does not match the account's time zone. OVERLAPPING_SCHEDULES_NOT_ALLOWED (int): Overlapping feed item schedule times (e.g., 7-10AM and 8-11AM) are not allowed. SCHEDULE_END_NOT_AFTER_START (int): Feed item schedule end time must be after start time. TOO_MANY_SCHEDULES_PER_DAY (int): There are too many feed item schedules per day. DUPLICATE_EXTENSION_FEED_ITEM_EDIT (int): Cannot edit the same extension feed item more than once in the same request. INVALID_SNIPPETS_HEADER (int): Invalid structured snippet header. PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY (int): Phone number with call tracking enabled is not supported for the specified country. CAMPAIGN_TARGETING_MISMATCH (int): The targeted adgroup must belong to the targeted campaign. CANNOT_OPERATE_ON_REMOVED_FEED (int): The feed used by the ExtensionSetting is removed and cannot be operated on. Remove the ExtensionSetting to allow a new one to be created using an active feed. EXTENSION_TYPE_REQUIRED (int): The ExtensionFeedItem type is required for this operation. INCOMPATIBLE_UNDERLYING_MATCHING_FUNCTION (int): The matching function that links the extension feed to the customer, campaign, or ad group is not compatible with the ExtensionSetting services. START_DATE_AFTER_END_DATE (int): Start date must be before end date. INVALID_PRICE_FORMAT (int): Input price is not in a valid format. PROMOTION_INVALID_TIME (int): The promotion time is invalid. PROMOTION_CANNOT_SET_PERCENT_DISCOUNT_AND_MONEY_DISCOUNT (int): Cannot set both percent discount and money discount fields. PROMOTION_CANNOT_SET_PROMOTION_CODE_AND_ORDERS_OVER_AMOUNT (int): Cannot set both promotion code and orders over amount fields. TOO_MANY_DECIMAL_PLACES_SPECIFIED (int): This field has too many decimal places specified. INVALID_LANGUAGE_CODE (int): The language code is not valid. UNSUPPORTED_LANGUAGE (int): The language is not supported. CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED (int): Customer hasn't consented for call recording, which is required for adding/updating call extensions. Please see https://support.google.com/google-ads/answer/7412639. EXTENSION_SETTING_UPDATE_IS_A_NOOP (int): The UPDATE operation does not specify any fields other than the resource name in the update mask. """ UNSPECIFIED = 0 UNKNOWN = 1 EXTENSIONS_REQUIRED = 2 FEED_TYPE_EXTENSION_TYPE_MISMATCH = 3 INVALID_FEED_TYPE = 4 INVALID_FEED_TYPE_FOR_CUSTOMER_EXTENSION_SETTING = 5 CANNOT_CHANGE_FEED_ITEM_ON_CREATE = 6 CANNOT_UPDATE_NEWLY_CREATED_EXTENSION = 7 NO_EXISTING_AD_GROUP_EXTENSION_SETTING_FOR_TYPE = 8 NO_EXISTING_CAMPAIGN_EXTENSION_SETTING_FOR_TYPE = 9 NO_EXISTING_CUSTOMER_EXTENSION_SETTING_FOR_TYPE = 10 AD_GROUP_EXTENSION_SETTING_ALREADY_EXISTS = 11 CAMPAIGN_EXTENSION_SETTING_ALREADY_EXISTS = 12 CUSTOMER_EXTENSION_SETTING_ALREADY_EXISTS = 13 AD_GROUP_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 14 CAMPAIGN_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 15 CUSTOMER_FEED_ALREADY_EXISTS_FOR_PLACEHOLDER_TYPE = 16 VALUE_OUT_OF_RANGE = 17 CANNOT_SET_FIELD_WITH_FINAL_URLS = 18 FINAL_URLS_NOT_SET = 19 INVALID_PHONE_NUMBER = 20 PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY = 21 CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED = 22 PREMIUM_RATE_NUMBER_NOT_ALLOWED = 23 DISALLOWED_NUMBER_TYPE = 24 INVALID_DOMESTIC_PHONE_NUMBER_FORMAT = 25 VANITY_PHONE_NUMBER_NOT_ALLOWED = 26 INVALID_COUNTRY_CODE = 27 INVALID_CALL_CONVERSION_TYPE_ID = 28 CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING = 29 CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY = 30 INVALID_APP_ID = 31 QUOTES_IN_REVIEW_EXTENSION_SNIPPET = 32 HYPHENS_IN_REVIEW_EXTENSION_SNIPPET = 33 REVIEW_EXTENSION_SOURCE_NOT_ELIGIBLE = 34 SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT = 35 MISSING_FIELD = 36 INCONSISTENT_CURRENCY_CODES = 37 PRICE_EXTENSION_HAS_DUPLICATED_HEADERS = 38 PRICE_ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION = 39 PRICE_EXTENSION_HAS_TOO_FEW_ITEMS = 40 PRICE_EXTENSION_HAS_TOO_MANY_ITEMS = 41 UNSUPPORTED_VALUE = 42 INVALID_DEVICE_PREFERENCE = 43 INVALID_SCHEDULE_END = 45 DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE = 47 OVERLAPPING_SCHEDULES_NOT_ALLOWED = 48 SCHEDULE_END_NOT_AFTER_START = 49 TOO_MANY_SCHEDULES_PER_DAY = 50 DUPLICATE_EXTENSION_FEED_ITEM_EDIT = 51 INVALID_SNIPPETS_HEADER = 52 PHONE_NUMBER_NOT_SUPPORTED_WITH_CALLTRACKING_FOR_COUNTRY = 53 CAMPAIGN_TARGETING_MISMATCH = 54 CANNOT_OPERATE_ON_REMOVED_FEED = 55 EXTENSION_TYPE_REQUIRED = 56 INCOMPATIBLE_UNDERLYING_MATCHING_FUNCTION = 57 START_DATE_AFTER_END_DATE = 58 INVALID_PRICE_FORMAT = 59 PROMOTION_INVALID_TIME = 60 PROMOTION_CANNOT_SET_PERCENT_DISCOUNT_AND_MONEY_DISCOUNT = 61 PROMOTION_CANNOT_SET_PROMOTION_CODE_AND_ORDERS_OVER_AMOUNT = 62 TOO_MANY_DECIMAL_PLACES_SPECIFIED = 63 INVALID_LANGUAGE_CODE = 64 UNSUPPORTED_LANGUAGE = 65 CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED = 66 EXTENSION_SETTING_UPDATE_IS_A_NOOP = 67 ''' ExtensionSettingErrorEnum = ExtensionSettingErrorEnum() # For __getattribute__ class ExtensionTypeEnum(_CreateEnumTypeUponFirstAccess): ExtensionTypeEnum = '''\ class ExtensionType(enum.IntEnum): """ Possible data types for an extension in an extension setting. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NONE (int): None. APP (int): App. CALL (int): Call. CALLOUT (int): Callout. MESSAGE (int): Message. PRICE (int): Price. PROMOTION (int): Promotion. SITELINK (int): Sitelink. STRUCTURED_SNIPPET (int): Structured snippet. LOCATION (int): Location. AFFILIATE_LOCATION (int): Affiliate location. HOTEL_CALLOUT (int): Hotel callout """ UNSPECIFIED = 0 UNKNOWN = 1 NONE = 2 APP = 3 CALL = 4 CALLOUT = 5 MESSAGE = 6 PRICE = 7 PROMOTION = 8 SITELINK = 10 STRUCTURED_SNIPPET = 11 LOCATION = 12 AFFILIATE_LOCATION = 13 HOTEL_CALLOUT = 15 ''' ExtensionTypeEnum = ExtensionTypeEnum() # For __getattribute__ class ExternalConversionSourceEnum(_CreateEnumTypeUponFirstAccess): ExternalConversionSourceEnum = '''\ class ExternalConversionSource(enum.IntEnum): """ The external conversion source that is associated with a ConversionAction. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Represents value unknown in this version. WEBPAGE (int): Conversion that occurs when a user navigates to a particular webpage after viewing an ad; Displayed in Google Ads UI as 'Website'. ANALYTICS (int): Conversion that comes from linked Google Analytics goal or transaction; Displayed in Google Ads UI as 'Analytics'. UPLOAD (int): Website conversion that is uploaded through ConversionUploadService; Displayed in Google Ads UI as 'Import from clicks'. AD_CALL_METRICS (int): Conversion that occurs when a user clicks on a call extension directly on an ad; Displayed in Google Ads UI as 'Calls from ads'. WEBSITE_CALL_METRICS (int): Conversion that occurs when a user calls a dynamically-generated phone number (by installed javascript) from an advertiser's website after clicking on an ad; Displayed in Google Ads UI as 'Calls from website'. STORE_VISITS (int): Conversion that occurs when a user visits an advertiser's retail store after clicking on a Google ad; Displayed in Google Ads UI as 'Store visits'. ANDROID_IN_APP (int): Conversion that occurs when a user takes an in-app action such as a purchase in an Android app; Displayed in Google Ads UI as 'Android in-app action'. IOS_IN_APP (int): Conversion that occurs when a user takes an in-app action such as a purchase in an iOS app; Displayed in Google Ads UI as 'iOS in-app action'. IOS_FIRST_OPEN (int): Conversion that occurs when a user opens an iOS app for the first time; Displayed in Google Ads UI as 'iOS app install (first open)'. APP_UNSPECIFIED (int): Legacy app conversions that do not have an AppPlatform provided; Displayed in Google Ads UI as 'Mobile app'. ANDROID_FIRST_OPEN (int): Conversion that occurs when a user opens an Android app for the first time; Displayed in Google Ads UI as 'Android app install (first open)'. UPLOAD_CALLS (int): Call conversion that is uploaded through ConversionUploadService; Displayed in Google Ads UI as 'Import from calls'. FIREBASE (int): Conversion that comes from a linked Firebase event; Displayed in Google Ads UI as 'Firebase'. CLICK_TO_CALL (int): Conversion that occurs when a user clicks on a mobile phone number; Displayed in Google Ads UI as 'Phone number clicks'. SALESFORCE (int): Conversion that comes from Salesforce; Displayed in Google Ads UI as 'Salesforce.com'. STORE_SALES_CRM (int): Conversion that comes from in-store purchases recorded by CRM; Displayed in Google Ads UI as 'Store sales (data partner)'. STORE_SALES_PAYMENT_NETWORK (int): Conversion that comes from in-store purchases from payment network; Displayed in Google Ads UI as 'Store sales (payment network)'. GOOGLE_PLAY (int): Codeless Google Play conversion; Displayed in Google Ads UI as 'Google Play'. THIRD_PARTY_APP_ANALYTICS (int): Conversion that comes from a linked third-party app analytics event; Displayed in Google Ads UI as 'Third-party app analytics'. GOOGLE_ATTRIBUTION (int): Conversion that is controlled by Google Attribution. STORE_SALES_DIRECT (int): Store Sales conversion based on first-party or third-party merchant data uploads. Displayed in Google Ads UI as 'Store sales (direct)'. """ UNSPECIFIED = 0 UNKNOWN = 1 WEBPAGE = 2 ANALYTICS = 3 UPLOAD = 4 AD_CALL_METRICS = 5 WEBSITE_CALL_METRICS = 6 STORE_VISITS = 7 ANDROID_IN_APP = 8 IOS_IN_APP = 9 IOS_FIRST_OPEN = 10 APP_UNSPECIFIED = 11 ANDROID_FIRST_OPEN = 12 UPLOAD_CALLS = 13 FIREBASE = 14 CLICK_TO_CALL = 15 SALESFORCE = 16 STORE_SALES_CRM = 17 STORE_SALES_PAYMENT_NETWORK = 18 GOOGLE_PLAY = 19 THIRD_PARTY_APP_ANALYTICS = 20 GOOGLE_ATTRIBUTION = 21 STORE_SALES_DIRECT = 22 ''' ExternalConversionSourceEnum = ExternalConversionSourceEnum() # For __getattribute__ class FeedAttributeOperation(_CreateEnumTypeUponFirstAccess): FeedAttributeOperation = '''\ class Operator(enum.IntEnum): """ The operator. Attributes: UNSPECIFIED (int): Unspecified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADD (int): Add the attribute to the existing attributes. """ UNSPECIFIED = 0 UNKNOWN = 1 ADD = 2 ''' FeedAttributeOperation = FeedAttributeOperation() # For __getattribute__ class FeedAttributeReferenceErrorEnum(_CreateEnumTypeUponFirstAccess): FeedAttributeReferenceErrorEnum = '''\ class FeedAttributeReferenceError(enum.IntEnum): """ Enum describing possible feed attribute reference errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_REFERENCE_REMOVED_FEED (int): A feed referenced by ID has been removed. INVALID_FEED_NAME (int): There is no enabled feed with the given name. INVALID_FEED_ATTRIBUTE_NAME (int): There is no feed attribute in an enabled feed with the given name. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_REFERENCE_REMOVED_FEED = 2 INVALID_FEED_NAME = 3 INVALID_FEED_ATTRIBUTE_NAME = 4 ''' FeedAttributeReferenceErrorEnum = FeedAttributeReferenceErrorEnum() # For __getattribute__ class FeedAttributeTypeEnum(_CreateEnumTypeUponFirstAccess): FeedAttributeTypeEnum = '''\ class FeedAttributeType(enum.IntEnum): """ Possible data types for a feed attribute. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INT64 (int): Int64. DOUBLE (int): Double. STRING (int): String. BOOLEAN (int): Boolean. URL (int): Url. DATE_TIME (int): Datetime. INT64_LIST (int): Int64 list. DOUBLE_LIST (int): Double (8 bytes) list. STRING_LIST (int): String list. BOOLEAN_LIST (int): Boolean list. URL_LIST (int): Url list. DATE_TIME_LIST (int): Datetime list. PRICE (int): Price. """ UNSPECIFIED = 0 UNKNOWN = 1 INT64 = 2 DOUBLE = 3 STRING = 4 BOOLEAN = 5 URL = 6 DATE_TIME = 7 INT64_LIST = 8 DOUBLE_LIST = 9 STRING_LIST = 10 BOOLEAN_LIST = 11 URL_LIST = 12 DATE_TIME_LIST = 13 PRICE = 14 ''' FeedAttributeTypeEnum = FeedAttributeTypeEnum() # For __getattribute__ class FeedErrorEnum(_CreateEnumTypeUponFirstAccess): FeedErrorEnum = '''\ class FeedError(enum.IntEnum): """ Enum describing possible feed errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ATTRIBUTE_NAMES_NOT_UNIQUE (int): The names of the FeedAttributes must be unique. ATTRIBUTES_DO_NOT_MATCH_EXISTING_ATTRIBUTES (int): The attribute list must be an exact copy of the existing list if the attribute ID's are present. CANNOT_SPECIFY_USER_ORIGIN_FOR_SYSTEM_FEED (int): Cannot specify USER origin for a system generated feed. CANNOT_SPECIFY_GOOGLE_ORIGIN_FOR_NON_SYSTEM_FEED (int): Cannot specify GOOGLE origin for a non-system generated feed. CANNOT_SPECIFY_FEED_ATTRIBUTES_FOR_SYSTEM_FEED (int): Cannot specify feed attributes for system feed. CANNOT_UPDATE_FEED_ATTRIBUTES_WITH_ORIGIN_GOOGLE (int): Cannot update FeedAttributes on feed with origin GOOGLE. FEED_REMOVED (int): The given ID refers to a removed Feed. Removed Feeds are immutable. INVALID_ORIGIN_VALUE (int): The origin of the feed is not valid for the client. FEED_ORIGIN_IS_NOT_USER (int): A user can only create and modify feeds with USER origin. INVALID_AUTH_TOKEN_FOR_EMAIL (int): Invalid auth token for the given email. INVALID_EMAIL (int): Invalid email specified. DUPLICATE_FEED_NAME (int): Feed name matches that of another active Feed. INVALID_FEED_NAME (int): Name of feed is not allowed. MISSING_OAUTH_INFO (int): Missing OAuthInfo. NEW_ATTRIBUTE_CANNOT_BE_PART_OF_UNIQUE_KEY (int): New FeedAttributes must not affect the unique key. TOO_MANY_ATTRIBUTES (int): Too many FeedAttributes for a Feed. INVALID_BUSINESS_ACCOUNT (int): The business account is not valid. BUSINESS_ACCOUNT_CANNOT_ACCESS_LOCATION_ACCOUNT (int): Business account cannot access Google My Business account. INVALID_AFFILIATE_CHAIN_ID (int): Invalid chain ID provided for affiliate location feed. DUPLICATE_SYSTEM_FEED (int): There is already a feed with the given system feed generation data. GMB_ACCESS_ERROR (int): An error occurred accessing GMB account. CANNOT_HAVE_LOCATION_AND_AFFILIATE_LOCATION_FEEDS (int): A customer cannot have both LOCATION and AFFILIATE\_LOCATION feeds. """ UNSPECIFIED = 0 UNKNOWN = 1 ATTRIBUTE_NAMES_NOT_UNIQUE = 2 ATTRIBUTES_DO_NOT_MATCH_EXISTING_ATTRIBUTES = 3 CANNOT_SPECIFY_USER_ORIGIN_FOR_SYSTEM_FEED = 4 CANNOT_SPECIFY_GOOGLE_ORIGIN_FOR_NON_SYSTEM_FEED = 5 CANNOT_SPECIFY_FEED_ATTRIBUTES_FOR_SYSTEM_FEED = 6 CANNOT_UPDATE_FEED_ATTRIBUTES_WITH_ORIGIN_GOOGLE = 7 FEED_REMOVED = 8 INVALID_ORIGIN_VALUE = 9 FEED_ORIGIN_IS_NOT_USER = 10 INVALID_AUTH_TOKEN_FOR_EMAIL = 11 INVALID_EMAIL = 12 DUPLICATE_FEED_NAME = 13 INVALID_FEED_NAME = 14 MISSING_OAUTH_INFO = 15 NEW_ATTRIBUTE_CANNOT_BE_PART_OF_UNIQUE_KEY = 16 TOO_MANY_ATTRIBUTES = 17 INVALID_BUSINESS_ACCOUNT = 18 BUSINESS_ACCOUNT_CANNOT_ACCESS_LOCATION_ACCOUNT = 19 INVALID_AFFILIATE_CHAIN_ID = 20 DUPLICATE_SYSTEM_FEED = 21 GMB_ACCESS_ERROR = 22 CANNOT_HAVE_LOCATION_AND_AFFILIATE_LOCATION_FEEDS = 23 ''' FeedErrorEnum = FeedErrorEnum() # For __getattribute__ class FeedItemErrorEnum(_CreateEnumTypeUponFirstAccess): FeedItemErrorEnum = '''\ class FeedItemError(enum.IntEnum): """ Enum describing possible feed item errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_CONVERT_ATTRIBUTE_VALUE_FROM_STRING (int): Cannot convert the feed attribute value from string to its real type. CANNOT_OPERATE_ON_REMOVED_FEED_ITEM (int): Cannot operate on removed feed item. DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE (int): Date time zone does not match the account's time zone. KEY_ATTRIBUTES_NOT_FOUND (int): Feed item with the key attributes could not be found. INVALID_URL (int): Url feed attribute value is not valid. MISSING_KEY_ATTRIBUTES (int): Some key attributes are missing. KEY_ATTRIBUTES_NOT_UNIQUE (int): Feed item has same key attributes as another feed item. CANNOT_MODIFY_KEY_ATTRIBUTE_VALUE (int): Cannot modify key attributes on an existing feed item. SIZE_TOO_LARGE_FOR_MULTI_VALUE_ATTRIBUTE (int): The feed attribute value is too large. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_CONVERT_ATTRIBUTE_VALUE_FROM_STRING = 2 CANNOT_OPERATE_ON_REMOVED_FEED_ITEM = 3 DATE_TIME_MUST_BE_IN_ACCOUNT_TIME_ZONE = 4 KEY_ATTRIBUTES_NOT_FOUND = 5 INVALID_URL = 6 MISSING_KEY_ATTRIBUTES = 7 KEY_ATTRIBUTES_NOT_UNIQUE = 8 CANNOT_MODIFY_KEY_ATTRIBUTE_VALUE = 9 SIZE_TOO_LARGE_FOR_MULTI_VALUE_ATTRIBUTE = 10 ''' FeedItemErrorEnum = FeedItemErrorEnum() # For __getattribute__ class FeedItemQualityApprovalStatusEnum(_CreateEnumTypeUponFirstAccess): FeedItemQualityApprovalStatusEnum = '''\ class FeedItemQualityApprovalStatus(enum.IntEnum): """ The possible quality evaluation approval statuses of a feed item. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. APPROVED (int): Meets all quality expectations. DISAPPROVED (int): Does not meet some quality expectations. The specific reason is found in the quality\_disapproval\_reasons field. """ UNSPECIFIED = 0 UNKNOWN = 1 APPROVED = 2 DISAPPROVED = 3 ''' FeedItemQualityApprovalStatusEnum = FeedItemQualityApprovalStatusEnum() # For __getattribute__ class FeedItemQualityDisapprovalReasonEnum(_CreateEnumTypeUponFirstAccess): FeedItemQualityDisapprovalReasonEnum = '''\ class FeedItemQualityDisapprovalReason(enum.IntEnum): """ The possible quality evaluation disapproval reasons of a feed item. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PRICE_TABLE_REPETITIVE_HEADERS (int): Price contains repetitive headers. PRICE_TABLE_REPETITIVE_DESCRIPTION (int): Price contains repetitive description. PRICE_TABLE_INCONSISTENT_ROWS (int): Price contains inconsistent items. PRICE_DESCRIPTION_HAS_PRICE_QUALIFIERS (int): Price contains qualifiers in description. PRICE_UNSUPPORTED_LANGUAGE (int): Price contains an unsupported language. PRICE_TABLE_ROW_HEADER_TABLE_TYPE_MISMATCH (int): Price item header is not relevant to the price type. PRICE_TABLE_ROW_HEADER_HAS_PROMOTIONAL_TEXT (int): Price item header has promotional text. PRICE_TABLE_ROW_DESCRIPTION_NOT_RELEVANT (int): Price item description is not relevant to the item header. PRICE_TABLE_ROW_DESCRIPTION_HAS_PROMOTIONAL_TEXT (int): Price item description contains promotional text. PRICE_TABLE_ROW_HEADER_DESCRIPTION_REPETITIVE (int): Price item header and description are repetitive. PRICE_TABLE_ROW_UNRATEABLE (int): Price item is in a foreign language, nonsense, or can't be rated. PRICE_TABLE_ROW_PRICE_INVALID (int): Price item price is invalid or inaccurate. PRICE_TABLE_ROW_URL_INVALID (int): Price item URL is invalid or irrelevant. PRICE_HEADER_OR_DESCRIPTION_HAS_PRICE (int): Price item header or description has price. STRUCTURED_SNIPPETS_HEADER_POLICY_VIOLATED (int): Structured snippet values do not match the header. STRUCTURED_SNIPPETS_REPEATED_VALUES (int): Structured snippet values are repeated. STRUCTURED_SNIPPETS_EDITORIAL_GUIDELINES (int): Structured snippet values violate editorial guidelines like punctuation. STRUCTURED_SNIPPETS_HAS_PROMOTIONAL_TEXT (int): Structured snippet contain promotional text. """ UNSPECIFIED = 0 UNKNOWN = 1 PRICE_TABLE_REPETITIVE_HEADERS = 2 PRICE_TABLE_REPETITIVE_DESCRIPTION = 3 PRICE_TABLE_INCONSISTENT_ROWS = 4 PRICE_DESCRIPTION_HAS_PRICE_QUALIFIERS = 5 PRICE_UNSUPPORTED_LANGUAGE = 6 PRICE_TABLE_ROW_HEADER_TABLE_TYPE_MISMATCH = 7 PRICE_TABLE_ROW_HEADER_HAS_PROMOTIONAL_TEXT = 8 PRICE_TABLE_ROW_DESCRIPTION_NOT_RELEVANT = 9 PRICE_TABLE_ROW_DESCRIPTION_HAS_PROMOTIONAL_TEXT = 10 PRICE_TABLE_ROW_HEADER_DESCRIPTION_REPETITIVE = 11 PRICE_TABLE_ROW_UNRATEABLE = 12 PRICE_TABLE_ROW_PRICE_INVALID = 13 PRICE_TABLE_ROW_URL_INVALID = 14 PRICE_HEADER_OR_DESCRIPTION_HAS_PRICE = 15 STRUCTURED_SNIPPETS_HEADER_POLICY_VIOLATED = 16 STRUCTURED_SNIPPETS_REPEATED_VALUES = 17 STRUCTURED_SNIPPETS_EDITORIAL_GUIDELINES = 18 STRUCTURED_SNIPPETS_HAS_PROMOTIONAL_TEXT = 19 ''' FeedItemQualityDisapprovalReasonEnum = FeedItemQualityDisapprovalReasonEnum() # For __getattribute__ class FeedItemStatusEnum(_CreateEnumTypeUponFirstAccess): FeedItemStatusEnum = '''\ class FeedItemStatus(enum.IntEnum): """ Possible statuses of a feed item. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Feed item is enabled. REMOVED (int): Feed item has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' FeedItemStatusEnum = FeedItemStatusEnum() # For __getattribute__ class FeedItemTargetDeviceEnum(_CreateEnumTypeUponFirstAccess): FeedItemTargetDeviceEnum = '''\ class FeedItemTargetDevice(enum.IntEnum): """ Possible data types for a feed item target device. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. MOBILE (int): Mobile. """ UNSPECIFIED = 0 UNKNOWN = 1 MOBILE = 2 ''' FeedItemTargetDeviceEnum = FeedItemTargetDeviceEnum() # For __getattribute__ class FeedItemTargetErrorEnum(_CreateEnumTypeUponFirstAccess): FeedItemTargetErrorEnum = '''\ class FeedItemTargetError(enum.IntEnum): """ Enum describing possible feed item target errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. MUST_SET_TARGET_ONEOF_ON_CREATE (int): On CREATE, the FeedItemTarget must have a populated field in the oneof target. FEED_ITEM_TARGET_ALREADY_EXISTS (int): The specified feed item target already exists, so it cannot be added. FEED_ITEM_SCHEDULES_CANNOT_OVERLAP (int): The schedules for a given feed item cannot overlap. TARGET_LIMIT_EXCEEDED_FOR_GIVEN_TYPE (int): Too many targets of a given type were added for a single feed item. TOO_MANY_SCHEDULES_PER_DAY (int): Too many AdSchedules are enabled for the feed item for the given day. CANNOT_HAVE_ENABLED_CAMPAIGN_AND_ENABLED_AD_GROUP_TARGETS (int): A feed item may either have an enabled campaign target or an enabled ad group target. DUPLICATE_AD_SCHEDULE (int): Duplicate ad schedules aren't allowed. DUPLICATE_KEYWORD (int): Duplicate keywords aren't allowed. """ UNSPECIFIED = 0 UNKNOWN = 1 MUST_SET_TARGET_ONEOF_ON_CREATE = 2 FEED_ITEM_TARGET_ALREADY_EXISTS = 3 FEED_ITEM_SCHEDULES_CANNOT_OVERLAP = 4 TARGET_LIMIT_EXCEEDED_FOR_GIVEN_TYPE = 5 TOO_MANY_SCHEDULES_PER_DAY = 6 CANNOT_HAVE_ENABLED_CAMPAIGN_AND_ENABLED_AD_GROUP_TARGETS = 7 DUPLICATE_AD_SCHEDULE = 8 DUPLICATE_KEYWORD = 9 ''' FeedItemTargetErrorEnum = FeedItemTargetErrorEnum() # For __getattribute__ class FeedItemTargetStatusEnum(_CreateEnumTypeUponFirstAccess): FeedItemTargetStatusEnum = '''\ class FeedItemTargetStatus(enum.IntEnum): """ Possible statuses of a feed item target. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Feed item target is enabled. REMOVED (int): Feed item target has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' FeedItemTargetStatusEnum = FeedItemTargetStatusEnum() # For __getattribute__ class FeedItemTargetTypeEnum(_CreateEnumTypeUponFirstAccess): FeedItemTargetTypeEnum = '''\ class FeedItemTargetType(enum.IntEnum): """ Possible type of a feed item target. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CAMPAIGN (int): Feed item targets a campaign. AD_GROUP (int): Feed item targets an ad group. CRITERION (int): Feed item targets a criterion. """ UNSPECIFIED = 0 UNKNOWN = 1 CAMPAIGN = 2 AD_GROUP = 3 CRITERION = 4 ''' FeedItemTargetTypeEnum = FeedItemTargetTypeEnum() # For __getattribute__ class FeedItemValidationErrorEnum(_CreateEnumTypeUponFirstAccess): FeedItemValidationErrorEnum = '''\ class FeedItemValidationError(enum.IntEnum): """ The possible validation errors of a feed item. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. STRING_TOO_SHORT (int): String is too short. STRING_TOO_LONG (int): String is too long. VALUE_NOT_SPECIFIED (int): Value is not provided. INVALID_DOMESTIC_PHONE_NUMBER_FORMAT (int): Phone number format is invalid for region. INVALID_PHONE_NUMBER (int): String does not represent a phone number. PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY (int): Phone number format is not compatible with country code. PREMIUM_RATE_NUMBER_NOT_ALLOWED (int): Premium rate number is not allowed. DISALLOWED_NUMBER_TYPE (int): Phone number type is not allowed. VALUE_OUT_OF_RANGE (int): Specified value is outside of the valid range. CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY (int): Call tracking is not supported in the selected country. CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING (int): Customer is not whitelisted for call tracking. INVALID_COUNTRY_CODE (int): Country code is invalid. INVALID_APP_ID (int): The specified mobile app id is invalid. MISSING_ATTRIBUTES_FOR_FIELDS (int): Some required field attributes are missing. INVALID_TYPE_ID (int): Invalid email button type for email extension. INVALID_EMAIL_ADDRESS (int): Email address is invalid. INVALID_HTTPS_URL (int): The HTTPS URL in email extension is invalid. MISSING_DELIVERY_ADDRESS (int): Delivery address is missing from email extension. START_DATE_AFTER_END_DATE (int): FeedItem scheduling start date comes after end date. MISSING_FEED_ITEM_START_TIME (int): FeedItem scheduling start time is missing. MISSING_FEED_ITEM_END_TIME (int): FeedItem scheduling end time is missing. MISSING_FEED_ITEM_ID (int): Cannot compute system attributes on a FeedItem that has no FeedItemId. VANITY_PHONE_NUMBER_NOT_ALLOWED (int): Call extension vanity phone numbers are not supported. INVALID_REVIEW_EXTENSION_SNIPPET (int): Invalid review text. INVALID_NUMBER_FORMAT (int): Invalid format for numeric value in ad parameter. INVALID_DATE_FORMAT (int): Invalid format for date value in ad parameter. INVALID_PRICE_FORMAT (int): Invalid format for price value in ad parameter. UNKNOWN_PLACEHOLDER_FIELD (int): Unrecognized type given for value in ad parameter. MISSING_ENHANCED_SITELINK_DESCRIPTION_LINE (int): Enhanced sitelinks must have both description lines specified. REVIEW_EXTENSION_SOURCE_INELIGIBLE (int): Review source is ineligible. HYPHENS_IN_REVIEW_EXTENSION_SNIPPET (int): Review text cannot contain hyphens or dashes. DOUBLE_QUOTES_IN_REVIEW_EXTENSION_SNIPPET (int): Review text cannot contain double quote characters. QUOTES_IN_REVIEW_EXTENSION_SNIPPET (int): Review text cannot contain quote characters. INVALID_FORM_ENCODED_PARAMS (int): Parameters are encoded in the wrong format. INVALID_URL_PARAMETER_NAME (int): URL parameter name must contain only letters, numbers, underscores, and dashes. NO_GEOCODING_RESULT (int): Cannot find address location. SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT (int): Review extension text has source name. CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED (int): Some phone numbers can be shorter than usual. Some of these short numbers are carrier-specific, and we disallow those in ad extensions because they will not be available to all users. INVALID_PLACEHOLDER_FIELD_ID (int): Triggered when a request references a placeholder field id that does not exist. INVALID_URL_TAG (int): URL contains invalid ValueTrack tags or format. LIST_TOO_LONG (int): Provided list exceeds acceptable size. INVALID_ATTRIBUTES_COMBINATION (int): Certain combinations of attributes aren't allowed to be specified in the same feed item. DUPLICATE_VALUES (int): An attribute has the same value repeatedly. INVALID_CALL_CONVERSION_ACTION_ID (int): Advertisers can link a conversion action with a phone number to indicate that sufficiently long calls forwarded to that phone number should be counted as conversions of the specified type. This is an error message indicating that the conversion action specified is invalid (e.g., the conversion action does not exist within the appropriate Google Ads account, or it is a type of conversion not appropriate to phone call conversions). CANNOT_SET_WITHOUT_FINAL_URLS (int): Tracking template requires final url to be set. APP_ID_DOESNT_EXIST_IN_APP_STORE (int): An app id was provided that doesn't exist in the given app store. INVALID_FINAL_URL (int): Invalid U2 final url. INVALID_TRACKING_URL (int): Invalid U2 tracking url. INVALID_FINAL_URL_FOR_APP_DOWNLOAD_URL (int): Final URL should start from App download URL. LIST_TOO_SHORT (int): List provided is too short. INVALID_USER_ACTION (int): User Action field has invalid value. INVALID_TYPE_NAME (int): Type field has invalid value. INVALID_EVENT_CHANGE_STATUS (int): Change status for event is invalid. INVALID_SNIPPETS_HEADER (int): The header of a structured snippets extension is not one of the valid headers. INVALID_ANDROID_APP_LINK (int): Android app link is not formatted correctly NUMBER_TYPE_WITH_CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY (int): Phone number incompatible with call tracking for country. RESERVED_KEYWORD_OTHER (int): The input is identical to a reserved keyword DUPLICATE_OPTION_LABELS (int): Each option label in the message extension must be unique. DUPLICATE_OPTION_PREFILLS (int): Each option prefill in the message extension must be unique. UNEQUAL_LIST_LENGTHS (int): In message extensions, the number of optional labels and optional prefills must be the same. INCONSISTENT_CURRENCY_CODES (int): All currency codes in an ad extension must be the same. PRICE_EXTENSION_HAS_DUPLICATED_HEADERS (int): Headers in price extension are not unique. ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION (int): Header and description in an item are the same. PRICE_EXTENSION_HAS_TOO_FEW_ITEMS (int): Price extension has too few items. UNSUPPORTED_VALUE (int): The given value is not supported. INVALID_FINAL_MOBILE_URL (int): Invalid final mobile url. INVALID_KEYWORDLESS_AD_RULE_LABEL (int): The given string value of Label contains invalid characters VALUE_TRACK_PARAMETER_NOT_SUPPORTED (int): The given URL contains value track parameters. UNSUPPORTED_VALUE_IN_SELECTED_LANGUAGE (int): The given value is not supported in the selected language of an extension. INVALID_IOS_APP_LINK (int): The iOS app link is not formatted correctly. MISSING_IOS_APP_LINK_OR_IOS_APP_STORE_ID (int): iOS app link or iOS app store id is missing. PROMOTION_INVALID_TIME (int): Promotion time is invalid. PROMOTION_CANNOT_SET_PERCENT_OFF_AND_MONEY_AMOUNT_OFF (int): Both the percent off and money amount off fields are set. PROMOTION_CANNOT_SET_PROMOTION_CODE_AND_ORDERS_OVER_AMOUNT (int): Both the promotion code and orders over amount fields are set. TOO_MANY_DECIMAL_PLACES_SPECIFIED (int): Too many decimal places are specified. AD_CUSTOMIZERS_NOT_ALLOWED (int): Ad Customizers are present and not allowed. INVALID_LANGUAGE_CODE (int): Language code is not valid. UNSUPPORTED_LANGUAGE (int): Language is not supported. IF_FUNCTION_NOT_ALLOWED (int): IF Function is present and not allowed. INVALID_FINAL_URL_SUFFIX (int): Final url suffix is not valid. INVALID_TAG_IN_FINAL_URL_SUFFIX (int): Final url suffix contains an invalid tag. INVALID_FINAL_URL_SUFFIX_FORMAT (int): Final url suffix is formatted incorrectly. CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED (int): Consent for call recording, which is required for the use of call extensions, was not provided by the advertiser. Please see https://support.google.com/google-ads/answer/7412639. ONLY_ONE_DELIVERY_OPTION_IS_ALLOWED (int): Multiple message delivery options are set. NO_DELIVERY_OPTION_IS_SET (int): No message delivery option is set. INVALID_CONVERSION_REPORTING_STATE (int): String value of conversion reporting state field is not valid. IMAGE_SIZE_WRONG (int): Image size is not right. EMAIL_DELIVERY_NOT_AVAILABLE_IN_COUNTRY (int): Email delivery is not supported in the country specified in the country code field. AUTO_REPLY_NOT_AVAILABLE_IN_COUNTRY (int): Auto reply is not supported in the country specified in the country code field. INVALID_LATITUDE_VALUE (int): Invalid value specified for latitude. INVALID_LONGITUDE_VALUE (int): Invalid value specified for longitude. TOO_MANY_LABELS (int): Too many label fields provided. INVALID_IMAGE_URL (int): Invalid image url. MISSING_LATITUDE_VALUE (int): Latitude value is missing. MISSING_LONGITUDE_VALUE (int): Longitude value is missing. ADDRESS_NOT_FOUND (int): Unable to find address. ADDRESS_NOT_TARGETABLE (int): Cannot target provided address. """ UNSPECIFIED = 0 UNKNOWN = 1 STRING_TOO_SHORT = 2 STRING_TOO_LONG = 3 VALUE_NOT_SPECIFIED = 4 INVALID_DOMESTIC_PHONE_NUMBER_FORMAT = 5 INVALID_PHONE_NUMBER = 6 PHONE_NUMBER_NOT_SUPPORTED_FOR_COUNTRY = 7 PREMIUM_RATE_NUMBER_NOT_ALLOWED = 8 DISALLOWED_NUMBER_TYPE = 9 VALUE_OUT_OF_RANGE = 10 CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY = 11 CUSTOMER_NOT_WHITELISTED_FOR_CALLTRACKING = 12 INVALID_COUNTRY_CODE = 13 INVALID_APP_ID = 14 MISSING_ATTRIBUTES_FOR_FIELDS = 15 INVALID_TYPE_ID = 16 INVALID_EMAIL_ADDRESS = 17 INVALID_HTTPS_URL = 18 MISSING_DELIVERY_ADDRESS = 19 START_DATE_AFTER_END_DATE = 20 MISSING_FEED_ITEM_START_TIME = 21 MISSING_FEED_ITEM_END_TIME = 22 MISSING_FEED_ITEM_ID = 23 VANITY_PHONE_NUMBER_NOT_ALLOWED = 24 INVALID_REVIEW_EXTENSION_SNIPPET = 25 INVALID_NUMBER_FORMAT = 26 INVALID_DATE_FORMAT = 27 INVALID_PRICE_FORMAT = 28 UNKNOWN_PLACEHOLDER_FIELD = 29 MISSING_ENHANCED_SITELINK_DESCRIPTION_LINE = 30 REVIEW_EXTENSION_SOURCE_INELIGIBLE = 31 HYPHENS_IN_REVIEW_EXTENSION_SNIPPET = 32 DOUBLE_QUOTES_IN_REVIEW_EXTENSION_SNIPPET = 33 QUOTES_IN_REVIEW_EXTENSION_SNIPPET = 34 INVALID_FORM_ENCODED_PARAMS = 35 INVALID_URL_PARAMETER_NAME = 36 NO_GEOCODING_RESULT = 37 SOURCE_NAME_IN_REVIEW_EXTENSION_TEXT = 38 CARRIER_SPECIFIC_SHORT_NUMBER_NOT_ALLOWED = 39 INVALID_PLACEHOLDER_FIELD_ID = 40 INVALID_URL_TAG = 41 LIST_TOO_LONG = 42 INVALID_ATTRIBUTES_COMBINATION = 43 DUPLICATE_VALUES = 44 INVALID_CALL_CONVERSION_ACTION_ID = 45 CANNOT_SET_WITHOUT_FINAL_URLS = 46 APP_ID_DOESNT_EXIST_IN_APP_STORE = 47 INVALID_FINAL_URL = 48 INVALID_TRACKING_URL = 49 INVALID_FINAL_URL_FOR_APP_DOWNLOAD_URL = 50 LIST_TOO_SHORT = 51 INVALID_USER_ACTION = 52 INVALID_TYPE_NAME = 53 INVALID_EVENT_CHANGE_STATUS = 54 INVALID_SNIPPETS_HEADER = 55 INVALID_ANDROID_APP_LINK = 56 NUMBER_TYPE_WITH_CALLTRACKING_NOT_SUPPORTED_FOR_COUNTRY = 57 RESERVED_KEYWORD_OTHER = 58 DUPLICATE_OPTION_LABELS = 59 DUPLICATE_OPTION_PREFILLS = 60 UNEQUAL_LIST_LENGTHS = 61 INCONSISTENT_CURRENCY_CODES = 62 PRICE_EXTENSION_HAS_DUPLICATED_HEADERS = 63 ITEM_HAS_DUPLICATED_HEADER_AND_DESCRIPTION = 64 PRICE_EXTENSION_HAS_TOO_FEW_ITEMS = 65 UNSUPPORTED_VALUE = 66 INVALID_FINAL_MOBILE_URL = 67 INVALID_KEYWORDLESS_AD_RULE_LABEL = 68 VALUE_TRACK_PARAMETER_NOT_SUPPORTED = 69 UNSUPPORTED_VALUE_IN_SELECTED_LANGUAGE = 70 INVALID_IOS_APP_LINK = 71 MISSING_IOS_APP_LINK_OR_IOS_APP_STORE_ID = 72 PROMOTION_INVALID_TIME = 73 PROMOTION_CANNOT_SET_PERCENT_OFF_AND_MONEY_AMOUNT_OFF = 74 PROMOTION_CANNOT_SET_PROMOTION_CODE_AND_ORDERS_OVER_AMOUNT = 75 TOO_MANY_DECIMAL_PLACES_SPECIFIED = 76 AD_CUSTOMIZERS_NOT_ALLOWED = 77 INVALID_LANGUAGE_CODE = 78 UNSUPPORTED_LANGUAGE = 79 IF_FUNCTION_NOT_ALLOWED = 80 INVALID_FINAL_URL_SUFFIX = 81 INVALID_TAG_IN_FINAL_URL_SUFFIX = 82 INVALID_FINAL_URL_SUFFIX_FORMAT = 83 CUSTOMER_CONSENT_FOR_CALL_RECORDING_REQUIRED = 84 ONLY_ONE_DELIVERY_OPTION_IS_ALLOWED = 85 NO_DELIVERY_OPTION_IS_SET = 86 INVALID_CONVERSION_REPORTING_STATE = 87 IMAGE_SIZE_WRONG = 88 EMAIL_DELIVERY_NOT_AVAILABLE_IN_COUNTRY = 89 AUTO_REPLY_NOT_AVAILABLE_IN_COUNTRY = 90 INVALID_LATITUDE_VALUE = 91 INVALID_LONGITUDE_VALUE = 92 TOO_MANY_LABELS = 93 INVALID_IMAGE_URL = 94 MISSING_LATITUDE_VALUE = 95 MISSING_LONGITUDE_VALUE = 96 ADDRESS_NOT_FOUND = 97 ADDRESS_NOT_TARGETABLE = 98 ''' FeedItemValidationErrorEnum = FeedItemValidationErrorEnum() # For __getattribute__ class FeedItemValidationStatusEnum(_CreateEnumTypeUponFirstAccess): FeedItemValidationStatusEnum = '''\ class FeedItemValidationStatus(enum.IntEnum): """ The possible validation statuses of a feed item. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): Validation pending. INVALID (int): An error was found. VALID (int): Feed item is semantically well-formed. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 INVALID = 3 VALID = 4 ''' FeedItemValidationStatusEnum = FeedItemValidationStatusEnum() # For __getattribute__ class FeedLinkStatusEnum(_CreateEnumTypeUponFirstAccess): FeedLinkStatusEnum = '''\ class FeedLinkStatus(enum.IntEnum): """ Possible statuses of a feed link. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Feed link is enabled. REMOVED (int): Feed link has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' FeedLinkStatusEnum = FeedLinkStatusEnum() # For __getattribute__ class FeedMappingCriterionTypeEnum(_CreateEnumTypeUponFirstAccess): FeedMappingCriterionTypeEnum = '''\ class FeedMappingCriterionType(enum.IntEnum): """ Possible placeholder types for a feed mapping. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LOCATION_EXTENSION_TARGETING (int): Allows campaign targeting at locations within a location feed. DSA_PAGE_FEED (int): Allows url targeting for your dynamic search ads within a page feed. """ UNSPECIFIED = 0 UNKNOWN = 1 LOCATION_EXTENSION_TARGETING = 4 DSA_PAGE_FEED = 3 ''' FeedMappingCriterionTypeEnum = FeedMappingCriterionTypeEnum() # For __getattribute__ class FeedMappingErrorEnum(_CreateEnumTypeUponFirstAccess): FeedMappingErrorEnum = '''\ class FeedMappingError(enum.IntEnum): """ Enum describing possible feed item errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_PLACEHOLDER_FIELD (int): The given placeholder field does not exist. INVALID_CRITERION_FIELD (int): The given criterion field does not exist. INVALID_PLACEHOLDER_TYPE (int): The given placeholder type does not exist. INVALID_CRITERION_TYPE (int): The given criterion type does not exist. NO_ATTRIBUTE_FIELD_MAPPINGS (int): A feed mapping must contain at least one attribute field mapping. FEED_ATTRIBUTE_TYPE_MISMATCH (int): The type of the feed attribute referenced in the attribute field mapping must match the type of the placeholder field. CANNOT_OPERATE_ON_MAPPINGS_FOR_SYSTEM_GENERATED_FEED (int): A feed mapping for a system generated feed cannot be operated on. MULTIPLE_MAPPINGS_FOR_PLACEHOLDER_TYPE (int): Only one feed mapping for a placeholder type is allowed per feed or customer (depending on the placeholder type). MULTIPLE_MAPPINGS_FOR_CRITERION_TYPE (int): Only one feed mapping for a criterion type is allowed per customer. MULTIPLE_MAPPINGS_FOR_PLACEHOLDER_FIELD (int): Only one feed attribute mapping for a placeholder field is allowed (depending on the placeholder type). MULTIPLE_MAPPINGS_FOR_CRITERION_FIELD (int): Only one feed attribute mapping for a criterion field is allowed (depending on the criterion type). UNEXPECTED_ATTRIBUTE_FIELD_MAPPINGS (int): This feed mapping may not contain any explicit attribute field mappings. LOCATION_PLACEHOLDER_ONLY_FOR_PLACES_FEEDS (int): Location placeholder feed mappings can only be created for Places feeds. CANNOT_MODIFY_MAPPINGS_FOR_TYPED_FEED (int): Mappings for typed feeds cannot be modified. INVALID_PLACEHOLDER_TYPE_FOR_NON_SYSTEM_GENERATED_FEED (int): The given placeholder type can only be mapped to system generated feeds. INVALID_PLACEHOLDER_TYPE_FOR_SYSTEM_GENERATED_FEED_TYPE (int): The given placeholder type cannot be mapped to a system generated feed with the given type. ATTRIBUTE_FIELD_MAPPING_MISSING_FIELD (int): The "field" oneof was not set in an AttributeFieldMapping. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_PLACEHOLDER_FIELD = 2 INVALID_CRITERION_FIELD = 3 INVALID_PLACEHOLDER_TYPE = 4 INVALID_CRITERION_TYPE = 5 NO_ATTRIBUTE_FIELD_MAPPINGS = 7 FEED_ATTRIBUTE_TYPE_MISMATCH = 8 CANNOT_OPERATE_ON_MAPPINGS_FOR_SYSTEM_GENERATED_FEED = 9 MULTIPLE_MAPPINGS_FOR_PLACEHOLDER_TYPE = 10 MULTIPLE_MAPPINGS_FOR_CRITERION_TYPE = 11 MULTIPLE_MAPPINGS_FOR_PLACEHOLDER_FIELD = 12 MULTIPLE_MAPPINGS_FOR_CRITERION_FIELD = 13 UNEXPECTED_ATTRIBUTE_FIELD_MAPPINGS = 14 LOCATION_PLACEHOLDER_ONLY_FOR_PLACES_FEEDS = 15 CANNOT_MODIFY_MAPPINGS_FOR_TYPED_FEED = 16 INVALID_PLACEHOLDER_TYPE_FOR_NON_SYSTEM_GENERATED_FEED = 17 INVALID_PLACEHOLDER_TYPE_FOR_SYSTEM_GENERATED_FEED_TYPE = 18 ATTRIBUTE_FIELD_MAPPING_MISSING_FIELD = 19 ''' FeedMappingErrorEnum = FeedMappingErrorEnum() # For __getattribute__ class FeedMappingStatusEnum(_CreateEnumTypeUponFirstAccess): FeedMappingStatusEnum = '''\ class FeedMappingStatus(enum.IntEnum): """ Possible statuses of a feed mapping. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Feed mapping is enabled. REMOVED (int): Feed mapping has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' FeedMappingStatusEnum = FeedMappingStatusEnum() # For __getattribute__ class FeedOriginEnum(_CreateEnumTypeUponFirstAccess): FeedOriginEnum = '''\ class FeedOrigin(enum.IntEnum): """ Possible values for a feed origin. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. USER (int): The FeedAttributes for this Feed are managed by the user. Users can add FeedAttributes to this Feed. GOOGLE (int): The FeedAttributes for an GOOGLE Feed are created by Google. A feed of this type is maintained by Google and will have the correct attributes for the placeholder type of the feed. """ UNSPECIFIED = 0 UNKNOWN = 1 USER = 2 GOOGLE = 3 ''' FeedOriginEnum = FeedOriginEnum() # For __getattribute__ class FeedStatusEnum(_CreateEnumTypeUponFirstAccess): FeedStatusEnum = '''\ class FeedStatus(enum.IntEnum): """ Possible statuses of a feed. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Feed is enabled. REMOVED (int): Feed has been removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' FeedStatusEnum = FeedStatusEnum() # For __getattribute__ class FieldErrorEnum(_CreateEnumTypeUponFirstAccess): FieldErrorEnum = '''\ class FieldError(enum.IntEnum): """ Enum describing possible field errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. REQUIRED (int): The required field was not present. IMMUTABLE_FIELD (int): The field attempted to be mutated is immutable. INVALID_VALUE (int): The field's value is invalid. VALUE_MUST_BE_UNSET (int): The field cannot be set. REQUIRED_NONEMPTY_LIST (int): The required repeated field was empty. FIELD_CANNOT_BE_CLEARED (int): The field cannot be cleared. BLACKLISTED_VALUE (int): The field's value is on a blacklist for this field. """ UNSPECIFIED = 0 UNKNOWN = 1 REQUIRED = 2 IMMUTABLE_FIELD = 3 INVALID_VALUE = 4 VALUE_MUST_BE_UNSET = 5 REQUIRED_NONEMPTY_LIST = 6 FIELD_CANNOT_BE_CLEARED = 7 BLACKLISTED_VALUE = 8 ''' FieldErrorEnum = FieldErrorEnum() # For __getattribute__ class FieldMaskErrorEnum(_CreateEnumTypeUponFirstAccess): FieldMaskErrorEnum = '''\ class FieldMaskError(enum.IntEnum): """ Enum describing possible field mask errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FIELD_MASK_MISSING (int): The field mask must be provided for update operations. FIELD_MASK_NOT_ALLOWED (int): The field mask must be empty for create and remove operations. FIELD_NOT_FOUND (int): The field mask contained an invalid field. FIELD_HAS_SUBFIELDS (int): The field mask updated a field with subfields. Fields with subfields may be cleared, but not updated. To fix this, the field mask should select all the subfields of the invalid field. """ UNSPECIFIED = 0 UNKNOWN = 1 FIELD_MASK_MISSING = 5 FIELD_MASK_NOT_ALLOWED = 4 FIELD_NOT_FOUND = 2 FIELD_HAS_SUBFIELDS = 3 ''' FieldMaskErrorEnum = FieldMaskErrorEnum() # For __getattribute__ class FlightPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): FlightPlaceholderFieldEnum = '''\ class FlightPlaceholderField(enum.IntEnum): """ Possible values for Flight placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DESTINATION_ID (int): Data Type: STRING. Required. Destination id. Example: PAR, LON. For feed items that only have destination id, destination id must be a unique key. For feed items that have both destination id and origin id, then the combination must be a unique key. ORIGIN_ID (int): Data Type: STRING. Origin id. Example: PAR, LON. Optional. Combination of destination id and origin id must be unique per offer. FLIGHT_DESCRIPTION (int): Data Type: STRING. Required. Main headline with product name to be shown in dynamic ad. ORIGIN_NAME (int): Data Type: STRING. Shorter names are recommended. DESTINATION_NAME (int): Data Type: STRING. Shorter names are recommended. FLIGHT_PRICE (int): Data Type: STRING. Price to be shown in the ad. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" FLIGHT_SALE_PRICE (int): Data Type: STRING. Sale price to be shown in the ad. Example: "80.00 USD" FORMATTED_SALE_PRICE (int): Data Type: STRING. Formatted sale price to be shown in the ad. Example: "On sale for $80.00", "$60 - $80" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. FINAL_URLS (int): Data Type: URL\_LIST. Required. Final URLs for the ad when using Upgraded URLs. User will be redirected to these URLs when they click on an ad, or when they click on a specific flight for ads that show multiple flights. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_DESTINATION_IDS (int): Data Type: STRING\_LIST. List of recommended destination IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 DESTINATION_ID = 2 ORIGIN_ID = 3 FLIGHT_DESCRIPTION = 4 ORIGIN_NAME = 5 DESTINATION_NAME = 6 FLIGHT_PRICE = 7 FORMATTED_PRICE = 8 FLIGHT_SALE_PRICE = 9 FORMATTED_SALE_PRICE = 10 IMAGE_URL = 11 FINAL_URLS = 12 FINAL_MOBILE_URLS = 13 TRACKING_URL = 14 ANDROID_APP_LINK = 15 SIMILAR_DESTINATION_IDS = 16 IOS_APP_LINK = 17 IOS_APP_STORE_ID = 18 ''' FlightPlaceholderFieldEnum = FlightPlaceholderFieldEnum() # For __getattribute__ class FrequencyCapEventTypeEnum(_CreateEnumTypeUponFirstAccess): FrequencyCapEventTypeEnum = '''\ class FrequencyCapEventType(enum.IntEnum): """ The type of event that the cap applies to (e.g. impression). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. IMPRESSION (int): The cap applies on ad impressions. VIDEO_VIEW (int): The cap applies on video ad views. """ UNSPECIFIED = 0 UNKNOWN = 1 IMPRESSION = 2 VIDEO_VIEW = 3 ''' FrequencyCapEventTypeEnum = FrequencyCapEventTypeEnum() # For __getattribute__ class FrequencyCapLevelEnum(_CreateEnumTypeUponFirstAccess): FrequencyCapLevelEnum = '''\ class FrequencyCapLevel(enum.IntEnum): """ The level on which the cap is to be applied (e.g ad group ad, ad group). Cap is applied to all the resources of this level. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AD_GROUP_AD (int): The cap is applied at the ad group ad level. AD_GROUP (int): The cap is applied at the ad group level. CAMPAIGN (int): The cap is applied at the campaign level. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_GROUP_AD = 2 AD_GROUP = 3 CAMPAIGN = 4 ''' FrequencyCapLevelEnum = FrequencyCapLevelEnum() # For __getattribute__ class FrequencyCapTimeUnitEnum(_CreateEnumTypeUponFirstAccess): FrequencyCapTimeUnitEnum = '''\ class FrequencyCapTimeUnit(enum.IntEnum): """ Unit of time the cap is defined at (e.g. day, week). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DAY (int): The cap would define limit per one day. WEEK (int): The cap would define limit per one week. MONTH (int): The cap would define limit per one month. """ UNSPECIFIED = 0 UNKNOWN = 1 DAY = 2 WEEK = 3 MONTH = 4 ''' FrequencyCapTimeUnitEnum = FrequencyCapTimeUnitEnum() # For __getattribute__ class FunctionErrorEnum(_CreateEnumTypeUponFirstAccess): FunctionErrorEnum = '''\ class FunctionError(enum.IntEnum): """ Enum describing possible function errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_FUNCTION_FORMAT (int): The format of the function is not recognized as a supported function format. DATA_TYPE_MISMATCH (int): Operand data types do not match. INVALID_CONJUNCTION_OPERANDS (int): The operands cannot be used together in a conjunction. INVALID_NUMBER_OF_OPERANDS (int): Invalid numer of Operands. INVALID_OPERAND_TYPE (int): Operand Type not supported. INVALID_OPERATOR (int): Operator not supported. INVALID_REQUEST_CONTEXT_TYPE (int): Request context type not supported. INVALID_FUNCTION_FOR_CALL_PLACEHOLDER (int): The matching function is not allowed for call placeholders INVALID_FUNCTION_FOR_PLACEHOLDER (int): The matching function is not allowed for the specified placeholder INVALID_OPERAND (int): Invalid operand. MISSING_CONSTANT_OPERAND_VALUE (int): Missing value for the constant operand. INVALID_CONSTANT_OPERAND_VALUE (int): The value of the constant operand is invalid. INVALID_NESTING (int): Invalid function nesting. MULTIPLE_FEED_IDS_NOT_SUPPORTED (int): The Feed ID was different from another Feed ID in the same function. INVALID_FUNCTION_FOR_FEED_WITH_FIXED_SCHEMA (int): The matching function is invalid for use with a feed with a fixed schema. INVALID_ATTRIBUTE_NAME (int): Invalid attribute name. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_FUNCTION_FORMAT = 2 DATA_TYPE_MISMATCH = 3 INVALID_CONJUNCTION_OPERANDS = 4 INVALID_NUMBER_OF_OPERANDS = 5 INVALID_OPERAND_TYPE = 6 INVALID_OPERATOR = 7 INVALID_REQUEST_CONTEXT_TYPE = 8 INVALID_FUNCTION_FOR_CALL_PLACEHOLDER = 9 INVALID_FUNCTION_FOR_PLACEHOLDER = 10 INVALID_OPERAND = 11 MISSING_CONSTANT_OPERAND_VALUE = 12 INVALID_CONSTANT_OPERAND_VALUE = 13 INVALID_NESTING = 14 MULTIPLE_FEED_IDS_NOT_SUPPORTED = 15 INVALID_FUNCTION_FOR_FEED_WITH_FIXED_SCHEMA = 16 INVALID_ATTRIBUTE_NAME = 17 ''' FunctionErrorEnum = FunctionErrorEnum() # For __getattribute__ class FunctionParsingErrorEnum(_CreateEnumTypeUponFirstAccess): FunctionParsingErrorEnum = '''\ class FunctionParsingError(enum.IntEnum): """ Enum describing possible function parsing errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NO_MORE_INPUT (int): Unexpected end of function string. EXPECTED_CHARACTER (int): Could not find an expected character. UNEXPECTED_SEPARATOR (int): Unexpected separator character. UNMATCHED_LEFT_BRACKET (int): Unmatched left bracket or parenthesis. UNMATCHED_RIGHT_BRACKET (int): Unmatched right bracket or parenthesis. TOO_MANY_NESTED_FUNCTIONS (int): Functions are nested too deeply. MISSING_RIGHT_HAND_OPERAND (int): Missing right-hand-side operand. INVALID_OPERATOR_NAME (int): Invalid operator/function name. FEED_ATTRIBUTE_OPERAND_ARGUMENT_NOT_INTEGER (int): Feed attribute operand's argument is not an integer. NO_OPERANDS (int): Missing function operands. TOO_MANY_OPERANDS (int): Function had too many operands. """ UNSPECIFIED = 0 UNKNOWN = 1 NO_MORE_INPUT = 2 EXPECTED_CHARACTER = 3 UNEXPECTED_SEPARATOR = 4 UNMATCHED_LEFT_BRACKET = 5 UNMATCHED_RIGHT_BRACKET = 6 TOO_MANY_NESTED_FUNCTIONS = 7 MISSING_RIGHT_HAND_OPERAND = 8 INVALID_OPERATOR_NAME = 9 FEED_ATTRIBUTE_OPERAND_ARGUMENT_NOT_INTEGER = 10 NO_OPERANDS = 11 TOO_MANY_OPERANDS = 12 ''' FunctionParsingErrorEnum = FunctionParsingErrorEnum() # For __getattribute__ class GenderTypeEnum(_CreateEnumTypeUponFirstAccess): GenderTypeEnum = '''\ class GenderType(enum.IntEnum): """ The type of demographic genders (e.g. female). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. MALE (int): Male. FEMALE (int): Female. UNDETERMINED (int): Undetermined gender. """ UNSPECIFIED = 0 UNKNOWN = 1 MALE = 10 FEMALE = 11 UNDETERMINED = 20 ''' GenderTypeEnum = GenderTypeEnum() # For __getattribute__ class GeoTargetConstantStatusEnum(_CreateEnumTypeUponFirstAccess): GeoTargetConstantStatusEnum = '''\ class GeoTargetConstantStatus(enum.IntEnum): """ The possible statuses of a geo target constant. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. ENABLED (int): The geo target constant is valid. REMOVAL_PLANNED (int): The geo target constant is obsolete and will be removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVAL_PLANNED = 3 ''' GeoTargetConstantStatusEnum = GeoTargetConstantStatusEnum() # For __getattribute__ class GeoTargetConstantSuggestionErrorEnum(_CreateEnumTypeUponFirstAccess): GeoTargetConstantSuggestionErrorEnum = '''\ class GeoTargetConstantSuggestionError(enum.IntEnum): """ Enum describing possible geo target constant suggestion errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. LOCATION_NAME_SIZE_LIMIT (int): A location name cannot be greater than 300 characters. LOCATION_NAME_LIMIT (int): At most 25 location names can be specified in a SuggestGeoTargetConstants method. INVALID_COUNTRY_CODE (int): The country code is invalid. REQUEST_PARAMETERS_UNSET (int): Geo target constant resource names or location names must be provided in the request. """ UNSPECIFIED = 0 UNKNOWN = 1 LOCATION_NAME_SIZE_LIMIT = 2 LOCATION_NAME_LIMIT = 3 INVALID_COUNTRY_CODE = 4 REQUEST_PARAMETERS_UNSET = 5 ''' GeoTargetConstantSuggestionErrorEnum = GeoTargetConstantSuggestionErrorEnum() # For __getattribute__ class GeoTargetingRestrictionEnum(_CreateEnumTypeUponFirstAccess): GeoTargetingRestrictionEnum = '''\ class GeoTargetingRestriction(enum.IntEnum): """ A restriction used to determine if the request context's geo should be matched. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LOCATION_OF_PRESENCE (int): Indicates that request context should match the physical location of the user. """ UNSPECIFIED = 0 UNKNOWN = 1 LOCATION_OF_PRESENCE = 2 ''' GeoTargetingRestrictionEnum = GeoTargetingRestrictionEnum() # For __getattribute__ class GeoTargetingTypeEnum(_CreateEnumTypeUponFirstAccess): GeoTargetingTypeEnum = '''\ class GeoTargetingType(enum.IntEnum): """ The possible geo targeting types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. AREA_OF_INTEREST (int): Location the user is interested in while making the query. LOCATION_OF_PRESENCE (int): Location of the user issuing the query. """ UNSPECIFIED = 0 UNKNOWN = 1 AREA_OF_INTEREST = 2 LOCATION_OF_PRESENCE = 3 ''' GeoTargetingTypeEnum = GeoTargetingTypeEnum() # For __getattribute__ class GoogleAdsFieldCategoryEnum(_CreateEnumTypeUponFirstAccess): GoogleAdsFieldCategoryEnum = '''\ class GoogleAdsFieldCategory(enum.IntEnum): """ The category of the artifact. Attributes: UNSPECIFIED (int): Unspecified UNKNOWN (int): Unknown RESOURCE (int): The described artifact is a resource. ATTRIBUTE (int): The described artifact is a field and is an attribute of a resource. Including a resource attribute field in a query may segment the query if the resource to which it is attributed segments the resource found in the FROM clause. SEGMENT (int): The described artifact is a field and always segments search queries. METRIC (int): The described artifact is a field and is a metric. It never segments search queries. """ UNSPECIFIED = 0 UNKNOWN = 1 RESOURCE = 2 ATTRIBUTE = 3 SEGMENT = 5 METRIC = 6 ''' GoogleAdsFieldCategoryEnum = GoogleAdsFieldCategoryEnum() # For __getattribute__ class GoogleAdsFieldDataTypeEnum(_CreateEnumTypeUponFirstAccess): GoogleAdsFieldDataTypeEnum = '''\ class GoogleAdsFieldDataType(enum.IntEnum): """ These are the various types a GoogleAdsService artifact may take on. Attributes: UNSPECIFIED (int): Unspecified UNKNOWN (int): Unknown BOOLEAN (int): Maps to google.protobuf.BoolValue Applicable operators: =, != DATE (int): Maps to google.protobuf.StringValue. It can be compared using the set of operators specific to dates however. Applicable operators: =, <, >, <=, >=, BETWEEN, DURING, and IN DOUBLE (int): Maps to google.protobuf.DoubleValue Applicable operators: =, !=, <, >, IN, NOT IN ENUM (int): Maps to an enum. It's specific definition can be found at type\_url. Applicable operators: =, !=, IN, NOT IN FLOAT (int): Maps to google.protobuf.FloatValue Applicable operators: =, !=, <, >, IN, NOT IN INT32 (int): Maps to google.protobuf.Int32Value Applicable operators: =, !=, <, >, <=, >=, BETWEEN, IN, NOT IN INT64 (int): Maps to google.protobuf.Int64Value Applicable operators: =, !=, <, >, <=, >=, BETWEEN, IN, NOT IN MESSAGE (int): Maps to a protocol buffer message type. The data type's details can be found in type\_url. No operators work with MESSAGE fields. RESOURCE_NAME (int): Maps to google.protobuf.StringValue. Represents the resource name (unique id) of a resource or one of its foreign keys. No operators work with RESOURCE\_NAME fields. STRING (int): Maps to google.protobuf.StringValue. Applicable operators: =, !=, LIKE, NOT LIKE, IN, NOT IN UINT64 (int): Maps to google.protobuf.UInt64Value Applicable operators: =, !=, <, >, <=, >=, BETWEEN, IN, NOT IN """ UNSPECIFIED = 0 UNKNOWN = 1 BOOLEAN = 2 DATE = 3 DOUBLE = 4 ENUM = 5 FLOAT = 6 INT32 = 7 INT64 = 8 MESSAGE = 9 RESOURCE_NAME = 10 STRING = 11 UINT64 = 12 ''' GoogleAdsFieldDataTypeEnum = GoogleAdsFieldDataTypeEnum() # For __getattribute__ class HeaderErrorEnum(_CreateEnumTypeUponFirstAccess): HeaderErrorEnum = '''\ class HeaderError(enum.IntEnum): """ Enum describing possible header errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_LOGIN_CUSTOMER_ID (int): The login customer id could not be validated. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_LOGIN_CUSTOMER_ID = 3 ''' HeaderErrorEnum = HeaderErrorEnum() # For __getattribute__ class HotelDateSelectionTypeEnum(_CreateEnumTypeUponFirstAccess): HotelDateSelectionTypeEnum = '''\ class HotelDateSelectionType(enum.IntEnum): """ Enum describing possible hotel date selection types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DEFAULT_SELECTION (int): Dates selected by default. USER_SELECTED (int): Dates selected by the user. """ UNSPECIFIED = 0 UNKNOWN = 1 DEFAULT_SELECTION = 50 USER_SELECTED = 51 ''' HotelDateSelectionTypeEnum = HotelDateSelectionTypeEnum() # For __getattribute__ class HotelPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): HotelPlaceholderFieldEnum = '''\ class HotelPlaceholderField(enum.IntEnum): """ Possible values for Hotel placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PROPERTY_ID (int): Data Type: STRING. Required. Unique ID. PROPERTY_NAME (int): Data Type: STRING. Required. Main headline with property name to be shown in dynamic ad. DESTINATION_NAME (int): Data Type: STRING. Name of destination to be shown in dynamic ad. DESCRIPTION (int): Data Type: STRING. Description of destination to be shown in dynamic ad. ADDRESS (int): Data Type: STRING. Complete property address, including postal code. PRICE (int): Data Type: STRING. Price to be shown in the ad. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" SALE_PRICE (int): Data Type: STRING. Sale price to be shown in the ad. Example: "80.00 USD" FORMATTED_SALE_PRICE (int): Data Type: STRING. Formatted sale price to be shown in the ad. Example: "On sale for $80.00", "$60 - $80" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. CATEGORY (int): Data Type: STRING. Category of property used to group like items together for recommendation engine. STAR_RATING (int): Data Type: INT64. Star rating (1 to 5) used to group like items together for recommendation engine. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. FINAL_URLS (int): Data Type: URL\_LIST. Required. Final URLs for the ad when using Upgraded URLs. User will be redirected to these URLs when they click on an ad, or when they click on a specific flight for ads that show multiple flights. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_PROPERTY_IDS (int): Data Type: STRING\_LIST. List of recommended property IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 PROPERTY_ID = 2 PROPERTY_NAME = 3 DESTINATION_NAME = 4 DESCRIPTION = 5 ADDRESS = 6 PRICE = 7 FORMATTED_PRICE = 8 SALE_PRICE = 9 FORMATTED_SALE_PRICE = 10 IMAGE_URL = 11 CATEGORY = 12 STAR_RATING = 13 CONTEXTUAL_KEYWORDS = 14 FINAL_URLS = 15 FINAL_MOBILE_URLS = 16 TRACKING_URL = 17 ANDROID_APP_LINK = 18 SIMILAR_PROPERTY_IDS = 19 IOS_APP_LINK = 20 IOS_APP_STORE_ID = 21 ''' HotelPlaceholderFieldEnum = HotelPlaceholderFieldEnum() # For __getattribute__ class HotelPriceBucketEnum(_CreateEnumTypeUponFirstAccess): HotelPriceBucketEnum = '''\ class HotelPriceBucket(enum.IntEnum): """ Enum describing possible hotel price buckets. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. LOWEST_TIED (int): Tied for lowest price. Partner is within a small variance of the lowest price. NOT_LOWEST (int): Not lowest price. Partner is not within a small variance of the lowest price. """ UNSPECIFIED = 0 UNKNOWN = 1 LOWEST_TIED = 3 NOT_LOWEST = 4 ''' HotelPriceBucketEnum = HotelPriceBucketEnum() # For __getattribute__ class HotelRateTypeEnum(_CreateEnumTypeUponFirstAccess): HotelRateTypeEnum = '''\ class HotelRateType(enum.IntEnum): """ Enum describing possible hotel rate types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. UNAVAILABLE (int): Rate type information is unavailable. PUBLIC_RATE (int): Rates available to everyone. QUALIFIED_RATE (int): A membership program rate is available and satisfies basic requirements like having a public rate available. UI treatment will strikethrough the public rate and indicate that a discount is available to the user. For more on Qualified Rates, visit https://developers.google.com/hotels/hotel-ads/dev-guide/qualified-rates PRIVATE_RATE (int): Rates available to users that satisfy some eligibility criteria. e.g. all signed-in users, 20% of mobile users, all mobile users in Canada, etc. """ UNSPECIFIED = 0 UNKNOWN = 1 UNAVAILABLE = 2 PUBLIC_RATE = 3 QUALIFIED_RATE = 4 PRIVATE_RATE = 5 ''' HotelRateTypeEnum = HotelRateTypeEnum() # For __getattribute__ class IdErrorEnum(_CreateEnumTypeUponFirstAccess): IdErrorEnum = '''\ class IdError(enum.IntEnum): """ Enum describing possible id errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NOT_FOUND (int): Id not found """ UNSPECIFIED = 0 UNKNOWN = 1 NOT_FOUND = 2 ''' IdErrorEnum = IdErrorEnum() # For __getattribute__ class ImageErrorEnum(_CreateEnumTypeUponFirstAccess): ImageErrorEnum = '''\ class ImageError(enum.IntEnum): """ Enum describing possible image errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_IMAGE (int): The image is not valid. STORAGE_ERROR (int): The image could not be stored. BAD_REQUEST (int): There was a problem with the request. UNEXPECTED_SIZE (int): The image is not of legal dimensions. ANIMATED_NOT_ALLOWED (int): Animated image are not permitted. ANIMATION_TOO_LONG (int): Animation is too long. SERVER_ERROR (int): There was an error on the server. CMYK_JPEG_NOT_ALLOWED (int): Image cannot be in CMYK color format. FLASH_NOT_ALLOWED (int): Flash images are not permitted. FLASH_WITHOUT_CLICKTAG (int): Flash images must support clickTag. FLASH_ERROR_AFTER_FIXING_CLICK_TAG (int): A flash error has occurred after fixing the click tag. ANIMATED_VISUAL_EFFECT (int): Unacceptable visual effects. FLASH_ERROR (int): There was a problem with the flash image. LAYOUT_PROBLEM (int): Incorrect image layout. PROBLEM_READING_IMAGE_FILE (int): There was a problem reading the image file. ERROR_STORING_IMAGE (int): There was an error storing the image. ASPECT_RATIO_NOT_ALLOWED (int): The aspect ratio of the image is not allowed. FLASH_HAS_NETWORK_OBJECTS (int): Flash cannot have network objects. FLASH_HAS_NETWORK_METHODS (int): Flash cannot have network methods. FLASH_HAS_URL (int): Flash cannot have a Url. FLASH_HAS_MOUSE_TRACKING (int): Flash cannot use mouse tracking. FLASH_HAS_RANDOM_NUM (int): Flash cannot have a random number. FLASH_SELF_TARGETS (int): Ad click target cannot be '\_self'. FLASH_BAD_GETURL_TARGET (int): GetUrl method should only use '\_blank'. FLASH_VERSION_NOT_SUPPORTED (int): Flash version is not supported. FLASH_WITHOUT_HARD_CODED_CLICK_URL (int): Flash movies need to have hard coded click URL or clickTAG INVALID_FLASH_FILE (int): Uploaded flash file is corrupted. FAILED_TO_FIX_CLICK_TAG_IN_FLASH (int): Uploaded flash file can be parsed, but the click tag can not be fixed properly. FLASH_ACCESSES_NETWORK_RESOURCES (int): Flash movie accesses network resources FLASH_EXTERNAL_JS_CALL (int): Flash movie attempts to call external javascript code FLASH_EXTERNAL_FS_CALL (int): Flash movie attempts to call flash system commands FILE_TOO_LARGE (int): Image file is too large. IMAGE_DATA_TOO_LARGE (int): Image data is too large. IMAGE_PROCESSING_ERROR (int): Error while processing the image. IMAGE_TOO_SMALL (int): Image is too small. INVALID_INPUT (int): Input was invalid. PROBLEM_READING_FILE (int): There was a problem reading the image file. IMAGE_CONSTRAINTS_VIOLATED (int): Image constraints are violated, but details like ASPECT\_RATIO\_NOT\_ALLOWED can't be provided. This happens when asset spec contains more than one constraint and different criteria of different constraints are violated. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_IMAGE = 2 STORAGE_ERROR = 3 BAD_REQUEST = 4 UNEXPECTED_SIZE = 5 ANIMATED_NOT_ALLOWED = 6 ANIMATION_TOO_LONG = 7 SERVER_ERROR = 8 CMYK_JPEG_NOT_ALLOWED = 9 FLASH_NOT_ALLOWED = 10 FLASH_WITHOUT_CLICKTAG = 11 FLASH_ERROR_AFTER_FIXING_CLICK_TAG = 12 ANIMATED_VISUAL_EFFECT = 13 FLASH_ERROR = 14 LAYOUT_PROBLEM = 15 PROBLEM_READING_IMAGE_FILE = 16 ERROR_STORING_IMAGE = 17 ASPECT_RATIO_NOT_ALLOWED = 18 FLASH_HAS_NETWORK_OBJECTS = 19 FLASH_HAS_NETWORK_METHODS = 20 FLASH_HAS_URL = 21 FLASH_HAS_MOUSE_TRACKING = 22 FLASH_HAS_RANDOM_NUM = 23 FLASH_SELF_TARGETS = 24 FLASH_BAD_GETURL_TARGET = 25 FLASH_VERSION_NOT_SUPPORTED = 26 FLASH_WITHOUT_HARD_CODED_CLICK_URL = 27 INVALID_FLASH_FILE = 28 FAILED_TO_FIX_CLICK_TAG_IN_FLASH = 29 FLASH_ACCESSES_NETWORK_RESOURCES = 30 FLASH_EXTERNAL_JS_CALL = 31 FLASH_EXTERNAL_FS_CALL = 32 FILE_TOO_LARGE = 33 IMAGE_DATA_TOO_LARGE = 34 IMAGE_PROCESSING_ERROR = 35 IMAGE_TOO_SMALL = 36 INVALID_INPUT = 37 PROBLEM_READING_FILE = 38 IMAGE_CONSTRAINTS_VIOLATED = 39 ''' ImageErrorEnum = ImageErrorEnum() # For __getattribute__ class IncomeRangeTypeEnum(_CreateEnumTypeUponFirstAccess): IncomeRangeTypeEnum = '''\ class IncomeRangeType(enum.IntEnum): """ The type of demographic income ranges (e.g. between 0% to 50%). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INCOME_RANGE_0_50 (int): 0%-50%. INCOME_RANGE_50_60 (int): 50% to 60%. INCOME_RANGE_60_70 (int): 60% to 70%. INCOME_RANGE_70_80 (int): 70% to 80%. INCOME_RANGE_80_90 (int): 80% to 90%. INCOME_RANGE_90_UP (int): Greater than 90%. INCOME_RANGE_UNDETERMINED (int): Undetermined income range. """ UNSPECIFIED = 0 UNKNOWN = 1 INCOME_RANGE_0_50 = 510001 INCOME_RANGE_50_60 = 510002 INCOME_RANGE_60_70 = 510003 INCOME_RANGE_70_80 = 510004 INCOME_RANGE_80_90 = 510005 INCOME_RANGE_90_UP = 510006 INCOME_RANGE_UNDETERMINED = 510000 ''' IncomeRangeTypeEnum = IncomeRangeTypeEnum() # For __getattribute__ class InteractionEventTypeEnum(_CreateEnumTypeUponFirstAccess): InteractionEventTypeEnum = '''\ class InteractionEventType(enum.IntEnum): """ Enum describing possible types of payable and free interactions. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CLICK (int): Click to site. In most cases, this interaction navigates to an external location, usually the advertiser's landing page. This is also the default InteractionEventType for click events. ENGAGEMENT (int): The user's expressed intent to engage with the ad in-place. VIDEO_VIEW (int): User viewed a video ad. NONE (int): The default InteractionEventType for ad conversion events. This is used when an ad conversion row does NOT indicate that the free interactions (i.e., the ad conversions) should be 'promoted' and reported as part of the core metrics. These are simply other (ad) conversions. """ UNSPECIFIED = 0 UNKNOWN = 1 CLICK = 2 ENGAGEMENT = 3 VIDEO_VIEW = 4 NONE = 5 ''' InteractionEventTypeEnum = InteractionEventTypeEnum() # For __getattribute__ class InteractionTypeEnum(_CreateEnumTypeUponFirstAccess): InteractionTypeEnum = '''\ class InteractionType(enum.IntEnum): """ Enum describing possible interaction types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CALLS (int): Calls. """ UNSPECIFIED = 0 UNKNOWN = 1 CALLS = 8000 ''' InteractionTypeEnum = InteractionTypeEnum() # For __getattribute__ class InternalErrorEnum(_CreateEnumTypeUponFirstAccess): InternalErrorEnum = '''\ class InternalError(enum.IntEnum): """ Enum describing possible internal errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INTERNAL_ERROR (int): Google Ads API encountered unexpected internal error. ERROR_CODE_NOT_PUBLISHED (int): The intended error code doesn't exist in specified API version. It will be released in a future API version. TRANSIENT_ERROR (int): Google Ads API encountered an unexpected transient error. The user should retry their request in these cases. DEADLINE_EXCEEDED (int): The request took longer than a deadline. """ UNSPECIFIED = 0 UNKNOWN = 1 INTERNAL_ERROR = 2 ERROR_CODE_NOT_PUBLISHED = 3 TRANSIENT_ERROR = 4 DEADLINE_EXCEEDED = 5 ''' InternalErrorEnum = InternalErrorEnum() # For __getattribute__ class InvoiceErrorEnum(_CreateEnumTypeUponFirstAccess): InvoiceErrorEnum = '''\ class InvoiceError(enum.IntEnum): """ Enum describing possible invoice errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. YEAR_MONTH_TOO_OLD (int): Cannot request invoices issued before 2019-01-01. NOT_INVOICED_CUSTOMER (int): Cannot request invoices for customer who doesn't receive invoices. """ UNSPECIFIED = 0 UNKNOWN = 1 YEAR_MONTH_TOO_OLD = 2 NOT_INVOICED_CUSTOMER = 3 ''' InvoiceErrorEnum = InvoiceErrorEnum() # For __getattribute__ class InvoiceTypeEnum(_CreateEnumTypeUponFirstAccess): InvoiceTypeEnum = '''\ class InvoiceType(enum.IntEnum): """ The possible type of invoices. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CREDIT_MEMO (int): An invoice with a negative amount. The account receives a credit. INVOICE (int): An invoice with a positive amount. The account owes a balance. """ UNSPECIFIED = 0 UNKNOWN = 1 CREDIT_MEMO = 2 INVOICE = 3 ''' InvoiceTypeEnum = InvoiceTypeEnum() # For __getattribute__ class JobPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): JobPlaceholderFieldEnum = '''\ class JobPlaceholderField(enum.IntEnum): """ Possible values for Job placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. JOB_ID (int): Data Type: STRING. Required. If only JOB\_ID is specified, then it must be unique. If both JOB\_ID and LOCATION\_ID are specified, then the pair must be unique. ID) pair must be unique. LOCATION_ID (int): Data Type: STRING. Combination of JOB\_ID and LOCATION\_ID must be unique per offer. TITLE (int): Data Type: STRING. Required. Main headline with job title to be shown in dynamic ad. SUBTITLE (int): Data Type: STRING. Job subtitle to be shown in dynamic ad. DESCRIPTION (int): Data Type: STRING. Description of job to be shown in dynamic ad. IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. Highly recommended for image ads. CATEGORY (int): Data Type: STRING. Category of property used to group like items together for recommendation engine. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. ADDRESS (int): Data Type: STRING. Complete property address, including postal code. SALARY (int): Data Type: STRING. Salary or salary range of job to be shown in dynamic ad. FINAL_URLS (int): Data Type: URL\_LIST. Required. Final URLs to be used in ad when using Upgraded URLs; the more specific the better (e.g. the individual URL of a specific job and its location). FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_JOB_IDS (int): Data Type: STRING\_LIST. List of recommended job IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 JOB_ID = 2 LOCATION_ID = 3 TITLE = 4 SUBTITLE = 5 DESCRIPTION = 6 IMAGE_URL = 7 CATEGORY = 8 CONTEXTUAL_KEYWORDS = 9 ADDRESS = 10 SALARY = 11 FINAL_URLS = 12 FINAL_MOBILE_URLS = 14 TRACKING_URL = 15 ANDROID_APP_LINK = 16 SIMILAR_JOB_IDS = 17 IOS_APP_LINK = 18 IOS_APP_STORE_ID = 19 ''' JobPlaceholderFieldEnum = JobPlaceholderFieldEnum() # For __getattribute__ class KeywordMatchTypeEnum(_CreateEnumTypeUponFirstAccess): KeywordMatchTypeEnum = '''\ class KeywordMatchType(enum.IntEnum): """ Possible Keyword match types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EXACT (int): Exact match. PHRASE (int): Phrase match. BROAD (int): Broad match. """ UNSPECIFIED = 0 UNKNOWN = 1 EXACT = 2 PHRASE = 3 BROAD = 4 ''' KeywordMatchTypeEnum = KeywordMatchTypeEnum() # For __getattribute__ class KeywordPlanAdGroupErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanAdGroupErrorEnum = '''\ class KeywordPlanAdGroupError(enum.IntEnum): """ Enum describing possible errors from applying a keyword plan ad group. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_NAME (int): The keyword plan ad group name is missing, empty, longer than allowed limit or contains invalid chars. DUPLICATE_NAME (int): The keyword plan ad group name is duplicate to an existing keyword plan AdGroup name or other keyword plan AdGroup name in the request. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_NAME = 2 DUPLICATE_NAME = 3 ''' KeywordPlanAdGroupErrorEnum = KeywordPlanAdGroupErrorEnum() # For __getattribute__ class KeywordPlanCampaignErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanCampaignErrorEnum = '''\ class KeywordPlanCampaignError(enum.IntEnum): """ Enum describing possible errors from applying a keyword plan campaign. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_NAME (int): A keyword plan campaign name is missing, empty, longer than allowed limit or contains invalid chars. INVALID_LANGUAGES (int): A keyword plan campaign contains one or more untargetable languages. INVALID_GEOS (int): A keyword plan campaign contains one or more invalid geo targets. DUPLICATE_NAME (int): The keyword plan campaign name is duplicate to an existing keyword plan campaign name or other keyword plan campaign name in the request. MAX_GEOS_EXCEEDED (int): The number of geo targets in the keyword plan campaign exceeds limits. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_NAME = 2 INVALID_LANGUAGES = 3 INVALID_GEOS = 4 DUPLICATE_NAME = 5 MAX_GEOS_EXCEEDED = 6 ''' KeywordPlanCampaignErrorEnum = KeywordPlanCampaignErrorEnum() # For __getattribute__ class KeywordPlanCompetitionLevelEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanCompetitionLevelEnum = '''\ class KeywordPlanCompetitionLevel(enum.IntEnum): """ Competition level of a keyword. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. LOW (int): Low competition. MEDIUM (int): Medium competition. HIGH (int): High competition. """ UNSPECIFIED = 0 UNKNOWN = 1 LOW = 2 MEDIUM = 3 HIGH = 4 ''' KeywordPlanCompetitionLevelEnum = KeywordPlanCompetitionLevelEnum() # For __getattribute__ class KeywordPlanErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanErrorEnum = '''\ class KeywordPlanError(enum.IntEnum): """ Enum describing possible errors from applying a keyword plan. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. BID_MULTIPLIER_OUT_OF_RANGE (int): The plan's bid multiplier value is outside the valid range. BID_TOO_HIGH (int): The plan's bid value is too high. BID_TOO_LOW (int): The plan's bid value is too low. BID_TOO_MANY_FRACTIONAL_DIGITS (int): The plan's cpc bid is not a multiple of the minimum billable unit. DAILY_BUDGET_TOO_LOW (int): The plan's daily budget value is too low. DAILY_BUDGET_TOO_MANY_FRACTIONAL_DIGITS (int): The plan's daily budget is not a multiple of the minimum billable unit. INVALID_VALUE (int): The input has an invalid value. KEYWORD_PLAN_HAS_NO_KEYWORDS (int): The plan has no keyword. KEYWORD_PLAN_NOT_ENABLED (int): The plan is not enabled and API cannot provide mutation, forecast or stats. KEYWORD_PLAN_NOT_FOUND (int): The requested plan cannot be found for providing forecast or stats. MISSING_BID (int): The plan is missing a cpc bid. MISSING_FORECAST_PERIOD (int): The plan is missing required forecast\_period field. INVALID_FORECAST_DATE_RANGE (int): The plan's forecast\_period has invalid forecast date range. INVALID_NAME (int): The plan's name is invalid. """ UNSPECIFIED = 0 UNKNOWN = 1 BID_MULTIPLIER_OUT_OF_RANGE = 2 BID_TOO_HIGH = 3 BID_TOO_LOW = 4 BID_TOO_MANY_FRACTIONAL_DIGITS = 5 DAILY_BUDGET_TOO_LOW = 6 DAILY_BUDGET_TOO_MANY_FRACTIONAL_DIGITS = 7 INVALID_VALUE = 8 KEYWORD_PLAN_HAS_NO_KEYWORDS = 9 KEYWORD_PLAN_NOT_ENABLED = 10 KEYWORD_PLAN_NOT_FOUND = 11 MISSING_BID = 13 MISSING_FORECAST_PERIOD = 14 INVALID_FORECAST_DATE_RANGE = 15 INVALID_NAME = 16 ''' KeywordPlanErrorEnum = KeywordPlanErrorEnum() # For __getattribute__ class KeywordPlanForecastIntervalEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanForecastIntervalEnum = '''\ class KeywordPlanForecastInterval(enum.IntEnum): """ Forecast intervals. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. NEXT_WEEK (int): The next week date range for keyword plan. The next week is based on the default locale of the user's account and is mostly SUN-SAT or MON-SUN. This can be different from next-7 days. NEXT_MONTH (int): The next month date range for keyword plan. NEXT_QUARTER (int): The next quarter date range for keyword plan. """ UNSPECIFIED = 0 UNKNOWN = 1 NEXT_WEEK = 3 NEXT_MONTH = 4 NEXT_QUARTER = 5 ''' KeywordPlanForecastIntervalEnum = KeywordPlanForecastIntervalEnum() # For __getattribute__ class KeywordPlanIdeaErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanIdeaErrorEnum = '''\ class KeywordPlanIdeaError(enum.IntEnum): """ Enum describing possible errors from KeywordPlanIdeaService. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. URL_CRAWL_ERROR (int): Error when crawling the input URL. INVALID_VALUE (int): The input has an invalid value. """ UNSPECIFIED = 0 UNKNOWN = 1 URL_CRAWL_ERROR = 2 INVALID_VALUE = 3 ''' KeywordPlanIdeaErrorEnum = KeywordPlanIdeaErrorEnum() # For __getattribute__ class KeywordPlanKeywordErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanKeywordErrorEnum = '''\ class KeywordPlanKeywordError(enum.IntEnum): """ Enum describing possible errors from applying a keyword plan keyword. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_KEYWORD_MATCH_TYPE (int): A keyword or negative keyword has invalid match type. DUPLICATE_KEYWORD (int): A keyword or negative keyword with same text and match type already exists. KEYWORD_TEXT_TOO_LONG (int): Keyword or negative keyword text exceeds the allowed limit. KEYWORD_HAS_INVALID_CHARS (int): Keyword or negative keyword text has invalid characters or symbols. KEYWORD_HAS_TOO_MANY_WORDS (int): Keyword or negative keyword text has too many words. INVALID_KEYWORD_TEXT (int): Keyword or negative keyword has invalid text. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_KEYWORD_MATCH_TYPE = 2 DUPLICATE_KEYWORD = 3 KEYWORD_TEXT_TOO_LONG = 4 KEYWORD_HAS_INVALID_CHARS = 5 KEYWORD_HAS_TOO_MANY_WORDS = 6 INVALID_KEYWORD_TEXT = 7 ''' KeywordPlanKeywordErrorEnum = KeywordPlanKeywordErrorEnum() # For __getattribute__ class KeywordPlanNegativeKeywordErrorEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanNegativeKeywordErrorEnum = '''\ class KeywordPlanNegativeKeywordError(enum.IntEnum): """ Enum describing possible errors from applying a keyword plan negative keyword. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. """ UNSPECIFIED = 0 UNKNOWN = 1 ''' KeywordPlanNegativeKeywordErrorEnum = KeywordPlanNegativeKeywordErrorEnum() # For __getattribute__ class KeywordPlanNetworkEnum(_CreateEnumTypeUponFirstAccess): KeywordPlanNetworkEnum = '''\ class KeywordPlanNetwork(enum.IntEnum): """ Enumerates keyword plan forecastable network types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. GOOGLE_SEARCH (int): Google Search. GOOGLE_SEARCH_AND_PARTNERS (int): Google Search + Search partners. """ UNSPECIFIED = 0 UNKNOWN = 1 GOOGLE_SEARCH = 2 GOOGLE_SEARCH_AND_PARTNERS = 3 ''' KeywordPlanNetworkEnum = KeywordPlanNetworkEnum() # For __getattribute__ class LabelErrorEnum(_CreateEnumTypeUponFirstAccess): LabelErrorEnum = '''\ class LabelError(enum.IntEnum): """ Enum describing possible label errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_APPLY_INACTIVE_LABEL (int): An inactive label cannot be applied. CANNOT_APPLY_LABEL_TO_DISABLED_AD_GROUP_CRITERION (int): A label cannot be applied to a disabled ad group criterion. CANNOT_APPLY_LABEL_TO_NEGATIVE_AD_GROUP_CRITERION (int): A label cannot be applied to a negative ad group criterion. EXCEEDED_LABEL_LIMIT_PER_TYPE (int): Cannot apply more than 50 labels per resource. INVALID_RESOURCE_FOR_MANAGER_LABEL (int): Labels from a manager account cannot be applied to campaign, ad group, ad group ad, or ad group criterion resources. DUPLICATE_NAME (int): Label names must be unique. INVALID_LABEL_NAME (int): Label names cannot be empty. CANNOT_ATTACH_LABEL_TO_DRAFT (int): Labels cannot be applied to a draft. CANNOT_ATTACH_NON_MANAGER_LABEL_TO_CUSTOMER (int): Labels not from a manager account cannot be applied to the customer resource. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_APPLY_INACTIVE_LABEL = 2 CANNOT_APPLY_LABEL_TO_DISABLED_AD_GROUP_CRITERION = 3 CANNOT_APPLY_LABEL_TO_NEGATIVE_AD_GROUP_CRITERION = 4 EXCEEDED_LABEL_LIMIT_PER_TYPE = 5 INVALID_RESOURCE_FOR_MANAGER_LABEL = 6 DUPLICATE_NAME = 7 INVALID_LABEL_NAME = 8 CANNOT_ATTACH_LABEL_TO_DRAFT = 9 CANNOT_ATTACH_NON_MANAGER_LABEL_TO_CUSTOMER = 10 ''' LabelErrorEnum = LabelErrorEnum() # For __getattribute__ class LabelStatusEnum(_CreateEnumTypeUponFirstAccess): LabelStatusEnum = '''\ class LabelStatus(enum.IntEnum): """ Possible statuses of a label. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): Label is enabled. REMOVED (int): Label is removed. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' LabelStatusEnum = LabelStatusEnum() # For __getattribute__ class LanguageCodeErrorEnum(_CreateEnumTypeUponFirstAccess): LanguageCodeErrorEnum = '''\ class LanguageCodeError(enum.IntEnum): """ Enum describing language code errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. LANGUAGE_CODE_NOT_FOUND (int): The input language code is not recognized. INVALID_LANGUAGE_CODE (int): The language is not allowed to use. """ UNSPECIFIED = 0 UNKNOWN = 1 LANGUAGE_CODE_NOT_FOUND = 2 INVALID_LANGUAGE_CODE = 3 ''' LanguageCodeErrorEnum = LanguageCodeErrorEnum() # For __getattribute__ class LegacyAppInstallAdAppStoreEnum(_CreateEnumTypeUponFirstAccess): LegacyAppInstallAdAppStoreEnum = '''\ class LegacyAppInstallAdAppStore(enum.IntEnum): """ App store type in a legacy app install ad. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. APPLE_APP_STORE (int): Apple iTunes. GOOGLE_PLAY (int): Google Play. WINDOWS_STORE (int): Windows Store. WINDOWS_PHONE_STORE (int): Windows Phone Store. CN_APP_STORE (int): The app is hosted in a Chinese app store. """ UNSPECIFIED = 0 UNKNOWN = 1 APPLE_APP_STORE = 2 GOOGLE_PLAY = 3 WINDOWS_STORE = 4 WINDOWS_PHONE_STORE = 5 CN_APP_STORE = 6 ''' LegacyAppInstallAdAppStoreEnum = LegacyAppInstallAdAppStoreEnum() # For __getattribute__ class ListOperationErrorEnum(_CreateEnumTypeUponFirstAccess): ListOperationErrorEnum = '''\ class ListOperationError(enum.IntEnum): """ Enum describing possible list operation errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. REQUIRED_FIELD_MISSING (int): Field required in value is missing. DUPLICATE_VALUES (int): Duplicate or identical value is sent in multiple list operations. """ UNSPECIFIED = 0 UNKNOWN = 1 REQUIRED_FIELD_MISSING = 7 DUPLICATE_VALUES = 8 ''' ListOperationErrorEnum = ListOperationErrorEnum() # For __getattribute__ class ListingGroupTypeEnum(_CreateEnumTypeUponFirstAccess): ListingGroupTypeEnum = '''\ class ListingGroupType(enum.IntEnum): """ The type of the listing group. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SUBDIVISION (int): Subdivision of products along some listing dimension. These nodes are not used by serving to target listing entries, but is purely to define the structure of the tree. UNIT (int): Listing group unit that defines a bid. """ UNSPECIFIED = 0 UNKNOWN = 1 SUBDIVISION = 2 UNIT = 3 ''' ListingGroupTypeEnum = ListingGroupTypeEnum() # For __getattribute__ class LocalPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): LocalPlaceholderFieldEnum = '''\ class LocalPlaceholderField(enum.IntEnum): """ Possible values for Local placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DEAL_ID (int): Data Type: STRING. Required. Unique ID. DEAL_NAME (int): Data Type: STRING. Required. Main headline with local deal title to be shown in dynamic ad. SUBTITLE (int): Data Type: STRING. Local deal subtitle to be shown in dynamic ad. DESCRIPTION (int): Data Type: STRING. Description of local deal to be shown in dynamic ad. PRICE (int): Data Type: STRING. Price to be shown in the ad. Highly recommended for dynamic ads. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" SALE_PRICE (int): Data Type: STRING. Sale price to be shown in the ad. Example: "80.00 USD" FORMATTED_SALE_PRICE (int): Data Type: STRING. Formatted sale price to be shown in the ad. Example: "On sale for $80.00", "$60 - $80" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. ADDRESS (int): Data Type: STRING. Complete property address, including postal code. CATEGORY (int): Data Type: STRING. Category of local deal used to group like items together for recommendation engine. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. FINAL_URLS (int): Data Type: URL\_LIST. Required. Final URLs to be used in ad when using Upgraded URLs; the more specific the better (e.g. the individual URL of a specific local deal and its location). FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_DEAL_IDS (int): Data Type: STRING\_LIST. List of recommended local deal IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 DEAL_ID = 2 DEAL_NAME = 3 SUBTITLE = 4 DESCRIPTION = 5 PRICE = 6 FORMATTED_PRICE = 7 SALE_PRICE = 8 FORMATTED_SALE_PRICE = 9 IMAGE_URL = 10 ADDRESS = 11 CATEGORY = 12 CONTEXTUAL_KEYWORDS = 13 FINAL_URLS = 14 FINAL_MOBILE_URLS = 15 TRACKING_URL = 16 ANDROID_APP_LINK = 17 SIMILAR_DEAL_IDS = 18 IOS_APP_LINK = 19 IOS_APP_STORE_ID = 20 ''' LocalPlaceholderFieldEnum = LocalPlaceholderFieldEnum() # For __getattribute__ class LocationExtensionTargetingCriterionFieldEnum(_CreateEnumTypeUponFirstAccess): LocationExtensionTargetingCriterionFieldEnum = '''\ class LocationExtensionTargetingCriterionField(enum.IntEnum): """ Possible values for Location Extension Targeting criterion fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADDRESS_LINE_1 (int): Data Type: STRING. Line 1 of the business address. ADDRESS_LINE_2 (int): Data Type: STRING. Line 2 of the business address. CITY (int): Data Type: STRING. City of the business address. PROVINCE (int): Data Type: STRING. Province of the business address. POSTAL_CODE (int): Data Type: STRING. Postal code of the business address. COUNTRY_CODE (int): Data Type: STRING. Country code of the business address. """ UNSPECIFIED = 0 UNKNOWN = 1 ADDRESS_LINE_1 = 2 ADDRESS_LINE_2 = 3 CITY = 4 PROVINCE = 5 POSTAL_CODE = 6 COUNTRY_CODE = 7 ''' LocationExtensionTargetingCriterionFieldEnum = LocationExtensionTargetingCriterionFieldEnum() # For __getattribute__ class LocationGroupRadiusUnitsEnum(_CreateEnumTypeUponFirstAccess): LocationGroupRadiusUnitsEnum = '''\ class LocationGroupRadiusUnits(enum.IntEnum): """ The unit of radius distance in location group (e.g. MILES) Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. METERS (int): Meters MILES (int): Miles """ UNSPECIFIED = 0 UNKNOWN = 1 METERS = 2 MILES = 3 ''' LocationGroupRadiusUnitsEnum = LocationGroupRadiusUnitsEnum() # For __getattribute__ class LocationPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): LocationPlaceholderFieldEnum = '''\ class LocationPlaceholderField(enum.IntEnum): """ Possible values for Location placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BUSINESS_NAME (int): Data Type: STRING. The name of the business. ADDRESS_LINE_1 (int): Data Type: STRING. Line 1 of the business address. ADDRESS_LINE_2 (int): Data Type: STRING. Line 2 of the business address. CITY (int): Data Type: STRING. City of the business address. PROVINCE (int): Data Type: STRING. Province of the business address. POSTAL_CODE (int): Data Type: STRING. Postal code of the business address. COUNTRY_CODE (int): Data Type: STRING. Country code of the business address. PHONE_NUMBER (int): Data Type: STRING. Phone number of the business. """ UNSPECIFIED = 0 UNKNOWN = 1 BUSINESS_NAME = 2 ADDRESS_LINE_1 = 3 ADDRESS_LINE_2 = 4 CITY = 5 PROVINCE = 6 POSTAL_CODE = 7 COUNTRY_CODE = 8 PHONE_NUMBER = 9 ''' LocationPlaceholderFieldEnum = LocationPlaceholderFieldEnum() # For __getattribute__ class ManagerLinkErrorEnum(_CreateEnumTypeUponFirstAccess): ManagerLinkErrorEnum = '''\ class ManagerLinkError(enum.IntEnum): """ Enum describing possible ManagerLink errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ACCOUNTS_NOT_COMPATIBLE_FOR_LINKING (int): The manager and client have incompatible account types. TOO_MANY_MANAGERS (int): Client is already linked to too many managers. TOO_MANY_INVITES (int): Manager has too many pending invitations. ALREADY_INVITED_BY_THIS_MANAGER (int): Client is already invited by this manager. ALREADY_MANAGED_BY_THIS_MANAGER (int): The client is already managed by this manager. ALREADY_MANAGED_IN_HIERARCHY (int): Client is already managed in hierarchy. DUPLICATE_CHILD_FOUND (int): Manger and sub-manager to be linked have duplicate client. CLIENT_HAS_NO_ADMIN_USER (int): Client has no active user that can access the client account. MAX_DEPTH_EXCEEDED (int): Adding this link would exceed the maximum hierarchy depth. CYCLE_NOT_ALLOWED (int): Adding this link will create a cycle. TOO_MANY_ACCOUNTS (int): Manager account has the maximum number of linked clients. TOO_MANY_ACCOUNTS_AT_MANAGER (int): Parent manager account has the maximum number of linked clients. NON_OWNER_USER_CANNOT_MODIFY_LINK (int): The account is not authorized owner. SUSPENDED_ACCOUNT_CANNOT_ADD_CLIENTS (int): Your manager account is suspended, and you are no longer allowed to link to clients. CLIENT_OUTSIDE_TREE (int): You are not allowed to move a client to a manager that is not under your current hierarchy. """ UNSPECIFIED = 0 UNKNOWN = 1 ACCOUNTS_NOT_COMPATIBLE_FOR_LINKING = 2 TOO_MANY_MANAGERS = 3 TOO_MANY_INVITES = 4 ALREADY_INVITED_BY_THIS_MANAGER = 5 ALREADY_MANAGED_BY_THIS_MANAGER = 6 ALREADY_MANAGED_IN_HIERARCHY = 7 DUPLICATE_CHILD_FOUND = 8 CLIENT_HAS_NO_ADMIN_USER = 9 MAX_DEPTH_EXCEEDED = 10 CYCLE_NOT_ALLOWED = 11 TOO_MANY_ACCOUNTS = 12 TOO_MANY_ACCOUNTS_AT_MANAGER = 13 NON_OWNER_USER_CANNOT_MODIFY_LINK = 14 SUSPENDED_ACCOUNT_CANNOT_ADD_CLIENTS = 15 CLIENT_OUTSIDE_TREE = 16 ''' ManagerLinkErrorEnum = ManagerLinkErrorEnum() # For __getattribute__ class ManagerLinkStatusEnum(_CreateEnumTypeUponFirstAccess): ManagerLinkStatusEnum = '''\ class ManagerLinkStatus(enum.IntEnum): """ Possible statuses of a link. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ACTIVE (int): Indicates current in-effect relationship INACTIVE (int): Indicates terminated relationship PENDING (int): Indicates relationship has been requested by manager, but the client hasn't accepted yet. REFUSED (int): Relationship was requested by the manager, but the client has refused. CANCELED (int): Indicates relationship has been requested by manager, but manager canceled it. """ UNSPECIFIED = 0 UNKNOWN = 1 ACTIVE = 2 INACTIVE = 3 PENDING = 4 REFUSED = 5 CANCELED = 6 ''' ManagerLinkStatusEnum = ManagerLinkStatusEnum() # For __getattribute__ class MatchingFunctionContextTypeEnum(_CreateEnumTypeUponFirstAccess): MatchingFunctionContextTypeEnum = '''\ class MatchingFunctionContextType(enum.IntEnum): """ Possible context types for an operand in a matching function. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. FEED_ITEM_ID (int): Feed item id in the request context. DEVICE_NAME (int): The device being used (possible values are 'Desktop' or 'Mobile'). """ UNSPECIFIED = 0 UNKNOWN = 1 FEED_ITEM_ID = 2 DEVICE_NAME = 3 ''' MatchingFunctionContextTypeEnum = MatchingFunctionContextTypeEnum() # For __getattribute__ class MatchingFunctionOperatorEnum(_CreateEnumTypeUponFirstAccess): MatchingFunctionOperatorEnum = '''\ class MatchingFunctionOperator(enum.IntEnum): """ Possible operators in a matching function. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. IN (int): The IN operator. IDENTITY (int): The IDENTITY operator. EQUALS (int): The EQUALS operator AND (int): Operator that takes two or more operands that are of type FunctionOperand and checks that all the operands evaluate to true. For functions related to ad formats, all the operands must be in left\_operands. CONTAINS_ANY (int): Operator that returns true if the elements in left\_operands contain any of the elements in right\_operands. Otherwise, return false. The right\_operands must contain at least 1 and no more than 3 ConstantOperands. """ UNSPECIFIED = 0 UNKNOWN = 1 IN = 2 IDENTITY = 3 EQUALS = 4 AND = 5 CONTAINS_ANY = 6 ''' MatchingFunctionOperatorEnum = MatchingFunctionOperatorEnum() # For __getattribute__ class MediaBundleErrorEnum(_CreateEnumTypeUponFirstAccess): MediaBundleErrorEnum = '''\ class MediaBundleError(enum.IntEnum): """ Enum describing possible media bundle errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. BAD_REQUEST (int): There was a problem with the request. DOUBLECLICK_BUNDLE_NOT_ALLOWED (int): HTML5 ads using DoubleClick Studio created ZIP files are not supported. EXTERNAL_URL_NOT_ALLOWED (int): Cannot reference URL external to the media bundle. FILE_TOO_LARGE (int): Media bundle file is too large. GOOGLE_WEB_DESIGNER_ZIP_FILE_NOT_PUBLISHED (int): ZIP file from Google Web Designer is not published. INVALID_INPUT (int): Input was invalid. INVALID_MEDIA_BUNDLE (int): There was a problem with the media bundle. INVALID_MEDIA_BUNDLE_ENTRY (int): There was a problem with one or more of the media bundle entries. INVALID_MIME_TYPE (int): The media bundle contains a file with an unknown mime type INVALID_PATH (int): The media bundle contain an invalid asset path. INVALID_URL_REFERENCE (int): HTML5 ad is trying to reference an asset not in .ZIP file MEDIA_DATA_TOO_LARGE (int): Media data is too large. MISSING_PRIMARY_MEDIA_BUNDLE_ENTRY (int): The media bundle contains no primary entry. SERVER_ERROR (int): There was an error on the server. STORAGE_ERROR (int): The image could not be stored. SWIFFY_BUNDLE_NOT_ALLOWED (int): Media bundle created with the Swiffy tool is not allowed. TOO_MANY_FILES (int): The media bundle contains too many files. UNEXPECTED_SIZE (int): The media bundle is not of legal dimensions. UNSUPPORTED_GOOGLE_WEB_DESIGNER_ENVIRONMENT (int): Google Web Designer not created for "Google Ads" environment. UNSUPPORTED_HTML5_FEATURE (int): Unsupported HTML5 feature in HTML5 asset. URL_IN_MEDIA_BUNDLE_NOT_SSL_COMPLIANT (int): URL in HTML5 entry is not ssl compliant. CUSTOM_EXIT_NOT_ALLOWED (int): Custom exits not allowed in HTML5 entry. """ UNSPECIFIED = 0 UNKNOWN = 1 BAD_REQUEST = 3 DOUBLECLICK_BUNDLE_NOT_ALLOWED = 4 EXTERNAL_URL_NOT_ALLOWED = 5 FILE_TOO_LARGE = 6 GOOGLE_WEB_DESIGNER_ZIP_FILE_NOT_PUBLISHED = 7 INVALID_INPUT = 8 INVALID_MEDIA_BUNDLE = 9 INVALID_MEDIA_BUNDLE_ENTRY = 10 INVALID_MIME_TYPE = 11 INVALID_PATH = 12 INVALID_URL_REFERENCE = 13 MEDIA_DATA_TOO_LARGE = 14 MISSING_PRIMARY_MEDIA_BUNDLE_ENTRY = 15 SERVER_ERROR = 16 STORAGE_ERROR = 17 SWIFFY_BUNDLE_NOT_ALLOWED = 18 TOO_MANY_FILES = 19 UNEXPECTED_SIZE = 20 UNSUPPORTED_GOOGLE_WEB_DESIGNER_ENVIRONMENT = 21 UNSUPPORTED_HTML5_FEATURE = 22 URL_IN_MEDIA_BUNDLE_NOT_SSL_COMPLIANT = 23 CUSTOM_EXIT_NOT_ALLOWED = 24 ''' MediaBundleErrorEnum = MediaBundleErrorEnum() # For __getattribute__ class MediaFileErrorEnum(_CreateEnumTypeUponFirstAccess): MediaFileErrorEnum = '''\ class MediaFileError(enum.IntEnum): """ Enum describing possible media file errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_CREATE_STANDARD_ICON (int): Cannot create a standard icon type. CANNOT_SELECT_STANDARD_ICON_WITH_OTHER_TYPES (int): May only select Standard Icons alone. CANNOT_SPECIFY_MEDIA_FILE_ID_AND_DATA (int): Image contains both a media file ID and data. DUPLICATE_MEDIA (int): A media file with given type and reference ID already exists. EMPTY_FIELD (int): A required field was not specified or is an empty string. RESOURCE_REFERENCED_IN_MULTIPLE_OPS (int): A media file may only be modified once per call. FIELD_NOT_SUPPORTED_FOR_MEDIA_SUB_TYPE (int): Field is not supported for the media sub type. INVALID_MEDIA_FILE_ID (int): The media file ID is invalid. INVALID_MEDIA_SUB_TYPE (int): The media subtype is invalid. INVALID_MEDIA_FILE_TYPE (int): The media file type is invalid. INVALID_MIME_TYPE (int): The mimetype is invalid. INVALID_REFERENCE_ID (int): The media reference ID is invalid. INVALID_YOU_TUBE_ID (int): The YouTube video ID is invalid. MEDIA_FILE_FAILED_TRANSCODING (int): Media file has failed transcoding MEDIA_NOT_TRANSCODED (int): Media file has not been transcoded. MEDIA_TYPE_DOES_NOT_MATCH_MEDIA_FILE_TYPE (int): The media type does not match the actual media file's type. NO_FIELDS_SPECIFIED (int): None of the fields have been specified. NULL_REFERENCE_ID_AND_MEDIA_ID (int): One of reference ID or media file ID must be specified. TOO_LONG (int): The string has too many characters. UNSUPPORTED_TYPE (int): The specified type is not supported. YOU_TUBE_SERVICE_UNAVAILABLE (int): YouTube is unavailable for requesting video data. YOU_TUBE_VIDEO_HAS_NON_POSITIVE_DURATION (int): The YouTube video has a non positive duration. YOU_TUBE_VIDEO_NOT_FOUND (int): The YouTube video ID is syntactically valid but the video was not found. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_CREATE_STANDARD_ICON = 2 CANNOT_SELECT_STANDARD_ICON_WITH_OTHER_TYPES = 3 CANNOT_SPECIFY_MEDIA_FILE_ID_AND_DATA = 4 DUPLICATE_MEDIA = 5 EMPTY_FIELD = 6 RESOURCE_REFERENCED_IN_MULTIPLE_OPS = 7 FIELD_NOT_SUPPORTED_FOR_MEDIA_SUB_TYPE = 8 INVALID_MEDIA_FILE_ID = 9 INVALID_MEDIA_SUB_TYPE = 10 INVALID_MEDIA_FILE_TYPE = 11 INVALID_MIME_TYPE = 12 INVALID_REFERENCE_ID = 13 INVALID_YOU_TUBE_ID = 14 MEDIA_FILE_FAILED_TRANSCODING = 15 MEDIA_NOT_TRANSCODED = 16 MEDIA_TYPE_DOES_NOT_MATCH_MEDIA_FILE_TYPE = 17 NO_FIELDS_SPECIFIED = 18 NULL_REFERENCE_ID_AND_MEDIA_ID = 19 TOO_LONG = 20 UNSUPPORTED_TYPE = 21 YOU_TUBE_SERVICE_UNAVAILABLE = 22 YOU_TUBE_VIDEO_HAS_NON_POSITIVE_DURATION = 23 YOU_TUBE_VIDEO_NOT_FOUND = 24 ''' MediaFileErrorEnum = MediaFileErrorEnum() # For __getattribute__ class MediaTypeEnum(_CreateEnumTypeUponFirstAccess): MediaTypeEnum = '''\ class MediaType(enum.IntEnum): """ The type of media. Attributes: UNSPECIFIED (int): The media type has not been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. IMAGE (int): Static image, used for image ad. ICON (int): Small image, used for map ad. MEDIA_BUNDLE (int): ZIP file, used in fields of template ads. AUDIO (int): Audio file. VIDEO (int): Video file. DYNAMIC_IMAGE (int): Animated image, such as animated GIF. """ UNSPECIFIED = 0 UNKNOWN = 1 IMAGE = 2 ICON = 3 MEDIA_BUNDLE = 4 AUDIO = 5 VIDEO = 6 DYNAMIC_IMAGE = 7 ''' MediaTypeEnum = MediaTypeEnum() # For __getattribute__ class MediaUploadErrorEnum(_CreateEnumTypeUponFirstAccess): MediaUploadErrorEnum = '''\ class MediaUploadError(enum.IntEnum): """ Enum describing possible media uploading errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. FILE_TOO_BIG (int): The uploaded file is too big. UNPARSEABLE_IMAGE (int): Image data is unparseable. ANIMATED_IMAGE_NOT_ALLOWED (int): Animated images are not allowed. FORMAT_NOT_ALLOWED (int): The image or media bundle format is not allowed. EXTERNAL_URL_NOT_ALLOWED (int): Cannot reference URL external to the media bundle. INVALID_URL_REFERENCE (int): HTML5 ad is trying to reference an asset not in .ZIP file. MISSING_PRIMARY_MEDIA_BUNDLE_ENTRY (int): The media bundle contains no primary entry. """ UNSPECIFIED = 0 UNKNOWN = 1 FILE_TOO_BIG = 2 UNPARSEABLE_IMAGE = 3 ANIMATED_IMAGE_NOT_ALLOWED = 4 FORMAT_NOT_ALLOWED = 5 EXTERNAL_URL_NOT_ALLOWED = 6 INVALID_URL_REFERENCE = 7 MISSING_PRIMARY_MEDIA_BUNDLE_ENTRY = 8 ''' MediaUploadErrorEnum = MediaUploadErrorEnum() # For __getattribute__ class MerchantCenterLinkStatusEnum(_CreateEnumTypeUponFirstAccess): MerchantCenterLinkStatusEnum = '''\ class MerchantCenterLinkStatus(enum.IntEnum): """ Describes the possible statuses for a link between a Google Ads customer and a Google Merchant Center account. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): The link is enabled. PENDING (int): The link has no effect. It was proposed by the Merchant Center Account owner and hasn't been confirmed by the customer. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 PENDING = 3 ''' MerchantCenterLinkStatusEnum = MerchantCenterLinkStatusEnum() # For __getattribute__ class MessagePlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): MessagePlaceholderFieldEnum = '''\ class MessagePlaceholderField(enum.IntEnum): """ Possible values for Message placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BUSINESS_NAME (int): Data Type: STRING. The name of your business. COUNTRY_CODE (int): Data Type: STRING. Country code of phone number. PHONE_NUMBER (int): Data Type: STRING. A phone number that's capable of sending and receiving text messages. MESSAGE_EXTENSION_TEXT (int): Data Type: STRING. The text that will go in your click-to-message ad. MESSAGE_TEXT (int): Data Type: STRING. The message text automatically shows in people's messaging apps when they tap to send you a message. """ UNSPECIFIED = 0 UNKNOWN = 1 BUSINESS_NAME = 2 COUNTRY_CODE = 3 PHONE_NUMBER = 4 MESSAGE_EXTENSION_TEXT = 5 MESSAGE_TEXT = 6 ''' MessagePlaceholderFieldEnum = MessagePlaceholderFieldEnum() # For __getattribute__ class MimeTypeEnum(_CreateEnumTypeUponFirstAccess): MimeTypeEnum = '''\ class MimeType(enum.IntEnum): """ The mime type Attributes: UNSPECIFIED (int): The mime type has not been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. IMAGE_JPEG (int): MIME type of image/jpeg. IMAGE_GIF (int): MIME type of image/gif. IMAGE_PNG (int): MIME type of image/png. FLASH (int): MIME type of application/x-shockwave-flash. TEXT_HTML (int): MIME type of text/html. PDF (int): MIME type of application/pdf. MSWORD (int): MIME type of application/msword. MSEXCEL (int): MIME type of application/vnd.ms-excel. RTF (int): MIME type of application/rtf. AUDIO_WAV (int): MIME type of audio/wav. AUDIO_MP3 (int): MIME type of audio/mp3. HTML5_AD_ZIP (int): MIME type of application/x-html5-ad-zip. """ UNSPECIFIED = 0 UNKNOWN = 1 IMAGE_JPEG = 2 IMAGE_GIF = 3 IMAGE_PNG = 4 FLASH = 5 TEXT_HTML = 6 PDF = 7 MSWORD = 8 MSEXCEL = 9 RTF = 10 AUDIO_WAV = 11 AUDIO_MP3 = 12 HTML5_AD_ZIP = 13 ''' MimeTypeEnum = MimeTypeEnum() # For __getattribute__ class MinuteOfHourEnum(_CreateEnumTypeUponFirstAccess): MinuteOfHourEnum = '''\ class MinuteOfHour(enum.IntEnum): """ Enumerates of quarter-hours. E.g. "FIFTEEN" Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. ZERO (int): Zero minutes past the hour. FIFTEEN (int): Fifteen minutes past the hour. THIRTY (int): Thirty minutes past the hour. FORTY_FIVE (int): Forty-five minutes past the hour. """ UNSPECIFIED = 0 UNKNOWN = 1 ZERO = 2 FIFTEEN = 3 THIRTY = 4 FORTY_FIVE = 5 ''' MinuteOfHourEnum = MinuteOfHourEnum() # For __getattribute__ class MobileDeviceTypeEnum(_CreateEnumTypeUponFirstAccess): MobileDeviceTypeEnum = '''\ class MobileDeviceType(enum.IntEnum): """ The type of mobile device. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. MOBILE (int): Mobile phones. TABLET (int): Tablets. """ UNSPECIFIED = 0 UNKNOWN = 1 MOBILE = 2 TABLET = 3 ''' MobileDeviceTypeEnum = MobileDeviceTypeEnum() # For __getattribute__ class MonthOfYearEnum(_CreateEnumTypeUponFirstAccess): MonthOfYearEnum = '''\ class MonthOfYear(enum.IntEnum): """ Enumerates months of the year, e.g., "January". Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. JANUARY (int): January. FEBRUARY (int): February. MARCH (int): March. APRIL (int): April. MAY (int): May. JUNE (int): June. JULY (int): July. AUGUST (int): August. SEPTEMBER (int): September. OCTOBER (int): October. NOVEMBER (int): November. DECEMBER (int): December. """ UNSPECIFIED = 0 UNKNOWN = 1 JANUARY = 2 FEBRUARY = 3 MARCH = 4 APRIL = 5 MAY = 6 JUNE = 7 JULY = 8 AUGUST = 9 SEPTEMBER = 10 OCTOBER = 11 NOVEMBER = 12 DECEMBER = 13 ''' MonthOfYearEnum = MonthOfYearEnum() # For __getattribute__ class MultiplierErrorEnum(_CreateEnumTypeUponFirstAccess): MultiplierErrorEnum = '''\ class MultiplierError(enum.IntEnum): """ Enum describing possible multiplier errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. MULTIPLIER_TOO_HIGH (int): Multiplier value is too high MULTIPLIER_TOO_LOW (int): Multiplier value is too low TOO_MANY_FRACTIONAL_DIGITS (int): Too many fractional digits MULTIPLIER_NOT_ALLOWED_FOR_BIDDING_STRATEGY (int): A multiplier cannot be set for this bidding strategy MULTIPLIER_NOT_ALLOWED_WHEN_BASE_BID_IS_MISSING (int): A multiplier cannot be set when there is no base bid (e.g., content max cpc) NO_MULTIPLIER_SPECIFIED (int): A bid multiplier must be specified MULTIPLIER_CAUSES_BID_TO_EXCEED_DAILY_BUDGET (int): Multiplier causes bid to exceed daily budget MULTIPLIER_CAUSES_BID_TO_EXCEED_MONTHLY_BUDGET (int): Multiplier causes bid to exceed monthly budget MULTIPLIER_CAUSES_BID_TO_EXCEED_CUSTOM_BUDGET (int): Multiplier causes bid to exceed custom budget MULTIPLIER_CAUSES_BID_TO_EXCEED_MAX_ALLOWED_BID (int): Multiplier causes bid to exceed maximum allowed bid BID_LESS_THAN_MIN_ALLOWED_BID_WITH_MULTIPLIER (int): Multiplier causes bid to become less than the minimum bid allowed MULTIPLIER_AND_BIDDING_STRATEGY_TYPE_MISMATCH (int): Multiplier type (cpc vs. cpm) needs to match campaign's bidding strategy """ UNSPECIFIED = 0 UNKNOWN = 1 MULTIPLIER_TOO_HIGH = 2 MULTIPLIER_TOO_LOW = 3 TOO_MANY_FRACTIONAL_DIGITS = 4 MULTIPLIER_NOT_ALLOWED_FOR_BIDDING_STRATEGY = 5 MULTIPLIER_NOT_ALLOWED_WHEN_BASE_BID_IS_MISSING = 6 NO_MULTIPLIER_SPECIFIED = 7 MULTIPLIER_CAUSES_BID_TO_EXCEED_DAILY_BUDGET = 8 MULTIPLIER_CAUSES_BID_TO_EXCEED_MONTHLY_BUDGET = 9 MULTIPLIER_CAUSES_BID_TO_EXCEED_CUSTOM_BUDGET = 10 MULTIPLIER_CAUSES_BID_TO_EXCEED_MAX_ALLOWED_BID = 11 BID_LESS_THAN_MIN_ALLOWED_BID_WITH_MULTIPLIER = 12 MULTIPLIER_AND_BIDDING_STRATEGY_TYPE_MISMATCH = 13 ''' MultiplierErrorEnum = MultiplierErrorEnum() # For __getattribute__ class MutateErrorEnum(_CreateEnumTypeUponFirstAccess): MutateErrorEnum = '''\ class MutateError(enum.IntEnum): """ Enum describing possible mutate errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. RESOURCE_NOT_FOUND (int): Requested resource was not found. ID_EXISTS_IN_MULTIPLE_MUTATES (int): Cannot mutate the same resource twice in one request. INCONSISTENT_FIELD_VALUES (int): The field's contents don't match another field that represents the same data. MUTATE_NOT_ALLOWED (int): Mutates are not allowed for the requested resource. RESOURCE_NOT_IN_GOOGLE_ADS (int): The resource isn't in Google Ads. It belongs to another ads system. RESOURCE_ALREADY_EXISTS (int): The resource being created already exists. """ UNSPECIFIED = 0 UNKNOWN = 1 RESOURCE_NOT_FOUND = 3 ID_EXISTS_IN_MULTIPLE_MUTATES = 7 INCONSISTENT_FIELD_VALUES = 8 MUTATE_NOT_ALLOWED = 9 RESOURCE_NOT_IN_GOOGLE_ADS = 10 RESOURCE_ALREADY_EXISTS = 11 ''' MutateErrorEnum = MutateErrorEnum() # For __getattribute__ class MutateJobErrorEnum(_CreateEnumTypeUponFirstAccess): MutateJobErrorEnum = '''\ class MutateJobError(enum.IntEnum): """ Enum describing possible request errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_MODIFY_JOB_AFTER_JOB_STARTS_RUNNING (int): The mutate job cannot add more operations or run after it has started running. EMPTY_OPERATIONS (int): The operations for an AddMutateJobOperations request were empty. INVALID_SEQUENCE_TOKEN (int): The sequence token for an AddMutateJobOperations request was invalid. RESULTS_NOT_READY (int): Mutate Job Results can only be retrieved once the job is finished. INVALID_PAGE_SIZE (int): The page size for ListMutateJobResults was invalid. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_MODIFY_JOB_AFTER_JOB_STARTS_RUNNING = 2 EMPTY_OPERATIONS = 3 INVALID_SEQUENCE_TOKEN = 4 RESULTS_NOT_READY = 5 INVALID_PAGE_SIZE = 6 ''' MutateJobErrorEnum = MutateJobErrorEnum() # For __getattribute__ class MutateJobStatusEnum(_CreateEnumTypeUponFirstAccess): MutateJobStatusEnum = '''\ class MutateJobStatus(enum.IntEnum): """ The mutate job statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PENDING (int): The job is not currently running. RUNNING (int): The job is running. DONE (int): The job is done. """ UNSPECIFIED = 0 UNKNOWN = 1 PENDING = 2 RUNNING = 3 DONE = 4 ''' MutateJobStatusEnum = MutateJobStatusEnum() # For __getattribute__ class NegativeGeoTargetTypeEnum(_CreateEnumTypeUponFirstAccess): NegativeGeoTargetTypeEnum = '''\ class NegativeGeoTargetType(enum.IntEnum): """ The possible negative geo target types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. PRESENCE_OR_INTEREST (int): Specifies that a user is excluded from seeing the ad if they are in, or show interest in, advertiser's excluded locations. PRESENCE (int): Specifies that a user is excluded from seeing the ad if they are in advertiser's excluded locations. """ UNSPECIFIED = 0 UNKNOWN = 1 PRESENCE_OR_INTEREST = 4 PRESENCE = 5 ''' NegativeGeoTargetTypeEnum = NegativeGeoTargetTypeEnum() # For __getattribute__ class NewResourceCreationErrorEnum(_CreateEnumTypeUponFirstAccess): NewResourceCreationErrorEnum = '''\ class NewResourceCreationError(enum.IntEnum): """ Enum describing possible new resource creation errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CANNOT_SET_ID_FOR_CREATE (int): Do not set the id field while creating new resources. DUPLICATE_TEMP_IDS (int): Creating more than one resource with the same temp ID is not allowed. TEMP_ID_RESOURCE_HAD_ERRORS (int): Parent resource with specified temp ID failed validation, so no validation will be done for this child resource. """ UNSPECIFIED = 0 UNKNOWN = 1 CANNOT_SET_ID_FOR_CREATE = 2 DUPLICATE_TEMP_IDS = 3 TEMP_ID_RESOURCE_HAD_ERRORS = 4 ''' NewResourceCreationErrorEnum = NewResourceCreationErrorEnum() # For __getattribute__ class NotEmptyErrorEnum(_CreateEnumTypeUponFirstAccess): NotEmptyErrorEnum = '''\ class NotEmptyError(enum.IntEnum): """ Enum describing possible not empty errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. EMPTY_LIST (int): Empty list. """ UNSPECIFIED = 0 UNKNOWN = 1 EMPTY_LIST = 2 ''' NotEmptyErrorEnum = NotEmptyErrorEnum() # For __getattribute__ class NotWhitelistedErrorEnum(_CreateEnumTypeUponFirstAccess): NotWhitelistedErrorEnum = '''\ class NotWhitelistedError(enum.IntEnum): """ Enum describing possible not whitelisted errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CUSTOMER_NOT_WHITELISTED_FOR_THIS_FEATURE (int): Customer is not whitelisted for accessing this feature. """ UNSPECIFIED = 0 UNKNOWN = 1 CUSTOMER_NOT_WHITELISTED_FOR_THIS_FEATURE = 2 ''' NotWhitelistedErrorEnum = NotWhitelistedErrorEnum() # For __getattribute__ class NullErrorEnum(_CreateEnumTypeUponFirstAccess): NullErrorEnum = '''\ class NullError(enum.IntEnum): """ Enum describing possible null errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NULL_CONTENT (int): Specified list/container must not contain any null elements """ UNSPECIFIED = 0 UNKNOWN = 1 NULL_CONTENT = 2 ''' NullErrorEnum = NullErrorEnum() # For __getattribute__ class OperatingSystemVersionOperatorTypeEnum(_CreateEnumTypeUponFirstAccess): OperatingSystemVersionOperatorTypeEnum = '''\ class OperatingSystemVersionOperatorType(enum.IntEnum): """ The type of operating system version. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EQUALS_TO (int): Equals to the specified version. GREATER_THAN_EQUALS_TO (int): Greater than or equals to the specified version. """ UNSPECIFIED = 0 UNKNOWN = 1 EQUALS_TO = 2 GREATER_THAN_EQUALS_TO = 4 ''' OperatingSystemVersionOperatorTypeEnum = OperatingSystemVersionOperatorTypeEnum() # For __getattribute__ class OperationAccessDeniedErrorEnum(_CreateEnumTypeUponFirstAccess): OperationAccessDeniedErrorEnum = '''\ class OperationAccessDeniedError(enum.IntEnum): """ Enum describing possible operation access denied errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ACTION_NOT_PERMITTED (int): Unauthorized invocation of a service's method (get, mutate, etc.) CREATE_OPERATION_NOT_PERMITTED (int): Unauthorized CREATE operation in invoking a service's mutate method. REMOVE_OPERATION_NOT_PERMITTED (int): Unauthorized REMOVE operation in invoking a service's mutate method. UPDATE_OPERATION_NOT_PERMITTED (int): Unauthorized UPDATE operation in invoking a service's mutate method. MUTATE_ACTION_NOT_PERMITTED_FOR_CLIENT (int): A mutate action is not allowed on this campaign, from this client. OPERATION_NOT_PERMITTED_FOR_CAMPAIGN_TYPE (int): This operation is not permitted on this campaign type CREATE_AS_REMOVED_NOT_PERMITTED (int): A CREATE operation may not set status to REMOVED. OPERATION_NOT_PERMITTED_FOR_REMOVED_RESOURCE (int): This operation is not allowed because the campaign or adgroup is removed. OPERATION_NOT_PERMITTED_FOR_AD_GROUP_TYPE (int): This operation is not permitted on this ad group type. MUTATE_NOT_PERMITTED_FOR_CUSTOMER (int): The mutate is not allowed for this customer. """ UNSPECIFIED = 0 UNKNOWN = 1 ACTION_NOT_PERMITTED = 2 CREATE_OPERATION_NOT_PERMITTED = 3 REMOVE_OPERATION_NOT_PERMITTED = 4 UPDATE_OPERATION_NOT_PERMITTED = 5 MUTATE_ACTION_NOT_PERMITTED_FOR_CLIENT = 6 OPERATION_NOT_PERMITTED_FOR_CAMPAIGN_TYPE = 7 CREATE_AS_REMOVED_NOT_PERMITTED = 8 OPERATION_NOT_PERMITTED_FOR_REMOVED_RESOURCE = 9 OPERATION_NOT_PERMITTED_FOR_AD_GROUP_TYPE = 10 MUTATE_NOT_PERMITTED_FOR_CUSTOMER = 11 ''' OperationAccessDeniedErrorEnum = OperationAccessDeniedErrorEnum() # For __getattribute__ class OperatorErrorEnum(_CreateEnumTypeUponFirstAccess): OperatorErrorEnum = '''\ class OperatorError(enum.IntEnum): """ Enum describing possible operator errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. OPERATOR_NOT_SUPPORTED (int): Operator not supported. """ UNSPECIFIED = 0 UNKNOWN = 1 OPERATOR_NOT_SUPPORTED = 2 ''' OperatorErrorEnum = OperatorErrorEnum() # For __getattribute__ class ParentalStatusTypeEnum(_CreateEnumTypeUponFirstAccess): ParentalStatusTypeEnum = '''\ class ParentalStatusType(enum.IntEnum): """ The type of parental statuses (e.g. not a parent). Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PARENT (int): Parent. NOT_A_PARENT (int): Not a parent. UNDETERMINED (int): Undetermined parental status. """ UNSPECIFIED = 0 UNKNOWN = 1 PARENT = 300 NOT_A_PARENT = 301 UNDETERMINED = 302 ''' ParentalStatusTypeEnum = ParentalStatusTypeEnum() # For __getattribute__ class PartialFailureErrorEnum(_CreateEnumTypeUponFirstAccess): PartialFailureErrorEnum = '''\ class PartialFailureError(enum.IntEnum): """ Enum describing possible partial failure errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. PARTIAL_FAILURE_MODE_REQUIRED (int): The partial failure field was false in the request. This method requires this field be set to true. """ UNSPECIFIED = 0 UNKNOWN = 1 PARTIAL_FAILURE_MODE_REQUIRED = 2 ''' PartialFailureErrorEnum = PartialFailureErrorEnum() # For __getattribute__ class PaymentModeEnum(_CreateEnumTypeUponFirstAccess): PaymentModeEnum = '''\ class PaymentMode(enum.IntEnum): """ Enum describing possible payment modes. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CLICKS (int): Pay per click. CONVERSION_VALUE (int): Pay per conversion value. This mode is only supported by campaigns with AdvertisingChannelType.HOTEL, BiddingStrategyType.COMMISSION, and BudgetType.HOTEL\_ADS\_COMMISSION. CONVERSIONS (int): Pay per conversion. This mode is only supported by campaigns with AdvertisingChannelType.DISPLAY (excluding AdvertisingChannelSubType.DISPLAY\_GMAIL), BiddingStrategyType.TARGET\_CPA, and BudgetType.FIXED\_CPA. The customer must also be eligible for this mode. See Customer.eligibility\_failure\_reasons for details. GUEST_STAY (int): Pay per guest stay value. This mode is only supported by campaigns with AdvertisingChannelType.HOTEL, BiddingStrategyType.COMMISSION, and BudgetType.HOTEL\_ADS\_COMMISSION. """ UNSPECIFIED = 0 UNKNOWN = 1 CLICKS = 4 CONVERSION_VALUE = 5 CONVERSIONS = 6 GUEST_STAY = 7 ''' PaymentModeEnum = PaymentModeEnum() # For __getattribute__ class PaymentsAccountErrorEnum(_CreateEnumTypeUponFirstAccess): PaymentsAccountErrorEnum = '''\ class PaymentsAccountError(enum.IntEnum): """ Enum describing possible errors in payments account service. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. NOT_SUPPORTED_FOR_MANAGER_CUSTOMER (int): Manager customers are not supported for payments account service. """ UNSPECIFIED = 0 UNKNOWN = 1 NOT_SUPPORTED_FOR_MANAGER_CUSTOMER = 2 ''' PaymentsAccountErrorEnum = PaymentsAccountErrorEnum() # For __getattribute__ class PlaceholderTypeEnum(_CreateEnumTypeUponFirstAccess): PlaceholderTypeEnum = '''\ class PlaceholderType(enum.IntEnum): """ Possible placeholder types for a feed mapping. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SITELINK (int): Lets you show links in your ad to pages from your website, including the main landing page. CALL (int): Lets you attach a phone number to an ad, allowing customers to call directly from the ad. APP (int): Lets you provide users with a link that points to a mobile app in addition to a website. LOCATION (int): Lets you show locations of businesses from your Google My Business account in your ad. This helps people find your locations by showing your ads with your address, a map to your location, or the distance to your business. This extension type is useful to draw customers to your brick-and-mortar location. AFFILIATE_LOCATION (int): If you sell your product through retail chains, affiliate location extensions let you show nearby stores that carry your products. CALLOUT (int): Lets you include additional text with your search ads that provide detailed information about your business, including products and services you offer. Callouts appear in ads at the top and bottom of Google search results. STRUCTURED_SNIPPET (int): Lets you add more info to your ad, specific to some predefined categories such as types, brands, styles, etc. A minimum of 3 text (SNIPPETS) values are required. MESSAGE (int): Allows users to see your ad, click an icon, and contact you directly by text message. With one tap on your ad, people can contact you to book an appointment, get a quote, ask for information, or request a service. PRICE (int): Lets you display prices for a list of items along with your ads. A price feed is composed of three to eight price table rows. PROMOTION (int): Allows you to highlight sales and other promotions that let users see how they can save by buying now. AD_CUSTOMIZER (int): Lets you dynamically inject custom data into the title and description of your ads. DYNAMIC_EDUCATION (int): Indicates that this feed is for education dynamic remarketing. DYNAMIC_FLIGHT (int): Indicates that this feed is for flight dynamic remarketing. DYNAMIC_CUSTOM (int): Indicates that this feed is for a custom dynamic remarketing type. Use this only if the other business types don't apply to your products or services. DYNAMIC_HOTEL (int): Indicates that this feed is for hotels and rentals dynamic remarketing. DYNAMIC_REAL_ESTATE (int): Indicates that this feed is for real estate dynamic remarketing. DYNAMIC_TRAVEL (int): Indicates that this feed is for travel dynamic remarketing. DYNAMIC_LOCAL (int): Indicates that this feed is for local deals dynamic remarketing. DYNAMIC_JOB (int): Indicates that this feed is for job dynamic remarketing. """ UNSPECIFIED = 0 UNKNOWN = 1 SITELINK = 2 CALL = 3 APP = 4 LOCATION = 5 AFFILIATE_LOCATION = 6 CALLOUT = 7 STRUCTURED_SNIPPET = 8 MESSAGE = 9 PRICE = 10 PROMOTION = 11 AD_CUSTOMIZER = 12 DYNAMIC_EDUCATION = 13 DYNAMIC_FLIGHT = 14 DYNAMIC_CUSTOM = 15 DYNAMIC_HOTEL = 16 DYNAMIC_REAL_ESTATE = 17 DYNAMIC_TRAVEL = 18 DYNAMIC_LOCAL = 19 DYNAMIC_JOB = 20 ''' PlaceholderTypeEnum = PlaceholderTypeEnum() # For __getattribute__ class PlacementTypeEnum(_CreateEnumTypeUponFirstAccess): PlacementTypeEnum = '''\ class PlacementType(enum.IntEnum): """ Possible placement types for a feed mapping. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. WEBSITE (int): Websites(e.g. 'www.flowers4sale.com'). MOBILE_APP_CATEGORY (int): Mobile application categories(e.g. 'Games'). MOBILE_APPLICATION (int): mobile applications(e.g. 'mobileapp::2-com.whatsthewordanswers'). YOUTUBE_VIDEO (int): YouTube videos(e.g. 'youtube.com/video/wtLJPvx7-ys'). YOUTUBE_CHANNEL (int): YouTube channels(e.g. 'youtube.com::L8ZULXASCc1I\_oaOT0NaOQ'). """ UNSPECIFIED = 0 UNKNOWN = 1 WEBSITE = 2 MOBILE_APP_CATEGORY = 3 MOBILE_APPLICATION = 4 YOUTUBE_VIDEO = 5 YOUTUBE_CHANNEL = 6 ''' PlacementTypeEnum = PlacementTypeEnum() # For __getattribute__ class PolicyApprovalStatusEnum(_CreateEnumTypeUponFirstAccess): PolicyApprovalStatusEnum = '''\ class PolicyApprovalStatus(enum.IntEnum): """ The possible policy approval statuses. When there are several approval statuses available the most severe one will be used. The order of severity is DISAPPROVED, AREA\_OF\_INTEREST\_ONLY, APPROVED\_LIMITED and APPROVED. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. DISAPPROVED (int): Will not serve. APPROVED_LIMITED (int): Serves with restrictions. APPROVED (int): Serves without restrictions. AREA_OF_INTEREST_ONLY (int): Will not serve in targeted countries, but may serve for users who are searching for information about the targeted countries. """ UNSPECIFIED = 0 UNKNOWN = 1 DISAPPROVED = 2 APPROVED_LIMITED = 3 APPROVED = 4 AREA_OF_INTEREST_ONLY = 5 ''' PolicyApprovalStatusEnum = PolicyApprovalStatusEnum() # For __getattribute__ class PolicyFindingErrorEnum(_CreateEnumTypeUponFirstAccess): PolicyFindingErrorEnum = '''\ class PolicyFindingError(enum.IntEnum): """ Enum describing possible policy finding errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. POLICY_FINDING (int): The resource has been disapproved since the policy summary includes policy topics of type PROHIBITED. POLICY_TOPIC_NOT_FOUND (int): The given policy topic does not exist. """ UNSPECIFIED = 0 UNKNOWN = 1 POLICY_FINDING = 2 POLICY_TOPIC_NOT_FOUND = 3 ''' PolicyFindingErrorEnum = PolicyFindingErrorEnum() # For __getattribute__ class PolicyReviewStatusEnum(_CreateEnumTypeUponFirstAccess): PolicyReviewStatusEnum = '''\ class PolicyReviewStatus(enum.IntEnum): """ The possible policy review statuses. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. REVIEW_IN_PROGRESS (int): Currently under review. REVIEWED (int): Primary review complete. Other reviews may be continuing. UNDER_APPEAL (int): The resource has been resubmitted for approval or its policy decision has been appealed. ELIGIBLE_MAY_SERVE (int): The resource is eligible and may be serving but could still undergo further review. """ UNSPECIFIED = 0 UNKNOWN = 1 REVIEW_IN_PROGRESS = 2 REVIEWED = 3 UNDER_APPEAL = 4 ELIGIBLE_MAY_SERVE = 5 ''' PolicyReviewStatusEnum = PolicyReviewStatusEnum() # For __getattribute__ class PolicyTopicEntryTypeEnum(_CreateEnumTypeUponFirstAccess): PolicyTopicEntryTypeEnum = '''\ class PolicyTopicEntryType(enum.IntEnum): """ The possible policy topic entry types. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. PROHIBITED (int): The resource will not be served. LIMITED (int): The resource will not be served under some circumstances. FULLY_LIMITED (int): The resource cannot serve at all because of the current targeting criteria. DESCRIPTIVE (int): May be of interest, but does not limit how the resource is served. BROADENING (int): Could increase coverage beyond normal. AREA_OF_INTEREST_ONLY (int): Constrained for all targeted countries, but may serve in other countries through area of interest. """ UNSPECIFIED = 0 UNKNOWN = 1 PROHIBITED = 2 LIMITED = 4 FULLY_LIMITED = 8 DESCRIPTIVE = 5 BROADENING = 6 AREA_OF_INTEREST_ONLY = 7 ''' PolicyTopicEntryTypeEnum = PolicyTopicEntryTypeEnum() # For __getattribute__ class PolicyTopicEvidenceDestinationMismatchUrlTypeEnum(_CreateEnumTypeUponFirstAccess): PolicyTopicEvidenceDestinationMismatchUrlTypeEnum = '''\ class PolicyTopicEvidenceDestinationMismatchUrlType(enum.IntEnum): """ The possible policy topic evidence destination mismatch url types. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. DISPLAY_URL (int): The display url. FINAL_URL (int): The final url. FINAL_MOBILE_URL (int): The final mobile url. TRACKING_URL (int): The tracking url template, with substituted desktop url. MOBILE_TRACKING_URL (int): The tracking url template, with substituted mobile url. """ UNSPECIFIED = 0 UNKNOWN = 1 DISPLAY_URL = 2 FINAL_URL = 3 FINAL_MOBILE_URL = 4 TRACKING_URL = 5 MOBILE_TRACKING_URL = 6 ''' PolicyTopicEvidenceDestinationMismatchUrlTypeEnum = PolicyTopicEvidenceDestinationMismatchUrlTypeEnum() # For __getattribute__ class PolicyTopicEvidenceDestinationNotWorkingDeviceEnum(_CreateEnumTypeUponFirstAccess): PolicyTopicEvidenceDestinationNotWorkingDeviceEnum = '''\ class PolicyTopicEvidenceDestinationNotWorkingDevice(enum.IntEnum): """ The possible policy topic evidence destination not working devices. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. DESKTOP (int): Landing page doesn't work on desktop device. ANDROID (int): Landing page doesn't work on Android device. IOS (int): Landing page doesn't work on iOS device. """ UNSPECIFIED = 0 UNKNOWN = 1 DESKTOP = 2 ANDROID = 3 IOS = 4 ''' PolicyTopicEvidenceDestinationNotWorkingDeviceEnum = PolicyTopicEvidenceDestinationNotWorkingDeviceEnum() # For __getattribute__ class PolicyTopicEvidenceDestinationNotWorkingDnsErrorTypeEnum(_CreateEnumTypeUponFirstAccess): PolicyTopicEvidenceDestinationNotWorkingDnsErrorTypeEnum = '''\ class PolicyTopicEvidenceDestinationNotWorkingDnsErrorType(enum.IntEnum): """ The possible policy topic evidence destination not working DNS error types. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. HOSTNAME_NOT_FOUND (int): Host name not found in DNS when fetching landing page. GOOGLE_CRAWLER_DNS_ISSUE (int): Google internal crawler issue when communicating with DNS. This error doesn't mean the landing page doesn't work. Google will recrawl the landing page. """ UNSPECIFIED = 0 UNKNOWN = 1 HOSTNAME_NOT_FOUND = 2 GOOGLE_CRAWLER_DNS_ISSUE = 3 ''' PolicyTopicEvidenceDestinationNotWorkingDnsErrorTypeEnum = PolicyTopicEvidenceDestinationNotWorkingDnsErrorTypeEnum() # For __getattribute__ class PolicyValidationParameterErrorEnum(_CreateEnumTypeUponFirstAccess): PolicyValidationParameterErrorEnum = '''\ class PolicyValidationParameterError(enum.IntEnum): """ Enum describing possible policy validation parameter errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. UNSUPPORTED_AD_TYPE_FOR_IGNORABLE_POLICY_TOPICS (int): Ignorable policy topics are not supported for the ad type. UNSUPPORTED_AD_TYPE_FOR_EXEMPT_POLICY_VIOLATION_KEYS (int): Exempt policy violation keys are not supported for the ad type. CANNOT_SET_BOTH_IGNORABLE_POLICY_TOPICS_AND_EXEMPT_POLICY_VIOLATION_KEYS (int): Cannot set ignorable policy topics and exempt policy violation keys in the same policy violation parameter. """ UNSPECIFIED = 0 UNKNOWN = 1 UNSUPPORTED_AD_TYPE_FOR_IGNORABLE_POLICY_TOPICS = 2 UNSUPPORTED_AD_TYPE_FOR_EXEMPT_POLICY_VIOLATION_KEYS = 3 CANNOT_SET_BOTH_IGNORABLE_POLICY_TOPICS_AND_EXEMPT_POLICY_VIOLATION_KEYS = 4 ''' PolicyValidationParameterErrorEnum = PolicyValidationParameterErrorEnum() # For __getattribute__ class PolicyViolationErrorEnum(_CreateEnumTypeUponFirstAccess): PolicyViolationErrorEnum = '''\ class PolicyViolationError(enum.IntEnum): """ Enum describing possible policy violation errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. POLICY_ERROR (int): A policy was violated. See PolicyViolationDetails for more detail. """ UNSPECIFIED = 0 UNKNOWN = 1 POLICY_ERROR = 2 ''' PolicyViolationErrorEnum = PolicyViolationErrorEnum() # For __getattribute__ class PositiveGeoTargetTypeEnum(_CreateEnumTypeUponFirstAccess): PositiveGeoTargetTypeEnum = '''\ class PositiveGeoTargetType(enum.IntEnum): """ The possible positive geo target types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. PRESENCE_OR_INTEREST (int): Specifies that an ad is triggered if the user is in, or shows interest in, advertiser's targeted locations. SEARCH_INTEREST (int): Specifies that an ad is triggered if the user searches for advertiser's targeted locations. PRESENCE (int): Specifies that an ad is triggered if the user is in or regularly in advertiser's targeted locations. """ UNSPECIFIED = 0 UNKNOWN = 1 PRESENCE_OR_INTEREST = 5 SEARCH_INTEREST = 6 PRESENCE = 7 ''' PositiveGeoTargetTypeEnum = PositiveGeoTargetTypeEnum() # For __getattribute__ class PreferredContentTypeEnum(_CreateEnumTypeUponFirstAccess): PreferredContentTypeEnum = '''\ class PreferredContentType(enum.IntEnum): """ Enumerates preferred content criterion type. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. YOUTUBE_TOP_CONTENT (int): Represents top content on YouTube. """ UNSPECIFIED = 0 UNKNOWN = 1 YOUTUBE_TOP_CONTENT = 400 ''' PreferredContentTypeEnum = PreferredContentTypeEnum() # For __getattribute__ class PriceExtensionPriceQualifierEnum(_CreateEnumTypeUponFirstAccess): PriceExtensionPriceQualifierEnum = '''\ class PriceExtensionPriceQualifier(enum.IntEnum): """ Enums of price extension price qualifier. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. FROM (int): 'From' qualifier for the price. UP_TO (int): 'Up to' qualifier for the price. AVERAGE (int): 'Average' qualifier for the price. """ UNSPECIFIED = 0 UNKNOWN = 1 FROM = 2 UP_TO = 3 AVERAGE = 4 ''' PriceExtensionPriceQualifierEnum = PriceExtensionPriceQualifierEnum() # For __getattribute__ class PriceExtensionPriceUnitEnum(_CreateEnumTypeUponFirstAccess): PriceExtensionPriceUnitEnum = '''\ class PriceExtensionPriceUnit(enum.IntEnum): """ Price extension price unit. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PER_HOUR (int): Per hour. PER_DAY (int): Per day. PER_WEEK (int): Per week. PER_MONTH (int): Per month. PER_YEAR (int): Per year. PER_NIGHT (int): Per night. """ UNSPECIFIED = 0 UNKNOWN = 1 PER_HOUR = 2 PER_DAY = 3 PER_WEEK = 4 PER_MONTH = 5 PER_YEAR = 6 PER_NIGHT = 7 ''' PriceExtensionPriceUnitEnum = PriceExtensionPriceUnitEnum() # For __getattribute__ class PriceExtensionTypeEnum(_CreateEnumTypeUponFirstAccess): PriceExtensionTypeEnum = '''\ class PriceExtensionType(enum.IntEnum): """ Price extension type. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BRANDS (int): The type for showing a list of brands. EVENTS (int): The type for showing a list of events. LOCATIONS (int): The type for showing locations relevant to your business. NEIGHBORHOODS (int): The type for showing sub-regions or districts within a city or region. PRODUCT_CATEGORIES (int): The type for showing a collection of product categories. PRODUCT_TIERS (int): The type for showing a collection of related product tiers. SERVICES (int): The type for showing a collection of services offered by your business. SERVICE_CATEGORIES (int): The type for showing a collection of service categories. SERVICE_TIERS (int): The type for showing a collection of related service tiers. """ UNSPECIFIED = 0 UNKNOWN = 1 BRANDS = 2 EVENTS = 3 LOCATIONS = 4 NEIGHBORHOODS = 5 PRODUCT_CATEGORIES = 6 PRODUCT_TIERS = 7 SERVICES = 8 SERVICE_CATEGORIES = 9 SERVICE_TIERS = 10 ''' PriceExtensionTypeEnum = PriceExtensionTypeEnum() # For __getattribute__ class PricePlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): PricePlaceholderFieldEnum = '''\ class PricePlaceholderField(enum.IntEnum): """ Possible values for Price placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. TYPE (int): Data Type: STRING. The type of your price feed. Must match one of the predefined price feed type exactly. PRICE_QUALIFIER (int): Data Type: STRING. The qualifier of each price. Must match one of the predefined price qualifiers exactly. TRACKING_TEMPLATE (int): Data Type: URL. Tracking template for the price feed when using Upgraded URLs. LANGUAGE (int): Data Type: STRING. Language of the price feed. Must match one of the available available locale codes exactly. FINAL_URL_SUFFIX (int): Data Type: STRING. Final URL suffix for the price feed when using parallel tracking. ITEM_1_HEADER (int): Data Type: STRING. The header of item 1 of the table. ITEM_1_DESCRIPTION (int): Data Type: STRING. The description of item 1 of the table. ITEM_1_PRICE (int): Data Type: MONEY. The price (money with currency) of item 1 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_1_UNIT (int): Data Type: STRING. The price unit of item 1 of the table. Must match one of the predefined price units. ITEM_1_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 1 of the table when using Upgraded URLs. ITEM_1_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 1 of the table when using Upgraded URLs. ITEM_2_HEADER (int): Data Type: STRING. The header of item 2 of the table. ITEM_2_DESCRIPTION (int): Data Type: STRING. The description of item 2 of the table. ITEM_2_PRICE (int): Data Type: MONEY. The price (money with currency) of item 2 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_2_UNIT (int): Data Type: STRING. The price unit of item 2 of the table. Must match one of the predefined price units. ITEM_2_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 2 of the table when using Upgraded URLs. ITEM_2_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 2 of the table when using Upgraded URLs. ITEM_3_HEADER (int): Data Type: STRING. The header of item 3 of the table. ITEM_3_DESCRIPTION (int): Data Type: STRING. The description of item 3 of the table. ITEM_3_PRICE (int): Data Type: MONEY. The price (money with currency) of item 3 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_3_UNIT (int): Data Type: STRING. The price unit of item 3 of the table. Must match one of the predefined price units. ITEM_3_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 3 of the table when using Upgraded URLs. ITEM_3_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 3 of the table when using Upgraded URLs. ITEM_4_HEADER (int): Data Type: STRING. The header of item 4 of the table. ITEM_4_DESCRIPTION (int): Data Type: STRING. The description of item 4 of the table. ITEM_4_PRICE (int): Data Type: MONEY. The price (money with currency) of item 4 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_4_UNIT (int): Data Type: STRING. The price unit of item 4 of the table. Must match one of the predefined price units. ITEM_4_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 4 of the table when using Upgraded URLs. ITEM_4_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 4 of the table when using Upgraded URLs. ITEM_5_HEADER (int): Data Type: STRING. The header of item 5 of the table. ITEM_5_DESCRIPTION (int): Data Type: STRING. The description of item 5 of the table. ITEM_5_PRICE (int): Data Type: MONEY. The price (money with currency) of item 5 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_5_UNIT (int): Data Type: STRING. The price unit of item 5 of the table. Must match one of the predefined price units. ITEM_5_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 5 of the table when using Upgraded URLs. ITEM_5_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 5 of the table when using Upgraded URLs. ITEM_6_HEADER (int): Data Type: STRING. The header of item 6 of the table. ITEM_6_DESCRIPTION (int): Data Type: STRING. The description of item 6 of the table. ITEM_6_PRICE (int): Data Type: MONEY. The price (money with currency) of item 6 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_6_UNIT (int): Data Type: STRING. The price unit of item 6 of the table. Must match one of the predefined price units. ITEM_6_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 6 of the table when using Upgraded URLs. ITEM_6_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 6 of the table when using Upgraded URLs. ITEM_7_HEADER (int): Data Type: STRING. The header of item 7 of the table. ITEM_7_DESCRIPTION (int): Data Type: STRING. The description of item 7 of the table. ITEM_7_PRICE (int): Data Type: MONEY. The price (money with currency) of item 7 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_7_UNIT (int): Data Type: STRING. The price unit of item 7 of the table. Must match one of the predefined price units. ITEM_7_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 7 of the table when using Upgraded URLs. ITEM_7_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 7 of the table when using Upgraded URLs. ITEM_8_HEADER (int): Data Type: STRING. The header of item 8 of the table. ITEM_8_DESCRIPTION (int): Data Type: STRING. The description of item 8 of the table. ITEM_8_PRICE (int): Data Type: MONEY. The price (money with currency) of item 8 of the table, e.g., 30 USD. The currency must match one of the available currencies. ITEM_8_UNIT (int): Data Type: STRING. The price unit of item 8 of the table. Must match one of the predefined price units. ITEM_8_FINAL_URLS (int): Data Type: URL\_LIST. The final URLs of item 8 of the table when using Upgraded URLs. ITEM_8_FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. The final mobile URLs of item 8 of the table when using Upgraded URLs. """ UNSPECIFIED = 0 UNKNOWN = 1 TYPE = 2 PRICE_QUALIFIER = 3 TRACKING_TEMPLATE = 4 LANGUAGE = 5 FINAL_URL_SUFFIX = 6 ITEM_1_HEADER = 100 ITEM_1_DESCRIPTION = 101 ITEM_1_PRICE = 102 ITEM_1_UNIT = 103 ITEM_1_FINAL_URLS = 104 ITEM_1_FINAL_MOBILE_URLS = 105 ITEM_2_HEADER = 200 ITEM_2_DESCRIPTION = 201 ITEM_2_PRICE = 202 ITEM_2_UNIT = 203 ITEM_2_FINAL_URLS = 204 ITEM_2_FINAL_MOBILE_URLS = 205 ITEM_3_HEADER = 300 ITEM_3_DESCRIPTION = 301 ITEM_3_PRICE = 302 ITEM_3_UNIT = 303 ITEM_3_FINAL_URLS = 304 ITEM_3_FINAL_MOBILE_URLS = 305 ITEM_4_HEADER = 400 ITEM_4_DESCRIPTION = 401 ITEM_4_PRICE = 402 ITEM_4_UNIT = 403 ITEM_4_FINAL_URLS = 404 ITEM_4_FINAL_MOBILE_URLS = 405 ITEM_5_HEADER = 500 ITEM_5_DESCRIPTION = 501 ITEM_5_PRICE = 502 ITEM_5_UNIT = 503 ITEM_5_FINAL_URLS = 504 ITEM_5_FINAL_MOBILE_URLS = 505 ITEM_6_HEADER = 600 ITEM_6_DESCRIPTION = 601 ITEM_6_PRICE = 602 ITEM_6_UNIT = 603 ITEM_6_FINAL_URLS = 604 ITEM_6_FINAL_MOBILE_URLS = 605 ITEM_7_HEADER = 700 ITEM_7_DESCRIPTION = 701 ITEM_7_PRICE = 702 ITEM_7_UNIT = 703 ITEM_7_FINAL_URLS = 704 ITEM_7_FINAL_MOBILE_URLS = 705 ITEM_8_HEADER = 800 ITEM_8_DESCRIPTION = 801 ITEM_8_PRICE = 802 ITEM_8_UNIT = 803 ITEM_8_FINAL_URLS = 804 ITEM_8_FINAL_MOBILE_URLS = 805 ''' PricePlaceholderFieldEnum = PricePlaceholderFieldEnum() # For __getattribute__ class ProductBiddingCategoryLevelEnum(_CreateEnumTypeUponFirstAccess): ProductBiddingCategoryLevelEnum = '''\ class ProductBiddingCategoryLevel(enum.IntEnum): """ Enum describing the level of the product bidding category. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LEVEL1 (int): Level 1. LEVEL2 (int): Level 2. LEVEL3 (int): Level 3. LEVEL4 (int): Level 4. LEVEL5 (int): Level 5. """ UNSPECIFIED = 0 UNKNOWN = 1 LEVEL1 = 2 LEVEL2 = 3 LEVEL3 = 4 LEVEL4 = 5 LEVEL5 = 6 ''' ProductBiddingCategoryLevelEnum = ProductBiddingCategoryLevelEnum() # For __getattribute__ class ProductBiddingCategoryStatusEnum(_CreateEnumTypeUponFirstAccess): ProductBiddingCategoryStatusEnum = '''\ class ProductBiddingCategoryStatus(enum.IntEnum): """ Enum describing the status of the product bidding category. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ACTIVE (int): The category is active and can be used for bidding. OBSOLETE (int): The category is obsolete. Used only for reporting purposes. """ UNSPECIFIED = 0 UNKNOWN = 1 ACTIVE = 2 OBSOLETE = 3 ''' ProductBiddingCategoryStatusEnum = ProductBiddingCategoryStatusEnum() # For __getattribute__ class ProductChannelEnum(_CreateEnumTypeUponFirstAccess): ProductChannelEnum = '''\ class ProductChannel(enum.IntEnum): """ Enum describing the locality of a product offer. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ONLINE (int): The item is sold online. LOCAL (int): The item is sold in local stores. """ UNSPECIFIED = 0 UNKNOWN = 1 ONLINE = 2 LOCAL = 3 ''' ProductChannelEnum = ProductChannelEnum() # For __getattribute__ class ProductChannelExclusivityEnum(_CreateEnumTypeUponFirstAccess): ProductChannelExclusivityEnum = '''\ class ProductChannelExclusivity(enum.IntEnum): """ Enum describing the availability of a product offer. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SINGLE_CHANNEL (int): The item is sold through one channel only, either local stores or online as indicated by its ProductChannel. MULTI_CHANNEL (int): The item is matched to its online or local stores counterpart, indicating it is available for purchase in both ShoppingProductChannels. """ UNSPECIFIED = 0 UNKNOWN = 1 SINGLE_CHANNEL = 2 MULTI_CHANNEL = 3 ''' ProductChannelExclusivityEnum = ProductChannelExclusivityEnum() # For __getattribute__ class ProductConditionEnum(_CreateEnumTypeUponFirstAccess): ProductConditionEnum = '''\ class ProductCondition(enum.IntEnum): """ Enum describing the condition of a product offer. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NEW (int): The product condition is new. REFURBISHED (int): The product condition is refurbished. USED (int): The product condition is used. """ UNSPECIFIED = 0 UNKNOWN = 1 NEW = 3 REFURBISHED = 4 USED = 5 ''' ProductConditionEnum = ProductConditionEnum() # For __getattribute__ class ProductCustomAttributeIndexEnum(_CreateEnumTypeUponFirstAccess): ProductCustomAttributeIndexEnum = '''\ class ProductCustomAttributeIndex(enum.IntEnum): """ The index of the product custom attribute. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INDEX0 (int): First product custom attribute. INDEX1 (int): Second product custom attribute. INDEX2 (int): Third product custom attribute. INDEX3 (int): Fourth product custom attribute. INDEX4 (int): Fifth product custom attribute. """ UNSPECIFIED = 0 UNKNOWN = 1 INDEX0 = 7 INDEX1 = 8 INDEX2 = 9 INDEX3 = 10 INDEX4 = 11 ''' ProductCustomAttributeIndexEnum = ProductCustomAttributeIndexEnum() # For __getattribute__ class ProductTypeLevelEnum(_CreateEnumTypeUponFirstAccess): ProductTypeLevelEnum = '''\ class ProductTypeLevel(enum.IntEnum): """ Enum describing the level of the type of a product offer. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LEVEL1 (int): Level 1. LEVEL2 (int): Level 2. LEVEL3 (int): Level 3. LEVEL4 (int): Level 4. LEVEL5 (int): Level 5. """ UNSPECIFIED = 0 UNKNOWN = 1 LEVEL1 = 7 LEVEL2 = 8 LEVEL3 = 9 LEVEL4 = 10 LEVEL5 = 11 ''' ProductTypeLevelEnum = ProductTypeLevelEnum() # For __getattribute__ class PromotionExtensionDiscountModifierEnum(_CreateEnumTypeUponFirstAccess): PromotionExtensionDiscountModifierEnum = '''\ class PromotionExtensionDiscountModifier(enum.IntEnum): """ A promotion extension discount modifier. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. UP_TO (int): 'Up to'. """ UNSPECIFIED = 0 UNKNOWN = 1 UP_TO = 2 ''' PromotionExtensionDiscountModifierEnum = PromotionExtensionDiscountModifierEnum() # For __getattribute__ class PromotionExtensionOccasionEnum(_CreateEnumTypeUponFirstAccess): PromotionExtensionOccasionEnum = '''\ class PromotionExtensionOccasion(enum.IntEnum): """ A promotion extension occasion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NEW_YEARS (int): New Year's. CHINESE_NEW_YEAR (int): Chinese New Year. VALENTINES_DAY (int): Valentine's Day. EASTER (int): Easter. MOTHERS_DAY (int): Mother's Day. FATHERS_DAY (int): Father's Day. LABOR_DAY (int): Labor Day. BACK_TO_SCHOOL (int): Back To School. HALLOWEEN (int): Halloween. BLACK_FRIDAY (int): Black Friday. CYBER_MONDAY (int): Cyber Monday. CHRISTMAS (int): Christmas. BOXING_DAY (int): Boxing Day. INDEPENDENCE_DAY (int): Independence Day in any country. NATIONAL_DAY (int): National Day in any country. END_OF_SEASON (int): End of any season. WINTER_SALE (int): Winter Sale. SUMMER_SALE (int): Summer sale. FALL_SALE (int): Fall Sale. SPRING_SALE (int): Spring Sale. RAMADAN (int): Ramadan. EID_AL_FITR (int): Eid al-Fitr. EID_AL_ADHA (int): Eid al-Adha. SINGLES_DAY (int): Singles Day. WOMENS_DAY (int): Women's Day. HOLI (int): Holi. PARENTS_DAY (int): Parent's Day. ST_NICHOLAS_DAY (int): St. Nicholas Day. CARNIVAL (int): Carnival. EPIPHANY (int): Epiphany, also known as Three Kings' Day. ROSH_HASHANAH (int): Rosh Hashanah. PASSOVER (int): Passover. HANUKKAH (int): Hanukkah. DIWALI (int): Diwali. NAVRATRI (int): Navratri. SONGKRAN (int): Available in Thai: Songkran. YEAR_END_GIFT (int): Available in Japanese: Year-end Gift. """ UNSPECIFIED = 0 UNKNOWN = 1 NEW_YEARS = 2 CHINESE_NEW_YEAR = 3 VALENTINES_DAY = 4 EASTER = 5 MOTHERS_DAY = 6 FATHERS_DAY = 7 LABOR_DAY = 8 BACK_TO_SCHOOL = 9 HALLOWEEN = 10 BLACK_FRIDAY = 11 CYBER_MONDAY = 12 CHRISTMAS = 13 BOXING_DAY = 14 INDEPENDENCE_DAY = 15 NATIONAL_DAY = 16 END_OF_SEASON = 17 WINTER_SALE = 18 SUMMER_SALE = 19 FALL_SALE = 20 SPRING_SALE = 21 RAMADAN = 22 EID_AL_FITR = 23 EID_AL_ADHA = 24 SINGLES_DAY = 25 WOMENS_DAY = 26 HOLI = 27 PARENTS_DAY = 28 ST_NICHOLAS_DAY = 29 CARNIVAL = 30 EPIPHANY = 31 ROSH_HASHANAH = 32 PASSOVER = 33 HANUKKAH = 34 DIWALI = 35 NAVRATRI = 36 SONGKRAN = 37 YEAR_END_GIFT = 38 ''' PromotionExtensionOccasionEnum = PromotionExtensionOccasionEnum() # For __getattribute__ class PromotionPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): PromotionPlaceholderFieldEnum = '''\ class PromotionPlaceholderField(enum.IntEnum): """ Possible values for Promotion placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PROMOTION_TARGET (int): Data Type: STRING. The text that appears on the ad when the extension is shown. DISCOUNT_MODIFIER (int): Data Type: STRING. Allows you to add "up to" phrase to the promotion, in case you have variable promotion rates. PERCENT_OFF (int): Data Type: INT64. Takes a value in micros, where 1 million micros represents 1%, and is shown as a percentage when rendered. MONEY_AMOUNT_OFF (int): Data Type: MONEY. Requires a currency and an amount of money. PROMOTION_CODE (int): Data Type: STRING. A string that the user enters to get the discount. ORDERS_OVER_AMOUNT (int): Data Type: MONEY. A minimum spend before the user qualifies for the promotion. PROMOTION_START (int): Data Type: DATE. The start date of the promotion. PROMOTION_END (int): Data Type: DATE. The end date of the promotion. OCCASION (int): Data Type: STRING. Describes the associated event for the promotion using one of the PromotionExtensionOccasion enum values, for example NEW\_YEARS. FINAL_URLS (int): Data Type: URL\_LIST. Final URLs to be used in the ad when using Upgraded URLs. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. LANGUAGE (int): Data Type: STRING. A string represented by a language code for the promotion. FINAL_URL_SUFFIX (int): Data Type: STRING. Final URL suffix for the ad when using parallel tracking. """ UNSPECIFIED = 0 UNKNOWN = 1 PROMOTION_TARGET = 2 DISCOUNT_MODIFIER = 3 PERCENT_OFF = 4 MONEY_AMOUNT_OFF = 5 PROMOTION_CODE = 6 ORDERS_OVER_AMOUNT = 7 PROMOTION_START = 8 PROMOTION_END = 9 OCCASION = 10 FINAL_URLS = 11 FINAL_MOBILE_URLS = 12 TRACKING_URL = 13 LANGUAGE = 14 FINAL_URL_SUFFIX = 15 ''' PromotionPlaceholderFieldEnum = PromotionPlaceholderFieldEnum() # For __getattribute__ class ProximityRadiusUnitsEnum(_CreateEnumTypeUponFirstAccess): ProximityRadiusUnitsEnum = '''\ class ProximityRadiusUnits(enum.IntEnum): """ The unit of radius distance in proximity (e.g. MILES) Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. MILES (int): Miles KILOMETERS (int): Kilometers """ UNSPECIFIED = 0 UNKNOWN = 1 MILES = 2 KILOMETERS = 3 ''' ProximityRadiusUnitsEnum = ProximityRadiusUnitsEnum() # For __getattribute__ class QualityScoreBucketEnum(_CreateEnumTypeUponFirstAccess): QualityScoreBucketEnum = '''\ class QualityScoreBucket(enum.IntEnum): """ Enum listing the possible quality score buckets. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BELOW_AVERAGE (int): Quality of the creative is below average. AVERAGE (int): Quality of the creative is average. ABOVE_AVERAGE (int): Quality of the creative is above average. """ UNSPECIFIED = 0 UNKNOWN = 1 BELOW_AVERAGE = 2 AVERAGE = 3 ABOVE_AVERAGE = 4 ''' QualityScoreBucketEnum = QualityScoreBucketEnum() # For __getattribute__ class QueryErrorEnum(_CreateEnumTypeUponFirstAccess): QueryErrorEnum = '''\ class QueryError(enum.IntEnum): """ Enum describing possible query errors. Attributes: UNSPECIFIED (int): Name unspecified. UNKNOWN (int): The received error code is not known in this version. QUERY_ERROR (int): Returned if all other query error reasons are not applicable. BAD_ENUM_CONSTANT (int): A condition used in the query references an invalid enum constant. BAD_ESCAPE_SEQUENCE (int): Query contains an invalid escape sequence. BAD_FIELD_NAME (int): Field name is invalid. BAD_LIMIT_VALUE (int): Limit value is invalid (i.e. not a number) BAD_NUMBER (int): Encountered number can not be parsed. BAD_OPERATOR (int): Invalid operator encountered. BAD_PARAMETER_NAME (int): Parameter unknown or not supported. BAD_PARAMETER_VALUE (int): Parameter have invalid value. BAD_RESOURCE_TYPE_IN_FROM_CLAUSE (int): Invalid resource type was specified in the FROM clause. BAD_SYMBOL (int): Non-ASCII symbol encountered outside of strings. BAD_VALUE (int): Value is invalid. DATE_RANGE_TOO_WIDE (int): Date filters fail to restrict date to a range smaller than 31 days. Applicable if the query is segmented by date. DATE_RANGE_TOO_NARROW (int): Filters on date/week/month/quarter have a start date after end date. EXPECTED_AND (int): Expected AND between values with BETWEEN operator. EXPECTED_BY (int): Expecting ORDER BY to have BY. EXPECTED_DIMENSION_FIELD_IN_SELECT_CLAUSE (int): There was no dimension field selected. EXPECTED_FILTERS_ON_DATE_RANGE (int): Missing filters on date related fields. EXPECTED_FROM (int): Missing FROM clause. EXPECTED_LIST (int): The operator used in the conditions requires the value to be a list. EXPECTED_REFERENCED_FIELD_IN_SELECT_CLAUSE (int): Fields used in WHERE or ORDER BY clauses are missing from the SELECT clause. EXPECTED_SELECT (int): SELECT is missing at the beginning of query. EXPECTED_SINGLE_VALUE (int): A list was passed as a value to a condition whose operator expects a single value. EXPECTED_VALUE_WITH_BETWEEN_OPERATOR (int): Missing one or both values with BETWEEN operator. INVALID_DATE_FORMAT (int): Invalid date format. Expected 'YYYY-MM-DD'. INVALID_STRING_VALUE (int): Value passed was not a string when it should have been. I.e., it was a number or unquoted literal. INVALID_VALUE_WITH_BETWEEN_OPERATOR (int): A String value passed to the BETWEEN operator does not parse as a date. INVALID_VALUE_WITH_DURING_OPERATOR (int): The value passed to the DURING operator is not a Date range literal INVALID_VALUE_WITH_LIKE_OPERATOR (int): A non-string value was passed to the LIKE operator. OPERATOR_FIELD_MISMATCH (int): An operator was provided that is inapplicable to the field being filtered. PROHIBITED_EMPTY_LIST_IN_CONDITION (int): A Condition was found with an empty list. PROHIBITED_ENUM_CONSTANT (int): A condition used in the query references an unsupported enum constant. PROHIBITED_FIELD_COMBINATION_IN_SELECT_CLAUSE (int): Fields that are not allowed to be selected together were included in the SELECT clause. PROHIBITED_FIELD_IN_ORDER_BY_CLAUSE (int): A field that is not orderable was included in the ORDER BY clause. PROHIBITED_FIELD_IN_SELECT_CLAUSE (int): A field that is not selectable was included in the SELECT clause. PROHIBITED_FIELD_IN_WHERE_CLAUSE (int): A field that is not filterable was included in the WHERE clause. PROHIBITED_RESOURCE_TYPE_IN_FROM_CLAUSE (int): Resource type specified in the FROM clause is not supported by this service. PROHIBITED_RESOURCE_TYPE_IN_SELECT_CLAUSE (int): A field that comes from an incompatible resource was included in the SELECT clause. PROHIBITED_RESOURCE_TYPE_IN_WHERE_CLAUSE (int): A field that comes from an incompatible resource was included in the WHERE clause. PROHIBITED_METRIC_IN_SELECT_OR_WHERE_CLAUSE (int): A metric incompatible with the main resource or other selected segmenting resources was included in the SELECT or WHERE clause. PROHIBITED_SEGMENT_IN_SELECT_OR_WHERE_CLAUSE (int): A segment incompatible with the main resource or other selected segmenting resources was included in the SELECT or WHERE clause. PROHIBITED_SEGMENT_WITH_METRIC_IN_SELECT_OR_WHERE_CLAUSE (int): A segment in the SELECT clause is incompatible with a metric in the SELECT or WHERE clause. LIMIT_VALUE_TOO_LOW (int): The value passed to the limit clause is too low. PROHIBITED_NEWLINE_IN_STRING (int): Query has a string containing a newline character. PROHIBITED_VALUE_COMBINATION_IN_LIST (int): List contains values of different types. PROHIBITED_VALUE_COMBINATION_WITH_BETWEEN_OPERATOR (int): The values passed to the BETWEEN operator are not of the same type. STRING_NOT_TERMINATED (int): Query contains unterminated string. TOO_MANY_SEGMENTS (int): Too many segments are specified in SELECT clause. UNEXPECTED_END_OF_QUERY (int): Query is incomplete and cannot be parsed. UNEXPECTED_FROM_CLAUSE (int): FROM clause cannot be specified in this query. UNRECOGNIZED_FIELD (int): Query contains one or more unrecognized fields. UNEXPECTED_INPUT (int): Query has an unexpected extra part. REQUESTED_METRICS_FOR_MANAGER (int): Metrics cannot be requested for a manager account. To retrieve metrics, issue separate requests against each client account under the manager account. """ UNSPECIFIED = 0 UNKNOWN = 1 QUERY_ERROR = 50 BAD_ENUM_CONSTANT = 18 BAD_ESCAPE_SEQUENCE = 7 BAD_FIELD_NAME = 12 BAD_LIMIT_VALUE = 15 BAD_NUMBER = 5 BAD_OPERATOR = 3 BAD_PARAMETER_NAME = 61 BAD_PARAMETER_VALUE = 62 BAD_RESOURCE_TYPE_IN_FROM_CLAUSE = 45 BAD_SYMBOL = 2 BAD_VALUE = 4 DATE_RANGE_TOO_WIDE = 36 DATE_RANGE_TOO_NARROW = 60 EXPECTED_AND = 30 EXPECTED_BY = 14 EXPECTED_DIMENSION_FIELD_IN_SELECT_CLAUSE = 37 EXPECTED_FILTERS_ON_DATE_RANGE = 55 EXPECTED_FROM = 44 EXPECTED_LIST = 41 EXPECTED_REFERENCED_FIELD_IN_SELECT_CLAUSE = 16 EXPECTED_SELECT = 13 EXPECTED_SINGLE_VALUE = 42 EXPECTED_VALUE_WITH_BETWEEN_OPERATOR = 29 INVALID_DATE_FORMAT = 38 INVALID_STRING_VALUE = 57 INVALID_VALUE_WITH_BETWEEN_OPERATOR = 26 INVALID_VALUE_WITH_DURING_OPERATOR = 22 INVALID_VALUE_WITH_LIKE_OPERATOR = 56 OPERATOR_FIELD_MISMATCH = 35 PROHIBITED_EMPTY_LIST_IN_CONDITION = 28 PROHIBITED_ENUM_CONSTANT = 54 PROHIBITED_FIELD_COMBINATION_IN_SELECT_CLAUSE = 31 PROHIBITED_FIELD_IN_ORDER_BY_CLAUSE = 40 PROHIBITED_FIELD_IN_SELECT_CLAUSE = 23 PROHIBITED_FIELD_IN_WHERE_CLAUSE = 24 PROHIBITED_RESOURCE_TYPE_IN_FROM_CLAUSE = 43 PROHIBITED_RESOURCE_TYPE_IN_SELECT_CLAUSE = 48 PROHIBITED_RESOURCE_TYPE_IN_WHERE_CLAUSE = 58 PROHIBITED_METRIC_IN_SELECT_OR_WHERE_CLAUSE = 49 PROHIBITED_SEGMENT_IN_SELECT_OR_WHERE_CLAUSE = 51 PROHIBITED_SEGMENT_WITH_METRIC_IN_SELECT_OR_WHERE_CLAUSE = 53 LIMIT_VALUE_TOO_LOW = 25 PROHIBITED_NEWLINE_IN_STRING = 8 PROHIBITED_VALUE_COMBINATION_IN_LIST = 10 PROHIBITED_VALUE_COMBINATION_WITH_BETWEEN_OPERATOR = 21 STRING_NOT_TERMINATED = 6 TOO_MANY_SEGMENTS = 34 UNEXPECTED_END_OF_QUERY = 9 UNEXPECTED_FROM_CLAUSE = 47 UNRECOGNIZED_FIELD = 32 UNEXPECTED_INPUT = 11 REQUESTED_METRICS_FOR_MANAGER = 59 ''' QueryErrorEnum = QueryErrorEnum() # For __getattribute__ class QuotaErrorEnum(_CreateEnumTypeUponFirstAccess): QuotaErrorEnum = '''\ class QuotaError(enum.IntEnum): """ Enum describing possible quota errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. RESOURCE_EXHAUSTED (int): Too many requests. ACCESS_PROHIBITED (int): Access is prohibited. RESOURCE_TEMPORARILY_EXHAUSTED (int): Too many requests in a short amount of time. """ UNSPECIFIED = 0 UNKNOWN = 1 RESOURCE_EXHAUSTED = 2 ACCESS_PROHIBITED = 3 RESOURCE_TEMPORARILY_EXHAUSTED = 4 ''' QuotaErrorEnum = QuotaErrorEnum() # For __getattribute__ class RangeErrorEnum(_CreateEnumTypeUponFirstAccess): RangeErrorEnum = '''\ class RangeError(enum.IntEnum): """ Enum describing possible range errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. TOO_LOW (int): Too low. TOO_HIGH (int): Too high. """ UNSPECIFIED = 0 UNKNOWN = 1 TOO_LOW = 2 TOO_HIGH = 3 ''' RangeErrorEnum = RangeErrorEnum() # For __getattribute__ class ReachPlanAdLengthEnum(_CreateEnumTypeUponFirstAccess): ReachPlanAdLengthEnum = '''\ class ReachPlanAdLength(enum.IntEnum): """ Possible ad length values. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. SIX_SECONDS (int): 6 seconds long ad. FIFTEEN_OR_TWENTY_SECONDS (int): 15 or 20 seconds long ad. TWENTY_SECONDS_OR_MORE (int): More than 20 seconds long ad. """ UNSPECIFIED = 0 UNKNOWN = 1 SIX_SECONDS = 2 FIFTEEN_OR_TWENTY_SECONDS = 3 TWENTY_SECONDS_OR_MORE = 4 ''' ReachPlanAdLengthEnum = ReachPlanAdLengthEnum() # For __getattribute__ class ReachPlanAgeRangeEnum(_CreateEnumTypeUponFirstAccess): ReachPlanAgeRangeEnum = '''\ class ReachPlanAgeRange(enum.IntEnum): """ Possible plannable age range values. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. AGE_RANGE_18_24 (int): Between 18 and 24 years old. AGE_RANGE_18_34 (int): Between 18 and 34 years old. AGE_RANGE_18_44 (int): Between 18 and 44 years old. AGE_RANGE_18_49 (int): Between 18 and 49 years old. AGE_RANGE_18_54 (int): Between 18 and 54 years old. AGE_RANGE_18_64 (int): Between 18 and 64 years old. AGE_RANGE_18_65_UP (int): Between 18 and 65+ years old. AGE_RANGE_21_34 (int): Between 21 and 34 years old. AGE_RANGE_25_34 (int): Between 25 and 34 years old. AGE_RANGE_25_44 (int): Between 25 and 44 years old. AGE_RANGE_25_49 (int): Between 25 and 49 years old. AGE_RANGE_25_54 (int): Between 25 and 54 years old. AGE_RANGE_25_64 (int): Between 25 and 64 years old. AGE_RANGE_25_65_UP (int): Between 25 and 65+ years old. AGE_RANGE_35_44 (int): Between 35 and 44 years old. AGE_RANGE_35_49 (int): Between 35 and 49 years old. AGE_RANGE_35_54 (int): Between 35 and 54 years old. AGE_RANGE_35_64 (int): Between 35 and 64 years old. AGE_RANGE_35_65_UP (int): Between 35 and 65+ years old. AGE_RANGE_45_54 (int): Between 45 and 54 years old. AGE_RANGE_45_64 (int): Between 45 and 64 years old. AGE_RANGE_45_65_UP (int): Between 45 and 65+ years old. AGE_RANGE_50_65_UP (int): Between 50 and 65+ years old. AGE_RANGE_55_64 (int): Between 55 and 64 years old. AGE_RANGE_55_65_UP (int): Between 55 and 65+ years old. AGE_RANGE_65_UP (int): 65 years old and beyond. """ UNSPECIFIED = 0 UNKNOWN = 1 AGE_RANGE_18_24 = 503001 AGE_RANGE_18_34 = 2 AGE_RANGE_18_44 = 3 AGE_RANGE_18_49 = 4 AGE_RANGE_18_54 = 5 AGE_RANGE_18_64 = 6 AGE_RANGE_18_65_UP = 7 AGE_RANGE_21_34 = 8 AGE_RANGE_25_34 = 503002 AGE_RANGE_25_44 = 9 AGE_RANGE_25_49 = 10 AGE_RANGE_25_54 = 11 AGE_RANGE_25_64 = 12 AGE_RANGE_25_65_UP = 13 AGE_RANGE_35_44 = 503003 AGE_RANGE_35_49 = 14 AGE_RANGE_35_54 = 15 AGE_RANGE_35_64 = 16 AGE_RANGE_35_65_UP = 17 AGE_RANGE_45_54 = 503004 AGE_RANGE_45_64 = 18 AGE_RANGE_45_65_UP = 19 AGE_RANGE_50_65_UP = 20 AGE_RANGE_55_64 = 503005 AGE_RANGE_55_65_UP = 21 AGE_RANGE_65_UP = 503006 ''' ReachPlanAgeRangeEnum = ReachPlanAgeRangeEnum() # For __getattribute__ class ReachPlanErrorEnum(_CreateEnumTypeUponFirstAccess): ReachPlanErrorEnum = '''\ class ReachPlanError(enum.IntEnum): """ Enum describing possible errors from ReachPlanService. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. """ UNSPECIFIED = 0 UNKNOWN = 1 ''' ReachPlanErrorEnum = ReachPlanErrorEnum() # For __getattribute__ class ReachPlanNetworkEnum(_CreateEnumTypeUponFirstAccess): ReachPlanNetworkEnum = '''\ class ReachPlanNetwork(enum.IntEnum): """ Possible plannable network values. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used as a return value only. Represents value unknown in this version. YOUTUBE (int): YouTube network. GOOGLE_VIDEO_PARTNERS (int): Google Video Partners (GVP) network. YOUTUBE_AND_GOOGLE_VIDEO_PARTNERS (int): A combination of the YouTube network and the Google Video Partners network. """ UNSPECIFIED = 0 UNKNOWN = 1 YOUTUBE = 2 GOOGLE_VIDEO_PARTNERS = 3 YOUTUBE_AND_GOOGLE_VIDEO_PARTNERS = 4 ''' ReachPlanNetworkEnum = ReachPlanNetworkEnum() # For __getattribute__ class RealEstatePlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): RealEstatePlaceholderFieldEnum = '''\ class RealEstatePlaceholderField(enum.IntEnum): """ Possible values for Real Estate placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LISTING_ID (int): Data Type: STRING. Unique ID. LISTING_NAME (int): Data Type: STRING. Main headline with listing name to be shown in dynamic ad. CITY_NAME (int): Data Type: STRING. City name to be shown in dynamic ad. DESCRIPTION (int): Data Type: STRING. Description of listing to be shown in dynamic ad. ADDRESS (int): Data Type: STRING. Complete listing address, including postal code. PRICE (int): Data Type: STRING. Price to be shown in the ad. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. PROPERTY_TYPE (int): Data Type: STRING. Type of property (house, condo, apartment, etc.) used to group like items together for recommendation engine. LISTING_TYPE (int): Data Type: STRING. Type of listing (resale, rental, foreclosure, etc.) used to group like items together for recommendation engine. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. FINAL_URLS (int): Data Type: URL\_LIST. Final URLs to be used in ad when using Upgraded URLs; the more specific the better (e.g. the individual URL of a specific listing and its location). FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_LISTING_IDS (int): Data Type: STRING\_LIST. List of recommended listing IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 LISTING_ID = 2 LISTING_NAME = 3 CITY_NAME = 4 DESCRIPTION = 5 ADDRESS = 6 PRICE = 7 FORMATTED_PRICE = 8 IMAGE_URL = 9 PROPERTY_TYPE = 10 LISTING_TYPE = 11 CONTEXTUAL_KEYWORDS = 12 FINAL_URLS = 13 FINAL_MOBILE_URLS = 14 TRACKING_URL = 15 ANDROID_APP_LINK = 16 SIMILAR_LISTING_IDS = 17 IOS_APP_LINK = 18 IOS_APP_STORE_ID = 19 ''' RealEstatePlaceholderFieldEnum = RealEstatePlaceholderFieldEnum() # For __getattribute__ class RecommendationErrorEnum(_CreateEnumTypeUponFirstAccess): RecommendationErrorEnum = '''\ class RecommendationError(enum.IntEnum): """ Enum describing possible errors from applying a recommendation. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. BUDGET_AMOUNT_TOO_SMALL (int): The specified budget amount is too low e.g. lower than minimum currency unit or lower than ad group minimum cost-per-click. BUDGET_AMOUNT_TOO_LARGE (int): The specified budget amount is too large. INVALID_BUDGET_AMOUNT (int): The specified budget amount is not a valid amount. e.g. not a multiple of minimum currency unit. POLICY_ERROR (int): The specified keyword or ad violates ad policy. INVALID_BID_AMOUNT (int): The specified bid amount is not valid. e.g. too many fractional digits, or negative amount. ADGROUP_KEYWORD_LIMIT (int): The number of keywords in ad group have reached the maximum allowed. RECOMMENDATION_ALREADY_APPLIED (int): The recommendation requested to apply has already been applied. RECOMMENDATION_INVALIDATED (int): The recommendation requested to apply has been invalidated. TOO_MANY_OPERATIONS (int): The number of operations in a single request exceeds the maximum allowed. NO_OPERATIONS (int): There are no operations in the request. DIFFERENT_TYPES_NOT_SUPPORTED (int): Operations with multiple recommendation types are not supported when partial failure mode is not enabled. DUPLICATE_RESOURCE_NAME (int): Request contains multiple operations with the same resource\_name. RECOMMENDATION_ALREADY_DISMISSED (int): The recommendation requested to dismiss has already been dismissed. INVALID_APPLY_REQUEST (int): The recommendation apply request was malformed and invalid. """ UNSPECIFIED = 0 UNKNOWN = 1 BUDGET_AMOUNT_TOO_SMALL = 2 BUDGET_AMOUNT_TOO_LARGE = 3 INVALID_BUDGET_AMOUNT = 4 POLICY_ERROR = 5 INVALID_BID_AMOUNT = 6 ADGROUP_KEYWORD_LIMIT = 7 RECOMMENDATION_ALREADY_APPLIED = 8 RECOMMENDATION_INVALIDATED = 9 TOO_MANY_OPERATIONS = 10 NO_OPERATIONS = 11 DIFFERENT_TYPES_NOT_SUPPORTED = 12 DUPLICATE_RESOURCE_NAME = 13 RECOMMENDATION_ALREADY_DISMISSED = 14 INVALID_APPLY_REQUEST = 15 ''' RecommendationErrorEnum = RecommendationErrorEnum() # For __getattribute__ class RecommendationTypeEnum(_CreateEnumTypeUponFirstAccess): RecommendationTypeEnum = '''\ class RecommendationType(enum.IntEnum): """ Types of recommendations. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CAMPAIGN_BUDGET (int): Budget recommendation for budget constrained campaigns. KEYWORD (int): Keyword recommendation. TEXT_AD (int): Recommendation to add a new text ad. TARGET_CPA_OPT_IN (int): Recommendation to update a campaign to use a Target CPA bidding strategy. MAXIMIZE_CONVERSIONS_OPT_IN (int): Recommendation to update a campaign to use the Maximize Conversions bidding strategy. ENHANCED_CPC_OPT_IN (int): Recommendation to enable Enhanced Cost Per Click for a campaign. SEARCH_PARTNERS_OPT_IN (int): Recommendation to start showing your campaign's ads on Google Search Partners Websites. MAXIMIZE_CLICKS_OPT_IN (int): Recommendation to update a campaign to use a Maximize Clicks bidding strategy. OPTIMIZE_AD_ROTATION (int): Recommendation to start using the "Optimize" ad rotation setting for the given ad group. CALLOUT_EXTENSION (int): Recommendation to add callout extensions to a campaign. SITELINK_EXTENSION (int): Recommendation to add sitelink extensions to a campaign. CALL_EXTENSION (int): Recommendation to add call extensions to a campaign. KEYWORD_MATCH_TYPE (int): Recommendation to change an existing keyword from one match type to a broader match type. MOVE_UNUSED_BUDGET (int): Recommendation to move unused budget from one budget to a constrained budget. """ UNSPECIFIED = 0 UNKNOWN = 1 CAMPAIGN_BUDGET = 2 KEYWORD = 3 TEXT_AD = 4 TARGET_CPA_OPT_IN = 5 MAXIMIZE_CONVERSIONS_OPT_IN = 6 ENHANCED_CPC_OPT_IN = 7 SEARCH_PARTNERS_OPT_IN = 8 MAXIMIZE_CLICKS_OPT_IN = 9 OPTIMIZE_AD_ROTATION = 10 CALLOUT_EXTENSION = 11 SITELINK_EXTENSION = 12 CALL_EXTENSION = 13 KEYWORD_MATCH_TYPE = 14 MOVE_UNUSED_BUDGET = 15 ''' RecommendationTypeEnum = RecommendationTypeEnum() # For __getattribute__ class RegionCodeErrorEnum(_CreateEnumTypeUponFirstAccess): RegionCodeErrorEnum = '''\ class RegionCodeError(enum.IntEnum): """ Enum describing possible region code errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_REGION_CODE (int): Invalid region code. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_REGION_CODE = 2 ''' RegionCodeErrorEnum = RegionCodeErrorEnum() # For __getattribute__ class RequestErrorEnum(_CreateEnumTypeUponFirstAccess): RequestErrorEnum = '''\ class RequestError(enum.IntEnum): """ Enum describing possible request errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. RESOURCE_NAME_MISSING (int): Resource name is required for this request. RESOURCE_NAME_MALFORMED (int): Resource name provided is malformed. BAD_RESOURCE_ID (int): Resource name provided is malformed. INVALID_CUSTOMER_ID (int): Customer ID is invalid. OPERATION_REQUIRED (int): Mutate operation should have either create, update, or remove specified. RESOURCE_NOT_FOUND (int): Requested resource not found. INVALID_PAGE_TOKEN (int): Next page token specified in user request is invalid. EXPIRED_PAGE_TOKEN (int): Next page token specified in user request has expired. INVALID_PAGE_SIZE (int): Page size specified in user request is invalid. REQUIRED_FIELD_MISSING (int): Required field is missing. IMMUTABLE_FIELD (int): The field cannot be modified because it's immutable. It's also possible that the field can be modified using 'create' operation but not 'update'. TOO_MANY_MUTATE_OPERATIONS (int): Received too many entries in request. CANNOT_BE_EXECUTED_BY_MANAGER_ACCOUNT (int): Request cannot be executed by a manager account. CANNOT_MODIFY_FOREIGN_FIELD (int): Mutate request was attempting to modify a readonly field. For instance, Budget fields can be requested for Ad Group, but are read-only for adGroups:mutate. INVALID_ENUM_VALUE (int): Enum value is not permitted. DEVELOPER_TOKEN_PARAMETER_MISSING (int): The developer-token parameter is required for all requests. LOGIN_CUSTOMER_ID_PARAMETER_MISSING (int): The login-customer-id parameter is required for this request. VALIDATE_ONLY_REQUEST_HAS_PAGE_TOKEN (int): page\_token is set in the validate only request CANNOT_RETURN_SUMMARY_ROW_FOR_REQUEST_WITHOUT_METRICS (int): return\_summary\_row cannot be enabled if request did not select any metrics field. CANNOT_RETURN_SUMMARY_ROW_FOR_VALIDATE_ONLY_REQUESTS (int): return\_summary\_row should not be enabled for validate only requests. INCONSISTENT_RETURN_SUMMARY_ROW_VALUE (int): return\_summary\_row parameter value should be the same between requests with page\_token field set and their original request. TOTAL_RESULTS_COUNT_NOT_ORIGINALLY_REQUESTED (int): The total results count cannot be returned if it was not requested in the original request. """ UNSPECIFIED = 0 UNKNOWN = 1 RESOURCE_NAME_MISSING = 3 RESOURCE_NAME_MALFORMED = 4 BAD_RESOURCE_ID = 17 INVALID_CUSTOMER_ID = 16 OPERATION_REQUIRED = 5 RESOURCE_NOT_FOUND = 6 INVALID_PAGE_TOKEN = 7 EXPIRED_PAGE_TOKEN = 8 INVALID_PAGE_SIZE = 22 REQUIRED_FIELD_MISSING = 9 IMMUTABLE_FIELD = 11 TOO_MANY_MUTATE_OPERATIONS = 13 CANNOT_BE_EXECUTED_BY_MANAGER_ACCOUNT = 14 CANNOT_MODIFY_FOREIGN_FIELD = 15 INVALID_ENUM_VALUE = 18 DEVELOPER_TOKEN_PARAMETER_MISSING = 19 LOGIN_CUSTOMER_ID_PARAMETER_MISSING = 20 VALIDATE_ONLY_REQUEST_HAS_PAGE_TOKEN = 21 CANNOT_RETURN_SUMMARY_ROW_FOR_REQUEST_WITHOUT_METRICS = 29 CANNOT_RETURN_SUMMARY_ROW_FOR_VALIDATE_ONLY_REQUESTS = 30 INCONSISTENT_RETURN_SUMMARY_ROW_VALUE = 31 TOTAL_RESULTS_COUNT_NOT_ORIGINALLY_REQUESTED = 32 ''' RequestErrorEnum = RequestErrorEnum() # For __getattribute__ class ResourceAccessDeniedErrorEnum(_CreateEnumTypeUponFirstAccess): ResourceAccessDeniedErrorEnum = '''\ class ResourceAccessDeniedError(enum.IntEnum): """ Enum describing possible resource access denied errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. WRITE_ACCESS_DENIED (int): User did not have write access. """ UNSPECIFIED = 0 UNKNOWN = 1 WRITE_ACCESS_DENIED = 3 ''' ResourceAccessDeniedErrorEnum = ResourceAccessDeniedErrorEnum() # For __getattribute__ class ResourceCountLimitExceededErrorEnum(_CreateEnumTypeUponFirstAccess): ResourceCountLimitExceededErrorEnum = '''\ class ResourceCountLimitExceededError(enum.IntEnum): """ Enum describing possible resource count limit exceeded errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ACCOUNT_LIMIT (int): Indicates that this request would exceed the number of allowed resources for the Google Ads account. The exact resource type and limit being checked can be inferred from accountLimitType. CAMPAIGN_LIMIT (int): Indicates that this request would exceed the number of allowed resources in a Campaign. The exact resource type and limit being checked can be inferred from accountLimitType, and the numeric id of the Campaign involved is given by enclosingId. ADGROUP_LIMIT (int): Indicates that this request would exceed the number of allowed resources in an ad group. The exact resource type and limit being checked can be inferred from accountLimitType, and the numeric id of the ad group involved is given by enclosingId. AD_GROUP_AD_LIMIT (int): Indicates that this request would exceed the number of allowed resources in an ad group ad. The exact resource type and limit being checked can be inferred from accountLimitType, and the enclosingId contains the ad group id followed by the ad id, separated by a single comma (,). AD_GROUP_CRITERION_LIMIT (int): Indicates that this request would exceed the number of allowed resources in an ad group criterion. The exact resource type and limit being checked can be inferred from accountLimitType, and the enclosingId contains the ad group id followed by the criterion id, separated by a single comma (,). SHARED_SET_LIMIT (int): Indicates that this request would exceed the number of allowed resources in this shared set. The exact resource type and limit being checked can be inferred from accountLimitType, and the numeric id of the shared set involved is given by enclosingId. MATCHING_FUNCTION_LIMIT (int): Exceeds a limit related to a matching function. RESPONSE_ROW_LIMIT_EXCEEDED (int): The response for this request would exceed the maximum number of rows that can be returned. RESOURCE_LIMIT (int): This request would exceed a limit on the number of allowed resources. The details of which type of limit was exceeded will eventually be returned in ErrorDetails. """ UNSPECIFIED = 0 UNKNOWN = 1 ACCOUNT_LIMIT = 2 CAMPAIGN_LIMIT = 3 ADGROUP_LIMIT = 4 AD_GROUP_AD_LIMIT = 5 AD_GROUP_CRITERION_LIMIT = 6 SHARED_SET_LIMIT = 7 MATCHING_FUNCTION_LIMIT = 8 RESPONSE_ROW_LIMIT_EXCEEDED = 9 RESOURCE_LIMIT = 10 ''' ResourceCountLimitExceededErrorEnum = ResourceCountLimitExceededErrorEnum() # For __getattribute__ class SearchEngineResultsPageTypeEnum(_CreateEnumTypeUponFirstAccess): SearchEngineResultsPageTypeEnum = '''\ class SearchEngineResultsPageType(enum.IntEnum): """ The type of the search engine results page. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADS_ONLY (int): Only ads were contained in the search engine results page. ORGANIC_ONLY (int): Only organic results were contained in the search engine results page. ADS_AND_ORGANIC (int): Both ads and organic results were contained in the search engine results page. """ UNSPECIFIED = 0 UNKNOWN = 1 ADS_ONLY = 2 ORGANIC_ONLY = 3 ADS_AND_ORGANIC = 4 ''' SearchEngineResultsPageTypeEnum = SearchEngineResultsPageTypeEnum() # For __getattribute__ class SearchTermMatchTypeEnum(_CreateEnumTypeUponFirstAccess): SearchTermMatchTypeEnum = '''\ class SearchTermMatchType(enum.IntEnum): """ Possible match types for a keyword triggering an ad, including variants. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. BROAD (int): Broad match. EXACT (int): Exact match. PHRASE (int): Phrase match. NEAR_EXACT (int): Exact match (close variant). NEAR_PHRASE (int): Phrase match (close variant). """ UNSPECIFIED = 0 UNKNOWN = 1 BROAD = 2 EXACT = 3 PHRASE = 4 NEAR_EXACT = 5 NEAR_PHRASE = 6 ''' SearchTermMatchTypeEnum = SearchTermMatchTypeEnum() # For __getattribute__ class SearchTermTargetingStatusEnum(_CreateEnumTypeUponFirstAccess): SearchTermTargetingStatusEnum = '''\ class SearchTermTargetingStatus(enum.IntEnum): """ Indicates whether the search term is one of your targeted or excluded keywords. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADDED (int): Search term is added to targeted keywords. EXCLUDED (int): Search term matches a negative keyword. ADDED_EXCLUDED (int): Search term has been both added and excluded. NONE (int): Search term is neither targeted nor excluded. """ UNSPECIFIED = 0 UNKNOWN = 1 ADDED = 2 EXCLUDED = 3 ADDED_EXCLUDED = 4 NONE = 5 ''' SearchTermTargetingStatusEnum = SearchTermTargetingStatusEnum() # For __getattribute__ class ServedAssetFieldTypeEnum(_CreateEnumTypeUponFirstAccess): ServedAssetFieldTypeEnum = '''\ class ServedAssetFieldType(enum.IntEnum): """ The possible asset field types. Attributes: UNSPECIFIED (int): No value has been specified. UNKNOWN (int): The received value is not known in this version. This is a response-only value. HEADLINE_1 (int): The asset is used in headline 1. HEADLINE_2 (int): The asset is used in headline 2. HEADLINE_3 (int): The asset is used in headline 3. DESCRIPTION_1 (int): The asset is used in description 1. DESCRIPTION_2 (int): The asset is used in description 2. """ UNSPECIFIED = 0 UNKNOWN = 1 HEADLINE_1 = 2 HEADLINE_2 = 3 HEADLINE_3 = 4 DESCRIPTION_1 = 5 DESCRIPTION_2 = 6 ''' ServedAssetFieldTypeEnum = ServedAssetFieldTypeEnum() # For __getattribute__ class SettingErrorEnum(_CreateEnumTypeUponFirstAccess): SettingErrorEnum = '''\ class SettingError(enum.IntEnum): """ Enum describing possible setting errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. SETTING_TYPE_IS_NOT_AVAILABLE (int): The campaign setting is not available for this Google Ads account. SETTING_TYPE_IS_NOT_COMPATIBLE_WITH_CAMPAIGN (int): The setting is not compatible with the campaign. TARGETING_SETTING_CONTAINS_INVALID_CRITERION_TYPE_GROUP (int): The supplied TargetingSetting contains an invalid CriterionTypeGroup. See CriterionTypeGroup documentation for CriterionTypeGroups allowed in Campaign or AdGroup TargetingSettings. TARGETING_SETTING_DEMOGRAPHIC_CRITERION_TYPE_GROUPS_MUST_BE_SET_TO_TARGET_ALL (int): TargetingSetting must not explicitly set any of the Demographic CriterionTypeGroups (AGE\_RANGE, GENDER, PARENT, INCOME\_RANGE) to false (it's okay to not set them at all, in which case the system will set them to true automatically). TARGETING_SETTING_CANNOT_CHANGE_TARGET_ALL_TO_FALSE_FOR_DEMOGRAPHIC_CRITERION_TYPE_GROUP (int): TargetingSetting cannot change any of the Demographic CriterionTypeGroups (AGE\_RANGE, GENDER, PARENT, INCOME\_RANGE) from true to false. DYNAMIC_SEARCH_ADS_SETTING_AT_LEAST_ONE_FEED_ID_MUST_BE_PRESENT (int): At least one feed id should be present. DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_INVALID_DOMAIN_NAME (int): The supplied DynamicSearchAdsSetting contains an invalid domain name. DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_SUBDOMAIN_NAME (int): The supplied DynamicSearchAdsSetting contains a subdomain name. DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_INVALID_LANGUAGE_CODE (int): The supplied DynamicSearchAdsSetting contains an invalid language code. TARGET_ALL_IS_NOT_ALLOWED_FOR_PLACEMENT_IN_SEARCH_CAMPAIGN (int): TargetingSettings in search campaigns should not have CriterionTypeGroup.PLACEMENT set to targetAll. """ UNSPECIFIED = 0 UNKNOWN = 1 SETTING_TYPE_IS_NOT_AVAILABLE = 3 SETTING_TYPE_IS_NOT_COMPATIBLE_WITH_CAMPAIGN = 4 TARGETING_SETTING_CONTAINS_INVALID_CRITERION_TYPE_GROUP = 5 TARGETING_SETTING_DEMOGRAPHIC_CRITERION_TYPE_GROUPS_MUST_BE_SET_TO_TARGET_ALL = 6 TARGETING_SETTING_CANNOT_CHANGE_TARGET_ALL_TO_FALSE_FOR_DEMOGRAPHIC_CRITERION_TYPE_GROUP = 7 DYNAMIC_SEARCH_ADS_SETTING_AT_LEAST_ONE_FEED_ID_MUST_BE_PRESENT = 8 DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_INVALID_DOMAIN_NAME = 9 DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_SUBDOMAIN_NAME = 10 DYNAMIC_SEARCH_ADS_SETTING_CONTAINS_INVALID_LANGUAGE_CODE = 11 TARGET_ALL_IS_NOT_ALLOWED_FOR_PLACEMENT_IN_SEARCH_CAMPAIGN = 12 ''' SettingErrorEnum = SettingErrorEnum() # For __getattribute__ class SharedCriterionErrorEnum(_CreateEnumTypeUponFirstAccess): SharedCriterionErrorEnum = '''\ class SharedCriterionError(enum.IntEnum): """ Enum describing possible shared criterion errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CRITERION_TYPE_NOT_ALLOWED_FOR_SHARED_SET_TYPE (int): The criterion is not appropriate for the shared set type. """ UNSPECIFIED = 0 UNKNOWN = 1 CRITERION_TYPE_NOT_ALLOWED_FOR_SHARED_SET_TYPE = 2 ''' SharedCriterionErrorEnum = SharedCriterionErrorEnum() # For __getattribute__ class SharedSetErrorEnum(_CreateEnumTypeUponFirstAccess): SharedSetErrorEnum = '''\ class SharedSetError(enum.IntEnum): """ Enum describing possible shared set errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. CUSTOMER_CANNOT_CREATE_SHARED_SET_OF_THIS_TYPE (int): The customer cannot create this type of shared set. DUPLICATE_NAME (int): A shared set with this name already exists. SHARED_SET_REMOVED (int): Removed shared sets cannot be mutated. SHARED_SET_IN_USE (int): The shared set cannot be removed because it is in use. """ UNSPECIFIED = 0 UNKNOWN = 1 CUSTOMER_CANNOT_CREATE_SHARED_SET_OF_THIS_TYPE = 2 DUPLICATE_NAME = 3 SHARED_SET_REMOVED = 4 SHARED_SET_IN_USE = 5 ''' SharedSetErrorEnum = SharedSetErrorEnum() # For __getattribute__ class SharedSetStatusEnum(_CreateEnumTypeUponFirstAccess): SharedSetStatusEnum = '''\ class SharedSetStatus(enum.IntEnum): """ Enum listing the possible shared set statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): The shared set is enabled. REMOVED (int): The shared set is removed and can no longer be used. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 REMOVED = 3 ''' SharedSetStatusEnum = SharedSetStatusEnum() # For __getattribute__ class SharedSetTypeEnum(_CreateEnumTypeUponFirstAccess): SharedSetTypeEnum = '''\ class SharedSetType(enum.IntEnum): """ Enum listing the possible shared set types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NEGATIVE_KEYWORDS (int): A set of keywords that can be excluded from targeting. NEGATIVE_PLACEMENTS (int): A set of placements that can be excluded from targeting. """ UNSPECIFIED = 0 UNKNOWN = 1 NEGATIVE_KEYWORDS = 2 NEGATIVE_PLACEMENTS = 3 ''' SharedSetTypeEnum = SharedSetTypeEnum() # For __getattribute__ class SimulationModificationMethodEnum(_CreateEnumTypeUponFirstAccess): SimulationModificationMethodEnum = '''\ class SimulationModificationMethod(enum.IntEnum): """ Enum describing the method by which a simulation modifies a field. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. UNIFORM (int): The values in a simulation were applied to all children of a given resource uniformly. Overrides on child resources were not respected. DEFAULT (int): The values in a simulation were applied to the given resource. Overrides on child resources were respected, and traffic estimates do not include these resources. """ UNSPECIFIED = 0 UNKNOWN = 1 UNIFORM = 2 DEFAULT = 3 ''' SimulationModificationMethodEnum = SimulationModificationMethodEnum() # For __getattribute__ class SimulationTypeEnum(_CreateEnumTypeUponFirstAccess): SimulationTypeEnum = '''\ class SimulationType(enum.IntEnum): """ Enum describing the field a simulation modifies. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CPC_BID (int): The simulation is for a cpc bid. CPV_BID (int): The simulation is for a cpv bid. TARGET_CPA (int): The simulation is for a cpa target. BID_MODIFIER (int): The simulation is for a bid modifier. """ UNSPECIFIED = 0 UNKNOWN = 1 CPC_BID = 2 CPV_BID = 3 TARGET_CPA = 4 BID_MODIFIER = 5 ''' SimulationTypeEnum = SimulationTypeEnum() # For __getattribute__ class SitelinkPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): SitelinkPlaceholderFieldEnum = '''\ class SitelinkPlaceholderField(enum.IntEnum): """ Possible values for Sitelink placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. TEXT (int): Data Type: STRING. The link text for your sitelink. LINE_1 (int): Data Type: STRING. First line of the sitelink description. LINE_2 (int): Data Type: STRING. Second line of the sitelink description. FINAL_URLS (int): Data Type: URL\_LIST. Final URLs for the sitelink when using Upgraded URLs. FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final Mobile URLs for the sitelink when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the sitelink when using Upgraded URLs. FINAL_URL_SUFFIX (int): Data Type: STRING. Final URL suffix for sitelink when using parallel tracking. """ UNSPECIFIED = 0 UNKNOWN = 1 TEXT = 2 LINE_1 = 3 LINE_2 = 4 FINAL_URLS = 5 FINAL_MOBILE_URLS = 6 TRACKING_URL = 7 FINAL_URL_SUFFIX = 8 ''' SitelinkPlaceholderFieldEnum = SitelinkPlaceholderFieldEnum() # For __getattribute__ class SizeLimitErrorEnum(_CreateEnumTypeUponFirstAccess): SizeLimitErrorEnum = '''\ class SizeLimitError(enum.IntEnum): """ Enum describing possible size limit errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. REQUEST_SIZE_LIMIT_EXCEEDED (int): The number of entries in the request exceeds the system limit. RESPONSE_SIZE_LIMIT_EXCEEDED (int): The number of entries in the response exceeds the system limit. """ UNSPECIFIED = 0 UNKNOWN = 1 REQUEST_SIZE_LIMIT_EXCEEDED = 2 RESPONSE_SIZE_LIMIT_EXCEEDED = 3 ''' SizeLimitErrorEnum = SizeLimitErrorEnum() # For __getattribute__ class SlotEnum(_CreateEnumTypeUponFirstAccess): SlotEnum = '''\ class Slot(enum.IntEnum): """ Enumerates possible positions of the Ad. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): The value is unknown in this version. SEARCH_SIDE (int): Google search: Side. SEARCH_TOP (int): Google search: Top. SEARCH_OTHER (int): Google search: Other. CONTENT (int): Google Display Network. SEARCH_PARTNER_TOP (int): Search partners: Top. SEARCH_PARTNER_OTHER (int): Search partners: Other. MIXED (int): Cross-network. """ UNSPECIFIED = 0 UNKNOWN = 1 SEARCH_SIDE = 2 SEARCH_TOP = 3 SEARCH_OTHER = 4 CONTENT = 5 SEARCH_PARTNER_TOP = 6 SEARCH_PARTNER_OTHER = 7 MIXED = 8 ''' SlotEnum = SlotEnum() # For __getattribute__ class SpendingLimitTypeEnum(_CreateEnumTypeUponFirstAccess): SpendingLimitTypeEnum = '''\ class SpendingLimitType(enum.IntEnum): """ The possible spending limit types used by certain resources as an alternative to absolute money values in micros. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. INFINITE (int): Infinite, indicates unlimited spending power. """ UNSPECIFIED = 0 UNKNOWN = 1 INFINITE = 2 ''' SpendingLimitTypeEnum = SpendingLimitTypeEnum() # For __getattribute__ class StringFormatErrorEnum(_CreateEnumTypeUponFirstAccess): StringFormatErrorEnum = '''\ class StringFormatError(enum.IntEnum): """ Enum describing possible string format errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. ILLEGAL_CHARS (int): The input string value contains disallowed characters. INVALID_FORMAT (int): The input string value is invalid for the associated field. """ UNSPECIFIED = 0 UNKNOWN = 1 ILLEGAL_CHARS = 2 INVALID_FORMAT = 3 ''' StringFormatErrorEnum = StringFormatErrorEnum() # For __getattribute__ class StringLengthErrorEnum(_CreateEnumTypeUponFirstAccess): StringLengthErrorEnum = '''\ class StringLengthError(enum.IntEnum): """ Enum describing possible string length errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. EMPTY (int): The specified field should have a least one non-whitespace character in it. TOO_SHORT (int): Too short. TOO_LONG (int): Too long. """ UNSPECIFIED = 0 UNKNOWN = 1 EMPTY = 4 TOO_SHORT = 2 TOO_LONG = 3 ''' StringLengthErrorEnum = StringLengthErrorEnum() # For __getattribute__ class StructuredSnippetPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): StructuredSnippetPlaceholderFieldEnum = '''\ class StructuredSnippetPlaceholderField(enum.IntEnum): """ Possible values for Structured Snippet placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. HEADER (int): Data Type: STRING. The category of snippet of your products/services. Must match exactly one of the predefined structured snippets headers. For a list, visit https://developers.google.com/adwords/api/docs/appendix/structured-snippet-headers SNIPPETS (int): Data Type: STRING\_LIST. Text values that describe your products/services. All text must be family safe. Special or non-ASCII characters are not permitted. A snippet can be at most 25 characters. """ UNSPECIFIED = 0 UNKNOWN = 1 HEADER = 2 SNIPPETS = 3 ''' StructuredSnippetPlaceholderFieldEnum = StructuredSnippetPlaceholderFieldEnum() # For __getattribute__ class SummaryRowSettingEnum(_CreateEnumTypeUponFirstAccess): SummaryRowSettingEnum = '''\ class SummaryRowSetting(enum.IntEnum): """ Enum describing return summary row settings. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Represent unknown values of return summary row. NO_SUMMARY_ROW (int): Do not return summary row. SUMMARY_ROW_WITH_RESULTS (int): Return summary row along with results. The summary row will be returned in the last batch alone (last batch will contain no results). SUMMARY_ROW_ONLY (int): Return summary row only and return no results. """ UNSPECIFIED = 0 UNKNOWN = 1 NO_SUMMARY_ROW = 2 SUMMARY_ROW_WITH_RESULTS = 3 SUMMARY_ROW_ONLY = 4 ''' SummaryRowSettingEnum = SummaryRowSettingEnum() # For __getattribute__ class SystemManagedResourceSourceEnum(_CreateEnumTypeUponFirstAccess): SystemManagedResourceSourceEnum = '''\ class SystemManagedResourceSource(enum.IntEnum): """ Enum listing the possible system managed entity sources. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AD_VARIATIONS (int): Generated ad variations experiment ad. """ UNSPECIFIED = 0 UNKNOWN = 1 AD_VARIATIONS = 2 ''' SystemManagedResourceSourceEnum = SystemManagedResourceSourceEnum() # For __getattribute__ class TargetCpaOptInRecommendationGoalEnum(_CreateEnumTypeUponFirstAccess): TargetCpaOptInRecommendationGoalEnum = '''\ class TargetCpaOptInRecommendationGoal(enum.IntEnum): """ Goal of TargetCpaOptIn recommendation. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. SAME_COST (int): Recommendation to set Target CPA to maintain the same cost. SAME_CONVERSIONS (int): Recommendation to set Target CPA to maintain the same conversions. SAME_CPA (int): Recommendation to set Target CPA to maintain the same CPA. CLOSEST_CPA (int): Recommendation to set Target CPA to a value that is as close as possible to, yet lower than, the actual CPA (computed for past 28 days). """ UNSPECIFIED = 0 UNKNOWN = 1 SAME_COST = 2 SAME_CONVERSIONS = 3 SAME_CPA = 4 CLOSEST_CPA = 5 ''' TargetCpaOptInRecommendationGoalEnum = TargetCpaOptInRecommendationGoalEnum() # For __getattribute__ class TargetImpressionShareLocationEnum(_CreateEnumTypeUponFirstAccess): TargetImpressionShareLocationEnum = '''\ class TargetImpressionShareLocation(enum.IntEnum): """ Enum describing possible goals. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ANYWHERE_ON_PAGE (int): Any location on the web page. TOP_OF_PAGE (int): Top box of ads. ABSOLUTE_TOP_OF_PAGE (int): Top slot in the top box of ads. """ UNSPECIFIED = 0 UNKNOWN = 1 ANYWHERE_ON_PAGE = 2 TOP_OF_PAGE = 3 ABSOLUTE_TOP_OF_PAGE = 4 ''' TargetImpressionShareLocationEnum = TargetImpressionShareLocationEnum() # For __getattribute__ class TargetRestrictionOperation(_CreateEnumTypeUponFirstAccess): TargetRestrictionOperation = '''\ class Operator(enum.IntEnum): """ The operator. Attributes: UNSPECIFIED (int): Unspecified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ADD (int): Add the restriction to the existing restrictions. REMOVE (int): Remove the restriction from the existing restrictions. """ UNSPECIFIED = 0 UNKNOWN = 1 ADD = 2 REMOVE = 3 ''' TargetRestrictionOperation = TargetRestrictionOperation() # For __getattribute__ class TargetingDimensionEnum(_CreateEnumTypeUponFirstAccess): TargetingDimensionEnum = '''\ class TargetingDimension(enum.IntEnum): """ Enum describing possible targeting dimensions. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. KEYWORD (int): Keyword criteria, e.g. 'mars cruise'. KEYWORD may be used as a custom bid dimension. Keywords are always a targeting dimension, so may not be set as a target "ALL" dimension with TargetRestriction. AUDIENCE (int): Audience criteria, which include user list, user interest, custom affinity, and custom in market. TOPIC (int): Topic criteria for targeting categories of content, e.g. 'category::Animals>Pets' Used for Display and Video targeting. GENDER (int): Criteria for targeting gender. AGE_RANGE (int): Criteria for targeting age ranges. PLACEMENT (int): Placement criteria, which include websites like 'www.flowers4sale.com', as well as mobile applications, mobile app categories, YouTube videos, and YouTube channels. PARENTAL_STATUS (int): Criteria for parental status targeting. INCOME_RANGE (int): Criteria for income range targeting. """ UNSPECIFIED = 0 UNKNOWN = 1 KEYWORD = 2 AUDIENCE = 3 TOPIC = 4 GENDER = 5 AGE_RANGE = 6 PLACEMENT = 7 PARENTAL_STATUS = 8 INCOME_RANGE = 9 ''' TargetingDimensionEnum = TargetingDimensionEnum() # For __getattribute__ class TimeTypeEnum(_CreateEnumTypeUponFirstAccess): TimeTypeEnum = '''\ class TimeType(enum.IntEnum): """ The possible time types used by certain resources as an alternative to absolute timestamps. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. NOW (int): As soon as possible. FOREVER (int): An infinite point in the future. """ UNSPECIFIED = 0 UNKNOWN = 1 NOW = 2 FOREVER = 3 ''' TimeTypeEnum = TimeTypeEnum() # For __getattribute__ class TimeZoneErrorEnum(_CreateEnumTypeUponFirstAccess): TimeZoneErrorEnum = '''\ class TimeZoneError(enum.IntEnum): """ Enum describing possible currency code errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_TIME_ZONE (int): Time zone is not valid. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_TIME_ZONE = 5 ''' TimeZoneErrorEnum = TimeZoneErrorEnum() # For __getattribute__ class TrackingCodePageFormatEnum(_CreateEnumTypeUponFirstAccess): TrackingCodePageFormatEnum = '''\ class TrackingCodePageFormat(enum.IntEnum): """ The format of the web page where the tracking tag and snippet will be installed. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. HTML (int): Standard HTML page format. AMP (int): Google AMP page format. """ UNSPECIFIED = 0 UNKNOWN = 1 HTML = 2 AMP = 3 ''' TrackingCodePageFormatEnum = TrackingCodePageFormatEnum() # For __getattribute__ class TrackingCodeTypeEnum(_CreateEnumTypeUponFirstAccess): TrackingCodeTypeEnum = '''\ class TrackingCodeType(enum.IntEnum): """ The type of the generated tag snippets for tracking conversions. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. WEBPAGE (int): The snippet that is fired as a result of a website page loading. WEBPAGE_ONCLICK (int): The snippet contains a JavaScript function which fires the tag. This function is typically called from an onClick handler added to a link or button element on the page. CLICK_TO_CALL (int): For embedding on a mobile webpage. The snippet contains a JavaScript function which fires the tag. WEBSITE_CALL (int): The snippet that is used to replace the phone number on your website with a Google forwarding number for call tracking purposes. """ UNSPECIFIED = 0 UNKNOWN = 1 WEBPAGE = 2 WEBPAGE_ONCLICK = 3 CLICK_TO_CALL = 4 WEBSITE_CALL = 5 ''' TrackingCodeTypeEnum = TrackingCodeTypeEnum() # For __getattribute__ class TravelPlaceholderFieldEnum(_CreateEnumTypeUponFirstAccess): TravelPlaceholderFieldEnum = '''\ class TravelPlaceholderField(enum.IntEnum): """ Possible values for Travel placeholder fields. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. DESTINATION_ID (int): Data Type: STRING. Required. Destination id. Example: PAR, LON. For feed items that only have destination id, destination id must be a unique key. For feed items that have both destination id and origin id, then the combination must be a unique key. ORIGIN_ID (int): Data Type: STRING. Origin id. Example: PAR, LON. Combination of DESTINATION\_ID and ORIGIN\_ID must be unique per offer. TITLE (int): Data Type: STRING. Required. Main headline with name to be shown in dynamic ad. DESTINATION_NAME (int): Data Type: STRING. The destination name. Shorter names are recommended. ORIGIN_NAME (int): Data Type: STRING. Origin name. Shorter names are recommended. PRICE (int): Data Type: STRING. Price to be shown in the ad. Highly recommended for dynamic ads. Example: "100.00 USD" FORMATTED_PRICE (int): Data Type: STRING. Formatted price to be shown in the ad. Example: "Starting at $100.00 USD", "$80 - $100" SALE_PRICE (int): Data Type: STRING. Sale price to be shown in the ad. Example: "80.00 USD" FORMATTED_SALE_PRICE (int): Data Type: STRING. Formatted sale price to be shown in the ad. Example: "On sale for $80.00", "$60 - $80" IMAGE_URL (int): Data Type: URL. Image to be displayed in the ad. CATEGORY (int): Data Type: STRING. Category of travel offer used to group like items together for recommendation engine. CONTEXTUAL_KEYWORDS (int): Data Type: STRING\_LIST. Keywords used for product retrieval. DESTINATION_ADDRESS (int): Data Type: STRING. Address of travel offer, including postal code. FINAL_URL (int): Data Type: URL\_LIST. Required. Final URLs to be used in ad, when using Upgraded URLs; the more specific the better (e.g. the individual URL of a specific travel offer and its location). FINAL_MOBILE_URLS (int): Data Type: URL\_LIST. Final mobile URLs for the ad when using Upgraded URLs. TRACKING_URL (int): Data Type: URL. Tracking template for the ad when using Upgraded URLs. ANDROID_APP_LINK (int): Data Type: STRING. Android app link. Must be formatted as: android-app://{package\_id}/{scheme}/{host\_path}. The components are defined as follows: package\_id: app ID as specified in Google Play. scheme: the scheme to pass to the application. Can be HTTP, or a custom scheme. host\_path: identifies the specific content within your application. SIMILAR_DESTINATION_IDS (int): Data Type: STRING\_LIST. List of recommended destination IDs to show together with this item. IOS_APP_LINK (int): Data Type: STRING. iOS app link. IOS_APP_STORE_ID (int): Data Type: INT64. iOS app store ID. """ UNSPECIFIED = 0 UNKNOWN = 1 DESTINATION_ID = 2 ORIGIN_ID = 3 TITLE = 4 DESTINATION_NAME = 5 ORIGIN_NAME = 6 PRICE = 7 FORMATTED_PRICE = 8 SALE_PRICE = 9 FORMATTED_SALE_PRICE = 10 IMAGE_URL = 11 CATEGORY = 12 CONTEXTUAL_KEYWORDS = 13 DESTINATION_ADDRESS = 14 FINAL_URL = 15 FINAL_MOBILE_URLS = 16 TRACKING_URL = 17 ANDROID_APP_LINK = 18 SIMILAR_DESTINATION_IDS = 19 IOS_APP_LINK = 20 IOS_APP_STORE_ID = 21 ''' TravelPlaceholderFieldEnum = TravelPlaceholderFieldEnum() # For __getattribute__ class UrlFieldErrorEnum(_CreateEnumTypeUponFirstAccess): UrlFieldErrorEnum = '''\ class UrlFieldError(enum.IntEnum): """ Enum describing possible url field errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. INVALID_TRACKING_URL_TEMPLATE (int): The tracking url template is invalid. INVALID_TAG_IN_TRACKING_URL_TEMPLATE (int): The tracking url template contains invalid tag. MISSING_TRACKING_URL_TEMPLATE_TAG (int): The tracking url template must contain at least one tag (e.g. {lpurl}), This applies only to tracking url template associated with website ads or product ads. MISSING_PROTOCOL_IN_TRACKING_URL_TEMPLATE (int): The tracking url template must start with a valid protocol (or lpurl tag). INVALID_PROTOCOL_IN_TRACKING_URL_TEMPLATE (int): The tracking url template starts with an invalid protocol. MALFORMED_TRACKING_URL_TEMPLATE (int): The tracking url template contains illegal characters. MISSING_HOST_IN_TRACKING_URL_TEMPLATE (int): The tracking url template must contain a host name (or lpurl tag). INVALID_TLD_IN_TRACKING_URL_TEMPLATE (int): The tracking url template has an invalid or missing top level domain extension. REDUNDANT_NESTED_TRACKING_URL_TEMPLATE_TAG (int): The tracking url template contains nested occurrences of the same conditional tag (i.e. {ifmobile:{ifmobile:x}}). INVALID_FINAL_URL (int): The final url is invalid. INVALID_TAG_IN_FINAL_URL (int): The final url contains invalid tag. REDUNDANT_NESTED_FINAL_URL_TAG (int): The final url contains nested occurrences of the same conditional tag (i.e. {ifmobile:{ifmobile:x}}). MISSING_PROTOCOL_IN_FINAL_URL (int): The final url must start with a valid protocol. INVALID_PROTOCOL_IN_FINAL_URL (int): The final url starts with an invalid protocol. MALFORMED_FINAL_URL (int): The final url contains illegal characters. MISSING_HOST_IN_FINAL_URL (int): The final url must contain a host name. INVALID_TLD_IN_FINAL_URL (int): The tracking url template has an invalid or missing top level domain extension. INVALID_FINAL_MOBILE_URL (int): The final mobile url is invalid. INVALID_TAG_IN_FINAL_MOBILE_URL (int): The final mobile url contains invalid tag. REDUNDANT_NESTED_FINAL_MOBILE_URL_TAG (int): The final mobile url contains nested occurrences of the same conditional tag (i.e. {ifmobile:{ifmobile:x}}). MISSING_PROTOCOL_IN_FINAL_MOBILE_URL (int): The final mobile url must start with a valid protocol. INVALID_PROTOCOL_IN_FINAL_MOBILE_URL (int): The final mobile url starts with an invalid protocol. MALFORMED_FINAL_MOBILE_URL (int): The final mobile url contains illegal characters. MISSING_HOST_IN_FINAL_MOBILE_URL (int): The final mobile url must contain a host name. INVALID_TLD_IN_FINAL_MOBILE_URL (int): The tracking url template has an invalid or missing top level domain extension. INVALID_FINAL_APP_URL (int): The final app url is invalid. INVALID_TAG_IN_FINAL_APP_URL (int): The final app url contains invalid tag. REDUNDANT_NESTED_FINAL_APP_URL_TAG (int): The final app url contains nested occurrences of the same conditional tag (i.e. {ifmobile:{ifmobile:x}}). MULTIPLE_APP_URLS_FOR_OSTYPE (int): More than one app url found for the same OS type. INVALID_OSTYPE (int): The OS type given for an app url is not valid. INVALID_PROTOCOL_FOR_APP_URL (int): The protocol given for an app url is not valid. (E.g. "android-app://") INVALID_PACKAGE_ID_FOR_APP_URL (int): The package id (app id) given for an app url is not valid. URL_CUSTOM_PARAMETERS_COUNT_EXCEEDS_LIMIT (int): The number of url custom parameters for an resource exceeds the maximum limit allowed. INVALID_CHARACTERS_IN_URL_CUSTOM_PARAMETER_KEY (int): An invalid character appears in the parameter key. INVALID_CHARACTERS_IN_URL_CUSTOM_PARAMETER_VALUE (int): An invalid character appears in the parameter value. INVALID_TAG_IN_URL_CUSTOM_PARAMETER_VALUE (int): The url custom parameter value fails url tag validation. REDUNDANT_NESTED_URL_CUSTOM_PARAMETER_TAG (int): The custom parameter contains nested occurrences of the same conditional tag (i.e. {ifmobile:{ifmobile:x}}). MISSING_PROTOCOL (int): The protocol (http:// or https://) is missing. INVALID_PROTOCOL (int): Unsupported protocol in URL. Only http and https are supported. INVALID_URL (int): The url is invalid. DESTINATION_URL_DEPRECATED (int): Destination Url is deprecated. INVALID_TAG_IN_URL (int): The url contains invalid tag. MISSING_URL_TAG (int): The url must contain at least one tag (e.g. {lpurl}), This applies only to urls associated with website ads or product ads. DUPLICATE_URL_ID (int): Duplicate url id. INVALID_URL_ID (int): Invalid url id. FINAL_URL_SUFFIX_MALFORMED (int): The final url suffix cannot begin with '?' or '&' characters and must be a valid query string. INVALID_TAG_IN_FINAL_URL_SUFFIX (int): The final url suffix cannot contain {lpurl} related or {ignore} tags. INVALID_TOP_LEVEL_DOMAIN (int): The top level domain is invalid, e.g, not a public top level domain listed in publicsuffix.org. MALFORMED_TOP_LEVEL_DOMAIN (int): Malformed top level domain in URL. MALFORMED_URL (int): Malformed URL. MISSING_HOST (int): No host found in URL. NULL_CUSTOM_PARAMETER_VALUE (int): Custom parameter value cannot be null. """ UNSPECIFIED = 0 UNKNOWN = 1 INVALID_TRACKING_URL_TEMPLATE = 2 INVALID_TAG_IN_TRACKING_URL_TEMPLATE = 3 MISSING_TRACKING_URL_TEMPLATE_TAG = 4 MISSING_PROTOCOL_IN_TRACKING_URL_TEMPLATE = 5 INVALID_PROTOCOL_IN_TRACKING_URL_TEMPLATE = 6 MALFORMED_TRACKING_URL_TEMPLATE = 7 MISSING_HOST_IN_TRACKING_URL_TEMPLATE = 8 INVALID_TLD_IN_TRACKING_URL_TEMPLATE = 9 REDUNDANT_NESTED_TRACKING_URL_TEMPLATE_TAG = 10 INVALID_FINAL_URL = 11 INVALID_TAG_IN_FINAL_URL = 12 REDUNDANT_NESTED_FINAL_URL_TAG = 13 MISSING_PROTOCOL_IN_FINAL_URL = 14 INVALID_PROTOCOL_IN_FINAL_URL = 15 MALFORMED_FINAL_URL = 16 MISSING_HOST_IN_FINAL_URL = 17 INVALID_TLD_IN_FINAL_URL = 18 INVALID_FINAL_MOBILE_URL = 19 INVALID_TAG_IN_FINAL_MOBILE_URL = 20 REDUNDANT_NESTED_FINAL_MOBILE_URL_TAG = 21 MISSING_PROTOCOL_IN_FINAL_MOBILE_URL = 22 INVALID_PROTOCOL_IN_FINAL_MOBILE_URL = 23 MALFORMED_FINAL_MOBILE_URL = 24 MISSING_HOST_IN_FINAL_MOBILE_URL = 25 INVALID_TLD_IN_FINAL_MOBILE_URL = 26 INVALID_FINAL_APP_URL = 27 INVALID_TAG_IN_FINAL_APP_URL = 28 REDUNDANT_NESTED_FINAL_APP_URL_TAG = 29 MULTIPLE_APP_URLS_FOR_OSTYPE = 30 INVALID_OSTYPE = 31 INVALID_PROTOCOL_FOR_APP_URL = 32 INVALID_PACKAGE_ID_FOR_APP_URL = 33 URL_CUSTOM_PARAMETERS_COUNT_EXCEEDS_LIMIT = 34 INVALID_CHARACTERS_IN_URL_CUSTOM_PARAMETER_KEY = 39 INVALID_CHARACTERS_IN_URL_CUSTOM_PARAMETER_VALUE = 40 INVALID_TAG_IN_URL_CUSTOM_PARAMETER_VALUE = 41 REDUNDANT_NESTED_URL_CUSTOM_PARAMETER_TAG = 42 MISSING_PROTOCOL = 43 INVALID_PROTOCOL = 52 INVALID_URL = 44 DESTINATION_URL_DEPRECATED = 45 INVALID_TAG_IN_URL = 46 MISSING_URL_TAG = 47 DUPLICATE_URL_ID = 48 INVALID_URL_ID = 49 FINAL_URL_SUFFIX_MALFORMED = 50 INVALID_TAG_IN_FINAL_URL_SUFFIX = 51 INVALID_TOP_LEVEL_DOMAIN = 53 MALFORMED_TOP_LEVEL_DOMAIN = 54 MALFORMED_URL = 55 MISSING_HOST = 56 NULL_CUSTOM_PARAMETER_VALUE = 57 ''' UrlFieldErrorEnum = UrlFieldErrorEnum() # For __getattribute__ class UserInterestTaxonomyTypeEnum(_CreateEnumTypeUponFirstAccess): UserInterestTaxonomyTypeEnum = '''\ class UserInterestTaxonomyType(enum.IntEnum): """ Enum containing the possible UserInterestTaxonomyTypes. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AFFINITY (int): The affinity for this user interest. IN_MARKET (int): The market for this user interest. MOBILE_APP_INSTALL_USER (int): Users known to have installed applications in the specified categories. VERTICAL_GEO (int): The geographical location of the interest-based vertical. NEW_SMART_PHONE_USER (int): User interest criteria for new smart phone users. """ UNSPECIFIED = 0 UNKNOWN = 1 AFFINITY = 2 IN_MARKET = 3 MOBILE_APP_INSTALL_USER = 4 VERTICAL_GEO = 5 NEW_SMART_PHONE_USER = 6 ''' UserInterestTaxonomyTypeEnum = UserInterestTaxonomyTypeEnum() # For __getattribute__ class UserListAccessStatusEnum(_CreateEnumTypeUponFirstAccess): UserListAccessStatusEnum = '''\ class UserListAccessStatus(enum.IntEnum): """ Enum containing possible user list access statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ENABLED (int): The access is enabled. DISABLED (int): The access is disabled. """ UNSPECIFIED = 0 UNKNOWN = 1 ENABLED = 2 DISABLED = 3 ''' UserListAccessStatusEnum = UserListAccessStatusEnum() # For __getattribute__ class UserListClosingReasonEnum(_CreateEnumTypeUponFirstAccess): UserListClosingReasonEnum = '''\ class UserListClosingReason(enum.IntEnum): """ Enum describing possible user list closing reasons. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. UNUSED (int): The userlist was closed because of not being used for over one year. """ UNSPECIFIED = 0 UNKNOWN = 1 UNUSED = 2 ''' UserListClosingReasonEnum = UserListClosingReasonEnum() # For __getattribute__ class UserListCombinedRuleOperatorEnum(_CreateEnumTypeUponFirstAccess): UserListCombinedRuleOperatorEnum = '''\ class UserListCombinedRuleOperator(enum.IntEnum): """ Enum describing possible user list combined rule operators. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AND (int): A AND B. AND_NOT (int): A AND NOT B. """ UNSPECIFIED = 0 UNKNOWN = 1 AND = 2 AND_NOT = 3 ''' UserListCombinedRuleOperatorEnum = UserListCombinedRuleOperatorEnum() # For __getattribute__ class UserListCrmDataSourceTypeEnum(_CreateEnumTypeUponFirstAccess): UserListCrmDataSourceTypeEnum = '''\ class UserListCrmDataSourceType(enum.IntEnum): """ Enum describing possible user list crm data source type. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. FIRST_PARTY (int): The uploaded data is first-party data. THIRD_PARTY_CREDIT_BUREAU (int): The uploaded data is from a third-party credit bureau. THIRD_PARTY_VOTER_FILE (int): The uploaded data is from a third-party voter file. """ UNSPECIFIED = 0 UNKNOWN = 1 FIRST_PARTY = 2 THIRD_PARTY_CREDIT_BUREAU = 3 THIRD_PARTY_VOTER_FILE = 4 ''' UserListCrmDataSourceTypeEnum = UserListCrmDataSourceTypeEnum() # For __getattribute__ class UserListDateRuleItemOperatorEnum(_CreateEnumTypeUponFirstAccess): UserListDateRuleItemOperatorEnum = '''\ class UserListDateRuleItemOperator(enum.IntEnum): """ Enum describing possible user list date rule item operators. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EQUALS (int): Equals. NOT_EQUALS (int): Not Equals. BEFORE (int): Before. AFTER (int): After. """ UNSPECIFIED = 0 UNKNOWN = 1 EQUALS = 2 NOT_EQUALS = 3 BEFORE = 4 AFTER = 5 ''' UserListDateRuleItemOperatorEnum = UserListDateRuleItemOperatorEnum() # For __getattribute__ class UserListErrorEnum(_CreateEnumTypeUponFirstAccess): UserListErrorEnum = '''\ class UserListError(enum.IntEnum): """ Enum describing possible user list errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. EXTERNAL_REMARKETING_USER_LIST_MUTATE_NOT_SUPPORTED (int): Creating and updating external remarketing user lists is not supported. CONCRETE_TYPE_REQUIRED (int): Concrete type of user list is required. CONVERSION_TYPE_ID_REQUIRED (int): Creating/updating user list conversion types requires specifying the conversion type Id. DUPLICATE_CONVERSION_TYPES (int): Remarketing user list cannot have duplicate conversion types. INVALID_CONVERSION_TYPE (int): Conversion type is invalid/unknown. INVALID_DESCRIPTION (int): User list description is empty or invalid. INVALID_NAME (int): User list name is empty or invalid. INVALID_TYPE (int): Type of the UserList does not match. CAN_NOT_ADD_LOGICAL_LIST_AS_LOGICAL_LIST_OPERAND (int): Embedded logical user lists are not allowed. INVALID_USER_LIST_LOGICAL_RULE_OPERAND (int): User list rule operand is invalid. NAME_ALREADY_USED (int): Name is already being used for another user list for the account. NEW_CONVERSION_TYPE_NAME_REQUIRED (int): Name is required when creating a new conversion type. CONVERSION_TYPE_NAME_ALREADY_USED (int): The given conversion type name has been used. OWNERSHIP_REQUIRED_FOR_SET (int): Only an owner account may edit a user list. USER_LIST_MUTATE_NOT_SUPPORTED (int): Creating user list without setting type in oneof user\_list field, or creating/updating read-only user list types is not allowed. INVALID_RULE (int): Rule is invalid. INVALID_DATE_RANGE (int): The specified date range is empty. CAN_NOT_MUTATE_SENSITIVE_USERLIST (int): A UserList which is privacy sensitive or legal rejected cannot be mutated by external users. MAX_NUM_RULEBASED_USERLISTS (int): Maximum number of rulebased user lists a customer can have. CANNOT_MODIFY_BILLABLE_RECORD_COUNT (int): BasicUserList's billable record field cannot be modified once it is set. APP_ID_NOT_SET (int): crm\_based\_user\_list.app\_id field must be set when upload\_key\_type is MOBILE\_ADVERTISING\_ID. USERLIST_NAME_IS_RESERVED_FOR_SYSTEM_LIST (int): Name of the user list is reserved for system generated lists and cannot be used. ADVERTISER_NOT_WHITELISTED_FOR_USING_UPLOADED_DATA (int): Advertiser needs to be whitelisted to use remarketing lists created from advertiser uploaded data (e.g., Customer Match lists). RULE_TYPE_IS_NOT_SUPPORTED (int): The provided rule\_type is not supported for the user list. CAN_NOT_ADD_A_SIMILAR_USERLIST_AS_LOGICAL_LIST_OPERAND (int): Similar user list cannot be used as a logical user list operand. CAN_NOT_MIX_CRM_BASED_IN_LOGICAL_LIST_WITH_OTHER_LISTS (int): Logical user list should not have a mix of CRM based user list and other types of lists in its rules. """ UNSPECIFIED = 0 UNKNOWN = 1 EXTERNAL_REMARKETING_USER_LIST_MUTATE_NOT_SUPPORTED = 2 CONCRETE_TYPE_REQUIRED = 3 CONVERSION_TYPE_ID_REQUIRED = 4 DUPLICATE_CONVERSION_TYPES = 5 INVALID_CONVERSION_TYPE = 6 INVALID_DESCRIPTION = 7 INVALID_NAME = 8 INVALID_TYPE = 9 CAN_NOT_ADD_LOGICAL_LIST_AS_LOGICAL_LIST_OPERAND = 10 INVALID_USER_LIST_LOGICAL_RULE_OPERAND = 11 NAME_ALREADY_USED = 12 NEW_CONVERSION_TYPE_NAME_REQUIRED = 13 CONVERSION_TYPE_NAME_ALREADY_USED = 14 OWNERSHIP_REQUIRED_FOR_SET = 15 USER_LIST_MUTATE_NOT_SUPPORTED = 16 INVALID_RULE = 17 INVALID_DATE_RANGE = 27 CAN_NOT_MUTATE_SENSITIVE_USERLIST = 28 MAX_NUM_RULEBASED_USERLISTS = 29 CANNOT_MODIFY_BILLABLE_RECORD_COUNT = 30 APP_ID_NOT_SET = 31 USERLIST_NAME_IS_RESERVED_FOR_SYSTEM_LIST = 32 ADVERTISER_NOT_WHITELISTED_FOR_USING_UPLOADED_DATA = 33 RULE_TYPE_IS_NOT_SUPPORTED = 34 CAN_NOT_ADD_A_SIMILAR_USERLIST_AS_LOGICAL_LIST_OPERAND = 35 CAN_NOT_MIX_CRM_BASED_IN_LOGICAL_LIST_WITH_OTHER_LISTS = 36 ''' UserListErrorEnum = UserListErrorEnum() # For __getattribute__ class UserListLogicalRuleOperatorEnum(_CreateEnumTypeUponFirstAccess): UserListLogicalRuleOperatorEnum = '''\ class UserListLogicalRuleOperator(enum.IntEnum): """ Enum describing possible user list logical rule operators. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. ALL (int): And - all of the operands. ANY (int): Or - at least one of the operands. NONE (int): Not - none of the operands. """ UNSPECIFIED = 0 UNKNOWN = 1 ALL = 2 ANY = 3 NONE = 4 ''' UserListLogicalRuleOperatorEnum = UserListLogicalRuleOperatorEnum() # For __getattribute__ class UserListMembershipStatusEnum(_CreateEnumTypeUponFirstAccess): UserListMembershipStatusEnum = '''\ class UserListMembershipStatus(enum.IntEnum): """ Enum containing possible user list membership statuses. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. OPEN (int): Open status - List is accruing members and can be targeted to. CLOSED (int): Closed status - No new members being added. Cannot be used for targeting. """ UNSPECIFIED = 0 UNKNOWN = 1 OPEN = 2 CLOSED = 3 ''' UserListMembershipStatusEnum = UserListMembershipStatusEnum() # For __getattribute__ class UserListNumberRuleItemOperatorEnum(_CreateEnumTypeUponFirstAccess): UserListNumberRuleItemOperatorEnum = '''\ class UserListNumberRuleItemOperator(enum.IntEnum): """ Enum describing possible user list number rule item operators. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. GREATER_THAN (int): Greater than. GREATER_THAN_OR_EQUAL (int): Greater than or equal. EQUALS (int): Equals. NOT_EQUALS (int): Not equals. LESS_THAN (int): Less than. LESS_THAN_OR_EQUAL (int): Less than or equal. """ UNSPECIFIED = 0 UNKNOWN = 1 GREATER_THAN = 2 GREATER_THAN_OR_EQUAL = 3 EQUALS = 4 NOT_EQUALS = 5 LESS_THAN = 6 LESS_THAN_OR_EQUAL = 7 ''' UserListNumberRuleItemOperatorEnum = UserListNumberRuleItemOperatorEnum() # For __getattribute__ class UserListPrepopulationStatusEnum(_CreateEnumTypeUponFirstAccess): UserListPrepopulationStatusEnum = '''\ class UserListPrepopulationStatus(enum.IntEnum): """ Enum describing possible user list prepopulation status. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. REQUESTED (int): Prepopoulation is being requested. FINISHED (int): Prepopulation is finished. FAILED (int): Prepopulation failed. """ UNSPECIFIED = 0 UNKNOWN = 1 REQUESTED = 2 FINISHED = 3 FAILED = 4 ''' UserListPrepopulationStatusEnum = UserListPrepopulationStatusEnum() # For __getattribute__ class UserListRuleTypeEnum(_CreateEnumTypeUponFirstAccess): UserListRuleTypeEnum = '''\ class UserListRuleType(enum.IntEnum): """ Enum describing possible user list rule types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. AND_OF_ORS (int): Conjunctive normal form. OR_OF_ANDS (int): Disjunctive normal form. """ UNSPECIFIED = 0 UNKNOWN = 1 AND_OF_ORS = 2 OR_OF_ANDS = 3 ''' UserListRuleTypeEnum = UserListRuleTypeEnum() # For __getattribute__ class UserListSizeRangeEnum(_CreateEnumTypeUponFirstAccess): UserListSizeRangeEnum = '''\ class UserListSizeRange(enum.IntEnum): """ Enum containing possible user list size ranges. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. LESS_THAN_FIVE_HUNDRED (int): User list has less than 500 users. LESS_THAN_ONE_THOUSAND (int): User list has number of users in range of 500 to 1000. ONE_THOUSAND_TO_TEN_THOUSAND (int): User list has number of users in range of 1000 to 10000. TEN_THOUSAND_TO_FIFTY_THOUSAND (int): User list has number of users in range of 10000 to 50000. FIFTY_THOUSAND_TO_ONE_HUNDRED_THOUSAND (int): User list has number of users in range of 50000 to 100000. ONE_HUNDRED_THOUSAND_TO_THREE_HUNDRED_THOUSAND (int): User list has number of users in range of 100000 to 300000. THREE_HUNDRED_THOUSAND_TO_FIVE_HUNDRED_THOUSAND (int): User list has number of users in range of 300000 to 500000. FIVE_HUNDRED_THOUSAND_TO_ONE_MILLION (int): User list has number of users in range of 500000 to 1 million. ONE_MILLION_TO_TWO_MILLION (int): User list has number of users in range of 1 to 2 millions. TWO_MILLION_TO_THREE_MILLION (int): User list has number of users in range of 2 to 3 millions. THREE_MILLION_TO_FIVE_MILLION (int): User list has number of users in range of 3 to 5 millions. FIVE_MILLION_TO_TEN_MILLION (int): User list has number of users in range of 5 to 10 millions. TEN_MILLION_TO_TWENTY_MILLION (int): User list has number of users in range of 10 to 20 millions. TWENTY_MILLION_TO_THIRTY_MILLION (int): User list has number of users in range of 20 to 30 millions. THIRTY_MILLION_TO_FIFTY_MILLION (int): User list has number of users in range of 30 to 50 millions. OVER_FIFTY_MILLION (int): User list has over 50 million users. """ UNSPECIFIED = 0 UNKNOWN = 1 LESS_THAN_FIVE_HUNDRED = 2 LESS_THAN_ONE_THOUSAND = 3 ONE_THOUSAND_TO_TEN_THOUSAND = 4 TEN_THOUSAND_TO_FIFTY_THOUSAND = 5 FIFTY_THOUSAND_TO_ONE_HUNDRED_THOUSAND = 6 ONE_HUNDRED_THOUSAND_TO_THREE_HUNDRED_THOUSAND = 7 THREE_HUNDRED_THOUSAND_TO_FIVE_HUNDRED_THOUSAND = 8 FIVE_HUNDRED_THOUSAND_TO_ONE_MILLION = 9 ONE_MILLION_TO_TWO_MILLION = 10 TWO_MILLION_TO_THREE_MILLION = 11 THREE_MILLION_TO_FIVE_MILLION = 12 FIVE_MILLION_TO_TEN_MILLION = 13 TEN_MILLION_TO_TWENTY_MILLION = 14 TWENTY_MILLION_TO_THIRTY_MILLION = 15 THIRTY_MILLION_TO_FIFTY_MILLION = 16 OVER_FIFTY_MILLION = 17 ''' UserListSizeRangeEnum = UserListSizeRangeEnum() # For __getattribute__ class UserListStringRuleItemOperatorEnum(_CreateEnumTypeUponFirstAccess): UserListStringRuleItemOperatorEnum = '''\ class UserListStringRuleItemOperator(enum.IntEnum): """ Enum describing possible user list string rule item operators. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. CONTAINS (int): Contains. EQUALS (int): Equals. STARTS_WITH (int): Starts with. ENDS_WITH (int): Ends with. NOT_EQUALS (int): Not equals. NOT_CONTAINS (int): Not contains. NOT_STARTS_WITH (int): Not starts with. NOT_ENDS_WITH (int): Not ends with. """ UNSPECIFIED = 0 UNKNOWN = 1 CONTAINS = 2 EQUALS = 3 STARTS_WITH = 4 ENDS_WITH = 5 NOT_EQUALS = 6 NOT_CONTAINS = 7 NOT_STARTS_WITH = 8 NOT_ENDS_WITH = 9 ''' UserListStringRuleItemOperatorEnum = UserListStringRuleItemOperatorEnum() # For __getattribute__ class UserListTypeEnum(_CreateEnumTypeUponFirstAccess): UserListTypeEnum = '''\ class UserListType(enum.IntEnum): """ Enum containing possible user list types. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. REMARKETING (int): UserList represented as a collection of conversion types. LOGICAL (int): UserList represented as a combination of other user lists/interests. EXTERNAL_REMARKETING (int): UserList created in the Google Ad Manager platform. RULE_BASED (int): UserList associated with a rule. SIMILAR (int): UserList with users similar to users of another UserList. CRM_BASED (int): UserList of first-party CRM data provided by advertiser in the form of emails or other formats. """ UNSPECIFIED = 0 UNKNOWN = 1 REMARKETING = 2 LOGICAL = 3 EXTERNAL_REMARKETING = 4 RULE_BASED = 5 SIMILAR = 6 CRM_BASED = 7 ''' UserListTypeEnum = UserListTypeEnum() # For __getattribute__ class VanityPharmaDisplayUrlModeEnum(_CreateEnumTypeUponFirstAccess): VanityPharmaDisplayUrlModeEnum = '''\ class VanityPharmaDisplayUrlMode(enum.IntEnum): """ Enum describing possible display modes for vanity pharma URLs. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. MANUFACTURER_WEBSITE_URL (int): Replace vanity pharma URL with manufacturer website url. WEBSITE_DESCRIPTION (int): Replace vanity pharma URL with description of the website. """ UNSPECIFIED = 0 UNKNOWN = 1 MANUFACTURER_WEBSITE_URL = 2 WEBSITE_DESCRIPTION = 3 ''' VanityPharmaDisplayUrlModeEnum = VanityPharmaDisplayUrlModeEnum() # For __getattribute__ class VanityPharmaTextEnum(_CreateEnumTypeUponFirstAccess): VanityPharmaTextEnum = '''\ class VanityPharmaText(enum.IntEnum): """ Enum describing possible text. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. PRESCRIPTION_TREATMENT_WEBSITE_EN (int): Prescription treatment website with website content in English. PRESCRIPTION_TREATMENT_WEBSITE_ES (int): Prescription treatment website with website content in Spanish (Sitio de tratamientos con receta). PRESCRIPTION_DEVICE_WEBSITE_EN (int): Prescription device website with website content in English. PRESCRIPTION_DEVICE_WEBSITE_ES (int): Prescription device website with website content in Spanish (Sitio de dispositivos con receta). MEDICAL_DEVICE_WEBSITE_EN (int): Medical device website with website content in English. MEDICAL_DEVICE_WEBSITE_ES (int): Medical device website with website content in Spanish (Sitio de dispositivos médicos). PREVENTATIVE_TREATMENT_WEBSITE_EN (int): Preventative treatment website with website content in English. PREVENTATIVE_TREATMENT_WEBSITE_ES (int): Preventative treatment website with website content in Spanish (Sitio de tratamientos preventivos). PRESCRIPTION_CONTRACEPTION_WEBSITE_EN (int): Prescription contraception website with website content in English. PRESCRIPTION_CONTRACEPTION_WEBSITE_ES (int): Prescription contraception website with website content in Spanish (Sitio de anticonceptivos con receta). PRESCRIPTION_VACCINE_WEBSITE_EN (int): Prescription vaccine website with website content in English. PRESCRIPTION_VACCINE_WEBSITE_ES (int): Prescription vaccine website with website content in Spanish (Sitio de vacunas con receta). """ UNSPECIFIED = 0 UNKNOWN = 1 PRESCRIPTION_TREATMENT_WEBSITE_EN = 2 PRESCRIPTION_TREATMENT_WEBSITE_ES = 3 PRESCRIPTION_DEVICE_WEBSITE_EN = 4 PRESCRIPTION_DEVICE_WEBSITE_ES = 5 MEDICAL_DEVICE_WEBSITE_EN = 6 MEDICAL_DEVICE_WEBSITE_ES = 7 PREVENTATIVE_TREATMENT_WEBSITE_EN = 8 PREVENTATIVE_TREATMENT_WEBSITE_ES = 9 PRESCRIPTION_CONTRACEPTION_WEBSITE_EN = 10 PRESCRIPTION_CONTRACEPTION_WEBSITE_ES = 11 PRESCRIPTION_VACCINE_WEBSITE_EN = 12 PRESCRIPTION_VACCINE_WEBSITE_ES = 13 ''' VanityPharmaTextEnum = VanityPharmaTextEnum() # For __getattribute__ class WebpageConditionOperandEnum(_CreateEnumTypeUponFirstAccess): WebpageConditionOperandEnum = '''\ class WebpageConditionOperand(enum.IntEnum): """ The webpage condition operand in webpage criterion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. URL (int): Operand denoting a webpage URL targeting condition. CATEGORY (int): Operand denoting a webpage category targeting condition. PAGE_TITLE (int): Operand denoting a webpage title targeting condition. PAGE_CONTENT (int): Operand denoting a webpage content targeting condition. CUSTOM_LABEL (int): Operand denoting a webpage custom label targeting condition. """ UNSPECIFIED = 0 UNKNOWN = 1 URL = 2 CATEGORY = 3 PAGE_TITLE = 4 PAGE_CONTENT = 5 CUSTOM_LABEL = 6 ''' WebpageConditionOperandEnum = WebpageConditionOperandEnum() # For __getattribute__ class WebpageConditionOperatorEnum(_CreateEnumTypeUponFirstAccess): WebpageConditionOperatorEnum = '''\ class WebpageConditionOperator(enum.IntEnum): """ The webpage condition operator in webpage criterion. Attributes: UNSPECIFIED (int): Not specified. UNKNOWN (int): Used for return value only. Represents value unknown in this version. EQUALS (int): The argument web condition is equal to the compared web condition. CONTAINS (int): The argument web condition is part of the compared web condition. """ UNSPECIFIED = 0 UNKNOWN = 1 EQUALS = 2 CONTAINS = 3 ''' WebpageConditionOperatorEnum = WebpageConditionOperatorEnum() # For __getattribute__ class YoutubeVideoRegistrationErrorEnum(_CreateEnumTypeUponFirstAccess): YoutubeVideoRegistrationErrorEnum = '''\ class YoutubeVideoRegistrationError(enum.IntEnum): """ Enum describing YouTube video registration errors. Attributes: UNSPECIFIED (int): Enum unspecified. UNKNOWN (int): The received error code is not known in this version. VIDEO_NOT_FOUND (int): Video to be registered wasn't found. VIDEO_NOT_ACCESSIBLE (int): Video to be registered is not accessible (e.g. private). VIDEO_NOT_ELIGIBLE (int): Video to be registered is not eligible (e.g. mature content). """ UNSPECIFIED = 0 UNKNOWN = 1 VIDEO_NOT_FOUND = 2 VIDEO_NOT_ACCESSIBLE = 3 VIDEO_NOT_ELIGIBLE = 4 ''' YoutubeVideoRegistrationErrorEnum = YoutubeVideoRegistrationErrorEnum() # For __getattribute__

46.501767

166

0.687753

a1297148dba1466ea88f7c774f54616096def0ec

90

py

Python

DataAnalysis/ProcessingTools/Queues/__init__.py

AdamSwenson/TwitterProject

8c5dc7a57eac611b555058736d609f2f204cb836

[ "MIT" ]

null

DataAnalysis/ProcessingTools/Queues/__init__.py

AdamSwenson/TwitterProject

8c5dc7a57eac611b555058736d609f2f204cb836

[ "MIT" ]

6

2020-03-24T17:34:24.000Z

2021-12-13T20:14:34.000Z

DataAnalysis/ProcessingTools/Queues/__init__.py

AdamSwenson/TwitterProject

8c5dc7a57eac611b555058736d609f2f204cb836

[ "MIT" ]

null

""" Created by adam on 5/4/18 """ __author__ = 'adam' if __name__ == '__main__': pass

12.857143

26

0.611111

11e0852edce6d92dee393e4fb99e5f1aeb16e80a

476

py

Python

packages/python/plotly/plotly/validators/layout/updatemenu/_borderwidth.py

mastermind88/plotly.py

efa70710df1af22958e1be080e105130042f1839

[ "MIT" ]

null

packages/python/plotly/plotly/validators/layout/updatemenu/_borderwidth.py

mastermind88/plotly.py

efa70710df1af22958e1be080e105130042f1839

[ "MIT" ]

null

packages/python/plotly/plotly/validators/layout/updatemenu/_borderwidth.py

mastermind88/plotly.py

efa70710df1af22958e1be080e105130042f1839

[ "MIT" ]

null

import _plotly_utils.basevalidators class BorderwidthValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name="borderwidth", parent_name="layout.updatemenu", **kwargs ): super(BorderwidthValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop("edit_type", "arraydraw"), min=kwargs.pop("min", 0), **kwargs, )

31.733333

82

0.64916

ad5f43ff5c201e5110a5d48df8de10b5ba4570a3

9,924

py

Python

bui/layout/radio.py

vincent-lg/bui

a4a783fea2269190fb69c6491a4bc91753ccc580

[ "BSD-3-Clause" ]

3

2019-07-28T17:15:56.000Z

2020-04-12T14:05:12.000Z

bui/layout/radio.py

vincent-lg/bui

a4a783fea2269190fb69c6491a4bc91753ccc580

[ "BSD-3-Clause" ]

null

bui/layout/radio.py

vincent-lg/bui

a4a783fea2269190fb69c6491a4bc91753ccc580

[ "BSD-3-Clause" ]

null

"""Radio button object represented in layout.""" from bui.layout.attr import Attr from bui.layout.component import Component class RadioButton(Component): """ Create a radio button with multiple choices. A radio button is represented by multiple, mutually-exclusive buttons. Each button in the group can't be selected without the other ones in the same group being de-selected. ``` <window title="Table demonstration"> <radio id=choices x=2 y=4> <choice>Choice 1</choice> <choice>Choice 2</choice> <choice>Choice 3</choice> </radio> </window> ``` Using the `<radio>` landmark creates an empty radio button group. A radio button must have at least two choices (if less, consider using a [checkbox](checkbox.md)). You can use the `<choice>` tag inside of the `<radio>` to create a selectable choice in the group, or use the method `add_choice` of a [RadioButton](../../widget/radio_button.py) widget to add the button programmatically. ## Attributes | Name | Required | Description | Example | | ------------ | -------- | ------------------------ | ----------- | | `x` | Yes | The widget's horizontal | `<radio | | | | position in columns (0 | x=5>` | | | | is left). This position | | | | | is relative to the | | | | | window width. | | | `y` | Yes | The widget's vertical | `<radio | | | | position in rows (0 | y=2>` | | | | is at the top). This | | | | | position is relative to | | | | | the window height. | | | `id` | Yes | The radio button group | `<radio | | | | identifier (ID). Contrary| id=actions>`| | | | to most other tags, you | | | | | need to set this | | | | | attribute. | | | `width` | No | The widget width, that | `<radio | | | | is, the number of | width=2>` | | | | columns it will use in | | | | | the window grid. A | | | | | widget with a width of | | | | | 2 will stretch one | | | | | additional column to the | | | | | right. A widget with `x` | | | | | set to 2 and `width` set | | | | | to 3 will span `x=2`, | | | | | `x=3`, and `x=4`. The | | | | | default is 1, so a | | | | | widget will remain in | | | | | its `x` column. | | | `height` | No | The widget height, that | `<radio | | | | is, the number of | height=2>` | | | | rows it will use in | | | | | the window grid. A | | | | | widget with a height of | | | | | 2 will stretch one | | | | | additional row downward. | | | | | A widget with `y` set | | | | | to 2 and `height` set | | | | | to 3 will span `y=2`, | | | | | `y=3`, and `y=4`. The | | | | | default is 1, so a | | | | | widget will remain in | | | | | its `y` row. | | See also the [choice](./choice.md) tag to define the choices in a radio button group. > Note: the radio button identifier is mandatory. You cannot dcreate a radio button without a valid identifier. The reason for this constraint is due to the fact that this widget, in particular, will be edited in your application. Lacking an identifier, you won't be able to do that. ## Data The [radio button widget](../../widget/radio_button.md) can be manipulated to add and remove choices, and know the selected choice. | Attribute | Meaning and type | Example | | -------------- | ---------------- | --------------------------- | | `choices` | The available | `self.choices = (("ch1", | | | choices (lsit) | "Choice 1"))` | | `selected` | ID of the | `self.selected = "ch1"` | | | selected choice | | | | (str) or indice | | | | of the choice if | | | | no ID is set | | | | (int). | | You can easily update the entire radio button this way. Just set the `choices` property on the [radio button widget](../../widget/RadioButton.md) instance, specifying a list of choices where each choice is a tuple of two values: ID (as a `str`) and label to be displayed (as a `str`). Th ID will be returned when this choice is selected: def on_click_update(self): # A button of ID 'update' was clicked # There is a radio button of ID 'actions' in the window actions = self["actions"] actions.choices = ( ("update", "Update the software"), ("repair", "Repair the software"), ("reinstall", "Remove the software configuration and install it again"), ("remove", "Uninstall this software"), ) action = actions.selected # action will be either "update", "repair", "reinstall" or "remove" # You can of course change the selected choice actions.selected = "reinstall" ## Controls | Control | Method | Description | | ------------------------------- | ------------ | ---------------- | | [focus](../../control/focus.md) | `on_focus` | The radio | | | | button is | | | | focused or | | | | loses focus. | | [init](../../control/init.md) | `on_init` | The radio is | | | | ready to be | | | | displayed, but | | | | is not displayed | | | | just yet. | | [press](../../control/press.md) | `on_press` | The user presses | | | | on a key from her| | | | keyboard. This | | | | control can have | | | | sub-controls. | | [release](../../ | `on_release` | The user | | control/release.md) | | relases a key on | | | | her keyboard. | | | | This control can | | | | have sub- | | | | controls. | | [select](../../ | `on_select` | The selected | | control/select.md) | | choice has | | | | changed for | | | | this radio | | | | button. | | [type](../../control/type.md) | `on_type` | The user types | | | | a character | | | | using her | | | | keyboard. This | | | | control can have | | | | sub-controls. | class MainWindow(Window): def on_select_actions(self, widget, selected): print(f"The user selected the action {selected}.") """ tag_name = "radio" attrs = ( Attr("x", help="The widget horizontal position", type=int), Attr("y", help="The widget vertical position", type=int), Attr("id", help="The widget identifier"), Attr("width", help="The widget width", type=int, default=1), Attr("height", help="The widget height", type=int, default=1), ) def __init__(self, layout, parent, x, y, id, width=1, height=1): super().__init__(layout, parent) self.x = x self.y = y self.id = id self.width = width self.height = height

52.787234

88

0.366385

9324713e71eeec4389cfc11ae3d9fbbc0e32cb7a

2,694

py

Python

setup.py

MelkyFB-zz/home-assistant

81453e9508d97270674415f0b197c69a60355e51

[ "Apache-2.0" ]

1

2018-10-24T02:43:48.000Z

setup.py

hassbeat/home-assistant

a08e5efe53b3d99fba3c6d46f37e1a8572a39278

[ "Apache-2.0" ]

null

setup.py

hassbeat/home-assistant

a08e5efe53b3d99fba3c6d46f37e1a8572a39278

[ "Apache-2.0" ]

1

2019-02-07T11:50:04.000Z

#!/usr/bin/env python3 """Home Assistant setup script.""" from setuptools import setup, find_packages import homeassistant.const as hass_const PROJECT_NAME = 'Home Assistant' PROJECT_PACKAGE_NAME = 'homeassistant' PROJECT_LICENSE = 'Apache License 2.0' PROJECT_AUTHOR = 'The Home Assistant Authors' PROJECT_COPYRIGHT = ' 2013-2018, {}'.format(PROJECT_AUTHOR) PROJECT_URL = 'https://home-assistant.io/' PROJECT_EMAIL = 'hello@home-assistant.io' PROJECT_DESCRIPTION = ('Open-source home automation platform ' 'running on Python 3.') PROJECT_LONG_DESCRIPTION = ('Home Assistant is an open-source ' 'home automation platform running on Python 3. ' 'Track and control all devices at home and ' 'automate control. ' 'Installation in less than a minute.') PROJECT_CLASSIFIERS = [ 'Development Status :: 4 - Beta', 'Intended Audience :: End Users/Desktop', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Home Automation' ] PROJECT_GITHUB_USERNAME = 'home-assistant' PROJECT_GITHUB_REPOSITORY = 'home-assistant' PYPI_URL = 'https://pypi.python.org/pypi/{}'.format(PROJECT_PACKAGE_NAME) GITHUB_PATH = '{}/{}'.format( PROJECT_GITHUB_USERNAME, PROJECT_GITHUB_REPOSITORY) GITHUB_URL = 'https://github.com/{}'.format(GITHUB_PATH) DOWNLOAD_URL = '{}/archive/{}.zip'.format(GITHUB_URL, hass_const.__version__) PACKAGES = find_packages(exclude=['tests', 'tests.*']) REQUIRES = [ 'requests==2.18.4', 'pyyaml>=3.11,<4', 'pytz>=2017.02', 'pip>=8.0.3', 'jinja2>=2.10', 'voluptuous==0.11.1', 'typing>=3,<4', 'aiohttp==3.1.0', 'async_timeout==2.0.1', 'astral==1.6', 'certifi>=2017.4.17', 'attrs==17.4.0', ] MIN_PY_VERSION = '.'.join(map(str, hass_const.REQUIRED_PYTHON_VER)) setup( name=PROJECT_PACKAGE_NAME, version=hass_const.__version__, license=PROJECT_LICENSE, url=PROJECT_URL, download_url=DOWNLOAD_URL, author=PROJECT_AUTHOR, author_email=PROJECT_EMAIL, description=PROJECT_DESCRIPTION, packages=PACKAGES, include_package_data=True, zip_safe=False, platforms='any', install_requires=REQUIRES, python_requires='>={}'.format(MIN_PY_VERSION), test_suite='tests', keywords=['home', 'automation'], entry_points={ 'console_scripts': [ 'hass = homeassistant.__main__:main' ] }, classifiers=PROJECT_CLASSIFIERS, )

31.694118

77

0.660356

8b8e343ce5e13ffd2c48aaed6fca2f02fa249892

6,835

py

Python

render/BitcoinTradingGraph.py

botemple/Bitcoin-Trader-RL

0da28ccf4fed4bdfcce7c77de2c1c8d04cad7477

[ "MIT" ]

10

2019-09-08T12:44:51.000Z

2021-07-19T09:05:21.000Z

render/BitcoinTradingGraph.py

botemple/Bitcoin-Trader-RL

0da28ccf4fed4bdfcce7c77de2c1c8d04cad7477

[ "MIT" ]

null

render/BitcoinTradingGraph.py

botemple/Bitcoin-Trader-RL

0da28ccf4fed4bdfcce7c77de2c1c8d04cad7477

[ "MIT" ]

2

2021-03-25T08:43:16.000Z

2021-04-20T12:53:40.000Z

import numpy as np import matplotlib import matplotlib.pyplot as plt from matplotlib import style from datetime import datetime # finance module is no longer part of matplotlib # see: https://github.com/matplotlib/mpl_finance from mpl_finance import candlestick_ochl as candlestick style.use('ggplot') VOLUME_CHART_HEIGHT = 0.33 class BitcoinTradingGraph: """A Bitcoin trading visualization using matplotlib made to render OpenAI gym environments""" def __init__(self, df, title=None): self.df = df self.net_worths = np.zeros(len(df)) # Create a figure on screen and set the title fig = plt.figure() fig.suptitle(title) # Create top subplot for net worth axis self.net_worth_ax = plt.subplot2grid( (6, 1), (0, 0), rowspan=2, colspan=1) # Create bottom subplot for shared price/volume axis self.price_ax = plt.subplot2grid( (6, 1), (2, 0), rowspan=8, colspan=1, sharex=self.net_worth_ax) # Create a new axis for volume which shares its x-axis with price self.volume_ax = self.price_ax.twinx() # Add padding to make graph easier to view plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0) # Show the graph without blocking the rest of the program plt.show(block=False) def _render_net_worth(self, current_step, net_worth, step_range, dates): # Clear the frame rendered last step self.net_worth_ax.clear() # Plot net worths self.net_worth_ax.plot_date( dates, self.net_worths[step_range], '-', label='Net Worth') # Show legend, which uses the label we defined for the plot above self.net_worth_ax.legend() legend = self.net_worth_ax.legend(loc=2, ncol=2, prop={'size': 8}) legend.get_frame().set_alpha(0.4) last_date = self.df['Timestamp'].values[current_step] last_net_worth = self.net_worths[current_step] # Annotate the current net worth on the net worth graph self.net_worth_ax.annotate('{0:.2f}'.format(net_worth), (last_date, last_net_worth), xytext=(last_date, last_net_worth), bbox=dict(boxstyle='round', fc='w', ec='k', lw=1), color="black", fontsize="small") # Add space above and below min/max net worth self.net_worth_ax.set_ylim( min(self.net_worths[np.nonzero(self.net_worths)]) / 1.25, max(self.net_worths) * 1.25) def _render_price(self, current_step, net_worth, step_range, dates): self.price_ax.clear() # Format data for OHCL candlestick graph candlesticks = zip(dates, self.df['Open'].values[step_range], self.df['Close'].values[step_range], self.df['High'].values[step_range], self.df['Low'].values[step_range]) # Plot price using candlestick graph from mpl_finance candlestick(self.price_ax, candlesticks, width=2) last_date = self.df['Timestamp'].values[current_step] last_close = self.df['Close'].values[current_step] last_high = self.df['High'].values[current_step] # Print the current price to the price axis self.price_ax.annotate('{0:.2f}'.format(last_close), (last_date, last_close), xytext=(last_date, last_high), bbox=dict(boxstyle='round', fc='w', ec='k', lw=1), color="black", fontsize="small") # Shift price axis up to give volume chart space ylim = self.price_ax.get_ylim() self.price_ax.set_ylim(ylim[0] - (ylim[1] - ylim[0]) * VOLUME_CHART_HEIGHT, ylim[1]) def _render_volume(self, current_step, net_worth, step_range, dates): self.volume_ax.clear() volume = np.array(self.df['Volume_(BTC)'].values[step_range]) pos = self.df['Open'].values[step_range] - \ self.df['Close'].values[step_range] < 0 neg = self.df['Open'].values[step_range] - \ self.df['Close'].values[step_range] > 0 # Color volume bars based on price direction on that date self.volume_ax.bar(dates[pos], volume[pos], width=2, color='g') self.volume_ax.bar(dates[neg], volume[neg], width=2, color='r') self.volume_ax.yaxis.set_ticks([]) def _render_trades(self, current_step, trades, step_range): for trade in trades: if trade['step'] in step_range: date = self.df['Timestamp'].values[trade['step']] close = self.df['Close'].values[trade['step']] high = self.df['High'].values[trade['step']] low = self.df['Low'].values[trade['step']] if trade['type'] == 'buy': high_low = low color = 'g' else: high_low = high color = 'r' total = '{0:.2f}'.format(trade['total']) self.price_ax.annotate('$' + str(total), (date, close), xytext=(date, high_low), bbox=dict(boxstyle='round', fc='w', ec='k', lw=1, alpha=0.4), color=color, alpha=0.4, fontsize="small") def render(self, current_step, net_worth, trades, window_size=40): self.net_worths[current_step] = net_worth window_start = max(current_step - window_size, 0) step_range = range(window_start, current_step + 1) dates = self.df['Timestamp'].values[step_range] self._render_net_worth(current_step, net_worth, step_range, dates) self._render_price(current_step, net_worth, step_range, dates) self._render_volume(current_step, net_worth, step_range, dates) self._render_trades(current_step, trades, step_range) date_labels = np.array([datetime.utcfromtimestamp(x).strftime( '%Y-%m-%d %H:%M') for x in self.df['Timestamp'].values[step_range]]) self.price_ax.set_xticklabels( date_labels, rotation=45, horizontalalignment='right') # Hide duplicate net worth date labels plt.setp(self.net_worth_ax.get_xticklabels(), visible=False) # Necessary to view frames before they are unrendered plt.pause(0.001) def close(self): plt.close()

40.205882

99

0.573958

84ede800b0b9cca1d7bdc1d6a25b37fee3de973b

2,097

py

Python

var/spack/repos/builtin/packages/biobambam2/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2,360

2017-11-06T08:47:01.000Z

2022-03-31T14:45:33.000Z

var/spack/repos/builtin/packages/biobambam2/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

13,838

2017-11-04T07:49:45.000Z

2022-03-31T23:38:39.000Z

var/spack/repos/builtin/packages/biobambam2/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1,793

2017-11-04T07:45:50.000Z

2022-03-30T14:31:53.000Z

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class Biobambam2(AutotoolsPackage): """Tools for early stage alignment file processing""" homepage = "https://gitlab.com/german.tischler/biobambam2" url = "https://gitlab.com/german.tischler/biobambam2/-/archive/2.0.177-release-20201112105453/biobambam2-2.0.177-release-20201112105453.tar.gz" version('2.0.177', sha256='ad0a418fb49a31996a105a1a275c0d1dfc8b84aa91d48fa1efb6ff4fe1e74181', url='https://gitlab.com/german.tischler/biobambam2/-/archive/2.0.177-release-20201112105453/biobambam2-2.0.177-release-20201112105453.tar.gz') depends_on('autoconf', type='build') depends_on('automake', type='build') depends_on('libtool', type='build') depends_on('m4', type='build') depends_on('libmaus2') test_src_dir = 'test' def configure_args(self): args = ['--with-libmaus2={0}'.format(self.spec['libmaus2'].prefix)] return args def _fix_shortsort(self): """Fix the testshortsort.sh file copied during installation.""" test_dir = join_path(self.install_test_root, self.test_src_dir) filter_file('../src/', '', join_path(test_dir, 'testshortsort.sh')) @run_after('install') def cache_test_sources(self): """Copy the test source files after the package is installed to an install test subdirectory for use during `spack test run`.""" self.cache_extra_test_sources(self.test_src_dir) self._fix_shortsort() def test(self): """Perform stand-alone/smoke test on installed package.""" test_dir = join_path(self.test_suite.current_test_cache_dir, self.test_src_dir) self.run_test('sh', ['testshortsort.sh'], expected='Alignments sorted by coordinate.', purpose='test: checking alignments', work_dir=test_dir)

41.94

154

0.67525

5734e017dc0541fc25d120148167be91c2166147

548

py

Python

utils/print_rejected_outlinks.py

truxonjm/BrozzlerAdmin

511fe3f07282cb9499e97877e62d7418cb9c1def

[ "Apache-2.0" ]

2

2019-12-23T23:33:46.000Z

2020-11-14T00:52:15.000Z

utils/print_rejected_outlinks.py

truxonjm/BrozzlerAdmin

511fe3f07282cb9499e97877e62d7418cb9c1def

[ "Apache-2.0" ]

1

2017-10-20T13:28:50.000Z

2017-10-20T17:13:22.000Z

utils/print_rejected_outlinks.py

truxonjm/BrozzlerAdmin

511fe3f07282cb9499e97877e62d7418cb9c1def

[ "Apache-2.0" ]

1

2021-06-12T11:40:02.000Z

import doublethink from argparse import ArgumentParser parser = ArgumentParser(description="Utility to print all outlinks from a job.") parser.add_argument('job_id',help="job_id we want to print the rejected outlinks") parser.add_argument('--rethinkdb_server', default="localhost:28015", help="RethinkDB server") args = parser.parse_args() rr = doublethink.Rethinker([args.rethinkdb_server], 'brozzler') links = rr.table('pages').filter({'job_id': args.job_id })['outlinks']['rejected'].run() for link in links: for e in link: print(e)

36.533333

93

0.75

5003b6fb507bbca926f169be33e5ede4a2a36812

527

py

Python

index/models.py

GadirMirzayev/Django-E-commerce

0ca289fdf584b29636a8fc9416319defad0be5a5

[ "MIT" ]

1

2021-08-20T07:44:39.000Z

index/models.py

GadirMirzayev/Django-E-commerce

0ca289fdf584b29636a8fc9416319defad0be5a5

[ "MIT" ]

null

index/models.py

GadirMirzayev/Django-E-commerce

0ca289fdf584b29636a8fc9416319defad0be5a5

[ "MIT" ]

null

from django.db import models # Create your models here. class Subscriber(models.Model): # information's email = models.EmailField('E-mail', max_length=63) # moderation's is_active = models.BooleanField(default=True) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) class Meta: verbose_name = 'Subscriber' verbose_name_plural = 'Subscribers' ordering = ('-created_at',) def __str__(self): return self.email

26.35

56

0.688805

e6cd7b5df9e506ba7a3e77fc3e6763aa18802799

53

py

Python

SadapWA.py

McybearX/GameTTs

d62b675394833038cb7df3992af0b3de08ec4fa6

[ "Apache-2.0" ]

null

SadapWA.py

McybearX/GameTTs

d62b675394833038cb7df3992af0b3de08ec4fa6

[ "Apache-2.0" ]

null

SadapWA.py

McybearX/GameTTs

d62b675394833038cb7df3992af0b3de08ec4fa6

[ "Apache-2.0" ]

null

os.system("xdg-open https://youtube.com/c/MBEWLEGS")

26.5

52

0.735849

96f1b94a0f4a437fb506f1c3321640141bbdaaa1

6,434

py

Python

openstack_dashboard/dashboards/project/access_and_security/floating_ips/tables.py

aristanetworks/horizon

6b4ba5194d46360bf1a436b6f9531facfbf5084a

[ "Apache-2.0" ]

null

openstack_dashboard/dashboards/project/access_and_security/floating_ips/tables.py

aristanetworks/horizon

6b4ba5194d46360bf1a436b6f9531facfbf5084a

[ "Apache-2.0" ]

null

openstack_dashboard/dashboards/project/access_and_security/floating_ips/tables.py

aristanetworks/horizon

6b4ba5194d46360bf1a436b6f9531facfbf5084a

[ "Apache-2.0" ]

null

# Copyright 2012 Nebula, Inc. # Copyright (c) 2012 X.commerce, a business unit of eBay Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import logging from django.conf import settings from django.core import urlresolvers from django import shortcuts from django.utils.http import urlencode from django.utils.translation import string_concat # noqa from django.utils.translation import ugettext_lazy as _ from horizon import exceptions from horizon import messages from horizon import tables from openstack_dashboard import api from openstack_dashboard.usage import quotas from openstack_dashboard.utils import filters LOG = logging.getLogger(__name__) POLICY_CHECK = getattr(settings, "POLICY_CHECK_FUNCTION", lambda p, r: True) class AllocateIP(tables.LinkAction): name = "allocate" verbose_name = _("Allocate IP To Project") classes = ("ajax-modal",) icon = "download-alt" url = "horizon:project:access_and_security:floating_ips:allocate" def single(self, data_table, request, *args): return shortcuts.redirect('horizon:project:access_and_security:index') def allowed(self, request, volume=None): usages = quotas.tenant_quota_usages(request) if usages['floating_ips']['available'] <= 0: if "disabled" not in self.classes: self.classes = [c for c in self.classes] + ['disabled'] self.verbose_name = string_concat(self.verbose_name, ' ', _("(Quota exceeded)")) else: self.verbose_name = _("Allocate IP To Project") classes = [c for c in self.classes if c != "disabled"] self.classes = classes if api.base.is_service_enabled(request, "network"): policy = (("network", "create_floatingip"),) else: policy = (("compute", "compute_extension:floating_ips"), ("compute", "network:allocate_floating_ip"),) return POLICY_CHECK(policy, request) class ReleaseIPs(tables.BatchAction): name = "release" action_present = _("Release") action_past = _("Released") data_type_singular = _("Floating IP") data_type_plural = _("Floating IPs") classes = ('btn-danger',) icon = "arrow-up" def allowed(self, request, fip=None): if api.base.is_service_enabled(request, "network"): policy = (("network", "delete_floatingip"),) else: policy = (("compute", "compute_extension:floating_ips"), ("compute", "network:release_floating_ip"),) return POLICY_CHECK(policy, request) def action(self, request, obj_id): api.network.tenant_floating_ip_release(request, obj_id) class AssociateIP(tables.LinkAction): name = "associate" verbose_name = _("Associate") url = "horizon:project:access_and_security:floating_ips:associate" classes = ("ajax-modal",) icon = "link" def allowed(self, request, fip): if api.base.is_service_enabled(request, "network"): policy = (("network", "update_floatingip"),) else: policy = (("compute", "compute_extension:floating_ips"), ("compute", "network:associate_floating_ip"),) return not fip.port_id and POLICY_CHECK(policy, request) def get_link_url(self, datum): base_url = urlresolvers.reverse(self.url) params = urlencode({"ip_id": self.table.get_object_id(datum)}) return "?".join([base_url, params]) class DisassociateIP(tables.Action): name = "disassociate" verbose_name = _("Disassociate") classes = ("btn-disassociate", "btn-danger") def allowed(self, request, fip): if api.base.is_service_enabled(request, "network"): policy = (("network", "update_floatingip"),) else: policy = (("compute", "compute_extension:floating_ips"), ("compute", "network:disassociate_floating_ip"),) return fip.port_id and POLICY_CHECK(policy, request) def single(self, table, request, obj_id): try: fip = table.get_object_by_id(filters.get_int_or_uuid(obj_id)) api.network.floating_ip_disassociate(request, fip.id, fip.port_id) LOG.info('Disassociating Floating IP "%s".' % obj_id) messages.success(request, _('Successfully disassociated Floating IP: %s') % fip.ip) except Exception: exceptions.handle(request, _('Unable to disassociate floating IP.')) return shortcuts.redirect('horizon:project:access_and_security:index') def get_instance_info(instance): return getattr(instance, "instance_name", None) def get_instance_link(datum): view = "horizon:project:instances:detail" if datum.instance_id: return urlresolvers.reverse(view, args=(datum.instance_id,)) else: return None class FloatingIPsTable(tables.DataTable): ip = tables.Column("ip", verbose_name=_("IP Address"), attrs={'data-type': "ip"}) instance = tables.Column(get_instance_info, link=get_instance_link, verbose_name=_("Instance"), empty_value="-") pool = tables.Column("pool_name", verbose_name=_("Floating IP Pool"), empty_value="-") def sanitize_id(self, obj_id): return filters.get_int_or_uuid(obj_id) def get_object_display(self, datum): return datum.ip class Meta: name = "floating_ips" verbose_name = _("Floating IPs") table_actions = (AllocateIP, ReleaseIPs) row_actions = (AssociateIP, DisassociateIP, ReleaseIPs)

36.146067

78

0.632577

220aad9bd6862c3b0e8bcaa6d4f340334793916b

17,723

py

Python

DPGAnalysis/SiStripTools/test/manyfederrors_cfg.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

DPGAnalysis/SiStripTools/test/manyfederrors_cfg.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

DPGAnalysis/SiStripTools/test/manyfederrors_cfg.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms import FWCore.ParameterSet.VarParsing as VarParsing process = cms.Process("ManyFEDErrors") #prepare options options = VarParsing.VarParsing("analysis") options.register ('globalTag', "DONOTEXIST", VarParsing.VarParsing.multiplicity.singleton, # singleton or list VarParsing.VarParsing.varType.string, # string, int, or float "GlobalTag") options.parseArguments() # process.options = cms.untracked.PSet( wantSummary = cms.untracked.bool(True), fileMode = cms.untracked.string("FULLMERGE") ) process.load("FWCore.MessageService.MessageLogger_cfi") process.MessageLogger.cout.enable = cms.untracked.bool(True) process.MessageLogger.cout.threshold = cms.untracked.string("DEBUG") process.MessageLogger.cout.default = cms.untracked.PSet( limit = cms.untracked.int32(10000000) ) process.MessageLogger.cout.FwkReport = cms.untracked.PSet( reportEvery = cms.untracked.int32(10000) ) process.MessageLogger.cerr.enable = cms.untracked.bool(True) process.MessageLogger.cerr.threshold = cms.untracked.string("WARNING") process.MessageLogger.cerr.default = cms.untracked.PSet( limit = cms.untracked.int32(10000000) ) process.MessageLogger.cerr.FwkReport = cms.untracked.PSet( reportEvery = cms.untracked.int32(100000) ) process.MessageLogger.debugModules=cms.untracked.vstring("eventtimedistribution") #------------------------------------------------------------------ process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) process.source = cms.Source("PoolSource", fileNames = cms.untracked.vstring(options.inputFiles), # skipBadFiles = cms.untracked.bool(True), inputCommands = cms.untracked.vstring("keep *", "drop *_MEtoEDMConverter_*_*") ) #-------------------------------------- process.load("Configuration.StandardSequences.RawToDigi_Data_cff") process.load("Configuration.StandardSequences.MagneticField_cff") process.load("Configuration.StandardSequences.GeometryDB_cff") process.load("Configuration.StandardSequences.Reconstruction_cff") process.froml1abcHEs = cms.EDProducer("EventWithHistoryProducerFromL1ABC", l1ABCCollection=cms.InputTag("scalersRawToDigi") ) process.load("DPGAnalysis.SiStripTools.apvcyclephaseproducerfroml1tsDB_cfi") process.load("DPGAnalysis.SiStripTools.l1TSDebugger_cfi") process.load("DPGAnalysis.SiStripTools.eventtimedistribution_cfi") process.eventtimedistribution.historyProduct = cms.InputTag("froml1abcHEs") process.eventtimedistribution.dbxHistosParams = cms.untracked.VPSet( cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(0),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(1),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(2),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(3),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(4),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(5),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(6),secondEvent=cms.uint32(7)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(0),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(1),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(2),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(3),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(4),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(5),secondEvent=cms.uint32(6)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(0),secondEvent=cms.uint32(5)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(1),secondEvent=cms.uint32(5)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(2),secondEvent=cms.uint32(5)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(3),secondEvent=cms.uint32(5)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(4),secondEvent=cms.uint32(5)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(0),secondEvent=cms.uint32(4)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(1),secondEvent=cms.uint32(4)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(2),secondEvent=cms.uint32(4)), cms.PSet(nbins=cms.int32(2000),min=cms.double(-0.5),max=cms.double(3999.5),firstEvent=cms.uint32(3),secondEvent=cms.uint32(4)), cms.PSet(nbins=cms.int32(1000),min=cms.double(-0.5),max=cms.double(999.5),firstEvent=cms.uint32(0),secondEvent=cms.uint32(1)), ) process.eventtimedistrmanyfederrorsallthr = process.eventtimedistribution.clone() process.eventtimedistrmanyfederrorshighthr = process.eventtimedistribution.clone() process.eventtimedistrmanyfederrorsmidthr = process.eventtimedistribution.clone() process.eventtimedistrmanyfederrorslowthr = process.eventtimedistribution.clone() process.eventtimedistrnomanyfederrors = process.eventtimedistribution.clone() process.seqEventHistoryReco = cms.Sequence(process.froml1abcHEs + process.APVPhases + process.l1TSDebugger) process.apvcyclephasemonitor = cms.EDAnalyzer('APVCyclePhaseMonitor', apvCyclePhaseCollection = cms.InputTag("APVPhases"), maxLSBeforeRebin = cms.untracked.uint32(250), startingLSFraction = cms.untracked.uint32(16), selectedPartitions = cms.untracked.vstring("TI","TO","TP","TM"), selectedVectorPartitions = cms.untracked.vstring("Any") ) process.seqEventHistory = cms.Sequence(process.eventtimedistribution + process.apvcyclephasemonitor) process.seqEventHistoryManyFEDErrorsAllThr = cms.Sequence(process.eventtimedistrmanyfederrorsallthr) process.seqEventHistoryManyFEDErrorsHighThr = cms.Sequence(process.eventtimedistrmanyfederrorshighthr) process.seqEventHistoryManyFEDErrorsMidThr = cms.Sequence(process.eventtimedistrmanyfederrorsmidthr) process.seqEventHistoryManyFEDErrorsLowThr = cms.Sequence(process.eventtimedistrmanyfederrorslowthr) process.seqEventHistoryNoManyFEDErrors = cms.Sequence(process.eventtimedistrnomanyfederrors) process.seqProducers = cms.Sequence(process.seqEventHistoryReco) #do not ignore APVe mismatches process.siStripDigis.DoAPVEmulatorCheck = cms.bool(True) #process.siStripDigis.ErrorThreshold = cms.uint32(30000) #process.siStripDigisMidThr = process.siStripDigis.clone(ErrorThreshold = cms.uint32(7174)) #process.siStripDigisLowThr = process.siStripDigis.clone(ErrorThreshold = cms.uint32(2800)) #process.siStripDigisAllThr = process.siStripDigis.clone(ErrorThreshold = cms.uint32(35500)) #process.seqSiStripDigis = cms.Sequence(process.siStripDigis + process.siStripDigisLowThr + process.siStripDigisMidThr + process.siStripDigisAllThr) process.seqSiStripDigis = cms.Sequence(process.siStripDigis) process.seqRECO = cms.Sequence(process.seqSiStripDigis + process.scalersRawToDigi + process.logErrorHarvester ) #process.logErrorTooManyErrors = cms.EDFilter("LogErrorEventFilter", # src = cms.InputTag("logErrorHarvester"), # maxErrorFractionInLumi = cms.double(1.0), # maxErrorFractionInRun = cms.double(1.0), # maxSavedEventsPerLumiAndError = cms.uint32(1000000), # # categoriesToIgnore = cms.vstring("HLTConfigProvider","FastCloningDisabled") # categoriesToWatch = cms.vstring("TooManyErrors"), # modulesToWatch = cms.vstring("SiStripRawToDigiModule:siStripDigis") # # ) #process.logErrorTooManyErrorsLowThr = process.logErrorTooManyErrors.clone(modulesToWatch = cms.vstring("SiStripRawToDigiModule:siStripDigisLowThr")) #process.logErrorTooManyErrorsMidThr = process.logErrorTooManyErrors.clone(modulesToWatch = cms.vstring("SiStripRawToDigiModule:siStripDigisMidThr")) #process.logErrorTooManyErrorsAllThr = process.logErrorTooManyErrors.clone(modulesToWatch = cms.vstring("SiStripRawToDigiModule:siStripDigisAllThr")) process.filterFEDBadModuleHighThr = cms.EDFilter("FEDBadModuleFilter", collectionName=cms.InputTag("siStripDigis"), badModThr = cms.uint32(30000), wantedHisto=cms.untracked.bool(False), maxLSBeforeRebin = cms.untracked.uint32(250), startingLSFraction = cms.untracked.uint32(16) ) process.filterFEDBadModuleAllThr = process.filterFEDBadModuleHighThr.clone(badModThr = cms.uint32(35500),wantedHisto=cms.untracked.bool(True)) process.filterFEDBadModuleMidThr = process.filterFEDBadModuleHighThr.clone(badModThr = cms.uint32(7174),wantedHisto=cms.untracked.bool(False)) process.filterFEDBadModuleLowThr = process.filterFEDBadModuleHighThr.clone(badModThr = cms.uint32(2000),wantedHisto=cms.untracked.bool(False)) process.badModuleFED74 = cms.EDFilter("FEDBadModuleFilter", collectionName=cms.InputTag("siStripDigis"), badModThr = cms.uint32(0), wantedHisto=cms.untracked.bool(True), moduleList = cms.untracked.vuint32(369158204), maxLSBeforeRebin = cms.untracked.uint32(250), startingLSFraction = cms.untracked.uint32(16) ) process.badModuleFED102 = cms.EDFilter("FEDBadModuleFilter", collectionName=cms.InputTag("siStripDigis"), badModThr = cms.uint32(0), wantedHisto=cms.untracked.bool(True), moduleList = cms.untracked.vuint32(369141837,369141833,369141829), maxLSBeforeRebin = cms.untracked.uint32(250), startingLSFraction = cms.untracked.uint32(16) ) process.badModuleFED108 = cms.EDFilter("FEDBadModuleFilter", collectionName=cms.InputTag("siStripDigis"), badModThr = cms.uint32(0), wantedHisto=cms.untracked.bool(True), moduleList = cms.untracked.vuint32(369142074,369142077,369142078), maxLSBeforeRebin = cms.untracked.uint32(250), startingLSFraction = cms.untracked.uint32(16) ) #process.seqTooManyErrorsAllThr = cms.Sequence(process.logErrorTooManyErrorsAllThr) #process.seqTooManyErrorsHighThr = cms.Sequence(~process.logErrorTooManyErrorsAllThr + process.logErrorTooManyErrors) #process.seqTooManyErrorsMidThr = cms.Sequence(~process.logErrorTooManyErrorsAllThr + ~process.logErrorTooManyErrors + process.logErrorTooManyErrorsMidThr) #process.seqTooManyErrorsLowThr = cms.Sequence(~process.logErrorTooManyErrorsAllThr + ~process.logErrorTooManyErrors + ~process.logErrorTooManyErrorsMidThr + # process.logErrorTooManyErrorsLowThr) #process.seqNoTooManyErrors = cms.Sequence(~process.logErrorTooManyErrorsAllThr + ~process.logErrorTooManyErrors + ~process.logErrorTooManyErrorsMidThr + # ~process.logErrorTooManyErrorsLowThr) process.seqTooManyErrorsAllThr = cms.Sequence(process.filterFEDBadModuleAllThr) process.seqTooManyErrorsHighThr = cms.Sequence(~process.filterFEDBadModuleAllThr + process.filterFEDBadModuleHighThr) process.seqTooManyErrorsMidThr = cms.Sequence(~process.filterFEDBadModuleAllThr + ~process.filterFEDBadModuleHighThr + process.filterFEDBadModuleMidThr) process.seqTooManyErrorsLowThr = cms.Sequence(~process.filterFEDBadModuleAllThr + ~process.filterFEDBadModuleHighThr + ~process.filterFEDBadModuleMidThr +process.filterFEDBadModuleLowThr) process.seqNoTooManyErrors = cms.Sequence(~process.filterFEDBadModuleAllThr + ~process.filterFEDBadModuleHighThr + ~process.filterFEDBadModuleMidThr + ~process.filterFEDBadModuleLowThr) process.load("DQM.SiStripCommon.TkHistoMap_cff") process.load("DQM.SiStripMonitorHardware.siStripFEDMonitor_Tier0_cff") process.siStripFEDMonitor.BadMajorityInPartitionHistogramConfig.Enabled = True process.siStripFEDMonitorAllThr = process.siStripFEDMonitor.clone( HistogramFolderName = cms.untracked.string('SiStrip/ReadoutView/FedSummaryAllThr')) process.siStripFEDMonitorHighThr = process.siStripFEDMonitor.clone( HistogramFolderName = cms.untracked.string('SiStrip/ReadoutView/FedSummaryHighThr')) process.siStripFEDMonitorMidThr = process.siStripFEDMonitor.clone( HistogramFolderName = cms.untracked.string('SiStrip/ReadoutView/FedSummaryMidThr')) process.siStripFEDMonitorLowThr = process.siStripFEDMonitor.clone( HistogramFolderName = cms.untracked.string('SiStrip/ReadoutView/FedSummaryLowThr')) process.siStripFEDMonitorNoErr = process.siStripFEDMonitor.clone( HistogramFolderName = cms.untracked.string('SiStrip/ReadoutView/FedSummaryNoErr')) process.load('Configuration.StandardSequences.EndOfProcess_cff') process.load("Configuration.EventContent.EventContent_cff") process.DQMoutput = cms.OutputModule("PoolOutputModule", splitLevel = cms.untracked.int32(0), outputCommands = process.DQMEventContent.outputCommands, fileName = cms.untracked.string('manyfederrors_DQM.root'), dataset = cms.untracked.PSet( filterName = cms.untracked.string(''), dataTier = cms.untracked.string('DQM') ) ) process.endjob_step = cms.EndPath(process.endOfProcess) process.DQMoutput_step = cms.EndPath(process.DQMoutput) process.p0 = cms.Path( process.seqRECO + process.seqProducers + process.siStripFEDMonitor + process.seqEventHistory + process.badModuleFED74 + process.badModuleFED102 + process.badModuleFED108 ) process.pnomanyfederrors = cms.Path( process.seqRECO + process.seqProducers + process.seqNoTooManyErrors + process.siStripFEDMonitorNoErr + process.seqEventHistoryNoManyFEDErrors ) process.pmanyfederrorslowthr = cms.Path( process.seqRECO + process.seqProducers + process.seqTooManyErrorsLowThr + process.siStripFEDMonitorLowThr + process.seqEventHistoryManyFEDErrorsLowThr ) process.pmanyfederrorsmidthr = cms.Path( process.seqRECO + process.seqProducers + process.seqTooManyErrorsMidThr + process.siStripFEDMonitorMidThr + process.seqEventHistoryManyFEDErrorsMidThr ) process.pmanyfederrorshighthr = cms.Path( process.seqRECO + process.seqProducers + process.seqTooManyErrorsHighThr + process.siStripFEDMonitorHighThr + process.seqEventHistoryManyFEDErrorsHighThr ) process.pmanyfederrorsallthr = cms.Path( process.seqRECO + process.seqProducers + process.seqTooManyErrorsAllThr + process.siStripFEDMonitorAllThr + process.seqEventHistoryManyFEDErrorsAllThr ) #----GlobalTag ------------------------ process.load("Configuration.StandardSequences.FrontierConditions_GlobalTag_cff") from Configuration.AlCa.GlobalTag import GlobalTag process.GlobalTag = GlobalTag(process.GlobalTag, options.globalTag, '') process.TFileService = cms.Service('TFileService', fileName = cms.string('ManyFEDErrors.root') ) #print process.dumpPython()

58.880399

157

0.680415

21516e9fd09718c5f6e7f9e0328f11d174452960

11,901

py

Python

python/sparkdl/transformers/tf_image.py

arthurtibame/spark-deep-learning

51cc6c030f3c4752c579aa29836666605477fa1d

[ "Apache-2.0" ]

1

2020-06-29T23:45:08.000Z

python/sparkdl/transformers/tf_image.py

diprup/spark-deep-learning

51cc6c030f3c4752c579aa29836666605477fa1d

[ "Apache-2.0" ]

null

python/sparkdl/transformers/tf_image.py

diprup/spark-deep-learning

51cc6c030f3c4752c579aa29836666605477fa1d

[ "Apache-2.0" ]

null

# Copyright 2017 Databricks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from deprecated import deprecated import numpy as np import tensorflow as tf import tensorframes as tfs # pylint: disable=import-error from pyspark import Row from pyspark.ml import Transformer from pyspark.ml.image import ImageSchema from pyspark.ml.param import Param, Params from pyspark.sql.functions import udf import sparkdl.graph.utils as tfx import sparkdl.image.imageIO as imageIO from sparkdl.param import keyword_only, HasInputCol, HasOutputCol, HasOutputMode from sparkdl.param import SparkDLTypeConverters import sparkdl.transformers.utils as utils import sparkdl.utils.jvmapi as JVMAPI __all__ = ['TFImageTransformer'] IMAGE_INPUT_TENSOR_NAME = tfx.tensor_name(utils.IMAGE_INPUT_PLACEHOLDER_NAME) USER_GRAPH_NAMESPACE = 'given' NEW_OUTPUT_PREFIX = 'sdl_flattened' @deprecated(reason="TFImageTransformer will be removed in the next release of sparkdl. " "Please use Pandas UDF for distributed model inference.") class TFImageTransformer(Transformer, HasInputCol, HasOutputCol, HasOutputMode): """ Applies the Tensorflow graph to the image column in DataFrame. Restrictions of the current API: * Does not use minibatches, which is a major low-hanging fruit for performance. * Only one output node can be specified. * The output is expected to be an image or a 1-d vector. * All images in the dataframe are expected be of the same numerical data type (i.e. the dtype of the values in the numpy array representation is the same.) We assume all graphs have a "minibatch" dimension (i.e. an unknown leading dimension) in the tensor shapes. .. note:: The input tensorflow graph should have appropriate weights constantified, since a new session is created inside this transformer. """ graph = Param( Params._dummy(), "graph", "A TensorFlow computation graph", typeConverter=SparkDLTypeConverters.toTFGraph) inputTensor = Param( Params._dummy(), "inputTensor", "A TensorFlow tensor object or name representing the input image", typeConverter=SparkDLTypeConverters.toTFTensorName) outputTensor = Param( Params._dummy(), "outputTensor", "A TensorFlow tensor object or name representing the output", typeConverter=SparkDLTypeConverters.toTFTensorName) channelOrder = Param( Params._dummy(), "channelOrder", "Strign specifying the expected color channel order, can be one of L,RGB,BGR", typeConverter=SparkDLTypeConverters.toChannelOrder) @keyword_only def __init__(self, channelOrder, inputCol=None, outputCol=None, graph=None, inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None, outputMode="vector"): """ __init__(self, channelOrder, inputCol=None, outputCol=None, graph=None, inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None, outputMode="vector") :param: channelOrder: specify the ordering of the color channel, can be one of RGB, BGR, L (grayscale) """ super(TFImageTransformer, self).__init__() kwargs = self._input_kwargs self.setParams(**kwargs) self._setDefault(inputTensor=IMAGE_INPUT_TENSOR_NAME) self.channelOrder = channelOrder @keyword_only def setParams(self, channelOrder=None, inputCol=None, outputCol=None, graph=None, inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None, outputMode="vector"): """ setParams(self, channelOrder=None, inputCol=None, outputCol=None, graph=None, inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None, outputMode="vector") """ kwargs = self._input_kwargs return self._set(**kwargs) def setGraph(self, value): return self._set(graph=value) def setInputTensor(self, value): return self._set(inputTensor=value) def setOutputTensor(self, value): return self._set(outputTensor=value) def getGraph(self): return self.getOrDefault(self.graph) def getInputTensor(self): tensor_name = self.getOrDefault(self.inputTensor) return self.getGraph().get_tensor_by_name(tensor_name) def getOutputTensor(self): tensor_name = self.getOrDefault(self.outputTensor) return self.getGraph().get_tensor_by_name(tensor_name) def _transform(self, dataset): graph = self.getGraph() composed_graph = self._addReshapeLayers(graph, self._getImageDtype(dataset)) final_graph = self._stripGraph(composed_graph) with final_graph.as_default(): # pylint: disable=not-context-manager image = dataset[self.getInputCol()] image_df_exploded = (dataset .withColumn("__sdl_image_height", image.height) .withColumn("__sdl_image_width", image.width) .withColumn("__sdl_image_nchannels", image.nChannels) .withColumn("__sdl_image_data", image.data) ) # yapf: disable final_output_name = self._getFinalOutputTensorName() output_tensor = final_graph.get_tensor_by_name(final_output_name) final_df = ( tfs.map_rows([output_tensor], image_df_exploded, feed_dict={ "height": "__sdl_image_height", "width": "__sdl_image_width", "num_channels": "__sdl_image_nchannels", "image_buffer": "__sdl_image_data"}) .drop("__sdl_image_height", "__sdl_image_width", "__sdl_image_nchannels", "__sdl_image_data") ) # yapf: disable tfs_output_name = tfx.op_name(output_tensor, final_graph) original_output_name = self._getOriginalOutputTensorName() output_shape = final_graph.get_tensor_by_name(original_output_name).shape output_mode = self.getOrDefault(self.outputMode) # TODO: support non-1d tensors (return np.array). if output_mode == "image": return self._convertOutputToImage(final_df, tfs_output_name, output_shape) else: assert output_mode == "vector", "Unknown output mode: %s" % output_mode return self._convertOutputToVector(final_df, tfs_output_name) def _getImageDtype(self, dataset): # This may not be the best way to get the type of image, but it is one way. # Assumes that the dtype for all images is the same in the given dataframe. pdf = dataset.select(self.getInputCol()).take(1) img = pdf[0][self.getInputCol()] img_type = imageIO.imageTypeByOrdinal(img.mode) return img_type.dtype # TODO: duplicate code, same functionality as sparkdl.graph.pieces.py::builSpImageConverter # TODO: It should be extracted as a util function and shared def _addReshapeLayers(self, tf_graph, dtype="uint8"): input_tensor_name = self.getInputTensor().name gdef = tf_graph.as_graph_def(add_shapes=True) g = tf.Graph() # pylint: disable=invalid-name with g.as_default(): # pylint: disable=not-context-manager # Flat image data -> image dimensions height = tf.placeholder(tf.int32, [], name="height") width = tf.placeholder(tf.int32, [], name="width") num_channels = tf.placeholder(tf.int32, [], name="num_channels") image_buffer = tf.placeholder(tf.string, [], name="image_buffer") # Note: the shape argument is required for tensorframes as it uses a # slightly older version of tensorflow. shape_tensor = tf.stack([height, width, num_channels], axis=0) shape = tf.reshape(shape_tensor, shape=(3, ), name='shape') if dtype == "uint8": image_uint8 = tf.decode_raw(image_buffer, tf.uint8, name="decode_raw") image_float = tf.to_float(image_uint8) else: assert dtype == "float32", "Unsupported dtype for image: %s" % dtype image_float = tf.decode_raw(image_buffer, tf.float32, name="decode_raw") image_reshaped = tf.reshape(image_float, shape, name="reshaped") image_reshaped = imageIO.fixColorChannelOrdering(self.channelOrder, image_reshaped) image_reshaped_expanded = tf.expand_dims(image_reshaped, 0, name="expanded") # Add on the original graph tf.import_graph_def( gdef, input_map={input_tensor_name: image_reshaped_expanded}, return_elements=[self.getOutputTensor().name], name=USER_GRAPH_NAMESPACE) # Flatten the output for tensorframes output_node = g.get_tensor_by_name(self._getOriginalOutputTensorName()) _ = tf.reshape(output_node[0], shape=[-1], name=self._getFinalOutputOpName()) return g # Sometimes the tf graph contains a bunch of stuff that doesn't lead to the # output. TensorFrames does not like that, so we strip out the parts that # are not necessary for the computation at hand. def _stripGraph(self, tf_graph): gdef = tfx.strip_and_freeze_until([self._getFinalOutputOpName()], tf_graph) g = tf.Graph() # pylint: disable=invalid-name with g.as_default(): # pylint: disable=not-context-manager tf.import_graph_def(gdef, name='') return g def _getOriginalOutputTensorName(self): return USER_GRAPH_NAMESPACE + '/' + self.getOutputTensor().name def _getFinalOutputTensorName(self): return NEW_OUTPUT_PREFIX + '_' + self.getOutputTensor().name def _getFinalOutputOpName(self): return tfx.op_name(self._getFinalOutputTensorName()) def _convertOutputToImage(self, df, tfs_output_col, output_shape): assert len(output_shape) == 4, str(output_shape) + " does not have 4 dimensions" height = int(output_shape[1]) width = int(output_shape[2]) def to_image(orig_image, numeric_data): # Assume the returned image has float pixels but same #channels as input mode = imageIO.imageTypeByName('CV_32FC%d' % orig_image.nChannels) data = bytearray(np.array(numeric_data).astype(np.float32).tobytes()) nChannels = orig_image.nChannels return Row( origin="", mode=mode.ord, height=height, width=width, nChannels=nChannels, data=data) to_image_udf = udf(to_image, ImageSchema.imageSchema['image'].dataType) resDf = df.withColumn(self.getOutputCol(), to_image_udf(df[self.getInputCol()], df[tfs_output_col])) return resDf.drop(tfs_output_col) def _convertOutputToVector(self, df, tfs_output_col): """ Converts the output python list to MLlib Vector. """ return df\ .withColumn(self.getOutputCol(), JVMAPI.listToMLlibVectorUDF(df[tfs_output_col]))\ .drop(tfs_output_col)

45.423664

95

0.659188

2e09cfa56b53db6721fbed3306045c7d62615215

2,283

py

Python

felpy/backend/wpg_converters.py

twguest/FELPy

90ed49cc4bddd2188f3284168152d043144ae1fb

[ "Apache-2.0" ]

null

felpy/backend/wpg_converters.py

twguest/FELPy

90ed49cc4bddd2188f3284168152d043144ae1fb

[ "Apache-2.0" ]

null

felpy/backend/wpg_converters.py

twguest/FELPy

90ed49cc4bddd2188f3284168152d043144ae1fb

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import numpy as np from felpy.model.wavefront import Wavefront def complex_to_wpg(arr): ### converter """ converter function to transform complex wavefield into wpg style electric field array :param arr: complex wavefield array [x,y,t] (complex128 type) :returns new_arr: wpg style electric field array [nx,ny,nz,2] (float64) """ new_arr = np.zeros([arr.shape[0], arr.shape[1], arr.shape[2], 2]) new_arr[:,:,:,0] = arr.real new_arr[:,:,:,1] = arr.imag return new_arr def wavefront_from_array(cfr,nx,ny,nz,dx,dy,dz,ekev, pulse_duration = 40e-15, sigma = 4, **kwargs): """ function to produce a wpg wavefront object instance from a complex valued wavefront definition :param cfr: complex valued wavefield array [x,y,t] (complex128) :param nx: number of pixels along x-axis :param ny: number of pixels along y-axis :param nz: number of pixels along z-axis :param dx: pixels size along x-axis :param dy: pixels size along y-axis :param dz: pixels size along z-axis :param ekev: energy in kev :param pulse_duration: duration of the pulse (fwhm) in seconds, defaults to 40e-15 :param sigma: integer number of width multiples over which time-axis should be defined, defaults to 4 :returns: DESCRIPTION """ # Initialize empty wavefront. wfr = Wavefront() # Setup E-field. wfr.data.arrEhor = np.zeros(shape=(nx, ny, nz, 2)) wfr.data.arrEver = np.zeros(shape=(nx, ny, nz, 2)) wfr.params.wEFieldUnit = 'sqrt(W/mm^2)' wfr.params.photonEnergy = ekev * 1000 wfr.params.wDomain = 'time' wfr.params.Mesh.nSlices = nz wfr.params.Mesh.nx = nx wfr.params.Mesh.ny = ny wfr.params.Mesh.sliceMin = -pulse_duration*sigma / 2. wfr.params.Mesh.sliceMax = pulse_duration*sigma / 2. range_x = dx*nx range_y = dy*ny wfr.params.Mesh.xMin = -range_x / 2. wfr.params.Mesh.xMax = range_x / 2. wfr.params.Mesh.yMin = -range_y / 2. wfr.params.Mesh.yMax = range_y / 2. wfr.params.Rx = 2 wfr.params.Ry = 1 wfr.data.arrEhor = complex_to_wpg(cfr) wfr.set_electric_field_representation('f') wfr.custom_fields.update(**kwargs) return wfr

29.269231

105

0.653088

c3ddd993b2b9eb359c85c7ca7ea5971fb33df643

635

py

Python

figures/Dots.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

figures/Dots.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

figures/Dots.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

import numpy.random as rnd import random from matplotlib.patches import Circle from figures.Figure import Figures class DotsBuilder(Figures): @staticmethod def generate(xy, count=0): elements = [] for i in range(count): xy = rnd.uniform(2.8, 7.2, 2) radius = rnd.uniform(0.01, 0.1) elements.append(Circle(xy=xy, radius=radius)) return elements def generate_random(self, count=0): xy = rnd.uniform(2.8, 7.2, 2) if count == 0: count = random.randint(3, 10) return self.generate(xy, count=count)

23.518519

58

0.577953

01ea7f5ec8560871513426399bd579a41dd7e242

1,087

py

Python

SampleEnvSchema142/ArrayByte.py

ScreamingUdder/python-streaming-deserialisation-tools

9410445ca0f517c904745bd211af05cff40dec5d

[ "BSD-2-Clause" ]

null

SampleEnvSchema142/ArrayByte.py

ScreamingUdder/python-streaming-deserialisation-tools

9410445ca0f517c904745bd211af05cff40dec5d

[ "BSD-2-Clause" ]

null

SampleEnvSchema142/ArrayByte.py

ScreamingUdder/python-streaming-deserialisation-tools

9410445ca0f517c904745bd211af05cff40dec5d

[ "BSD-2-Clause" ]

null

# automatically generated, do not modify # namespace: import flatbuffers class ArrayByte(object): __slots__ = ['_tab'] # ArrayByte def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # ArrayByte def Value(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Int8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) return 0 # ArrayByte def ValueLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.VectorLen(o) return 0 def ArrayByteStart(builder): builder.StartObject(1) def ArrayByteAddValue(builder, value): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(value), 0) def ArrayByteStartValueVector(builder, numElems): return builder.StartVector(1, numElems, 1) def ArrayByteEnd(builder): return builder.EndObject()

32.939394

135

0.696412

bdd60322cd578ec69ca349f5076338adfba9ad1a

11,624

py

Python

tests/inference/qmhl_loss_test.py

zaqqwerty/qhbm-library

a4886adf91a3e2e2bc6f6b07a6e13ffe8cf0aa2e

[ "Apache-2.0" ]

19

2022-02-17T19:32:36.000Z

2022-03-31T01:07:19.000Z

tests/inference/qmhl_loss_test.py

zaqqwerty/qhbm-library

a4886adf91a3e2e2bc6f6b07a6e13ffe8cf0aa2e

[ "Apache-2.0" ]

37

2022-02-17T17:26:01.000Z

2022-03-31T21:32:16.000Z

tests/inference/qmhl_loss_test.py

zaqqwerty/qhbm-library

a4886adf91a3e2e2bc6f6b07a6e13ffe8cf0aa2e

[ "Apache-2.0" ]

8

2022-02-17T17:26:32.000Z

2022-03-18T12:11:33.000Z

# Copyright 2021 The QHBM Library Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for qhbmlib.inference.qmhl_loss""" import functools import math import cirq import sympy import tensorflow as tf import tensorflow_probability as tfp from qhbmlib import data from qhbmlib import inference from qhbmlib import models from tests import test_util class QMHLTest(tf.test.TestCase): """Tests for the QMHL loss and gradients.""" def setUp(self): """Initializes test objects.""" super().setUp() self.num_qubits_list = [1, 2] self.tf_random_seed = 4 self.tf_random_seed_alt = 7 self.tfp_seed = tf.constant([3, 6], tf.int32) # TODO(#190) self.num_samples = int(1e6) self.close_rtol = 2e-2 self.zero_atol = 2e-3 self.not_zero_atol = 2e-3 @test_util.eager_mode_toggle def test_self_qmhl(self): """Confirms known value of the QMHL loss of a model against itself.""" num_layers = 1 qmhl_wrapper = tf.function(inference.qmhl) for num_qubits in self.num_qubits_list: qubits = cirq.GridQubit.rect(1, num_qubits) data_h, data_infer = test_util.get_random_hamiltonian_and_inference( qubits, num_layers, f"data_objects_{num_qubits}", self.num_samples) model_h, model_infer = test_util.get_random_hamiltonian_and_inference( qubits, num_layers, f"hamiltonian_objects_{num_qubits}", self.num_samples, initializer_seed=self.tf_random_seed) # Set data equal to the model data_h.set_weights(model_h.get_weights()) actual_data = data.QHBMData(data_infer) # Trained loss is the entropy. expected_loss = model_infer.e_inference.entropy() # Since this is the optimum, derivatives should all be zero. expected_loss_derivative = [ tf.zeros_like(v) for v in model_h.trainable_variables ] with tf.GradientTape() as tape: actual_loss = qmhl_wrapper(actual_data, model_infer) actual_loss_derivative = tape.gradient(actual_loss, model_h.trainable_variables) self.assertAllClose(actual_loss, expected_loss, rtol=self.close_rtol) self.assertAllClose( actual_loss_derivative, expected_loss_derivative, atol=self.zero_atol) @test_util.eager_mode_toggle def test_hamiltonian_qmhl(self): """Tests derivatives of QMHL with respect to the model.""" # TODO(#171): Delta function seems generalizable. def delta_qmhl(k, var, actual_data, model_qhbm, delta): """Calculates the qmhl loss with the kth entry of `var` perturbed.""" num_elts = tf.size(var) old_value = var.read_value() var.assign(old_value + delta * tf.one_hot(k, num_elts, 1.0, 0.0)) delta_loss = inference.qmhl(actual_data, model_qhbm) var.assign(old_value) return delta_loss qmhl_wrapper = tf.function(inference.qmhl) for num_qubits in self.num_qubits_list: qubits = cirq.GridQubit.rect(1, num_qubits) num_layers = 2 _, data_qhbm = test_util.get_random_hamiltonian_and_inference( qubits, num_layers, f"data_objects_{num_qubits}", self.num_samples, initializer_seed=self.tf_random_seed, ebm_seed=self.tfp_seed) actual_data = data.QHBMData(data_qhbm) model_h, model_qhbm = test_util.get_random_hamiltonian_and_inference( qubits, num_layers, f"model_objects_{num_qubits}", self.num_samples, initializer_seed=self.tf_random_seed_alt, ebm_seed=self.tfp_seed) # Make sure variables are trainable self.assertGreater(len(model_h.trainable_variables), 1) with tf.GradientTape() as tape: actual_loss = qmhl_wrapper(actual_data, model_qhbm) actual_derivative = tape.gradient(actual_loss, model_h.trainable_variables) expected_derivative = test_util.approximate_gradient( functools.partial(qmhl_wrapper, actual_data, model_qhbm), model_h.trainable_variables) # Changing model parameters is working if finite difference derivatives # are non-zero. Also confirms that model_h and data_h are different. tf.nest.map_structure( lambda x: self.assertAllGreater(tf.abs(x), self.not_zero_atol), expected_derivative) self.assertAllClose( actual_derivative, expected_derivative, rtol=self.close_rtol) def test_loss_value_x_rot(self): """Confirms correct values for a single qubit X rotation QHBM. We use a data state which is a Y rotation of an initially diagonal density operator. The QHBM is a Bernoulli latent state with X rotation QNN. See the colab notebook at the following link for derivations: https://colab.research.google.com/drive/14987JCMju_8AVvvVoojwe6hA7Nlw-Dhe?usp=sharing Since each qubit is independent, the loss is the sum over the individual qubit losses, and the gradients are the the per-qubit gradients. """ ebm_const = 1.0 q_const = math.pi for num_qubits in self.num_qubits_list: # EBM ebm_init = tf.keras.initializers.RandomUniform( minval=ebm_const / 4, maxval=ebm_const, seed=self.tf_random_seed) actual_energy = models.BernoulliEnergy(list(range(num_qubits)), ebm_init) e_infer = inference.BernoulliEnergyInference( actual_energy, self.num_samples, initial_seed=self.tfp_seed) # QNN qubits = cirq.GridQubit.rect(1, num_qubits) r_symbols = [sympy.Symbol(f"phi_{n}") for n in range(num_qubits)] r_circuit = cirq.Circuit( cirq.rx(r_s)(q) for r_s, q in zip(r_symbols, qubits)) qnn_init = tf.keras.initializers.RandomUniform( minval=q_const / 4, maxval=q_const, seed=self.tf_random_seed) actual_circuit = models.DirectQuantumCircuit(r_circuit, qnn_init) q_infer = inference.AnalyticQuantumInference(actual_circuit) qhbm_infer = inference.QHBM(e_infer, q_infer) model = qhbm_infer.modular_hamiltonian # Confirm qhbm_model QHBM test_thetas = model.energy.trainable_variables[0] test_phis = model.circuit.trainable_variables[0] with tf.GradientTape() as log_partition_tape: actual_log_partition = qhbm_infer.e_inference.log_partition() expected_log_partition = tf.reduce_sum( tf.math.log(2 * tf.math.cosh(test_thetas))) self.assertAllClose( actual_log_partition, expected_log_partition, rtol=self.close_rtol) # Confirm qhbm_model modular Hamiltonian for 1 qubit case if num_qubits == 1: actual_dm = inference.density_matrix(model) actual_log_dm = tf.linalg.logm(actual_dm) actual_ktp = -actual_log_dm - tf.eye( 2, dtype=tf.complex64) * tf.cast(actual_log_partition, tf.complex64) a = (test_thetas[0] * tf.math.cos(test_phis[0])).numpy() + 0j b = 1j * (test_thetas[0] * tf.math.sin(test_phis[0])).numpy() c = -1j * (test_thetas[0] * tf.math.sin(test_phis[0])).numpy() d = -(test_thetas[0] * tf.math.cos(test_phis[0])).numpy() + 0j expected_ktp = tf.constant([[a, b], [c, d]], dtype=tf.complex64) self.assertAllClose(actual_ktp, expected_ktp, rtol=self.close_rtol) # Build target data alphas = tf.random.uniform([num_qubits], -q_const, q_const, tf.float32, self.tf_random_seed) y_rot = cirq.Circuit( cirq.ry(r.numpy())(q) for r, q in zip(alphas, qubits)) data_circuit = models.DirectQuantumCircuit(y_rot) data_q_infer = inference.AnalyticQuantumInference(data_circuit) data_probs = tf.random.uniform([num_qubits], dtype=tf.float32, seed=self.tf_random_seed) data_samples = tfp.distributions.Bernoulli( probs=1 - data_probs, dtype=tf.int8).sample( self.num_samples, seed=self.tfp_seed) # Load target data into a QuantumData class class FixedData(data.QuantumData): """Contains a fixed quantum data set.""" def __init__(self, samples, q_infer): """Initializes a FixedData.""" self.samples = samples self.q_infer = q_infer def expectation(self, observable): """Averages over the fixed quantum data set.""" raw_expectations = self.q_infer.expectation(self.samples, observable) return tf.math.reduce_mean(raw_expectations) actual_data = FixedData(data_samples, data_q_infer) qmhl_wrapper = tf.function(inference.qmhl) with tf.GradientTape() as loss_tape: actual_loss = qmhl_wrapper(actual_data, qhbm_infer) # TODO(zaqqwerty): add way to use a log QHBM as observable on states expected_expectation = tf.reduce_sum(test_thetas * (2 * data_probs - 1) * tf.math.cos(alphas) * tf.math.cos(test_phis)) with tf.GradientTape() as expectation_tape: actual_expectation = actual_data.expectation( qhbm_infer.modular_hamiltonian) self.assertAllClose(actual_expectation, expected_expectation, self.close_rtol) expected_loss = expected_expectation + expected_log_partition self.assertAllClose(actual_loss, expected_loss, rtol=self.close_rtol) expected_log_partition_grad = tf.math.tanh(test_thetas) actual_log_partition_grad = log_partition_tape.gradient( actual_log_partition, test_thetas) self.assertAllClose( actual_log_partition_grad, expected_log_partition_grad, rtol=self.close_rtol) expected_expectation_thetas_grad = ( 2 * data_probs - 1) * tf.math.cos(alphas) * tf.math.cos(test_phis) expected_expectation_phis_grad = -test_thetas * ( 2 * data_probs - 1) * tf.math.cos(alphas) * tf.math.sin(test_phis) (actual_expectation_thetas_grad, actual_expectation_phis_grad) = expectation_tape.gradient( actual_expectation, (test_thetas, test_phis)) self.assertAllClose( actual_expectation_thetas_grad, expected_expectation_thetas_grad, rtol=self.close_rtol) self.assertAllClose( actual_expectation_phis_grad, expected_expectation_phis_grad, rtol=self.close_rtol) actual_thetas_grads, actual_phis_grads = loss_tape.gradient( actual_loss, (test_thetas, test_phis)) expected_thetas_grads = ( expected_expectation_thetas_grad + expected_log_partition_grad) expected_phis_grads = expected_expectation_phis_grad self.assertAllClose( actual_thetas_grads, expected_thetas_grads, rtol=self.close_rtol) self.assertAllClose( actual_phis_grads, expected_phis_grads, rtol=self.close_rtol) if __name__ == "__main__": print("Running qmhl_loss_test.py ...") tf.test.main()

41.81295

89

0.678768

859b921d912edbaee5dddf15826f6e4ffc5e7355

6,668

py

Python

tests/flytekit/unit/core/test_workflows.py

frsann/flytekit

6c032035563ae645b0b93558b3fe3362080057ea

[ "Apache-2.0" ]

null

tests/flytekit/unit/core/test_workflows.py

frsann/flytekit

6c032035563ae645b0b93558b3fe3362080057ea

[ "Apache-2.0" ]

null

tests/flytekit/unit/core/test_workflows.py

frsann/flytekit

6c032035563ae645b0b93558b3fe3362080057ea

[ "Apache-2.0" ]

null

import typing from collections import OrderedDict import pytest from flytekit.common.exceptions.user import FlyteValidationException, FlyteValueException from flytekit.common.translator import get_serializable from flytekit.core import context_manager from flytekit.core.condition import conditional from flytekit.core.context_manager import Image, ImageConfig from flytekit.core.task import task from flytekit.core.workflow import WorkflowFailurePolicy, WorkflowMetadata, WorkflowMetadataDefaults, workflow default_img = Image(name="default", fqn="test", tag="tag") serialization_settings = context_manager.SerializationSettings( project="project", domain="domain", version="version", env=None, image_config=ImageConfig(default_image=default_img, images=[default_img]), ) def test_metadata_values(): with pytest.raises(FlyteValidationException): WorkflowMetadata(on_failure=0) wm = WorkflowMetadata(on_failure=WorkflowFailurePolicy.FAIL_IMMEDIATELY) assert wm.on_failure == WorkflowFailurePolicy.FAIL_IMMEDIATELY def test_default_metadata_values(): with pytest.raises(FlyteValidationException): WorkflowMetadataDefaults(3) wm = WorkflowMetadataDefaults(interruptible=False) assert wm.interruptible is False def test_workflow_values(): @task def t1(a: int) -> typing.NamedTuple("OutputsBC", t1_int_output=int, c=str): a = a + 2 return a, "world-" + str(a) @workflow(interruptible=True, failure_policy=WorkflowFailurePolicy.FAIL_AFTER_EXECUTABLE_NODES_COMPLETE) def wf(a: int) -> (str, str): x, y = t1(a=a) u, v = t1(a=x) return y, v wf_spec = get_serializable(OrderedDict(), serialization_settings, wf) assert wf_spec.template.metadata_defaults.interruptible assert wf_spec.template.metadata.on_failure == 1 def test_default_values(): @task def t() -> bool: return True @task def f() -> bool: return False @workflow def wf(a: bool = True) -> bool: return conditional("bool").if_(a.is_true()).then(t()).else_().then(f()) # type: ignore assert wf() is True assert wf(a=False) is False def test_list_output_wf(): @task def t1(a: int) -> int: a = a + 5 return a @workflow def list_output_wf() -> typing.List[int]: v = [] for i in range(2): v.append(t1(a=i)) return v x = list_output_wf() assert x == [5, 6] def test_sub_wf_single_named_tuple(): nt = typing.NamedTuple("SingleNamedOutput", named1=int) @task def t1(a: int) -> nt: a = a + 2 return nt(a) @workflow def subwf(a: int) -> nt: return t1(a=a) @workflow def wf(b: int) -> nt: out = subwf(a=b) return t1(a=out.named1) x = wf(b=3) assert x == (7,) def test_sub_wf_multi_named_tuple(): nt = typing.NamedTuple("Multi", named1=int, named2=int) @task def t1(a: int) -> nt: a = a + 2 return nt(a, a) @workflow def subwf(a: int) -> nt: return t1(a=a) @workflow def wf(b: int) -> nt: out = subwf(a=b) return t1(a=out.named1) x = wf(b=3) assert x == (7, 7) def test_unexpected_outputs(): @task def t1(a: int) -> int: a = a + 5 return a @workflow def no_outputs_wf(): return t1(a=3) # Should raise an exception because the workflow returns something when it shouldn't with pytest.raises(FlyteValueException): no_outputs_wf() # Should raise an exception because it doesn't return something when it should with pytest.raises(AssertionError): @workflow def one_output_wf() -> int: # noqa t1(a=3) def test_wf_no_output(): @task def t1(a: int) -> int: a = a + 5 return a @workflow def no_outputs_wf(): t1(a=3) assert no_outputs_wf() is None def test_wf_nested_comp(): @task def t1(a: int) -> int: a = a + 5 return a @workflow def outer() -> typing.Tuple[int, int]: # You should not do this. This is just here for testing. @workflow def wf2() -> int: return t1(a=5) return t1(a=3), wf2() assert (8, 10) == outer() entity_mapping = OrderedDict() model_wf = get_serializable(entity_mapping, serialization_settings, outer) assert len(model_wf.template.interface.outputs) == 2 assert len(model_wf.template.nodes) == 2 assert model_wf.template.nodes[1].workflow_node is not None sub_wf = model_wf.sub_workflows[0] assert len(sub_wf.nodes) == 1 assert sub_wf.nodes[0].id == "wf2-n0" assert sub_wf.nodes[0].task_node.reference_id.name == "test_workflows.t1" @task def add_5(a: int) -> int: a = a + 5 return a @workflow def simple_wf() -> int: return add_5(a=1) @workflow def my_wf_example(a: int) -> typing.Tuple[int, int]: """example Workflows can have inputs and return outputs of simple or complex types. :param a: input a :return: outputs """ x = add_5(a=a) # You can use outputs of a previous task as inputs to other nodes. z = add_5(a=x) # You can call other workflows from within this workflow d = simple_wf() # You can add conditions that can run on primitive types and execute different branches e = conditional("bool").if_(a == 5).then(add_5(a=d)).else_().then(add_5(a=z)) # Outputs of the workflow have to be outputs returned by prior nodes. # No outputs and single or multiple outputs are supported return x, e def test_all_node_types(): assert my_wf_example(a=1) == (6, 16) entity_mapping = OrderedDict() model_wf = get_serializable(entity_mapping, serialization_settings, my_wf_example) assert len(model_wf.template.interface.outputs) == 2 assert len(model_wf.template.nodes) == 4 assert model_wf.template.nodes[2].workflow_node is not None sub_wf = model_wf.sub_workflows[0] assert len(sub_wf.nodes) == 1 assert sub_wf.nodes[0].id == "n0" assert sub_wf.nodes[0].task_node.reference_id.name == "test_workflows.add_5" def test_wf_docstring(): model_wf = get_serializable(OrderedDict(), serialization_settings, my_wf_example) assert len(model_wf.template.interface.outputs) == 2 assert model_wf.template.interface.outputs["o0"].description == "outputs" assert model_wf.template.interface.outputs["o1"].description == "outputs" assert len(model_wf.template.interface.inputs) == 1 assert model_wf.template.interface.inputs["a"].description == "input a"

25.745174

110

0.657618

a75c94205ca5f6a9ad5dbf0bd477f45d6852ca87

1,119

py

Python

shoobsblog/users/tests/test_forms.py

mattshoobs/AgileStoryboard

80f14192702330c51b2ff130b25b4bc6d0c326a0

[ "Apache-1.1" ]

null

shoobsblog/users/tests/test_forms.py

mattshoobs/AgileStoryboard

80f14192702330c51b2ff130b25b4bc6d0c326a0

[ "Apache-1.1" ]

null

shoobsblog/users/tests/test_forms.py

mattshoobs/AgileStoryboard

80f14192702330c51b2ff130b25b4bc6d0c326a0

[ "Apache-1.1" ]

null

import pytest from shoobsblog.users.forms import UserCreationForm from shoobsblog.users.tests.factories import UserFactory pytestmark = pytest.mark.django_db class TestUserCreationForm: def test_clean_username(self): # A user with proto_user params does not exist yet. proto_user = UserFactory.build() form = UserCreationForm( { "username": proto_user.username, "password1": proto_user._password, "password2": proto_user._password, } ) assert form.is_valid() assert form.clean_username() == proto_user.username # Creating a user. form.save() # The user with proto_user params already exists, # hence cannot be created. form = UserCreationForm( { "username": proto_user.username, "password1": proto_user._password, "password2": proto_user._password, } ) assert not form.is_valid() assert len(form.errors) == 1 assert "username" in form.errors

27.292683

59

0.595174

68cf4b5a4056e02ad1b141e38907bfb306a13b0d

328

py

Python

auto_erpnext/config/docs.py

faztp12/auto_erpnext

a38f28cb3c4bae541426bd7375a68d01bb360d66

[ "MIT" ]

2

2020-12-28T16:34:34.000Z

2021-01-15T13:35:44.000Z

auto_erpnext/config/docs.py

faztp12/auto_erpnext

a38f28cb3c4bae541426bd7375a68d01bb360d66

[ "MIT" ]

null

auto_erpnext/config/docs.py

faztp12/auto_erpnext

a38f28cb3c4bae541426bd7375a68d01bb360d66

[ "MIT" ]

4

2018-12-20T10:50:41.000Z

2020-09-13T16:46:58.000Z

""" Configuration for docs """ # source_link = "https://github.com/[org_name]/auto_erpnext" # docs_base_url = "https://[org_name].github.io/auto_erpnext" # headline = "App that does everything" # sub_heading = "Yes, you got that right the first time, everything" def get_context(context): context.brand_html = "Auto Erpnext"

27.333333

68

0.731707

1c3c16f2f9c12a308f80775d111741f371af1209

1,044

py

Python

python/bitcoin_101/bitcoin_client.py

fcracker79/python_bitcoin_101

3af1b1e1b817d9676981f3c6c87d93064ac8febe

[ "MIT" ]

1

2018-09-26T18:10:36.000Z

python/bitcoin_101/bitcoin_client.py

fcracker79/python_bitcoin_101

3af1b1e1b817d9676981f3c6c87d93064ac8febe

[ "MIT" ]

null

python/bitcoin_101/bitcoin_client.py

fcracker79/python_bitcoin_101

3af1b1e1b817d9676981f3c6c87d93064ac8febe

[ "MIT" ]

null

import base64 from bitcoin_101 import bitjson_dumps, bitjson_loads import requests class BitcoinClient: def __init__(self): btcd_auth_header = b'Basic ' + base64.b64encode(b'bitcoin:qwertyuiop') self.btcd_headers = {'content-type': 'application/json', 'Authorization': btcd_auth_header} def __getattr__(self, item): if item == '_call': return self._call return lambda *a, **kw: self._call(item, *a, **kw) def _call(self, method: str, *params, expect_json: bool=True): payload = bitjson_dumps({ 'method': method, 'params': params, 'jsonrpc': '2.0', 'id': 0, }) resp = requests.post('http://localhost:18332', data=payload, headers=self.btcd_headers) content = resp.text if expect_json: content = bitjson_loads(content) if content['error']: raise ValueError('Error: {}'.format(content['error'])) return content['result'] return content

30.705882

99

0.59387

5282bca7fce8729741798cb0dce38a5acd071213

1,220

py

Python

data/youtube/download.py

tagny/iLID

38f5dcae0dc84fd9b78e170748aa38cd8f524c70

[ "MIT" ]

90

2016-02-19T12:37:20.000Z

2022-02-25T19:52:46.000Z

data/youtube/download.py

vyas97/iLID

4d124b76fdbc37fbafd12e860281a4bc3ddf87d9

[ "MIT" ]

7

2017-03-24T04:12:09.000Z

2020-06-16T11:27:54.000Z

data/youtube/download.py

vyas97/iLID

4d124b76fdbc37fbafd12e860281a4bc3ddf87d9

[ "MIT" ]

31

2016-02-01T12:52:51.000Z

2021-08-16T04:27:59.000Z

import yaml import subprocess import os def read_yaml(file_name): with open(file_name, "r") as f: return yaml.load(f) def download(language, source, source_name, source_type): output_path = "{0}/{1}".format(language, source_name) if os.path.exists(output_path): print "skipping {0} {1} because the target folder already exists".format(source_type, source_name) else: print "downloading {0} {1}".format(source_type, source_name) command = """youtube-dl -i --max-downloads 500 --extract-audio --audio-format mp3 {0} -o "{1}/{2}/%(title)s.%(ext)s" """.format(source,language,source_name) subprocess.call(command, shell=True) def download_user(language, user): user_selector = "ytuser:%s" % user download(language, user_selector, user, "user") def download_playlist(language, playlist_name, playlist_id): download(language, playlist_id, playlist_name, "playlist") if __name__ == '__main__': sources = read_yaml("sources.yml") for language, categories in sources.items(): for user in categories["users"]: download_user(language, user) #for playlist_name, playlist_id in categories["playlists"].items(): # download_playlist(language, playlist_name, playlist_id)

35.882353

160

0.722131

5631814b4c9c0c56f06fb651305b7cf15ec952b4

2,906

py

Python

tests/test_source_file/test_source_file.py

ethanio12345/OpenMC

3b0c044974c59773dac4e3ce261a87e18fc53de5

[ "MIT" ]

null

tests/test_source_file/test_source_file.py

ethanio12345/OpenMC

3b0c044974c59773dac4e3ce261a87e18fc53de5

[ "MIT" ]

null

tests/test_source_file/test_source_file.py

ethanio12345/OpenMC

3b0c044974c59773dac4e3ce261a87e18fc53de5

[ "MIT" ]

null

#!/usr/bin/env python import glob import os import sys sys.path.insert(0, os.pardir) from testing_harness import * settings1="""<?xml version="1.0"?> <settings> <state_point batches="10" /> <source_point separate="true" /> <eigenvalue> <batches>10</batches> <inactive>5</inactive> <particles>1000</particles> </eigenvalue> <source> <space type="box"> <parameters>-4 -4 -4 4 4 4</parameters> </space> </source> </settings> """ settings2 = """<?xml version="1.0"?> <settings> <eigenvalue> <batches>10</batches> <inactive>5</inactive> <particles>1000</particles> </eigenvalue> <source> <file> source.10.{0} </file> </source> </settings> """ class SourceFileTestHarness(TestHarness): def execute_test(self): """Run OpenMC with the appropriate arguments and check the outputs.""" try: self._run_openmc() self._test_output_created() self._run_openmc_restart() results = self._get_results() self._write_results(results) self._compare_results() finally: self._cleanup() def update_results(self): """Update the results_true using the current version of OpenMC.""" try: self._run_openmc() self._test_output_created() self._run_openmc_restart() results = self._get_results() self._write_results(results) self._overwrite_results() finally: self._cleanup() def _test_output_created(self): """Make sure statepoint and source files have been created.""" statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name)) assert len(statepoint) == 1, 'Either multiple or no statepoint files ' \ 'exist.' assert statepoint[0].endswith('h5'), \ 'Statepoint file is not a HDF5 file.' source = glob.glob(os.path.join(os.getcwd(), 'source.10.*')) assert len(source) == 1, 'Either multiple or no source files exist.' assert source[0].endswith('h5'), \ 'Source file is not a HDF5 file.' def _run_openmc_restart(self): # Get the name of the source file. source = glob.glob(os.path.join(os.getcwd(), 'source.10.*')) # Write the new settings.xml file. with open('settings.xml','w') as fh: fh.write(settings2.format(source[0].split('.')[-1])) # Run OpenMC. self._run_openmc() def _cleanup(self): TestHarness._cleanup(self) output = glob.glob(os.path.join(os.getcwd(), 'source.*')) for f in output: if os.path.exists(f): os.remove(f) with open('settings.xml','w') as fh: fh.write(settings1) if __name__ == '__main__': harness = SourceFileTestHarness('statepoint.10.h5') harness.main()

28.213592

80

0.593255

89749c3e40460646ee17cfadfc9ba17fc593a687

1,977

py

Python

src/Graphing/image_to_plot.py

njanirudh/R-D-HBRS

220777628406366179106069eb9ce211e50e7543

[ "MIT" ]

3

2019-08-27T22:00:56.000Z

2020-04-15T06:31:39.000Z

src/Graphing/image_to_plot.py

njanirudh/R-D-HBRS

220777628406366179106069eb9ce211e50e7543

[ "MIT" ]

1

2019-10-29T13:21:21.000Z

2019-10-29T14:01:28.000Z

src/Graphing/image_to_plot.py

njanirudh/Research-Development-HBRS

220777628406366179106069eb9ce211e50e7543

[ "MIT" ]

null

from pylab import * from mpl_toolkits.mplot3d import Axes3D from matplotlib.cbook import get_sample_data from matplotlib._png import read_png import matplotlib.pyplot as plt import numpy as np import pandas import pandas as pd import sklearn.preprocessing as preproc fig = plt.figure() ax = fig.add_subplot(111, projection='3d') X_val = [] Y_val = [] Z_val = [] with open('/home/nj/HBRS/RnD/Research-Development-HBRS/reports/drawer_handle_grasp - drawer.csv') as csv_file: # with open('/home/nj/HBRS/RnD/Research-Development-HBRS/reports/drawer_handle_grasp - fridge.csv') as csv_file: csv_reader = pd.read_csv(csv_file, delimiter=',') # print(csv_reader.head()) # print(csv_reader['handle_x'].tolist()) X_val = csv_reader['handle_x'].tolist() Y_val = csv_reader['handle_y'].tolist() Z_val = csv_reader['handle_z'].tolist() marker = np.array(csv_reader['Success'].tolist()) # marker[np.where(marker == 0)] = 'red' # marker[np.where(marker == 1)] = 'green' color_list = ['r' if i == 0 else 'g' for i in marker] # print(color_list) #------------------------------------------------------ fn = get_sample_data("/home/nj/HBRS/RnD/Research-Development-HBRS/images/frcnn.png", asfileobj=False) img = read_png(fn) x_lim, y_lim = ogrid[0:img.shape[0], 0:img.shape[1]] # lower_x, upper_x = min(X_val),max(X_val) # x_norm = np.array([lower_x + (upper_x - lower_x) * x for x in x_lim]) y_norm = preproc.minmax_scale(x_lim,(min(Y_val),max(Y_val))) z_norm = preproc.minmax_scale(np.array(y_lim).T,(min(Z_val),max(Z_val))) # print(min(y_norm),max(y_norm)) # print(min(Z_val),max(Z_val)) # print(z_norm) ax = gca(projection='3d') ax.plot_surface(np.atleast_2d(0.80), y_norm, z_norm.T, rstride=10, cstride=10, facecolors=img) #------------------------------------------------------ ax.scatter(X_val, Y_val, Z_val,color=color_list) ax.set_xlabel('X Distance - meters') ax.set_ylabel('Y Distance - meters') ax.set_zlabel('Z Distance - meters') plt.show()

33.508475

112

0.679312

3b534eaeb07df4e83f6454fec30f2f280d0634c5

1,779

py

Python

modules/m_wolframalpha.py

LunaNyan/Luna_Libertin_Discord_Bot

db7def99602b65b682a128430118e3941aabcd6e

[ "MIT" ]

8

2019-08-17T17:30:10.000Z

2021-07-21T01:59:32.000Z

modules/m_wolframalpha.py

LunaNyan/Luna_Libertin_Discord_Bot

db7def99602b65b682a128430118e3941aabcd6e

[ "MIT" ]

2

2019-07-16T10:01:39.000Z

2019-12-30T11:30:29.000Z

modules/m_wolframalpha.py

LunaNyan/Luna_Libertin_Discord_Bot

db7def99602b65b682a128430118e3941aabcd6e

[ "MIT" ]

3

2019-08-20T22:43:16.000Z

2020-07-13T23:24:08.000Z

import sys, requests, wolframalpha, xmltodict if __name__=="__main__": print("FATAL : Run this bot from right way.") sys.exit(1) client = "" def load(conf): global client client = wolframalpha.Client(conf.get("wolframalpha", "appid")) def wa_calc(query): res = client.query(query) answer = next(res.results).text return answer def wa_img(conf, query): url = 'http://api.wolframalpha.com/v1/query?input=%s&appid=%s' %(query, conf.get("wolframalpha", "appid")) res = requests.get(url) xml_content = res.content dict_content = xmltodict.parse(xml_content) results = [] pods = dict_content["queryresult"]["pod"] try: plot_url = pods["subpod"]["img"]["@src"] except: try: plot_url = pods[3]["subpod"][0]["img"]["@src"] except: plot_url = pods[2]["subpod"]["img"]["@src"] img_data = requests.get(plot_url).content with open('wa_temp_img.gif', 'wb') as handler: handler.write(img_data) return 'wa_temp_img.gif' def captcha(conf, query): url = 'http://api.wolframalpha.com/v1/query?input=%s&appid=%s' %(query, conf.get("wolframalpha", "appid")) res = requests.get(url) xml_content = res.content dict_content = xmltodict.parse(xml_content) results = [] for pod in dict_content['queryresult']['pod']: result = {} title = pod['@title'] link = pod['subpod']['img']['@src'] data = pod['subpod']['img']['@alt'] result['caption'] = title result['image'] = link result['data'] = data results.append(result) img_data = requests.get(result['image']).content with open('wa_temp_img.gif', 'wb') as handler: handler.write(img_data) return 'wa_temp_img.gif'

32.345455

110

0.611017

cb7d34d1a1eb767227b03d4c2d5f535f41f883fe

4,143

py

Python

google/bigtable/admin/v2/bigtable-admin-v2-py/google/cloud/bigtable_admin_v2/types/__init__.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/bigtable/admin/v2/bigtable-admin-v2-py/google/cloud/bigtable_admin_v2/types/__init__.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/bigtable/admin/v2/bigtable-admin-v2-py/google/cloud/bigtable_admin_v2/types/__init__.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

4

2021-01-28T23:25:45.000Z

2021-08-30T01:55:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from .bigtable_instance_admin import ( CreateAppProfileRequest, CreateClusterMetadata, CreateClusterRequest, CreateInstanceMetadata, CreateInstanceRequest, DeleteAppProfileRequest, DeleteClusterRequest, DeleteInstanceRequest, GetAppProfileRequest, GetClusterRequest, GetInstanceRequest, ListAppProfilesRequest, ListAppProfilesResponse, ListClustersRequest, ListClustersResponse, ListInstancesRequest, ListInstancesResponse, PartialUpdateInstanceRequest, UpdateAppProfileMetadata, UpdateAppProfileRequest, UpdateClusterMetadata, UpdateInstanceMetadata, ) from .bigtable_table_admin import ( CheckConsistencyRequest, CheckConsistencyResponse, CreateBackupMetadata, CreateBackupRequest, CreateTableFromSnapshotMetadata, CreateTableFromSnapshotRequest, CreateTableRequest, DeleteBackupRequest, DeleteSnapshotRequest, DeleteTableRequest, DropRowRangeRequest, GenerateConsistencyTokenRequest, GenerateConsistencyTokenResponse, GetBackupRequest, GetSnapshotRequest, GetTableRequest, ListBackupsRequest, ListBackupsResponse, ListSnapshotsRequest, ListSnapshotsResponse, ListTablesRequest, ListTablesResponse, ModifyColumnFamiliesRequest, OptimizeRestoredTableMetadata, RestoreTableMetadata, RestoreTableRequest, SnapshotTableMetadata, SnapshotTableRequest, UpdateBackupRequest, ) from .common import ( OperationProgress, StorageType, ) from .instance import ( AppProfile, Cluster, Instance, ) from .table import ( Backup, BackupInfo, ColumnFamily, EncryptionInfo, GcRule, RestoreInfo, Snapshot, Table, RestoreSourceType, ) __all__ = ( 'CreateAppProfileRequest', 'CreateClusterMetadata', 'CreateClusterRequest', 'CreateInstanceMetadata', 'CreateInstanceRequest', 'DeleteAppProfileRequest', 'DeleteClusterRequest', 'DeleteInstanceRequest', 'GetAppProfileRequest', 'GetClusterRequest', 'GetInstanceRequest', 'ListAppProfilesRequest', 'ListAppProfilesResponse', 'ListClustersRequest', 'ListClustersResponse', 'ListInstancesRequest', 'ListInstancesResponse', 'PartialUpdateInstanceRequest', 'UpdateAppProfileMetadata', 'UpdateAppProfileRequest', 'UpdateClusterMetadata', 'UpdateInstanceMetadata', 'CheckConsistencyRequest', 'CheckConsistencyResponse', 'CreateBackupMetadata', 'CreateBackupRequest', 'CreateTableFromSnapshotMetadata', 'CreateTableFromSnapshotRequest', 'CreateTableRequest', 'DeleteBackupRequest', 'DeleteSnapshotRequest', 'DeleteTableRequest', 'DropRowRangeRequest', 'GenerateConsistencyTokenRequest', 'GenerateConsistencyTokenResponse', 'GetBackupRequest', 'GetSnapshotRequest', 'GetTableRequest', 'ListBackupsRequest', 'ListBackupsResponse', 'ListSnapshotsRequest', 'ListSnapshotsResponse', 'ListTablesRequest', 'ListTablesResponse', 'ModifyColumnFamiliesRequest', 'OptimizeRestoredTableMetadata', 'RestoreTableMetadata', 'RestoreTableRequest', 'SnapshotTableMetadata', 'SnapshotTableRequest', 'UpdateBackupRequest', 'OperationProgress', 'StorageType', 'AppProfile', 'Cluster', 'Instance', 'Backup', 'BackupInfo', 'ColumnFamily', 'EncryptionInfo', 'GcRule', 'RestoreInfo', 'Snapshot', 'Table', 'RestoreSourceType', )

26.056604

74

0.732078

2b35e5686a7f62feaae4462a96f11f2cebcb570f

323

py

Python

tests/test_lyrics_prediction_file.py

castillogo/lyrics_project

59855bfe91b5a41a68d52540a7ca6a8494a94ded

[ "MIT" ]

null

tests/test_lyrics_prediction_file.py

castillogo/lyrics_project

59855bfe91b5a41a68d52540a7ca6a8494a94ded

[ "MIT" ]

1

2021-11-15T17:50:49.000Z

tests/test_lyrics_prediction_file.py

castillogo/lyrics_project

59855bfe91b5a41a68d52540a7ca6a8494a94ded

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Mar 16 15:59:40 2020 @author: casti """ import pytest from lyrics_prediction_file import print_evaluations Y_TEST = [1, 2, 3, 4, 5, 6, 7, 8, 9] Y_PRED = [1, 2, 3, 4, 5, 6, 7, 8, 9] def test_print_evaluations(): assert print_evaluations(Y_TEST, Y_PRED, 'Randomforest') > 0

20.1875

64

0.656347

3227f5ea7b7d29ea7e4a9ac76db7d798389a8570

4,052

py

Python

render/render_depth.py

StefanieStoppel/pcn

71b17791380798d349c18f96514099183d89836e

[ "MIT" ]

9

2021-04-10T07:42:36.000Z

2022-03-27T15:21:38.000Z

render/render_depth.py

StefanieStoppel/pcn

71b17791380798d349c18f96514099183d89836e

[ "MIT" ]

null

render/render_depth.py

StefanieStoppel/pcn

71b17791380798d349c18f96514099183d89836e

[ "MIT" ]

2

2021-04-21T01:26:31.000Z

2021-05-23T13:31:10.000Z

# Author: Wentao Yuan (wyuan1@cs.cmu.edu) 05/31/2018 import bpy import mathutils import numpy as np import os import sys import time # Usage: blender -b -P render_depth.py [ShapeNet directory] [model list] [output directory] [num scans per model] def random_pose(): angle_x = np.random.uniform() * 2 * np.pi angle_y = np.random.uniform() * 2 * np.pi angle_z = np.random.uniform() * 2 * np.pi Rx = np.array([[1, 0, 0], [0, np.cos(angle_x), -np.sin(angle_x)], [0, np.sin(angle_x), np.cos(angle_x)]]) Ry = np.array([[np.cos(angle_y), 0, np.sin(angle_y)], [0, 1, 0], [-np.sin(angle_y), 0, np.cos(angle_y)]]) Rz = np.array([[np.cos(angle_z), -np.sin(angle_z), 0], [np.sin(angle_z), np.cos(angle_z), 0], [0, 0, 1]]) R = np.dot(Rz, np.dot(Ry, Rx)) # Set camera pointing to the origin and 1 unit away from the origin t = np.expand_dims(R[:, 2], 1) pose = np.concatenate([np.concatenate([R, t], 1), [[0, 0, 0, 1]]], 0) return pose def setup_blender(width, height, focal_length): # camera camera = bpy.data.objects['Camera'] camera.data.angle = np.arctan(width / 2 / focal_length) * 2 # render layer scene = bpy.context.scene scene.render.filepath = 'buffer' scene.render.image_settings.color_depth = '16' scene.render.resolution_percentage = 100 scene.render.resolution_x = width scene.render.resolution_y = height # compositor nodes scene.use_nodes = True tree = scene.node_tree rl = tree.nodes.new('CompositorNodeRLayers') output = tree.nodes.new('CompositorNodeOutputFile') output.base_path = '' output.format.file_format = 'OPEN_EXR' tree.links.new(rl.outputs['Depth'], output.inputs[0]) # remove default cube bpy.data.objects['Cube'].select = True bpy.ops.object.delete() return scene, camera, output if __name__ == '__main__': model_dir = sys.argv[-4] list_path = sys.argv[-3] output_dir = sys.argv[-2] num_scans = int(sys.argv[-1]) width = 160 height = 120 focal = 100 scene, camera, output = setup_blender(width, height, focal) intrinsics = np.array([[focal, 0, width / 2], [0, focal, height / 2], [0, 0, 1]]) with open(os.path.join(list_path)) as file: model_list = [line.strip() for line in file] open('blender.log', 'w+').close() os.system('rm -rf %s' % output_dir) os.makedirs(output_dir) np.savetxt(os.path.join(output_dir, 'intrinsics.txt'), intrinsics, '%f') for model_id in model_list: start = time.time() exr_dir = os.path.join(output_dir, 'exr', model_id) pose_dir = os.path.join(output_dir, 'pose', model_id) os.makedirs(exr_dir) os.makedirs(pose_dir) # Redirect output to log file old_os_out = os.dup(1) os.close(1) os.open('blender.log', os.O_WRONLY) # Import mesh model model_path = os.path.join(model_dir, model_id, 'model.obj') bpy.ops.import_scene.obj(filepath=model_path) # Rotate model by 90 degrees around x-axis (z-up => y-up) to match ShapeNet's coordinates bpy.ops.transform.rotate(value=-np.pi / 2, axis=(1, 0, 0)) # Render for i in range(num_scans): scene.frame_set(i) pose = random_pose() camera.matrix_world = mathutils.Matrix(pose) output.file_slots[0].path = os.path.join(exr_dir, '#.exr') bpy.ops.render.render(write_still=True) np.savetxt(os.path.join(pose_dir, '%d.txt' % i), pose, '%f') # Clean up bpy.ops.object.delete() for m in bpy.data.meshes: bpy.data.meshes.remove(m) for m in bpy.data.materials: m.user_clear() bpy.data.materials.remove(m) # Show time os.close(1) os.dup(old_os_out) os.close(old_os_out) print('%s done, time=%.4f sec' % (model_id, time.time() - start))

33.213115

113

0.598963

2f2ae01c5dc0c8dc36854cb4c64f4b985bf765eb

783

pyde

Python

mode/examples/Topics/Simulate/MultipleParticleSystems/MultipleParticleSystems.pyde

katnino/processing.py

ff6450a6606908a014ce795340c06c8f6d90023d

[ "Apache-2.0" ]

4

2016-08-09T14:14:36.000Z

2021-12-10T07:51:35.000Z

mode/examples/Topics/Simulate/MultipleParticleSystems/MultipleParticleSystems.pyde

katnino/processing.py

ff6450a6606908a014ce795340c06c8f6d90023d

[ "Apache-2.0" ]

null

mode/examples/Topics/Simulate/MultipleParticleSystems/MultipleParticleSystems.pyde

katnino/processing.py

ff6450a6606908a014ce795340c06c8f6d90023d

[ "Apache-2.0" ]

null

""" Multiple Particle Systems by Daniel Shiffman. Click the mouse to generate a burst of particles at mouse location. Each burst is one instance of a particle system with Particles and CrazyParticles (a subclass of Particle). Note use of Inheritance and Polymorphism here. """ from crazy_particle import CrazyParticle from particle import Particle from particle_system import ParticleSystem systems = None def setup(): size(640, 360) systems = [] def draw(): background(0) for ps in systems: ps.run() ps.addParticle() if not systems: fill(255) textAlign(CENTER) text("click mouse to add particle systems", width / 2, height / 2) def mousePressed(): systems.append(ParticleSystem(1, PVector(mouseX, mouseY)))

20.076923

74

0.704981

7642637d3c29176328f3ca35a05a393c8d4bf9f7

7,369

py

Python

app.py

Rodrigoh2702/calendarizaciones

e1e9f1fac0a1a53da3a833dd3e94261a5c350989

[ "MIT" ]

null

app.py

Rodrigoh2702/calendarizaciones

e1e9f1fac0a1a53da3a833dd3e94261a5c350989

[ "MIT" ]

null

app.py

Rodrigoh2702/calendarizaciones

e1e9f1fac0a1a53da3a833dd3e94261a5c350989

[ "MIT" ]

null

import tkinter as tk from backend.proceso import Proceso from backend.algo.srtf import SRTF import copy class App(tk.Tk): def __init__(self, *args, **kwargs): tk.Tk.__init__(self, *args, **kwargs) container = tk.Frame(self) container.pack(side="top", fill="both", expand=True) container.grid_rowconfigure(0, weight=1) container.grid_columnconfigure(0, weight=1) self.frames = {} for F in (MainWindow, SRTFWindow): frame = F(container, self) self.frames[F] = frame frame.grid(row=0, column=0, sticky="nsew") self.show_frame(MainWindow) def show_frame(self, cont): frame = self.frames[cont] frame.tkraise() class MainWindow(tk.Frame): def __init__(self, parent, controller): tk.Frame.__init__(self, parent) # self.parent.geometry("500x500") self.frame = tk.Frame(self) self.label = tk.Label(self.frame, text="Simulador de calendarización de procesos", justify='center') self.label.grid(row=0, column=0, sticky="we", pady=40, padx=20, columnspan=2) self.button1 = tk.Button(self.frame, text='SRTF', command=lambda: controller.show_frame(SRTFWindow)) self.button1.grid(row=1, column=0, pady=(0, 40), sticky="we") self.button2 = tk.Button(self.frame, text='Round Robin') self.button2.grid(row=1, column=1, pady=(0, 40), sticky="we") self.frame.pack() def srtf_window(self): self.newWindow = tk.Toplevel(self.parent) self.app = SRTFWindow(self.newWindow, 0, []) class SRTFWindow(tk.Frame): procesos_list = [] count_proceso = 0 def __init__(self, parent, controller): tk.Frame.__init__(self, parent) self.frame = tk.Frame(self, background="black") self.create_table_task() self.create_wait_table() self.frame.pack() def add_process_window(self): AddProcessWindowSRTF(tk.Toplevel(self), self.count_proceso) def create_table_task(self): # Table of the processes added self.process_table = tk.Frame(self.frame) self.process_table.grid(row=0, column=0, sticky="nwe", padx=(15, 30), pady=(10, 40)) self.name_process_head = tk.Label(self.process_table, text="Proceso", justify='center', background="white") self.name_process_head.grid(row=0, column=0, sticky="we") self.entry_process_head = tk.Label(self.process_table, text="Tiempo de entrada", justify='center', background="white") self.entry_process_head.grid(row=0, column=1, sticky="we") self.burst_process_head = tk.Label(self.process_table, text="Ráfaga", justify='center', background="white") self.burst_process_head.grid(row=0, column=2, sticky="we") self.add_process_button_head = tk.Button(self.process_table, text="+", command=self.add_process_window) self.add_process_button_head.grid(row=0, column=3, sticky="we") self.process_table_body = tk.Frame(self.process_table, background="red") self.process_table_body.grid(row=1, column=0, sticky="we", columnspan=4) def update_process_list_table(self): i = 0 for p in self.procesos_list: name_process = tk.Label(self.process_table_body, text=p.nombre, justify='center', name=f"np{i+1}") name_process.grid(row=i, column=0, sticky="we") entry_process = tk.Label(self.process_table_body, text=p.entrada, justify='center', name=f"ep{i+1}") entry_process.grid(row=i, column=1, sticky="we") burst_process = tk.Label(self.process_table_body, text=p.rafaga, justify='center', name=f"bp{i+1}") burst_process.grid(row=i, column=2, sticky="we") remove_process_button = tk.Button(self.process_table_body, text="-", command=lambda: self.remove_process(p), name=f"rp{i+1}") remove_process_button.grid(row=i, column=3, sticky="we") i += 1 def create_wait_table(self): self.wait_table = tk.Frame(self.frame) self.wait_table.grid(row=0, column=1, sticky="nwe", padx=(30, 15), pady=(10, 40)) self.wait_process_head = tk.Label(self.wait_table, text="Proceso", justify='center', background="white") self.wait_process_head.grid(row=0, column=0, sticky="we") self.wburst_process_head = tk.Label(self.wait_table, text="Tiempo de espera", justify='center', background="white") self.wburst_process_head.grid(row=0, column=1, sticky="we") self.wprocess_table_body = tk.Frame(self.wait_table, background="red") self.wprocess_table_body.grid(row=1, column=0, sticky="we", columnspan=2) def update_process_wait_table(self): i = 0 for p in self.procesos_list: name_process = tk.Label(self.wprocess_table_body, text=p.nombre, justify='center', name=f"wnp{i+1}") name_process.grid(row=i, column=0, sticky="we") burst_process = tk.Label(self.wprocess_table_body, text=p.wait, justify='center', name=f"wwp{i+1}") burst_process.grid(row=i, column=1, sticky="we") i += 1 def remove_process(self, process): for w in self.process_table_body.winfo_children(): w.destroy() for w in self.wprocess_table_body.winfo_children(): w.destroy() self.count_proceso -= 1 self.procesos_list.remove(process) self.update_process_list_table() self.update_process_wait_table() def update_gantt(self): aux = self.procesos_list for p in aux: print("ayuda dios ", p) algo = SRTF(*aux) result = algo.srtf() print(result) class AddProcessWindowSRTF(): def __init__(self, parent, count_p): self.parent = parent self.count_p = count_p self.frame = tk.Frame(parent) self.frame.grid(row=0, column=0) self.label1 = tk.Label(self.frame, text="Ráfaga", justify='right') self.label1.grid(row=0, column=0, sticky="we") self.input1 = tk.Entry(self.frame) self.input1.grid(row=0, column=1, sticky="we") self.label2 = tk.Label(self.frame, text="Tiempo de llegada", justify='right') self.label2.grid(row=1, column=0, sticky="we") self.input2 = tk.Entry(self.frame) self.input2.grid(row=1, column=1, sticky="we") self.add_process_button = tk.Button(self.frame, text="Añadir proceso", command=self.add_process_) self.add_process_button.grid(row=2, column=0, columnspan=2) self.count = tk.Label(self.frame, text=f"{self.parent.master.count_proceso}", justify='right') self.count.grid(row=3, column=0, columnspan=2) def add_process_(self): burst = self.input1.get() entry = self.input2.get() self.parent.master.count_proceso += 1 name = f"p{self.parent.master.count_proceso}" process = Proceso(int(entry), int(burst), name) self.parent.master.procesos_list.append(process) self.close_windows() def close_windows(self): self.parent.master.update_gantt() self.parent.master.update_process_list_table() self.parent.master.update_process_wait_table() self.parent.destroy() def main(): app = App() app.mainloop() if __name__ == '__main__': main()

36.300493

137

0.645135

a18ffc222cc5338097804fe2d91f57fd741d28e0

2,391

py

Python

tests/contrib/django/test_connection.py

vijayperiasamy-eb/dd-trace-py

2b0d396fc7f76582e8ffedff48933245a77ebaf2

[ "BSD-3-Clause" ]

null

tests/contrib/django/test_connection.py

vijayperiasamy-eb/dd-trace-py

2b0d396fc7f76582e8ffedff48933245a77ebaf2

[ "BSD-3-Clause" ]

null

tests/contrib/django/test_connection.py

vijayperiasamy-eb/dd-trace-py

2b0d396fc7f76582e8ffedff48933245a77ebaf2

[ "BSD-3-Clause" ]

1

2021-02-11T10:20:14.000Z

import mock import time # 3rd party from django.contrib.auth.models import User from ddtrace.contrib.django.conf import settings from ddtrace.contrib.django.patch import apply_django_patches, connections # testing from .utils import DjangoTraceTestCase, override_ddtrace_settings class DjangoConnectionTest(DjangoTraceTestCase): """ Ensures that database connections are properly traced """ def test_connection(self): # trace a simple query start = time.time() users = User.objects.count() assert users == 0 end = time.time() # tests spans = self.tracer.writer.pop() assert spans, spans assert len(spans) == 1 span = spans[0] assert span.name == 'sqlite.query' assert span.service == 'defaultdb' assert span.span_type == 'sql' assert span.get_tag('django.db.vendor') == 'sqlite' assert span.get_tag('django.db.alias') == 'default' assert start < span.start < span.start + span.duration < end def test_django_db_query_in_resource_not_in_tags(self): User.objects.count() spans = self.tracer.writer.pop() assert spans[0].name == 'sqlite.query' assert spans[0].resource == 'SELECT COUNT(*) AS "__count" FROM "auth_user"' assert spans[0].get_tag('sql.query') is None @override_ddtrace_settings(INSTRUMENT_DATABASE=False) def test_connection_disabled(self): # trace a simple query users = User.objects.count() assert users == 0 # tests spans = self.tracer.writer.pop() assert len(spans) == 0 def test_should_append_database_prefix(self): # trace a simple query and check if the prefix is correctly # loaded from Django settings settings.DEFAULT_DATABASE_PREFIX = 'my_prefix_db' User.objects.count() traces = self.tracer.writer.pop_traces() assert len(traces) == 1 assert len(traces[0]) == 1 span = traces[0][0] assert span.service == 'my_prefix_db-defaultdb' def test_apply_django_patches_calls_connections_all(self): with mock.patch.object(connections, 'all') as mock_connections: apply_django_patches(patch_rest_framework=False) assert mock_connections.call_count == 1 assert mock_connections.mock_calls == [mock.call()]

32.753425

83

0.658302

c1004b9d5a10d906d787d22a81489d2591cc6768

18,652

py

Python

src/transformers/convert_graph_to_onnx.py

kct22aws/transformers

04cddaf402591e9f5bdb5f116a111d829a0ce4f4

[ "Apache-2.0" ]

5

2020-10-30T13:07:02.000Z

2021-03-17T12:18:30.000Z

src/transformers/convert_graph_to_onnx.py

kct22aws/transformers

04cddaf402591e9f5bdb5f116a111d829a0ce4f4

[ "Apache-2.0" ]

null

src/transformers/convert_graph_to_onnx.py

kct22aws/transformers

04cddaf402591e9f5bdb5f116a111d829a0ce4f4

[ "Apache-2.0" ]

1

2020-08-06T05:23:13.000Z

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from os import listdir, makedirs from pathlib import Path from typing import Dict, List, Optional, Tuple from packaging.version import Version, parse from transformers.file_utils import ModelOutput, is_tf_available, is_torch_available from transformers.pipelines import Pipeline, pipeline from transformers.tokenization_utils import BatchEncoding # This is the minimal required version to # support some ONNX Runtime features ORT_QUANTIZE_MINIMUM_VERSION = parse("1.4.0") SUPPORTED_PIPELINES = [ "feature-extraction", "ner", "sentiment-analysis", "fill-mask", "question-answering", "text-generation", "translation_en_to_fr", "translation_en_to_de", "translation_en_to_ro", ] class OnnxConverterArgumentParser(ArgumentParser): """ Wraps all the script arguments supported to export transformers models to ONNX IR """ def __init__(self): super().__init__("ONNX Converter") self.add_argument( "--pipeline", type=str, choices=SUPPORTED_PIPELINES, default="feature-extraction", ) self.add_argument( "--model", type=str, required=True, help="Model's id or path (ex: bert-base-cased)", ) self.add_argument("--tokenizer", type=str, help="Tokenizer's id or path (ex: bert-base-cased)") self.add_argument( "--framework", type=str, choices=["pt", "tf"], help="Framework for loading the model", ) self.add_argument("--opset", type=int, default=11, help="ONNX opset to use") self.add_argument( "--check-loading", action="store_true", help="Check ONNX is able to load the model", ) self.add_argument( "--use-external-format", action="store_true", help="Allow exporting model >= than 2Gb", ) self.add_argument( "--quantize", action="store_true", help="Quantize the neural network to be run with int8", ) self.add_argument("output") def generate_identified_filename(filename: Path, identifier: str) -> Path: """ Append a string-identifier at the end (before the extension, if any) to the provided filepath Args: filename: pathlib.Path The actual path object we would like to add an identifier suffix identifier: The suffix to add Returns: String with concatenated identifier at the end of the filename """ return filename.parent.joinpath(filename.stem + identifier).with_suffix(filename.suffix) def check_onnxruntime_requirements(minimum_version: Version): """ Check onnxruntime is installed and if the installed version match is recent enough Raises: ImportError: If onnxruntime is not installed or too old version is found """ try: import onnxruntime # Parse the version of the installed onnxruntime ort_version = parse(onnxruntime.__version__) # We require 1.4.0 minimum if ort_version < ORT_QUANTIZE_MINIMUM_VERSION: raise ImportError( f"We found an older version of onnxruntime ({onnxruntime.__version__}) " f"but we require onnxruntime to be >= {minimum_version} to enable all the conversions options.\n" f"Please update onnxruntime by running `pip install --upgrade onnxruntime`" ) except ImportError: raise ImportError( "onnxruntime doesn't seem to be currently installed. " "Please install the onnxruntime by running `pip install onnxruntime`" " and relaunch the conversion." ) def ensure_valid_input(model, tokens, input_names): """ Ensure input are presented in the correct order, without any Non Args: model: The model used to forward the input data tokens: BatchEncoding holding the input data input_names: The name of the inputs Returns: Tuple """ print("Ensuring inputs are in correct order") model_args_name = model.forward.__code__.co_varnames model_args, ordered_input_names = [], [] for arg_name in model_args_name[1:]: # start at index 1 to skip "self" argument if arg_name in input_names: ordered_input_names.append(arg_name) model_args.append(tokens[arg_name]) else: print(f"{arg_name} is not present in the generated input list.") break print(f"Generated inputs order: {ordered_input_names}") return ordered_input_names, tuple(model_args) def infer_shapes(nlp: Pipeline, framework: str) -> Tuple[List[str], List[str], Dict, BatchEncoding]: """ Attempt to infer the static vs dynamic axes for each input and output tensors for a specific model Args: nlp: The pipeline object holding the model to be exported framework: The framework identifier to dispatch to the correct inference scheme (pt/tf) Returns: - List of the inferred input variable names - List of the inferred output variable names - Dictionary with input/output variables names as key and shape tensor as value - a BatchEncoding reference which was used to infer all the above information """ def build_shape_dict(name: str, tensor, is_input: bool, seq_len: int): if isinstance(tensor, (tuple, list)): return [build_shape_dict(name, t, is_input, seq_len) for t in tensor] else: # Let's assume batch is the first axis with only 1 element (~~ might not be always true ...) axes = {[axis for axis, numel in enumerate(tensor.shape) if numel == 1][0]: "batch"} if is_input: if len(tensor.shape) == 2: axes[1] = "sequence" else: raise ValueError(f"Unable to infer tensor axes ({len(tensor.shape)})") else: seq_axes = [dim for dim, shape in enumerate(tensor.shape) if shape == seq_len] axes.update({dim: "sequence" for dim in seq_axes}) print(f"Found {'input' if is_input else 'output'} {name} with shape: {axes}") return axes tokens = nlp.tokenizer("This is a sample output", return_tensors=framework) seq_len = tokens.input_ids.shape[-1] outputs = nlp.model(**tokens) if framework == "pt" else nlp.model(tokens) if isinstance(outputs, ModelOutput): outputs = outputs.to_tuple() if not isinstance(outputs, (list, tuple)): outputs = (outputs,) # Generate input names & axes input_vars = list(tokens.keys()) input_dynamic_axes = {k: build_shape_dict(k, v, True, seq_len) for k, v in tokens.items()} # flatten potentially grouped outputs (past for gpt2, attentions) outputs_flat = [] for output in outputs: if isinstance(output, (tuple, list)): outputs_flat.extend(output) else: outputs_flat.append(output) # Generate output names & axes output_names = [f"output_{i}" for i in range(len(outputs_flat))] output_dynamic_axes = {k: build_shape_dict(k, v, False, seq_len) for k, v in zip(output_names, outputs_flat)} # Create the aggregated axes representation dynamic_axes = dict(input_dynamic_axes, **output_dynamic_axes) return input_vars, output_names, dynamic_axes, tokens def load_graph_from_args( pipeline_name: str, framework: str, model: str, tokenizer: Optional[str] = None, **models_kwargs ) -> Pipeline: """ Convert the set of arguments provided through the CLI to an actual pipeline reference (tokenizer + model Args: pipeline_name: The kind of pipeline to use (ner, question-answering, etc.) framework: The actual model to convert the pipeline from ("pt" or "tf") model: The model name which will be loaded by the pipeline tokenizer: The tokenizer name which will be loaded by the pipeline, default to the model's value Returns: Pipeline object """ # If no tokenizer provided if tokenizer is None: tokenizer = model # Check the wanted framework is available if framework == "pt" and not is_torch_available(): raise Exception("Cannot convert because PyTorch is not installed. Please install torch first.") if framework == "tf" and not is_tf_available(): raise Exception("Cannot convert because TF is not installed. Please install tensorflow first.") print(f"Loading pipeline (model: {model}, tokenizer: {tokenizer})") # Allocate tokenizer and model return pipeline(pipeline_name, model=model, tokenizer=tokenizer, framework=framework, model_kwargs=models_kwargs) def convert_pytorch(nlp: Pipeline, opset: int, output: Path, use_external_format: bool): """ Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR Args: nlp: The pipeline to be exported opset: The actual version of the ONNX operator set to use output: Path where will be stored the generated ONNX model use_external_format: Split the model definition from its parameters to allow model bigger than 2GB Returns: """ if not is_torch_available(): raise Exception("Cannot convert because PyTorch is not installed. Please install torch first.") import torch from torch.onnx import export print(f"Using framework PyTorch: {torch.__version__}") with torch.no_grad(): input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "pt") ordered_input_names, model_args = ensure_valid_input(nlp.model, tokens, input_names) export( nlp.model, model_args, f=output.as_posix(), input_names=ordered_input_names, output_names=output_names, dynamic_axes=dynamic_axes, do_constant_folding=True, use_external_data_format=use_external_format, enable_onnx_checker=True, opset_version=opset, ) def convert_tensorflow(nlp: Pipeline, opset: int, output: Path): """ Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR Args: nlp: The pipeline to be exported opset: The actual version of the ONNX operator set to use output: Path where will be stored the generated ONNX model Notes: TensorFlow cannot export model bigger than 2GB due to internal constraint from TensorFlow """ if not is_tf_available(): raise Exception("Cannot convert because TF is not installed. Please install tensorflow first.") print("/!\\ Please note TensorFlow doesn't support exporting model > 2Gb /!\\") try: import tensorflow as tf from keras2onnx import __version__ as k2ov from keras2onnx import convert_keras, save_model print(f"Using framework TensorFlow: {tf.version.VERSION}, keras2onnx: {k2ov}") # Build input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "tf") # Forward nlp.model.predict(tokens.data) onnx_model = convert_keras(nlp.model, nlp.model.name, target_opset=opset) save_model(onnx_model, output.as_posix()) except ImportError as e: raise Exception(f"Cannot import {e.name} required to convert TF model to ONNX. Please install {e.name} first.") def convert( framework: str, model: str, output: Path, opset: int, tokenizer: Optional[str] = None, use_external_format: bool = False, pipeline_name: str = "feature-extraction", **model_kwargs ): """ Convert the pipeline object to the ONNX Intermediate Representation (IR) format Args: framework: The framework the pipeline is backed by ("pt" or "tf") model: The name of the model to load for the pipeline output: The path where the ONNX graph will be stored opset: The actual version of the ONNX operator set to use tokenizer: The name of the model to load for the pipeline, default to the model's name if not provided use_external_format: Split the model definition from its parameters to allow model bigger than 2GB (PyTorch only) pipeline_name: The kind of pipeline to instantiate (ner, question-answering, etc.) model_kwargs: Keyword arguments to be forwarded to the model constructor Returns: """ print(f"ONNX opset version set to: {opset}") # Load the pipeline nlp = load_graph_from_args(pipeline_name, framework, model, tokenizer, **model_kwargs) if not output.parent.exists(): print(f"Creating folder {output.parent}") makedirs(output.parent.as_posix()) elif len(listdir(output.parent.as_posix())) > 0: raise Exception(f"Folder {output.parent.as_posix()} is not empty, aborting conversion") # Export the graph if framework == "pt": convert_pytorch(nlp, opset, output, use_external_format) else: convert_tensorflow(nlp, opset, output) def optimize(onnx_model_path: Path) -> Path: """ Load the model at the specified path and let onnxruntime look at transformations on the graph to enable all the optimizations possibl Args: onnx_model_path: filepath where the model binary description is stored Returns: Path where the optimized model binary description has been saved """ from onnxruntime import InferenceSession, SessionOptions # Generate model name with suffix "optimized" opt_model_path = generate_identified_filename(onnx_model_path, "-optimized") sess_option = SessionOptions() sess_option.optimized_model_filepath = opt_model_path.as_posix() _ = InferenceSession(onnx_model_path.as_posix(), sess_option) print(f"Optimized model has been written at {opt_model_path}: \N{heavy check mark}") print("/!\\ Optimized model contains hardware specific operators which might not be portable. /!\\") return opt_model_path def quantize(onnx_model_path: Path) -> Path: """ Quantize the weights of the model from float32 to in8 to allow very efficient inference on modern CPU Args: onnx_model_path: Path to location the exported ONNX model is stored Returns: The Path generated for the quantized """ import onnx from onnxruntime.quantization import QuantizationMode, quantize onnx_model = onnx.load(onnx_model_path.as_posix()) # Discussed with @yufenglee from ONNX runtime, this will be address in the next release of onnxruntime print( "As of onnxruntime 1.4.0, models larger than 2GB will fail to quantize due to protobuf constraint.\n" "This limitation will be removed in the next release of onnxruntime." ) quantized_model = quantize( model=onnx_model, quantization_mode=QuantizationMode.IntegerOps, force_fusions=True, symmetric_weight=True, ) # Append "-quantized" at the end of the model's name quantized_model_path = generate_identified_filename(onnx_model_path, "-quantized") # Save model print(f"Quantized model has been written at {quantized_model_path}: \N{heavy check mark}") onnx.save_model(quantized_model, quantized_model_path.as_posix()) return quantized_model_path def verify(path: Path): from onnxruntime import InferenceSession, SessionOptions from onnxruntime.capi.onnxruntime_pybind11_state import RuntimeException print(f"Checking ONNX model loading from: {path} ...") try: onnx_options = SessionOptions() _ = InferenceSession(path.as_posix(), onnx_options, providers=["CPUExecutionProvider"]) print(f"Model {path} correctly loaded: \N{heavy check mark}") except RuntimeException as re: print(f"Error while loading the model {re}: \N{heavy ballot x}") if __name__ == "__main__": parser = OnnxConverterArgumentParser() args = parser.parse_args() # Make sure output is absolute path args.output = Path(args.output).absolute() try: print("\n====== Converting model to ONNX ======") # Convert convert( args.framework, args.model, args.output, args.opset, args.tokenizer, args.use_external_format, args.pipeline, ) if args.quantize: # Ensure requirements for quantization on onnxruntime is met check_onnxruntime_requirements(ORT_QUANTIZE_MINIMUM_VERSION) # onnxruntime optimizations doesn't provide the same level of performances on TensorFlow than PyTorch if args.framework == "tf": print( "\t Using TensorFlow might not provide the same optimization level compared to PyTorch.\n" "\t For TensorFlow users you can try optimizing the model directly through onnxruntime_tools.\n" "\t For more information, please refer to the onnxruntime documentation:\n" "\t\thttps://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers\n" ) print("\n====== Optimizing ONNX model ======") # Quantization works best when using the optimized version of the model args.optimized_output = optimize(args.output) # Do the quantization on the right graph args.quantized_output = quantize(args.optimized_output) # And verify if args.check_loading: print("\n====== Check exported ONNX model(s) ======") verify(args.output) if hasattr(args, "optimized_output"): verify(args.optimized_output) if hasattr(args, "quantized_output"): verify(args.quantized_output) except Exception as e: print(f"Error while converting the model: {e}") exit(1)

36.788955

119

0.66958

08dafe6011e7ec9c20106396fe2461e7f4b7f55b

5,061

py

Python

disentanglement_lib/evaluation/metrics/local_modularity_test.py

travers-rhodes/disentanglement_lib

73d4b995e88efdd5ffbe98a72e48a620c58f4dc7

[ "Apache-2.0" ]

null

disentanglement_lib/evaluation/metrics/local_modularity_test.py

travers-rhodes/disentanglement_lib

73d4b995e88efdd5ffbe98a72e48a620c58f4dc7

[ "Apache-2.0" ]

null

disentanglement_lib/evaluation/metrics/local_modularity_test.py

travers-rhodes/disentanglement_lib

73d4b995e88efdd5ffbe98a72e48a620c58f4dc7

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2018 The DisentanglementLib Authors. All rights reserved. # Copyright 2021 Travers Rhodes. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This file was modified by Travers Rhodes """Tests for local_modularity.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import absltest from disentanglement_lib.data.ground_truth import dummy_data from disentanglement_lib.evaluation.metrics import utils, local_modularity import numpy as np from six.moves import range import gin.tf def _identity_discretizer(target, num_bins): del num_bins return target def initialize_gin(): gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer) #gin.bind_parameter("discretizer.discretizer_fn", utils._histogram_discretize) gin.bind_parameter("discretizer.num_bins", 10) gin.bind_parameter("local_sample_factors.denylist_factors", []) gin.bind_parameter("local_sample_factors.locality_proportion", 1.0) gin.bind_parameter("local_sample_factors.continuity_cutoff", 0) gin.bind_parameter("local_modularity.num_train", 20000) gin.bind_parameter("local_modularity.num_local_clusters", 3) class ModularityExplicitnessTest(absltest.TestCase): def test_metric(self): initialize_gin() ground_truth_data = dummy_data.IdentityObservationsData() representation_function = lambda x: np.array(x, dtype=np.float64) random_state = np.random.RandomState(0) scores = local_modularity.compute_local_modularity( ground_truth_data, representation_function, random_state) self.assertBetween(scores["modularity_score"], 0.9, 1.0) def test_bad_metric(self): initialize_gin() ground_truth_data = dummy_data.IdentityObservationsData() random_state_rep = np.random.RandomState(0) # The representation which randomly permutes the factors, should have equal # non-zero MI which should give a low modularity score. def representation_function(x): code = np.array(x, dtype=np.float64) for i in range(code.shape[0]): code[i, :] = random_state_rep.permutation(code[i, :]) return code random_state = np.random.RandomState(0) scores = local_modularity.compute_local_modularity( ground_truth_data, representation_function, random_state) self.assertBetween(scores["modularity_score"], 0.0, 0.2) def test_duplicated_latent_space(self): initialize_gin() ground_truth_data = dummy_data.IdentityObservationsData() def representation_function(x): x = np.array(x, dtype=np.float64) return np.hstack([x, x]) random_state = np.random.RandomState(0) scores = local_modularity.compute_local_modularity( ground_truth_data, representation_function, random_state) self.assertBetween(scores["modularity_score"], 0.9, 1.0) # the representation here folds the dataset in on itself. # it looks like MIG counts this as a relatively good discretization which # makes sense because mutual information is not correlation def test_locally_good_metric(self): initialize_gin() gin.bind_parameter("discretizer.discretizer_fn", utils._histogram_discretize) num_factors = 3 num_possible_values = 40 ground_truth_data = dummy_data.IdentityObservationsCustomSize( [num_possible_values] * num_factors) random_state = np.random.RandomState(30) # modularity is so weird. We want a factor that globally # depends on multiple variables but locally depends on only # one variable. Easiest is something like Floor[(A+B) / 5] + C / 40 # which for small changes of A,B,C only depends on changes of C # and globally depends jointly on A,B def representation_function(x): representation = np.floor((x[:,0:1] + x[:,1:2])/12) + x[:,2:3] / 40 return representation # if you sample globally (lp = 1) then you get a low MIG score # if you sample locally (lp=0.1) then you get a high MIG score for lp, exp_min_score, exp_max_score in [(1.0, 0.0, 0.3), (0.1, 0.8, 1.0)]: gin.bind_parameter("local_sample_factors.locality_proportion" ,lp) gin.bind_parameter("local_modularity.num_local_clusters",3) scores = local_modularity.compute_local_modularity( ground_truth_data, representation_function, random_state, batch_size=30) self.assertBetween(scores["modularity_score"], exp_min_score, exp_max_score) if __name__ == "__main__": logging.set_verbosity(logging.DEBUG) absltest.main()

43.25641

84

0.753013

ccf2161242a1d75bd20b4a0b7769d6579c86d46e

774

py

Python

trigger/pulseRatioWOnoise.py

greatofdream/pmtTest

d8f1fc8e94c3999516b6eef01e228311e6135053

[ "MIT" ]

null

trigger/pulseRatioWOnoise.py

greatofdream/pmtTest

d8f1fc8e94c3999516b6eef01e228311e6135053

[ "MIT" ]

null

trigger/pulseRatioWOnoise.py

greatofdream/pmtTest

d8f1fc8e94c3999516b6eef01e228311e6135053

[ "MIT" ]

null

import numpy as np, h5py import argparse if __name__=="__main__": psr = argparse.ArgumentParser() psr.add_argument('-i', dest='ipt', help='input text file') psr.add_argument('-o', dest='opt', help='output text file') psr.add_argument('--ref', help='the reference of noise') args = psr.parse_args() noise = np.genfromtxt(args.ref, dtype=[('thre', '<i2'), ('noi', '<f4')]) pulse = np.genfromtxt(args.ipt, dtype=[('prompt', '<f4'), ('delay1', '<f4'), ('delay10', '<f4'), ('triggerNum', '<i4')]) timeInterval = np.array([200, 700, 9000]) noiserate = noise['noi'].reshape((2,1))*timeInterval*1e-6 pulse['prompt'] -= noiserate[:,0] pulse['delay1'] -= noiserate[:,1] pulse['delay10'] -= noiserate[:,2] np.savetxt(args.opt, pulse)

48.375

124

0.614987

7121561ed9035de74e1c98ca82d378979367c3eb

1,062

py

Python

kubernetes_asyncio/test/test_v1beta1_network_policy_spec.py

hubo1016/kubernetes_asyncio

d57e9e9be11f6789e1ce8d5b161acb64d29acf35

[ "Apache-2.0" ]

1

2021-01-13T09:28:57.000Z

kubernetes_asyncio/test/test_v1beta1_network_policy_spec.py

hubo1016/kubernetes_asyncio

d57e9e9be11f6789e1ce8d5b161acb64d29acf35

[ "Apache-2.0" ]

null

kubernetes_asyncio/test/test_v1beta1_network_policy_spec.py

hubo1016/kubernetes_asyncio

d57e9e9be11f6789e1ce8d5b161acb64d29acf35

[ "Apache-2.0" ]

null

# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: v1.12.4 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import kubernetes_asyncio.client from kubernetes_asyncio.client.models.v1beta1_network_policy_spec import V1beta1NetworkPolicySpec # noqa: E501 from kubernetes_asyncio.client.rest import ApiException class TestV1beta1NetworkPolicySpec(unittest.TestCase): """V1beta1NetworkPolicySpec unit test stubs""" def setUp(self): pass def tearDown(self): pass def testV1beta1NetworkPolicySpec(self): """Test V1beta1NetworkPolicySpec""" # FIXME: construct object with mandatory attributes with example values # model = kubernetes_asyncio.client.models.v1beta1_network_policy_spec.V1beta1NetworkPolicySpec() # noqa: E501 pass if __name__ == '__main__': unittest.main()

25.902439

119

0.741055

aabc6334a63c35c9447a1544824aaf83e3382dcb

14,202

py

Python

requests_oauthlib/oauth2_session.py

Huddle/requests-oauthlib

503146ae39c1c050abf39d2fd5a43595e621cdf7

[ "0BSD" ]

null

requests_oauthlib/oauth2_session.py

Huddle/requests-oauthlib

503146ae39c1c050abf39d2fd5a43595e621cdf7

[ "0BSD" ]

null

requests_oauthlib/oauth2_session.py

Huddle/requests-oauthlib

503146ae39c1c050abf39d2fd5a43595e621cdf7

[ "0BSD" ]

null

from __future__ import unicode_literals import logging from oauthlib.common import generate_token, urldecode from oauthlib.oauth2 import WebApplicationClient, InsecureTransportError from oauthlib.oauth2 import TokenExpiredError, is_secure_transport import requests log = logging.getLogger(__name__) class TokenUpdated(Warning): def __init__(self, token): super(TokenUpdated, self).__init__() self.token = token class OAuth2Session(requests.Session): """Versatile OAuth 2 extension to :class:`requests.Session`. Supports any grant type adhering to :class:`oauthlib.oauth2.Client` spec including the four core OAuth 2 grants. Can be used to create authorization urls, fetch tokens and access protected resources using the :class:`requests.Session` interface you are used to. - :class:`oauthlib.oauth2.WebApplicationClient` (default): Authorization Code Grant - :class:`oauthlib.oauth2.MobileApplicationClient`: Implicit Grant - :class:`oauthlib.oauth2.LegacyApplicationClient`: Password Credentials Grant - :class:`oauthlib.oauth2.BackendApplicationClient`: Client Credentials Grant Note that the only time you will be using Implicit Grant from python is if you are driving a user agent able to obtain URL fragments. """ def __init__(self, client_id=None, client=None, auto_refresh_url=None, auto_refresh_kwargs=None, scope=None, redirect_uri=None, token=None, state=None, token_updater=None, **kwargs): """Construct a new OAuth 2 client session. :param client_id: Client id obtained during registration :param client: :class:`oauthlib.oauth2.Client` to be used. Default is WebApplicationClient which is useful for any hosted application but not mobile or desktop. :param scope: List of scopes you wish to request access to :param redirect_uri: Redirect URI you registered as callback :param token: Token dictionary, must include access_token and token_type. :param state: State string used to prevent CSRF. This will be given when creating the authorization url and must be supplied when parsing the authorization response. Can be either a string or a no argument callable. :auto_refresh_url: Refresh token endpoint URL, must be HTTPS. Supply this if you wish the client to automatically refresh your access tokens. :auto_refresh_kwargs: Extra arguments to pass to the refresh token endpoint. :token_updater: Method with one argument, token, to be used to update your token databse on automatic token refresh. If not set a TokenUpdated warning will be raised when a token has been refreshed. This warning will carry the token in its token argument. :param kwargs: Arguments to pass to the Session constructor. """ super(OAuth2Session, self).__init__(**kwargs) self.client_id = client_id or client.client_id self.scope = scope self.redirect_uri = redirect_uri self.token = token or {} self.state = state or generate_token self._state = state self.auto_refresh_url = auto_refresh_url self.auto_refresh_kwargs = auto_refresh_kwargs or {} self.token_updater = token_updater self._client = client or WebApplicationClient(client_id, token=token) self._client._populate_attributes(token or {}) # Allow customizations for non compliant providers through various # hooks to adjust requests and responses. self.compliance_hook = { 'access_token_response': set([]), 'refresh_token_response': set([]), 'protected_request': set([]), } def new_state(self): """Generates a state string to be used in authorizations.""" try: self._state = self.state() log.debug('Generated new state %s.', self._state) except TypeError: self._state = self.state log.debug('Re-using previously supplied state %s.', self._state) return self._state def authorization_url(self, url, state=None, **kwargs): """Form an authorization URL. :param url: Authorization endpoint url, must be HTTPS. :param state: An optional state string for CSRF protection. If not given it will be generated for you. :param kwargs: Extra parameters to include. :return: authorization_url, state """ state = state or self.new_state() return self._client.prepare_request_uri(url, redirect_uri=self.redirect_uri, scope=self.scope, state=state, **kwargs), state def fetch_token(self, token_url, code=None, authorization_response=None, body='', auth=None, username=None, password=None, method='POST', headers=None, verify=True, **kwargs): """Generic method for fetching an access token from the token endpoint. If you are using the MobileApplicationClient you will want to use token_from_fragment instead of fetch_token. :param token_url: Token endpoint URL, must use HTTPS. :param code: Authorization code (used by WebApplicationClients). :param authorization_response: Authorization response URL, the callback URL of the request back to you. Used by WebApplicationClients instead of code. :param body: Optional application/x-www-form-urlencoded body to add the include in the token request. Prefer kwargs over body. :param auth: An auth tuple or method as accepted by requests. :param username: Username used by LegacyApplicationClients. :param password: Password used by LegacyApplicationClients. :param method: The HTTP method used to make the request. Defaults to POST, but may also be GET. Other methods should be added as needed. :param headers: Dict to default request headers with. :param verify: Verify SSL certificate. :param kwargs: Extra parameters to include in the token request. :return: A token dict """ if not is_secure_transport(token_url): raise InsecureTransportError() if not code and authorization_response: self._client.parse_request_uri_response(authorization_response, state=self._state) code = self._client.code elif not code and isinstance(self._client, WebApplicationClient): code = self._client.code if not code: raise ValueError('Please supply either code or ' 'authorization_code parameters.') body = self._client.prepare_request_body(code=code, body=body, redirect_uri=self.redirect_uri, username=username, password=password, **kwargs) headers = headers or { 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8', } if method.upper() == 'POST': r = self.post(token_url, data=dict(urldecode(body)), headers=headers, auth=auth, verify=verify) log.debug('Prepared fetch token request body %s', body) elif method.upper() == 'GET': # if method is not 'POST', switch body to querystring and GET r = self.get(token_url, params=dict(urldecode(body)), headers=headers, auth=auth, verify=verify) log.debug('Prepared fetch token request querystring %s', body) else: raise ValueError('The method kwarg must be POST or GET.') log.debug('Request to fetch token completed with status %s.', r.status_code) log.debug('Request headers were %s', r.request.headers) log.debug('Request body was %s', r.request.body) log.debug('Response headers were %s and content %s.', r.headers, r.text) log.debug('Invoking %d token response hooks.', len(self.compliance_hook['access_token_response'])) for hook in self.compliance_hook['access_token_response']: log.debug('Invoking hook %s.', hook) r = hook(r) self._client.parse_request_body_response(r.text, scope=self.scope) self.token = self._client.token log.debug('Obtained token %s.', self.token) return self.token def token_from_fragment(self, authorization_response): """Parse token from the URI fragment, used by MobileApplicationClients. :param authorization_response: The full URL of the redirect back to you :return: A token dict """ self._client.parse_request_uri_response(authorization_response, state=self._state) self.token = self._client.token return self.token def refresh_token(self, token_url, refresh_token=None, body='', auth=None, **kwargs): """Fetch a new access token using a refresh token. :param token_url: The token endpoint, must be HTTPS. :param refresh_token: The refresh_token to use. :param body: Optional application/x-www-form-urlencoded body to add the include in the token request. Prefer kwargs over body. :param auth: An auth tuple or method as accepted by requests. :param kwargs: Extra parameters to include in the token request. :return: A token dict """ if not token_url: raise ValueError('No token endpoint set for auto_refresh.') if not is_secure_transport(token_url): raise InsecureTransportError() # Need to nullify token to prevent it from being added to the request refresh_token = refresh_token or self.token.get('refresh_token') self.token = {} log.debug('Adding auto refresh key word arguments %s.', self.auto_refresh_kwargs) kwargs.update(self.auto_refresh_kwargs) body = self._client.prepare_refresh_body(body=body, refresh_token=refresh_token, scope=self.scope, **kwargs) log.debug('Prepared refresh token request body %s', body) r = self.post(token_url, data=dict(urldecode(body)), auth=auth) log.debug('Request to refresh token completed with status %s.', r.status_code) log.debug('Response headers were %s and content %s.', r.headers, r.text) log.debug('Invoking %d token response hooks.', len(self.compliance_hook['refresh_token_response'])) for hook in self.compliance_hook['refresh_token_response']: log.debug('Invoking hook %s.', hook) r = hook(r) self.token = self._client.parse_request_body_response(r.text, scope=self.scope) if not 'refresh_token' in self.token: log.debug('No new refresh token given. Re-using old.') self.token['refresh_token'] = refresh_token return self.token def request(self, method, url, data=None, headers=None, **kwargs): """Intercept all requests and add the OAuth 2 token if present.""" if not is_secure_transport(url): raise InsecureTransportError() if self.token: log.debug('Invoking %d protected resource request hooks.', len(self.compliance_hook['protected_request'])) for hook in self.compliance_hook['protected_request']: log.debug('Invoking hook %s.', hook) url, headers, data = hook(url, headers, data) log.debug('Adding token %s to request.', self.token) try: url, headers, data = self._client.add_token(url, http_method=method, body=data, headers=headers) # Attempt to retrieve and save new access token if expired except TokenExpiredError: if self.auto_refresh_url: log.debug('Auto refresh is set, attempting to refresh at %s.', self.auto_refresh_url) token = self.refresh_token(self.auto_refresh_url) if self.token_updater: log.debug('Updating token to %s using %s.', token, self.token_updater) self.token_updater(token) url, headers, data = self._client.add_token(url, http_method=method, body=data, headers=headers) else: raise TokenUpdated(token) else: raise log.debug('Requesting url %s using method %s.', url, method) log.debug('Supplying headers %s and data %s', headers, data) log.debug('Passing through key word arguments %s.', kwargs) return super(OAuth2Session, self).request(method, url, headers=headers, data=data, **kwargs) def register_compliance_hook(self, hook_type, hook): """Register a hook for request/response tweaking. Available hooks are: access_token_response invoked before token parsing. refresh_token_response invoked before refresh token parsing. protected_request invoked before making a request. If you find a new hook is needed please send a GitHub PR request or open an issue. """ if hook_type not in self.compliance_hook: raise ValueError('Hook type %s is not in %s.', hook_type, self.compliance_hook) self.compliance_hook[hook_type].add(hook)

47.182724

87

0.623715

0299b702b90ae551cb69cf100ae411e60e0cc0f1

34,917

py

Python

dis_snek/models/snek/application_commands.py

ShardlessBun/Dis-Snek

fd15b06c531e82a9682a7b139deec0df7b2aa2d0

[ "MIT" ]

null

dis_snek/models/snek/application_commands.py

ShardlessBun/Dis-Snek

fd15b06c531e82a9682a7b139deec0df7b2aa2d0

[ "MIT" ]

null

dis_snek/models/snek/application_commands.py

ShardlessBun/Dis-Snek

fd15b06c531e82a9682a7b139deec0df7b2aa2d0

[ "MIT" ]

null

import asyncio import inspect import logging import re from enum import IntEnum from typing import TYPE_CHECKING, Callable, Coroutine, Dict, List, Union, Optional, Any import attr import dis_snek.models.discord.channel as channel from dis_snek.client.const import ( GLOBAL_SCOPE, CONTEXT_MENU_NAME_LENGTH, SLASH_OPTION_NAME_LENGTH, SLASH_CMD_NAME_LENGTH, SLASH_CMD_MAX_OPTIONS, SLASH_CMD_MAX_DESC_LENGTH, MISSING, logger_name, Absent, ) from dis_snek.client.mixins.serialization import DictSerializationMixin from dis_snek.client.utils.attr_utils import docs from dis_snek.client.utils.misc_utils import get_parameters from dis_snek.client.utils.serializer import no_export_meta from dis_snek.models.discord.enums import ChannelTypes, CommandTypes from dis_snek.models.discord.role import Role from dis_snek.models.discord.snowflake import to_snowflake, to_snowflake_list from dis_snek.models.discord.user import BaseUser from dis_snek.models.snek.auto_defer import AutoDefer from dis_snek.models.snek.command import BaseCommand if TYPE_CHECKING: from dis_snek.models.discord.snowflake import Snowflake_Type from dis_snek.models.snek.context import Context __all__ = [ "OptionTypes", "PermissionTypes", "CallbackTypes", "Permission", "InteractionCommand", "ContextMenu", "SlashCommandChoice", "SlashCommand", "ComponentCommand", "slash_command", "subcommand", "context_menu", "component_callback", "slash_option", "slash_permission", "auto_defer", "application_commands_to_dict", "sync_needed", ] log = logging.getLogger(logger_name) class OptionTypes(IntEnum): """Option types supported by slash commands.""" SUB_COMMAND = 1 SUB_COMMAND_GROUP = 2 STRING = 3 INTEGER = 4 BOOLEAN = 5 USER = 6 CHANNEL = 7 ROLE = 8 MENTIONABLE = 9 NUMBER = 10 @classmethod def from_type(cls, t: type) -> "OptionTypes": """ Convert data types to their corresponding OptionType. parameters: t: The datatype to convert returns: OptionType or None """ if issubclass(t, str): return cls.STRING if issubclass(t, int): return cls.INTEGER if issubclass(t, bool): return cls.BOOLEAN if issubclass(t, BaseUser): return cls.USER if issubclass(t, channel.BaseChannel): return cls.CHANNEL if issubclass(t, Role): return cls.ROLE if issubclass(t, float): return cls.NUMBER class PermissionTypes(IntEnum): """Types of target supported by the interaction permission.""" ROLE = 1 USER = 2 @classmethod def from_type(cls, t: type) -> "PermissionTypes": if issubclass(t, Role): return cls.ROLE if issubclass(t, BaseUser): return cls.USER class CallbackTypes(IntEnum): """Types of callback supported by interaction response.""" PONG = 1 CHANNEL_MESSAGE_WITH_SOURCE = 4 DEFERRED_CHANNEL_MESSAGE_WITH_SOURCE = 5 DEFERRED_UPDATE_MESSAGE = 6 UPDATE_MESSAGE = 7 AUTOCOMPLETE_RESULT = 8 @attr.s(slots=True, hash=True) class Permission: """ Represents a interaction permission. parameters: id: The id of the role or user. guild_id: The guild this permission belongs to type: The type of id (user or role) permission: The state of permission. ``True`` to allow, ``False``, to disallow. """ id: "Snowflake_Type" = attr.ib(converter=to_snowflake) guild_id: "Snowflake_Type" = attr.ib(converter=to_snowflake, metadata=no_export_meta) type: Union[PermissionTypes, int] = attr.ib(converter=PermissionTypes) permission: bool = attr.ib(default=True) def to_dict(self) -> dict: """ Convert this object into a dict ready for discord. returns: Representation of this object """ data = attr.asdict(self) data.pop("guild_id", None) data["id"] = str(data["id"]) return data @attr.s(slots=True, kw_only=True, on_setattr=[attr.setters.convert, attr.setters.validate]) class InteractionCommand(BaseCommand): """ Represents a discord abstract interaction command. parameters: scope: Denotes whether its global or for specific guild. default_permission: Is this command available to all users? permissions: Map of guild id and its respective list of permissions to apply. cmd_id: The id of this command given by discord. callback: The coroutine to callback when this interaction is received. """ name: str = attr.ib(metadata=docs("1-32 character name") | no_export_meta) scopes: List["Snowflake_Type"] = attr.ib( default=[GLOBAL_SCOPE], converter=to_snowflake_list, metadata=docs("The scopes of this interaction. Global or guild ids") | no_export_meta, ) default_permission: bool = attr.ib( default=True, metadata=docs("whether this command is enabled by default when the app is added to a guild") ) permissions: Optional[List[Union[Permission, Dict]]] = attr.ib( factory=dict, metadata=docs("The permissions of this interaction") ) cmd_id: Dict[str, "Snowflake_Type"] = attr.ib( factory=dict, metadata=docs("The unique IDs of this commands") | no_export_meta ) # scope: cmd_id callback: Callable[..., Coroutine] = attr.ib( default=None, metadata=docs("The coroutine to call when this interaction is received") | no_export_meta ) auto_defer: "AutoDefer" = attr.ib( default=MISSING, metadata=docs("A system to automatically defer this command after a set duration") | no_export_meta, ) def __attrs_post_init__(self) -> None: if self.callback is not None: if hasattr(self.callback, "auto_defer"): self.auto_defer = self.callback.auto_defer super().__attrs_post_init__() @property def resolved_name(self) -> str: """A representation of this interaction's name.""" return self.name def get_cmd_id(self, scope: "Snowflake_Type") -> "Snowflake_Type": return self.cmd_id.get(scope, self.cmd_id.get(GLOBAL_SCOPE, None)) @property def is_subcommand(self) -> bool: return False async def _permission_enforcer(self, ctx: "Context") -> bool: """A check that enforces Discord permissions.""" # I wish this wasn't needed, but unfortunately Discord permissions cant be trusted to actually prevent usage for perm in self.permissions or []: if perm.type == PermissionTypes.ROLE: if ctx.author.has_role(perm.id): if perm.permission is True: return True elif self.default_permission is True: return False elif perm.type == PermissionTypes.USER: if ctx.author.id == perm.id: if perm.permission is True: return True elif self.default_permission is True: return False return self.default_permission @attr.s(slots=True, kw_only=True, on_setattr=[attr.setters.convert, attr.setters.validate]) class ContextMenu(InteractionCommand): """ Represents a discord context menu. parameters: name: The name of this entry. type: The type of entry (user or message). """ name: str = attr.ib(metadata=docs("1-32 character name")) type: CommandTypes = attr.ib(metadata=docs("The type of command, defaults to 1 if not specified")) @name.validator def _name_validator(self, attribute: str, value: str) -> None: if not 1 <= len(value) <= CONTEXT_MENU_NAME_LENGTH: raise ValueError("Context Menu name attribute must be between 1 and 32 characters") @type.validator def _type_validator(self, attribute: str, value: int): if not isinstance(value, CommandTypes): if value not in CommandTypes.__members__.values(): raise ValueError("Context Menu type not recognised, please consult the docs.") elif value == CommandTypes.CHAT_INPUT: raise ValueError( "The CHAT_INPUT type is basically slash commands. Please use the @slash_command() " "decorator instead." ) @attr.s(slots=True) class SlashCommandChoice(DictSerializationMixin): """ Represents a discord slash command choice. parameters: name: The name the user will see value: The data sent to your code when this choice is used """ name: str = attr.ib() value: Union[str, int, float] = attr.ib() @attr.s(slots=True, on_setattr=[attr.setters.convert, attr.setters.validate]) class SlashCommandOption(DictSerializationMixin): """ Represents a discord slash command option. parameters: name: The name of this option type: The type of option description: The description of this option required: "This option must be filled to use the command" choices: A list of choices the user has to pick between channel_types: The channel types permitted. The option needs to be a channel min_value: The minimum value permitted. The option needs to be an integer or float max_value: The maximum value permitted. The option needs to be an integer or float """ name: str = attr.ib() type: Union[OptionTypes, int] = attr.ib() description: str = attr.ib(default="No Description Set") required: bool = attr.ib(default=True) autocomplete: bool = attr.ib(default=False) choices: List[Union[SlashCommandChoice, Dict]] = attr.ib(factory=list) channel_types: Optional[list[Union[ChannelTypes, int]]] = attr.ib(default=None) min_value: Optional[float] = attr.ib(default=None) max_value: Optional[float] = attr.ib(default=None) @name.validator def _name_validator(self, attribute: str, value: str) -> None: if not re.match(rf"^[\w-]{{1,{SLASH_CMD_NAME_LENGTH}}}$", value) or value != value.lower(): raise ValueError( f"Options names must be lower case and match this regex: ^[\w-]{1, {SLASH_CMD_NAME_LENGTH} }$" # noqa: W605 ) @description.validator def _description_validator(self, attribute: str, value: str) -> None: if not 1 <= len(value) <= SLASH_OPTION_NAME_LENGTH: raise ValueError("Options must be between 1 and 100 characters long") @type.validator def _type_validator(self, attribute: str, value: int) -> None: if value == OptionTypes.SUB_COMMAND or value == OptionTypes.SUB_COMMAND_GROUP: raise ValueError( "Options cannot be SUB_COMMAND or SUB_COMMAND_GROUP. If you want to use subcommands, " "see the @sub_command() decorator." ) @channel_types.validator def _channel_types_validator(self, attribute: str, value: Optional[list[OptionTypes]]) -> None: if value is not None: if self.type != OptionTypes.CHANNEL: raise ValueError("The option needs to be CHANNEL to use this") allowed_int = [channel_type.value for channel_type in ChannelTypes] for item in value: if (item not in allowed_int) and (item not in ChannelTypes): raise ValueError(f"{value} is not allowed here") @min_value.validator def _min_value_validator(self, attribute: str, value: Optional[float]) -> None: if value is not None: if self.type != OptionTypes.INTEGER and self.type != OptionTypes.NUMBER: raise ValueError("`min_value` can only be supplied with int or float options") if self.type == OptionTypes.INTEGER: if isinstance(value, float): raise ValueError("`min_value` needs to be an int in an int option") if self.max_value is not None and self.min_value is not None: if self.max_value < self.min_value: raise ValueError("`min_value` needs to be <= than `max_value`") @max_value.validator def _max_value_validator(self, attribute: str, value: Optional[float]) -> None: if value is not None: if self.type != OptionTypes.INTEGER and self.type != OptionTypes.NUMBER: raise ValueError("`max_value` can only be supplied with int or float options") if self.type == OptionTypes.INTEGER: if isinstance(value, float): raise ValueError("`max_value` needs to be an int in an int option") if self.max_value and self.min_value: if self.max_value < self.min_value: raise ValueError("`min_value` needs to be <= than `max_value`") @attr.s(slots=True, kw_only=True, on_setattr=[attr.setters.convert, attr.setters.validate]) class SlashCommand(InteractionCommand): name: str = attr.ib() description: str = attr.ib("No Description Set") group_name: str = attr.ib(default=None, metadata=no_export_meta) group_description: str = attr.ib(default="No Description Set", metadata=no_export_meta) sub_cmd_name: str = attr.ib(default=None, metadata=no_export_meta) sub_cmd_description: str = attr.ib(default="No Description Set", metadata=no_export_meta) options: List[Union[SlashCommandOption, Dict]] = attr.ib(factory=list) autocomplete_callbacks: dict = attr.ib(factory=dict, metadata=no_export_meta) @property def resolved_name(self) -> str: return f"{self.name}{f' {self.group_name}' if self.group_name else ''}{f' {self.sub_cmd_name}' if self.sub_cmd_name else ''}" @property def is_subcommand(self) -> bool: return self.sub_cmd_name is not None def __attrs_post_init__(self) -> None: params = get_parameters(self.callback) for name, val in params.items(): if val.annotation and isinstance(val.annotation, SlashCommandOption): if not self.options: self.options = [] val.annotation.name = name self.options.append(val.annotation) if self.callback is not None: if hasattr(self.callback, "options"): if not self.options: self.options = [] self.options += self.callback.options if hasattr(self.callback, "permissions"): self.permissions = self.callback.permissions super().__attrs_post_init__() def to_dict(self) -> dict: data = super().to_dict() if self.is_subcommand: data["name"] = self.sub_cmd_name data["description"] = self.sub_cmd_description data.pop("default_permission", None) data.pop("permissions", None) return data @name.validator @group_name.validator @sub_cmd_name.validator def name_validator(self, attribute: str, value: str) -> None: if value: if not re.match(rf"^[\w-]{{1,{SLASH_CMD_NAME_LENGTH}}}$", value) or value != value.lower(): raise ValueError( f"Slash Command names must be lower case and match this regex: ^[\w-]{1, {SLASH_CMD_NAME_LENGTH} }$" # noqa: W605 ) @description.validator @group_description.validator @sub_cmd_description.validator def description_validator(self, attribute: str, value: str) -> None: if not 1 <= len(value) <= SLASH_CMD_MAX_DESC_LENGTH: raise ValueError(f"Description must be between 1 and {SLASH_CMD_MAX_DESC_LENGTH} characters long") @options.validator def options_validator(self, attribute: str, value: List) -> None: if value: if isinstance(value, list): if len(value) > SLASH_CMD_MAX_OPTIONS: raise ValueError(f"Slash commands can only hold {SLASH_CMD_MAX_OPTIONS} options") if value != sorted( value, key=lambda x: x.required if isinstance(x, SlashCommandOption) else x["required"], reverse=True, ): raise ValueError("Required options must go before optional options") else: raise TypeError("Options attribute must be either None or a list of options") def autocomplete(self, option_name: str) -> Callable[..., Coroutine]: """A decorator to declare a coroutine as an option autocomplete.""" def wrapper(call: Callable[..., Coroutine]) -> Callable[..., Coroutine]: if not asyncio.iscoroutinefunction(call): raise TypeError("autocomplete must be coroutine") self.autocomplete_callbacks[option_name] = call # automatically set the option's autocomplete attribute to True for opt in self.options: if isinstance(opt, dict) and opt["name"] == option_name: opt["autocomplete"] = True elif isinstance(opt, SlashCommandOption) and opt.name == option_name: opt.autocomplete = True return call option_name = option_name.lower() return wrapper def subcommand( self, sub_cmd_name: str, group_name: str = None, group_description: str = "No Description Set", sub_cmd_description: Absent[str] = MISSING, options: List[Union[SlashCommandOption, Dict]] = None, ) -> Callable[..., "SlashCommand"]: def wrapper(call: Callable[..., Coroutine]) -> "SlashCommand": if not asyncio.iscoroutinefunction(call): raise TypeError("Subcommand must be coroutine") _description = sub_cmd_description if _description is MISSING: _description = call.__doc__ if call.__doc__ else "No Description Set" return SlashCommand( name=self.name, description=self.description, group_name=group_name, group_description=group_description, sub_cmd_name=sub_cmd_name, sub_cmd_description=_description, options=options, callback=call, ) return wrapper @attr.s(slots=True, kw_only=True, on_setattr=[attr.setters.convert, attr.setters.validate]) class ComponentCommand(InteractionCommand): # right now this adds no extra functionality, but for future dev ive implemented it listeners: list[str] = attr.ib(factory=list) ############## # Decorators # ############## def slash_command( name: str, description: Absent[str] = MISSING, scopes: Absent[List["Snowflake_Type"]] = MISSING, options: Optional[List[Union[SlashCommandOption, Dict]]] = None, default_permission: bool = True, permissions: Optional[List[Union[Permission, Dict]]] = None, sub_cmd_name: str = None, group_name: str = None, sub_cmd_description: str = "No Description Set", group_description: str = "No Description Set", ) -> Callable[[Coroutine], SlashCommand]: """ A decorator to declare a coroutine as a slash command. note: While the base and group descriptions arent visible in the discord client, currently. We strongly advise defining them anyway, if you're using subcommands, as Discord has said they will be visible in one of the future ui updates. parameters: name: 1-32 character name of the command description: 1-100 character description of the command scope: The scope this command exists within options: The parameters for the command, max 25 default_permission: Whether the command is enabled by default when the app is added to a guild permissions: The roles or users who can use this command sub_cmd_name: 1-32 character name of the subcommand sub_cmd_description: 1-100 character description of the subcommand group_name: 1-32 character name of the group group_description: 1-100 character description of the group returns: SlashCommand Object """ def wrapper(func) -> SlashCommand: if not asyncio.iscoroutinefunction(func): raise ValueError("Commands must be coroutines") _description = description if _description is MISSING: _description = func.__doc__ if func.__doc__ else "No Description Set" cmd = SlashCommand( name=name, group_name=group_name, group_description=group_description, sub_cmd_name=sub_cmd_name, sub_cmd_description=sub_cmd_description, description=_description, scopes=scopes if scopes else [GLOBAL_SCOPE], default_permission=default_permission, permissions=permissions or {}, callback=func, options=options, ) return cmd return wrapper def subcommand( base: str, *, subcommand_group: Optional[str] = None, name: Optional[str] = None, description: Absent[str] = MISSING, base_description: Optional[str] = None, base_desc: Optional[str] = None, base_default_permission: bool = True, base_permissions: Optional[dict] = None, subcommand_group_description: Optional[str] = None, sub_group_desc: Optional[str] = None, scopes: List["Snowflake_Type"] = None, options: List[dict] = None, ) -> Callable[[Coroutine], SlashCommand]: """ A decorator specifically tailored for creating subcommands. Args: base: The name of the base command subcommand_group: The name of the subcommand group, if any. name: The name of the subcommand, defaults to the name of the coroutine. description: The description of the subcommand base_description: The description of the base command base_desc: An alias of `base_description` base_default_permission: If users have permission to run the command by default when no permissions have been set base_permissions: Permissions of the command subcommand_group_description: Description of the subcommand group sub_group_desc: An alias for `subcommand_group_description` scopes: The scopes of which this command is available, defaults to GLOBAL_SCOPE options: The options for this command Returns: A SlashCommand object """ def wrapper(func) -> SlashCommand: if not asyncio.iscoroutinefunction(func): raise ValueError("Commands must be coroutines") _description = description if _description is MISSING: _description = func.__doc__ if func.__doc__ else "No Description Set" cmd = SlashCommand( name=base, description=(base_description or base_desc) or "No Description Set", group_name=subcommand_group, group_description=(subcommand_group_description or sub_group_desc) or "No Description Set", sub_cmd_name=name, sub_cmd_description=_description, default_permission=base_default_permission, permissions=base_permissions or {}, scopes=scopes if scopes else [GLOBAL_SCOPE], callback=func, options=options, ) return cmd return wrapper def context_menu( name: str, context_type: "CommandTypes", scopes: Absent[List["Snowflake_Type"]] = MISSING, default_permission: bool = True, permissions: Optional[List[Union[Permission, Dict]]] = None, ) -> Callable[[Coroutine], ContextMenu]: """ A decorator to declare a coroutine as a Context Menu. parameters: name: 1-32 character name of the context menu context_type: The type of context menu scope: The scope this command exists within default_permission: Whether the menu is enabled by default when the app is added to a guild permissions: The roles or users who can use this menu returns: ContextMenu object """ def wrapper(func) -> ContextMenu: if not asyncio.iscoroutinefunction(func): raise ValueError("Commands must be coroutines") perm = permissions or {} if hasattr(func, "permissions"): if perm: perm.update(func.permissions) else: perm = func.permissions cmd = ContextMenu( name=name, type=context_type, scopes=scopes if scopes else [GLOBAL_SCOPE], default_permission=default_permission, permissions=perm, callback=func, ) return cmd return wrapper def component_callback(*custom_id: str) -> Callable[[Coroutine], ComponentCommand]: """ Register a coroutine as a component callback. Component callbacks work the same way as commands, just using components as a way of invoking, instead of messages. Your callback will be given a single argument, `ComponentContext` Args: custom_id: The custom ID of the component to wait for """ def wrapper(func) -> ComponentCommand: if not asyncio.iscoroutinefunction(func): raise ValueError("Commands must be coroutines") return ComponentCommand(name=f"ComponentCallback::{custom_id}", callback=func, listeners=custom_id) # allows a mixture of generators and strings to be passed unpack = [] for c in custom_id: if inspect.isgenerator(c): unpack += list(c) else: unpack.append(c) custom_id = unpack return wrapper def slash_option( name: str, description: str, opt_type: Union[OptionTypes, int], required: bool = False, autocomplete: bool = False, choices: List[Union[SlashCommandChoice, dict]] = None, channel_types: Optional[list[Union[ChannelTypes, int]]] = None, min_value: Optional[float] = None, max_value: Optional[float] = None, ) -> Any: r""" A decorator to add an option to a slash command. parameters: name: 1-32 lowercase character name matching ^[\w-]{1,32}$ opt_type: The type of option description: 1-100 character description of option required: If the parameter is required or optional--default false choices: A list of choices the user has to pick between (max 25) channel_types: The channel types permitted. The option needs to be a channel min_value: The minimum value permitted. The option needs to be an integer or float max_value: The maximum value permitted. The option needs to be an integer or float """ def wrapper(func: Coroutine) -> Coroutine: if hasattr(func, "cmd_id"): raise Exception("slash_option decorators must be positioned under a slash_command decorator") option = SlashCommandOption( name=name, type=opt_type, description=description, required=required, autocomplete=autocomplete, choices=choices if choices else [], channel_types=channel_types, min_value=min_value, max_value=max_value, ) if not hasattr(func, "options"): func.options = [] func.options.insert(0, option) return func return wrapper def slash_permission(*permission: Union[Permission, Dict]) -> Any: """ A decorator to add permissions for a guild to a slash command or context menu. parameters: *permission: The permissions to apply to this command """ def wrapper(func: Coroutine) -> Coroutine: if hasattr(func, "cmd_id"): raise Exception("slash_permission decorators must be positioned under a slash_command decorator") if not hasattr(func, "permissions"): func.permissions = [] func.permissions += list(permission) return func return wrapper def auto_defer(ephemeral: bool = False, time_until_defer: float = 0.0) -> Callable[[Coroutine], Coroutine]: """ A decorator to add an auto defer to a application command. Args: ephemeral: Should the command be deferred as ephemeral time_until_defer: How long to wait before deferring automatically """ def wrapper(func: Coroutine) -> Coroutine: if hasattr(func, "cmd_id"): raise Exception("auto_defer decorators must be positioned under a slash_command decorator") func.auto_defer = AutoDefer(enabled=True, ephemeral=ephemeral, time_until_defer=time_until_defer) return func return wrapper def application_commands_to_dict(commands: Dict["Snowflake_Type", Dict[str, InteractionCommand]]) -> dict: """ Convert the command list into a format that would be accepted by discord. `Snake.interactions` should be the variable passed to this """ cmd_bases = {} # {cmd_base: [commands]} """A store of commands organised by their base command""" output = {} """The output dictionary""" def squash_subcommand(subcommands: List) -> Dict: output_data = {} groups = {} sub_cmds = [] for subcommand in subcommands: if not output_data: output_data = { "name": subcommand.name, "description": subcommand.description, "options": [], "permissions": [s.to_dict() if not isinstance(s, dict) else s for s in subcommand.permissions], "default_permission": subcommand.default_permission, } if subcommand.group_name: if subcommand.group_name not in groups: groups[subcommand.group_name] = { "name": subcommand.group_name, "description": subcommand.group_description, "type": int(OptionTypes.SUB_COMMAND_GROUP), "options": [], } groups[subcommand.group_name]["options"].append( subcommand.to_dict() | {"type": int(OptionTypes.SUB_COMMAND)} ) elif subcommand.is_subcommand: sub_cmds.append(subcommand.to_dict() | {"type": int(OptionTypes.SUB_COMMAND)}) options = list(groups.values()) + sub_cmds output_data["options"] = options return output_data for _scope, cmds in commands.items(): for cmd in cmds.values(): if cmd.name not in cmd_bases: cmd_bases[cmd.name] = [cmd] continue if cmd not in cmd_bases[cmd.name]: cmd_bases[cmd.name].append(cmd) for cmd_list in cmd_bases.values(): if any(c.is_subcommand for c in cmd_list): # validate all commands share required attributes scopes: list[Snowflake_Type] = list({s for c in cmd_list for s in c.scopes}) permissions: list = list({d for c in cmd_list for d in c.permissions}) base_description = next( (c.description for c in cmd_list if c.description is not None), "No Description Set" ) if not all(c.description in (base_description, "No Description Set") for c in cmd_list): log.warning( f"Conflicting descriptions found in `{cmd_list[0].name}` subcommands; `{base_description}` will be used" ) if not all(c.default_permission == cmd_list[0].default_permission for c in cmd_list): raise ValueError(f"Conflicting `default_permission` values found in `{cmd_list[0].name}`") for cmd in cmd_list: cmd.scopes = list(scopes) cmd.permissions = permissions cmd.description = base_description # end validation of attributes cmd_data = squash_subcommand(cmd_list) else: scopes = cmd_list[0].scopes cmd_data = cmd_list[0].to_dict() for s in scopes: if s not in output: output[s] = [cmd_data] continue output[s].append(cmd_data) return output def _compare_options(local_opt_list: dict, remote_opt_list: dict): if local_opt_list != remote_opt_list: if len(local_opt_list) != len(remote_opt_list): return False for i in range(len(local_opt_list)): local_option = local_opt_list[i] remote_option = remote_opt_list[i] if local_option["type"] == remote_option["type"]: if local_option["type"] in (OptionTypes.SUB_COMMAND_GROUP, OptionTypes.SUB_COMMAND): if not _compare_options(local_option.get("options", []), remote_option.get("options", [])): return False else: if ( local_option["name"] != remote_option["name"] or local_option["description"] != remote_option["description"] or local_option["required"] != remote_option.get("required", False) or local_option["autocomplete"] != remote_option.get("autocomplete", False) or local_option["choices"] != remote_option.get("choices", []) ): return False else: return False return True def sync_needed(local_cmd: dict, remote_cmd: Optional[dict] = None) -> bool: """ Compares a local application command to its remote counterpart to determine if a sync is required. Args: local_cmd: The local json representation of the command remote_cmd: The json representation of the command from Discord Returns: Boolean indicating if a sync is needed """ if not remote_cmd: # No remote version, command must be new return True if ( local_cmd["name"] != remote_cmd["name"] or local_cmd.get("description", "") != remote_cmd.get("description", "") or local_cmd["default_permission"] != remote_cmd["default_permission"] ): # basic comparison of attributes return True if remote_cmd["type"] == CommandTypes.CHAT_INPUT: try: if not _compare_options(local_cmd["options"], remote_cmd["options"]): # options are not the same, sync needed return True except KeyError: if "options" in local_cmd or "options" in remote_cmd: return True return False

36.793467

134

0.63482

e0fbe8e2486effc235dfc7636d289fd16a8fef33

96,271

py

Python

tensorflow/lite/python/lite.py

henriwoodcock/tensorflow

d9ad2a9f62ada961b26d09c8f4856cb0c94d9f8a

[ "Apache-2.0" ]

null

tensorflow/lite/python/lite.py

henriwoodcock/tensorflow

d9ad2a9f62ada961b26d09c8f4856cb0c94d9f8a

[ "Apache-2.0" ]

null

tensorflow/lite/python/lite.py

henriwoodcock/tensorflow

d9ad2a9f62ada961b26d09c8f4856cb0c94d9f8a

[ "Apache-2.0" ]

1

2021-04-20T18:26:18.000Z

# Lint as: python2, python3 # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """TensorFlow Lite tooling helper functionality.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import enum import pprint import shutil import tempfile import warnings from absl import logging import six from six import PY2 from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.core.framework import graph_pb2 as _graph_pb2 from tensorflow.lite.experimental.examples.lstm.rnn import dynamic_rnn # pylint: disable=unused-import from tensorflow.lite.experimental.examples.lstm.rnn_cell import TFLiteLSTMCell # pylint: disable=unused-import from tensorflow.lite.experimental.examples.lstm.rnn_cell import TfLiteRNNCell # pylint: disable=unused-import from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op # pylint: disable=unused-import from tensorflow.lite.experimental.tensorboard.ops_util import get_potentially_supported_ops # pylint: disable=unused-import from tensorflow.lite.python import lite_constants as constants from tensorflow.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import from tensorflow.lite.python.convert import convert_saved_model as _convert_saved_model from tensorflow.lite.python.convert import ConverterError # pylint: disable=unused-import from tensorflow.lite.python.convert import mlir_quantize as _mlir_quantize from tensorflow.lite.python.convert import mlir_sparsify as _mlir_sparsify from tensorflow.lite.python.convert import OpsSet from tensorflow.lite.python.convert import toco_convert # pylint: disable=unused-import from tensorflow.lite.python.convert import toco_convert_graph_def as _toco_convert_graph_def from tensorflow.lite.python.convert import toco_convert_impl as _toco_convert_impl from tensorflow.lite.python.convert import toco_convert_protos # pylint: disable=unused-import from tensorflow.lite.python.convert_saved_model import freeze_saved_model as _freeze_saved_model from tensorflow.lite.python.interpreter import Interpreter # pylint: disable=unused-import from tensorflow.lite.python.interpreter import load_delegate # pylint: disable=unused-import from tensorflow.lite.python.interpreter import OpResolverType # pylint: disable=unused-import from tensorflow.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import from tensorflow.lite.python.op_hint import is_ophint_converted as _is_ophint_converted from tensorflow.lite.python.op_hint import OpHint # pylint: disable=unused-import from tensorflow.lite.python.optimize import calibrator as _calibrator from tensorflow.lite.python.util import build_debug_info_func as _build_debug_info_func from tensorflow.lite.python.util import convert_debug_info_func as _convert_debug_info_func from tensorflow.lite.python.util import freeze_graph as _freeze_graph from tensorflow.lite.python.util import get_debug_info as _get_debug_info from tensorflow.lite.python.util import get_grappler_config as _get_grappler_config from tensorflow.lite.python.util import get_tensor_name as _get_tensor_name from tensorflow.lite.python.util import get_tensors_from_tensor_names as _get_tensors_from_tensor_names from tensorflow.lite.python.util import get_tf_type_name as _get_tf_type_name from tensorflow.lite.python.util import is_frozen_graph as _is_frozen_graph from tensorflow.lite.python.util import model_input_signature as _model_input_signature from tensorflow.lite.python.util import modify_model_io_type as _modify_model_io_type from tensorflow.lite.python.util import run_graph_optimizations as _run_graph_optimizations from tensorflow.lite.python.util import set_tensor_shapes as _set_tensor_shapes from tensorflow.lite.python.util import trace_model_call as _trace_model_call from tensorflow.python.client import session as _session from tensorflow.python.eager import context from tensorflow.python.eager import def_function as _def_function from tensorflow.python.eager import function as _function from tensorflow.python.framework import convert_to_constants as _convert_to_constants from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import ops as _ops from tensorflow.python.framework.errors_impl import NotFoundError as _NotFoundError from tensorflow.python.framework.importer import import_graph_def as _import_graph_def from tensorflow.python.lib.io import file_io as _file_io from tensorflow.python.saved_model import loader_impl as _loader_impl from tensorflow.python.saved_model import signature_constants as _signature_constants from tensorflow.python.saved_model import tag_constants as _tag_constants from tensorflow.python.saved_model.load import load as _load from tensorflow.python.saved_model.loader_impl import parse_saved_model_with_debug_info as _parse_saved_model_with_debug_info from tensorflow.python.util import deprecation as _deprecation from tensorflow.python.util import keras_deps from tensorflow.python.util.tf_export import tf_export as _tf_export # pylint: disable=g-import-not-at-top try: from tensorflow.lite.python import metrics_portable as metrics except ImportError: from tensorflow.lite.python import metrics_nonportable as metrics # pylint: enable=g-import-not-at-top @_tf_export("lite.Optimize") class Optimize(enum.Enum): """Enum defining the optimizations to apply when generating a tflite model. DEFAULT Default optimization strategy that quantizes model weights. Enhanced optimizations are gained by providing a representative dataset that quantizes biases and activations as well. Converter will do its best to reduce size and latency, while minimizing the loss in accuracy. OPTIMIZE_FOR_SIZE Deprecated. Does the same as DEFAULT. OPTIMIZE_FOR_LATENCY Deprecated. Does the same as DEFAULT. EXPERIMENTAL_SPARSITY Experimental flag, subject to change. Enable optimization by taking advantage of the sparse model weights trained with pruning. The converter will inspect the sparsity pattern of the model weights and do its best to improve size and latency. The flag can be used alone to optimize float32 models with sparse weights. It can also be used together with the DEFAULT optimization mode to optimize quantized models with sparse weights. """ # Default optimization strategy that quantizes model weights. Enhanced # optimizations are gained by providing a representative dataset that # quantizes biases and activations as well. # Converter will do its best to reduce size and latency, while minimizing # the loss in accuracy. DEFAULT = "DEFAULT" # Deprecated. Does the same as DEFAULT. OPTIMIZE_FOR_SIZE = "OPTIMIZE_FOR_SIZE" # Deprecated. Does the same as DEFAULT. OPTIMIZE_FOR_LATENCY = "OPTIMIZE_FOR_LATENCY" # Experimental flag, subject to change. # Enable optimization by taking advantage of the sparse model weights trained # with pruning. # # The converter will inspect the sparsity pattern of the model weights and do # its best to improve size and latency. # The flag can be used alone to optimize float32 models with sparse weights. # It can also be used together with the DEFAULT optimization mode to optimize # quantized models with sparse weights. # TODO(b/161560631): Add log message when this optimization is applied. EXPERIMENTAL_SPARSITY = "EXPERIMENTAL_SPARSITY" def __str__(self): return str(self.value) @_tf_export("lite.RepresentativeDataset") class RepresentativeDataset(object): """Representative dataset used to optimize the model. This is a generator function that provides a small dataset to calibrate or estimate the range, i.e, (min, max) of all floating-point arrays in the model (such as model input, activation outputs of intermediate layers, and model output) for quantization. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. """ def __init__(self, input_gen): """Creates a representative dataset. Args: input_gen: A generator function that generates input samples for the model and has the same order, type and shape as the inputs to the model. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. """ self.input_gen = input_gen @_tf_export("lite.TargetSpec") class TargetSpec(object): """Specification of target device used to optimize the model. Attributes: supported_ops: Experimental flag, subject to change. Set of `tf.lite.OpsSet` options, where each option represents a set of operators supported by the target device. (default {tf.lite.OpsSet.TFLITE_BUILTINS})) supported_types: Set of `tf.dtypes.DType` data types supported on the target device. If initialized, optimization might be driven by the smallest type in this set. (default set()) experimental_select_user_tf_ops: Experimental flag, subject to change. Set of user's TensorFlow operators' names that are required in the TensorFlow Lite runtime. These ops will be exported as select TensorFlow ops in the model (in conjunction with the tf.lite.OpsSet.SELECT_TF_OPS flag). This is an advanced feature that should only be used if the client is using TF ops that may not be linked in by default with the TF ops that are provided when using the SELECT_TF_OPS path. The client is responsible for linking these ops into the target runtime. """ def __init__(self, supported_ops=None, supported_types=None, experimental_select_user_tf_ops=None): if supported_ops is None: supported_ops = {OpsSet.TFLITE_BUILTINS} self.supported_ops = supported_ops if supported_types is None: supported_types = set() self.supported_types = supported_types if experimental_select_user_tf_ops is None: experimental_select_user_tf_ops = set() self.experimental_select_user_tf_ops = experimental_select_user_tf_ops self._experimental_custom_op_registerers = [] class QuantizationMode(object): """QuantizationMode determines the quantization type from user options.""" def __init__(self, optimizations, target_spec, representative_dataset, graph_def, disable_per_channel=False): self._optimizations = optimizations self._target_spec = target_spec self._representative_dataset = representative_dataset self._graph_def = graph_def self._validate_int8_required() self._disable_per_channel = disable_per_channel # TODO(b/162537905): Refactor the following quantization functions - # re-organize and refactor for better readability. def post_training_int8_no_float(self): return (self._any_optimization_enabled() and self._is_int8_target_required() and not self._is_int16x8_target_required() and not self._is_allow_float() and self._representative_dataset is not None) def post_training_int8_allow_float(self): return (self._any_optimization_enabled() and not self._is_int16x8_target_required() and self._representative_dataset is not None and self._smallest_supported_type() == _dtypes.int8) def is_post_training_integer_quantize_8(self): return (self.post_training_int8_no_float() or self.post_training_int8_allow_float()) def is_post_training_integer_quantize_16x8(self): return (self.post_training_int16x8_no_float() or self.post_training_int16x8_allow_float()) def is_integer_quantize(self): return (self.is_post_training_integer_quantize_8() or self.is_post_training_integer_quantize_16x8() or self.is_training_time_int8_allow_float()) def is_training_time_int8_allow_float(self): return (self._any_optimization_enabled() and self.contains_training_quant_op()) def post_training_int16x8_no_float(self): return (self._any_optimization_enabled() and not self._is_int8_target_required() and self._is_int16x8_target_required() and not self._is_allow_float() and self._representative_dataset is not None) def post_training_int16x8_allow_float(self): return (self._any_optimization_enabled() and self._is_int16x8_target_required() and self._is_allow_float()) def post_training_dynamic_range_int8(self): # Post-training dynamic range quantization is only enabled if post-training # int8 quantization and training time quantization was not done. return (self._any_optimization_enabled() and self._representative_dataset is None and not self.contains_training_quant_op() and self._smallest_supported_type() == _dtypes.int8) def post_training_fp16(self): return (self._any_optimization_enabled() and self._smallest_supported_type() == _dtypes.float16) def fp32_execution(self): """If none of the above are true.""" return not (self.is_integer_quantize() or self.post_training_dynamic_range_int8() or self.post_training_fp16()) def activations_type(self): return _dtypes.int16 if self._is_int16x8_target_required() \ else _dtypes.int8 def converter_flags(self, inference_ty=None, inference_input_ty=None): """Flags to the converter.""" if self.is_integer_quantize(): return { "inference_type": inference_ty if inference_ty else \ self.activations_type(), "inference_input_type": _dtypes.float32, "post_training_quantize": False, # disable dynamic range quantization "quantize_to_float16": False # disable float16 quantization } elif self.post_training_dynamic_range_int8(): return { "inference_type": _dtypes.float32, "inference_input_type": _dtypes.float32, "post_training_quantize": True, # enable dynamic range quantization "quantize_to_float16": False # disable float16 quantization } elif self.post_training_fp16(): return { "inference_type": _dtypes.float32, "inference_input_type": _dtypes.float32, "post_training_quantize": True, "quantize_to_float16": True # enable float16 quantization } else: # Note this might still trigger (uint8) quantization to be compatible with # TOCO. return { "inference_type": inference_ty if inference_ty else _dtypes.float32, "inference_input_type": inference_input_ty, "post_training_quantize": False, # enable dynamic range quantization "quantize_to_float16": False # disable float16 quantization } def quantizer_flags(self, input_ty=None, output_ty=None): """Default flags to the TFMOT quantizer.""" inference_input_type = input_ty if input_ty else _dtypes.float32 inference_output_type = output_ty if output_ty else _dtypes.float32 if self.post_training_int8_no_float() \ or self.post_training_int16x8_no_float(): return True, { "inference_input_type": inference_input_type, "inference_output_type": inference_output_type, "activations_type": self.activations_type(), "allow_float": False, "disable_per_channel": self._disable_per_channel, } elif self.post_training_int8_allow_float() \ or self.post_training_int16x8_allow_float(): return True, { "inference_input_type": inference_input_type, "inference_output_type": inference_output_type, "activations_type": self.activations_type(), "allow_float": True, "disable_per_channel": self._disable_per_channel, } else: return False, None def flags_modify_model_io_type(self, input_ty=None, output_ty=None): """Flags for modifying the input and output type of a tflite model.""" if self.is_integer_quantize(): return { "inference_input_type": input_ty if input_ty else _dtypes.float32, "inference_output_type": output_ty if output_ty else _dtypes.float32, } else: return None # Below are helpers for the above functions. def _validate_int8_required(self): """Int8 mode requires certain parameters to exist and be compatible.""" if not self._is_int8_target_required(): return if self._target_spec.supported_types and (self._smallest_supported_type() != _dtypes.int8): raise ValueError("TFLITE_BUILTINS_INT8 requires smallest supported " "type to be INT8.") if self._representative_dataset: if not isinstance(self._representative_dataset, RepresentativeDataset): self._representative_dataset = RepresentativeDataset( self._representative_dataset) if self._representative_dataset.input_gen is None: raise ValueError( "Provide an input generator for representative_dataset") else: # TODO(b/162537905): Relax this check for QAT. raise ValueError("representative_dataset is required when specifying " "TFLITE_BUILTINS_INT8 or INT8 supported types.") def _is_int8_target_required(self): return (OpsSet.TFLITE_BUILTINS_INT8 in set( self._target_spec.supported_ops)) or (set( self._target_spec.supported_types) == set([_dtypes.int8])) def _is_int16x8_target_required(self): return (OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 in set(self._target_spec.supported_ops)) def _is_allow_float(self): return (OpsSet.TFLITE_BUILTINS in set( self._target_spec.supported_ops)) or (OpsSet.SELECT_TF_OPS in set( self._target_spec.supported_ops)) def _any_optimization_enabled(self): return bool( set(self._optimizations).intersection([ Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE, Optimize.DEFAULT ])) def _smallest_supported_type(self): if self._target_spec.supported_types: return min(self._target_spec.supported_types, key=lambda x: x.size) else: # The default smallest supported type is INT8. return _dtypes.int8 def contains_training_quant_op(self): """Checks if the graph contains any training-time quantization ops.""" training_quant_ops = frozenset({ "FakeQuantWithMinMaxVars", "FakeQuantWithMinMaxVarsPerChannel", "FakeQuantWithMinMaxArgs", "FakeQuantWithMinMaxArgsPerChannel", "QuantizeAndDequantizeV2", "QuantizeAndDequantizeV3" }) for node_def in self._graph_def.node: if node_def.op in training_quant_ops: return True for function in self._graph_def.library.function: for node_def in function.node_def: if node_def.op in training_quant_ops: return True return False # The metrics are unregistered if their variables get garbage-collected. So use # a global variable to keep them alive till program exits. _global_metrics = metrics.TFLiteMetrics() class TFLiteConverterBase(object): """Converter subclass to share functionality between V1 and V2 converters.""" def __init__(self): self.optimizations = set() self.representative_dataset = None self.target_spec = TargetSpec() self.allow_custom_ops = False self.experimental_new_converter = True self.experimental_new_quantizer = True self._experimental_new_quantizer = None self._experimental_calibrate_only = False self._experimental_sparsify_model = False self._experimental_disable_per_channel = False self._debug_info = None # contains the stack traces of all the original # nodes in the `GraphDef` to the converter. self.saved_model_dir = None self._saved_model_tags = None self._saved_model_version = 0 self._saved_model_exported_names = [] # Variable for converter metrics. self._tflite_metrics = _global_metrics self._experimental_disable_batchmatmul_unfold = False self._experimental_lower_tensor_list_ops = True def _grappler_config(self, optimizers=None): """Creates a tf.compat.v1.ConfigProto for configuring Grappler. Args: optimizers: List of strings that represents the list of optimizers. Returns: tf.ConfigProto. """ if not optimizers: optimizers = [] # MLIR converter will take care of constant folding instead of grappler. if not self.experimental_new_converter: optimizers.append("constfold") is_only_flex_enabled = ( set([OpsSet.SELECT_TF_OPS]) == set(self.target_spec.supported_ops)) if is_only_flex_enabled: # The layout optimizer turns NHCW to NCHW. This provides performance # optimizations when Flex mode is enabled. However, this is not compatible # with builtin ops. optimizers.append("layout") return _get_grappler_config(optimizers) def _calibrate_quantize_model(self, result, inference_input_type, inference_output_type, activations_type, allow_float, disable_per_channel): """Calibrate and quantize the model.""" # pylint: disable=protected-access custom_op_registerers_by_name = [ x for x in self.target_spec._experimental_custom_op_registerers if isinstance(x, str) ] custom_op_registerers_by_func = [ x for x in self.target_spec._experimental_custom_op_registerers if not isinstance(x, str) ] # pylint: enable=protected-access if not isinstance(self.representative_dataset, RepresentativeDataset): self.representative_dataset = RepresentativeDataset( self.representative_dataset) # Add intermediate tensors to the model if needed. result = _calibrator.add_intermediate_tensors(result) calibrate_quantize = _calibrator.Calibrator(result, custom_op_registerers_by_name, custom_op_registerers_by_func) if self._experimental_calibrate_only or self.experimental_new_quantizer: calibrated = calibrate_quantize.calibrate( self.representative_dataset.input_gen) if self._experimental_calibrate_only: return calibrated elif self.experimental_new_quantizer and ( activations_type != _dtypes.int16): # TODO(b/175659372): remove the activations_type restriction and enable # it for all the activation types. return _mlir_quantize(calibrated, disable_per_channel, input_data_type=inference_input_type, output_data_type=inference_output_type) else: return calibrate_quantize.calibrate_and_quantize( self.representative_dataset.input_gen, inference_input_type, inference_output_type, allow_float, activations_type, disable_per_channel=disable_per_channel) def _is_unknown_shapes_allowed(self): # Unknown dimensions are only allowed with the new converter. return self.experimental_new_converter def _get_base_converter_args(self): """Returns the base converter args. Returns: {key str: val} """ args = { "input_format": constants.TENSORFLOW_GRAPHDEF, "allow_custom_ops": self.allow_custom_ops, "debug_info": self._debug_info, "target_ops": self.target_spec.supported_ops, "enable_mlir_converter": self.experimental_new_converter, "select_user_tf_ops": self.target_spec.experimental_select_user_tf_ops, "unfold_batchmatmul": not self._experimental_disable_batchmatmul_unfold, "lower_tensor_list_ops": self._experimental_lower_tensor_list_ops, } if self.saved_model_dir: args.update({ "saved_model_dir": self.saved_model_dir, "saved_model_version": self._saved_model_version, "saved_model_tags": self._saved_model_tags, "saved_model_exported_names": self._saved_model_exported_names, }) return args def _contains_function_with_implements_attr(self, saved_model_proto): meta_graph = saved_model_proto.meta_graphs[0] for function in meta_graph.graph_def.library.function: if function.attr.get("_implements", None) or function.attr.get( "api_implements", None): return True return False def _parse_saved_model_args(self, always_enable_saved_model_import=False): """Parses SavedModel arguments from the given Keras/RNN SavedModel. Args: always_enable_saved_model_import: Bool. When the value is true, it enables MLIR saved model import path regardless of checking the conditions. """ if not self.experimental_new_converter: self.saved_model_dir = None return if self.saved_model_dir: try: saved_model_proto, _ = ( _parse_saved_model_with_debug_info(self.saved_model_dir)) except OSError: # If it fails to read the given saved model, it will fall back to the # frozen graph def path. self.saved_model_dir = None return if (not always_enable_saved_model_import and not self._contains_function_with_implements_attr(saved_model_proto)): self.saved_model_dir = None return if not self._saved_model_exported_names: self._saved_model_exported_names = [] self._saved_model_version = saved_model_proto.saved_model_schema_version if self._saved_model_version == 0: self.saved_model_dir = None logging.warning("SavedModel schema version is zero.") return if self._saved_model_version not in [1, 2]: raise ValueError("SavedModel file format({0}) is not supported".format( self._saved_model_version)) def _sparsify_model(self): return Optimize.EXPERIMENTAL_SPARSITY in self.optimizations def _validate_experimental_new_quantizer_flag(self): if self._experimental_new_quantizer is not None: raise ValueError("Please use 'experimental_new_quantizer' instead.") def _increase_conversion_attempt_metric(self): self._tflite_metrics.increase_counter_converter_attempt() def _increase_conversion_success_metric(self, result): if result: self._tflite_metrics.increase_counter_converter_success() def _save_conversion_params_metric(self, converter_params, graph_def=None, inference_type=None, inference_input_type=None): """Set conversion parameter metrics.""" converter_kwargs = converter_params.copy() converter_kwargs.update(self._get_base_converter_args()) # quantization-replated parameters. try: quant_mode = QuantizationMode(self.optimizations, self.target_spec, self.representative_dataset, graph_def) calibrate_and_quantize, flags = quant_mode.quantizer_flags( inference_type, inference_input_type) converter_kwargs.update({ "calibrate_and_quantize": calibrate_and_quantize, }) if calibrate_and_quantize: converter_kwargs.update(flags) converter_kwargs.update( quant_mode.converter_flags(inference_type, inference_input_type)) except Exception: # pylint: disable=broad-except # Still updates other params. pass # Optimization parameters. optimization_default = set(self.optimizations).intersection([ Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE, Optimize.DEFAULT ]) converter_kwargs.update({ "optimization_sparsify_model": self._sparsify_model(), "optimization_default": bool(optimization_default), }) def format_element(elem): if isinstance(elem, enum.Enum): return str(elem.value) return pprint.pformat(elem) def format_param(param): if isinstance(param, (list, tuple, set)): if not param: return "None" # Return None if empty. string_list = [format_element(x) for x in param] return ",".join(sorted(string_list)) return format_element(param) for key, value in converter_kwargs.items(): self._tflite_metrics.set_converter_param(key, format_param(value)) class TFLiteConverterBaseV2(TFLiteConverterBase): """Converter subclass to share functionality between V2 converters.""" def __init__(self): """Constructor for TFLiteConverter.""" super(TFLiteConverterBaseV2, self).__init__() self.inference_input_type = _dtypes.float32 self.inference_output_type = _dtypes.float32 def _validate_inference_input_output_types(self, quant_mode): """Validate inference_input_type and inference_output_type flags.""" default_types = [_dtypes.float32] # We support integer input/output for integer quantized models only. if quant_mode.is_integer_quantize(): if quant_mode.is_post_training_integer_quantize_16x8(): all_types = default_types + [_dtypes.int16] else: all_types = default_types + [_dtypes.int8, _dtypes.uint8] if self.inference_input_type not in all_types or \ self.inference_output_type not in all_types: all_types_names = ["tf." + t.name for t in all_types] raise ValueError("The inference_input_type and inference_output_type " "must be in {}.".format(all_types_names)) elif self.inference_input_type not in default_types or \ self.inference_output_type not in default_types: raise ValueError("The inference_input_type and inference_output_type " "must be tf.float32.") def _save_conversion_params_metric(self, converter_params, graph_def=None, inference_type=None, inference_input_type=None): converter_kwargs = converter_params.copy() converter_kwargs.update({ "api_version": 2, }) super(TFLiteConverterBaseV2, self)._save_conversion_params_metric(converter_kwargs, graph_def, inference_type, inference_input_type) def convert(self, graph_def, input_tensors, output_tensors): """Converts a TensorFlow GraphDef based on instance variables. Args: graph_def: Frozen TensorFlow GraphDef. input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). Returns: The converted data in serialized format. Raises: ValueError: No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. """ # Update conversion params with graph_def. self._save_conversion_params_metric({}, graph_def) quant_mode = QuantizationMode(self.optimizations, self.target_spec, self.representative_dataset, graph_def, self._experimental_disable_per_channel) self._validate_inference_input_output_types(quant_mode) self._validate_experimental_new_quantizer_flag() if not self._is_unknown_shapes_allowed(): # Checks dimensions in input tensor. for tensor in input_tensors: # Note that shape_list might be empty for scalar shapes. shape_list = tensor.shape.as_list() if None in shape_list[1:]: raise ValueError( "None is only supported in the 1st dimension. Tensor '{0}' has " "invalid shape '{1}'.".format( _get_tensor_name(tensor), shape_list)) elif shape_list and shape_list[0] is None: # Set the batch size to 1 if undefined. shape = tensor.shape.as_list() shape[0] = 1 tensor.set_shape(shape) if self._trackable_obj is None: self._debug_info = _get_debug_info( _build_debug_info_func(self._funcs[0].graph), graph_def) else: self._debug_info = _get_debug_info( _convert_debug_info_func(self._trackable_obj.graph_debug_info), graph_def) converter_kwargs = self._get_base_converter_args() converter_kwargs.update(quant_mode.converter_flags()) if not self.experimental_new_converter: logging.warning( "Please consider switching to the new converter by setting " "experimental_new_converter=True. " "The old converter (TOCO) is deprecated.") else: logging.info("Using new converter: If you encounter a problem " "please file a bug. You can opt-out " "by setting experimental_new_converter=False") # Converts model. result = _toco_convert_impl( input_data=graph_def, input_tensors=input_tensors, output_tensors=output_tensors, **converter_kwargs) if self.experimental_new_quantizer: calibrate_and_quantize, flags = quant_mode.quantizer_flags( self.inference_input_type, self.inference_output_type) else: calibrate_and_quantize, flags = quant_mode.quantizer_flags() if calibrate_and_quantize: result = self._calibrate_quantize_model(result, **flags) flags_modify_model_io_type = quant_mode.flags_modify_model_io_type( self.inference_input_type, self.inference_output_type) if flags_modify_model_io_type: result = _modify_model_io_type(result, **flags_modify_model_io_type) if self._sparsify_model(): result = _mlir_sparsify(result) return result class TFLiteSavedModelConverterV2(TFLiteConverterBaseV2): """Converts the given SavedModel into TensorFlow Lite model. Attributes: saved_model_dir: Directory of the SavedModel. """ def __init__(self, saved_model_dir, saved_model_tags=None, saved_model_exported_names=None, trackable_obj=None): """Constructor for TFLiteConverter. Args: saved_model_dir: Directory of the SavedModel. saved_model_tags: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default {tf.saved_model.SERVING}). saved_model_exported_names: Names to be exported when the saved model import path is on. trackable_obj: tf.AutoTrackable object associated with `funcs`. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. `from_saved_model`). """ super(TFLiteSavedModelConverterV2, self).__init__() self.saved_model_dir = saved_model_dir self._saved_model_tags = saved_model_tags self._saved_model_exported_names = saved_model_exported_names self._trackable_obj = trackable_obj self._parse_saved_model_args(always_enable_saved_model_import=True) self._enable_tflite_resource_variables = False def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Raises: ValueError: No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. """ converter_kwargs = { "enable_tflite_resource_variables": self._enable_tflite_resource_variables } self._increase_conversion_attempt_metric() self._save_conversion_params_metric(converter_kwargs) graph = _ops.Graph() saved_model = _loader_impl.SavedModelLoader(self.saved_model_dir) saved_model.load_graph(graph, tags=self._saved_model_tags) meta_graph = saved_model.get_meta_graph_def_from_tags( self._saved_model_tags) # If we can't use saved model importer, then fallback # to frozen graph conversion path. if self.saved_model_dir is None or not self.experimental_new_converter: signature_def = meta_graph.signature_def[ _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] input_tensors = [ graph.get_tensor_by_name(signature_def.inputs[key].name) for key in signature_def.inputs ] output_tensors = [ graph.get_tensor_by_name(signature_def.outputs[key].name) for key in signature_def.outputs ] result = _freeze_saved_model( self.saved_model_dir, None, None, None, self._saved_model_tags, _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY) graph_def = result[0] # We make sure to clear the saved_model_dir as there is some # legacy code down in the caller that checks this. # TODO(b/162537905): Clean these indirect dependencies. self.saved_model_dir = None conversion_result = super(TFLiteSavedModelConverterV2, self).convert(graph_def, input_tensors, output_tensors) self._increase_conversion_success_metric(conversion_result) return conversion_result if self._trackable_obj is None: self._debug_info = _get_debug_info( _build_debug_info_func(self._funcs[0].graph), meta_graph.graph_def) else: self._debug_info = _get_debug_info( _convert_debug_info_func(self._trackable_obj.graph_debug_info), meta_graph.graph_def) # Update conversion params with graph_def. self._save_conversion_params_metric(converter_kwargs, meta_graph.graph_def) # Get quantization options and do some sanity checks. quant_mode = QuantizationMode(self.optimizations, self.target_spec, self.representative_dataset, meta_graph.graph_def, self._experimental_disable_per_channel) self._validate_inference_input_output_types(quant_mode) converter_kwargs.update(self._get_base_converter_args()) converter_kwargs.update(quant_mode.converter_flags()) result = _convert_saved_model(**converter_kwargs) if self.experimental_new_quantizer: calibrate_and_quantize, flags = quant_mode.quantizer_flags( self.inference_input_type, self.inference_output_type) else: calibrate_and_quantize, flags = quant_mode.quantizer_flags() if calibrate_and_quantize: result = self._calibrate_quantize_model(result, **flags) flags_modify_model_io_type = quant_mode.flags_modify_model_io_type( self.inference_input_type, self.inference_output_type) if flags_modify_model_io_type: result = _modify_model_io_type(result, **flags_modify_model_io_type) if self._sparsify_model(): result = _mlir_sparsify(result) self._increase_conversion_success_metric(result) return result class TFLiteKerasModelConverterV2(TFLiteConverterBaseV2): """Converts the given Keras model into TensorFlow Lite model.""" def __init__(self, keras_model, trackable_obj=None): """Constructor for TFLiteConverter. Args: keras_model: tf.Keras.Model. trackable_obj: tf.AutoTrackable object associated with `funcs`. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. `from_saved_model`). """ super(TFLiteKerasModelConverterV2, self).__init__() self._keras_model = keras_model self._trackable_obj = trackable_obj def _convert_as_saved_model(self): """Converts a Keras model as a saved model. Returns: The converted data in serialized format. """ temp_dir = tempfile.mkdtemp() try: try: self._keras_model.save(temp_dir, save_format="tf") except Exception: # pylint: disable=broad-except # When storing the given keras model to a saved model is failed, let's # use original keras model conversion pipeline. return None self.saved_model_dir = temp_dir self._saved_model_tags = set([_tag_constants.SERVING]) self._saved_model_exported_names = [ _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY ] self._parse_saved_model_args() if self.saved_model_dir: graph = _ops.Graph() saved_model = _loader_impl.SavedModelLoader(self.saved_model_dir) saved_model.load_graph(graph, tags=self._saved_model_tags) meta_graph = saved_model.get_meta_graph_def_from_tags( self._saved_model_tags) signature_def = meta_graph.signature_def[ _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] input_tensors = [ graph.get_tensor_by_name(signature_def.inputs[key].name) for key in signature_def.inputs ] output_tensors = [ graph.get_tensor_by_name(signature_def.outputs[key].name) for key in signature_def.outputs ] self._trackable_obj = _load(self.saved_model_dir, self._saved_model_tags) return super(TFLiteKerasModelConverterV2, self).convert(meta_graph.graph_def, input_tensors, output_tensors) finally: shutil.rmtree(temp_dir, True) def convert(self): """Converts a keras model based on instance variables. Returns: The converted data in serialized format. Raises: ValueError: Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. """ self._increase_conversion_attempt_metric() self._save_conversion_params_metric({}) saved_model_convert_result = self._convert_as_saved_model() if saved_model_convert_result: self._increase_conversion_success_metric(saved_model_convert_result) return saved_model_convert_result input_signature = None # If the model's call is not a `tf.function`, then we need to first get its # input signature from `model_input_signature` method. We can't directly # call `trace_model_call` because otherwise the batch dimension is set # to None. # Once we have better support for dynamic shapes, we can remove this. if not isinstance(self._keras_model.call, _def_function.Function): # Pass `keep_original_batch_size=True` will ensure that we get an input # signature including the batch dimension specified by the user. # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF input_signature = _model_input_signature( self._keras_model, keep_original_batch_size=True) # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF func = _trace_model_call(self._keras_model, input_signature) concrete_func = func.get_concrete_function() self._funcs = [concrete_func] frozen_func, graph_def = ( _convert_to_constants.convert_variables_to_constants_v2_as_graph( self._funcs[0], lower_control_flow=False)) input_tensors = [ tensor for tensor in frozen_func.inputs if tensor.dtype != _dtypes.resource ] output_tensors = frozen_func.outputs # Run a Grappler pass. grappler_config = self._grappler_config() # Skip running grappler when there are no optimizers to run. If not, # grappler will run with the default optimizer set and it will lead to # causing an unexpected behavior. if grappler_config.graph_options.rewrite_options.optimizers: graph_def = _run_graph_optimizations( graph_def, input_tensors, output_tensors, config=grappler_config, graph=frozen_func.graph) result = super(TFLiteKerasModelConverterV2, self).convert(graph_def, input_tensors, output_tensors) self._increase_conversion_success_metric(result) return result class TFLiteFrozenGraphConverterV2(TFLiteConverterBaseV2): """Converts the given frozen graph into TensorFlow Lite model.""" def __init__(self, funcs, trackable_obj=None): """Constructor for TFLiteConverter. Args: funcs: List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements. trackable_obj: tf.AutoTrackable object associated with `funcs`. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. `from_saved_model`). """ super(TFLiteFrozenGraphConverterV2, self).__init__() self._funcs = funcs self._trackable_obj = trackable_obj def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Raises: ValueError: No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. """ # TODO(b/130297984): Add support for converting multiple function. if len(self._funcs) == 0: # pylint: disable=g-explicit-length-test raise ValueError("No ConcreteFunction is specified.") if len(self._funcs) > 1: raise ValueError("This converter can only convert a single " "ConcreteFunction. Converting multiple functions is " "under development.") self._increase_conversion_attempt_metric() self._save_conversion_params_metric({}) frozen_func, graph_def = ( _convert_to_constants.convert_variables_to_constants_v2_as_graph( self._funcs[0], lower_control_flow=False)) input_tensors = [ tensor for tensor in frozen_func.inputs if tensor.dtype != _dtypes.resource ] output_tensors = frozen_func.outputs # Run a Grappler pass. grappler_config = self._grappler_config() # Skip running grappler when there are no optimizers to run. If not, # grappler will run with the default optimizer set and it will lead to # causing an unexpected behavior. if grappler_config.graph_options.rewrite_options.optimizers: graph_def = _run_graph_optimizations( graph_def, input_tensors, output_tensors, config=grappler_config, graph=frozen_func.graph) result = super(TFLiteFrozenGraphConverterV2, self).convert(graph_def, input_tensors, output_tensors) self._increase_conversion_success_metric(result) return result @_tf_export("lite.TFLiteConverter", v1=[]) class TFLiteConverterV2(TFLiteFrozenGraphConverterV2): """Converts a TensorFlow model into TensorFlow Lite model. Attributes: optimizations: Experimental flag, subject to change. Set of optimizations to apply. e.g {tf.lite.Optimize.DEFAULT}. (default None, must be None or a set of values of type `tf.lite.Optimize`) representative_dataset: A generator function used for integer quantization where each generated sample has the same order, type and shape as the inputs to the model. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. This is an optional attribute, but required for full integer quantization, i.e, if `tf.int8` is the only supported type in `target_spec.supported_types`. Refer to `tf.lite.RepresentativeDataset`. (default None) target_spec: Experimental flag, subject to change. Specifications of target device, including supported ops set, supported types and a set of user's defined TensorFlow operators required in the TensorFlow Lite runtime. Refer to `tf.lite.TargetSpec`. inference_input_type: Data type of the input layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) inference_output_type: Data type of the output layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) allow_custom_ops: Boolean indicating whether to allow custom operations. When False, any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer needs to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True) experimental_new_quantizer: Experimental flag, subject to change. Enables MLIR-based quantization conversion instead of Flatbuffer-based conversion. (default True) Example usage: ```python # Converting a SavedModel to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) tflite_model = converter.convert() # Converting a tf.Keras model to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() # Converting ConcreteFunctions to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_concrete_functions([func]) tflite_model = converter.convert() ``` """ # pylint: disable=useless-super-delegation def __init__(self, funcs, trackable_obj=None): """Constructor for TFLiteConverter. Args: funcs: List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements. trackable_obj: tf.AutoTrackable object associated with `funcs`. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. `from_saved_model`). """ super(TFLiteConverterV2, self).__init__(funcs, trackable_obj) @classmethod def from_concrete_functions(cls, funcs): """Creates a TFLiteConverter object from ConcreteFunctions. Args: funcs: List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements. Currently converter can only convert a single ConcreteFunction. Converting multiple functions is under development. Returns: TFLiteConverter object. Raises: Invalid input type. """ for func in funcs: if not isinstance(func, _function.ConcreteFunction): message = "This function takes in a list of ConcreteFunction." if isinstance(func, _def_function.Function): message += (" To get the ConcreteFunction from a Function," " call get_concrete_function.") raise ValueError(message) return cls(funcs) @classmethod def from_saved_model(cls, saved_model_dir, signature_keys=None, tags=None): """Creates a TFLiteConverter object from a SavedModel directory. Args: saved_model_dir: SavedModel directory to convert. signature_keys: List of keys identifying SignatureDef containing inputs and outputs. Elements should not be duplicated. By default the `signatures` attribute of the MetaGraphdef is used. (default saved_model.signatures) tags: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default {tf.saved_model.SERVING} or {'serve'}) Returns: TFLiteConverter object. Raises: Invalid signature keys. """ # When run without eager enabled, this will return the legacy # TFLiteConverter. if not context.executing_eagerly(): signature_key = None if signature_keys: if len(signature_keys) != 1: raise ValueError("Only support a single signature key.") else: signature_key = signature_keys[0] logging.warning("Invoking the TF1 implementation of TFLiteConverter " "because eager is disabled. Consider enabling eager.") return TFLiteConverter.from_saved_model( saved_model_dir, signature_key=signature_key, tag_set=tags) # Ensures any graphs created in Eager mode are able to run. This is required # in order to create a tf.estimator.Exporter that exports a TFLite model. if tags is None: tags = set([_tag_constants.SERVING]) with context.eager_mode(): saved_model = _load(saved_model_dir, tags) if not signature_keys: signature_keys = saved_model.signatures if not signature_keys: raise ValueError("Only support at least one signature key.") funcs = [] for key in signature_keys: if key not in saved_model.signatures: raise ValueError("Invalid signature key '{}' found. Valid keys are " "'{}'.".format(key, ",".join(saved_model.signatures))) funcs.append(saved_model.signatures[key]) saved_model_converter = TFLiteSavedModelConverterV2(saved_model_dir, tags, signature_keys, saved_model) if saved_model_converter.saved_model_dir: return saved_model_converter return cls(funcs, saved_model) @classmethod def from_keras_model(cls, model): """Creates a TFLiteConverter object from a Keras model. Args: model: tf.Keras.Model Returns: TFLiteConverter object. """ return TFLiteKerasModelConverterV2(model) # pylint: disable=useless-super-delegation def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Raises: ValueError: No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. """ return super(TFLiteConverterV2, self).convert() class TFLiteConverterBaseV1(TFLiteConverterBase): """Converter subclass to share functionality between V1 converters.""" def __init__(self, experimental_debug_info_func): """Constructor for TFLiteConverter. Args: experimental_debug_info_func: An experimental function to retrieve the graph debug info for a set of nodes from the `graph_def`. """ super(TFLiteConverterBaseV1, self).__init__() self.inference_type = _dtypes.float32 self.inference_input_type = None self.inference_output_type = None self.output_format = constants.TFLITE self.quantized_input_stats = {} self.default_ranges_stats = None self.drop_control_dependency = True self.reorder_across_fake_quant = False self.change_concat_input_ranges = False self.dump_graphviz_dir = None self.dump_graphviz_video = False self.conversion_summary_dir = None self._debug_info_func = experimental_debug_info_func def __setattr__(self, name, value): if name == "post_training_quantize": warnings.warn("Property %s is deprecated, " "please use optimizations=[Optimize.DEFAULT]" " instead." % name) if value: self.optimizations = [Optimize.DEFAULT] else: self.optimizations = [] return if name == "target_ops": warnings.warn("Property %s is deprecated, please use " "target_spec.supported_ops instead." % name) self.target_spec.supported_ops = value return object.__setattr__(self, name, value) def __getattribute__(self, name): if name == "post_training_quantize": warnings.warn("Property %s is deprecated, " "please use optimizations=[Optimize.DEFAULT]" " instead." % name) return Optimize.DEFAULT in set(self.optimizations) if name == "target_ops": warnings.warn("Property %s is deprecated, please use " "target_spec.supported_ops instead." % name) return self.target_spec.supported_ops return object.__getattribute__(self, name) def _validate_quantized_input_stats(self, converter_kwargs, calibrate): """Ensure the `quantized_input_stats` flag is provided if required.""" quantized_types = frozenset({_dtypes.int8, _dtypes.uint8}) requires_quantized_input_stats = ( (converter_kwargs["inference_type"] in quantized_types or converter_kwargs["inference_input_type"] in quantized_types) and not calibrate) if (requires_quantized_input_stats and not converter_kwargs["quantized_input_stats"]): raise ValueError( "The `quantized_input_stats` flag must be defined when either " "`inference_type` flag or `inference_input_type` flag is set to " "tf.int8 or tf.uint8. Currently, `inference_type={}` and " "`inference_input_type={}`.".format( _get_tf_type_name(converter_kwargs["inference_type"]), _get_tf_type_name(converter_kwargs["inference_input_type"]))) def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. Raises: ValueError: Input shape is not specified. None value for dimension in input_tensor. """ quant_mode = QuantizationMode(self.optimizations, self.target_spec, self.representative_dataset, self._graph_def, self._experimental_disable_per_channel) if (not self._is_unknown_shapes_allowed() and self._has_valid_tensors()): # Checks dimensions in input tensor. for tensor in self._input_tensors: shape = tensor.shape if not shape: raise ValueError("Provide an input shape for input array " "'{0}'.".format(_get_tensor_name(tensor))) # Note that shape_list might be empty for scalar shapes. shape_list = shape.as_list() if None in shape_list[1:]: raise ValueError( "None is only supported in the 1st dimension. Tensor '{0}' has " "invalid shape '{1}'.".format( _get_tensor_name(tensor), shape_list)) elif shape_list and shape_list[0] is None: self._set_batch_size(batch_size=1) # Get quantization stats. Ensures there is one stat per name if the stats # are specified. if self.quantized_input_stats: quantized_stats = [] invalid_stats = [] for name in self.get_input_arrays(): if name in self.quantized_input_stats: quantized_stats.append(self.quantized_input_stats[name]) else: invalid_stats.append(name) if invalid_stats: raise ValueError("Quantization input stats are not available for input " "tensors '{0}'.".format(",".join(invalid_stats))) else: quantized_stats = None optimized_graph = self._graph_def if not self.saved_model_dir: # Disable grappler constant folding if there are training quant ops. if not quant_mode.contains_training_quant_op(): try: # TODO(b/150163103): Merge `disabling lower using switch merge' calls. # Grappler will also try to lower while loop into switch merge # representation which is undesired for Ophints, so we simply remove # those attributes to prevent Grappler from doing so. graph_def = _convert_to_constants.disable_lower_using_switch_merge( optimized_graph) # Run function inlining optimization to ensure any models generated # through the from_frozen_graph path have been inlined. optimized_graph = _run_graph_optimizations( graph_def, self._input_tensors, self._output_tensors, config=self._grappler_config(["function"])) except Exception: # pylint: disable=broad-except optimized_graph = self._graph_def self._debug_info = _get_debug_info(self._debug_info_func, optimized_graph) converter_kwargs = self._get_base_converter_args() converter_kwargs.update( quant_mode.converter_flags(self.inference_type, self.inference_input_type)) converter_kwargs.update({ "output_format": self.output_format, "quantized_input_stats": quantized_stats, "default_ranges_stats": self.default_ranges_stats, "drop_control_dependency": self.drop_control_dependency, "reorder_across_fake_quant": self.reorder_across_fake_quant, "change_concat_input_ranges": self.change_concat_input_ranges, "dump_graphviz_dir": self.dump_graphviz_dir, "dump_graphviz_video": self.dump_graphviz_video, "conversion_summary_dir": self.conversion_summary_dir, }) if not self.experimental_new_converter: logging.warning( "Please consider switching to the new converter by setting " "experimental_new_converter=True. " "The old converter (TOCO) is deprecated.") else: logging.info("Using experimental converter: If you encountered a problem " "please file a bug. You can opt-out " "by setting experimental_new_converter=False") if not self.experimental_new_converter: calibrate_quantize, flags = quant_mode.quantizer_flags( self.inference_input_type, self.inference_output_type) else: calibrate_quantize, flags = quant_mode.quantizer_flags() self._validate_quantized_input_stats(converter_kwargs, calibrate_quantize) self._validate_experimental_new_quantizer_flag() # Converts model. if self._has_valid_tensors(): result = _toco_convert_impl( input_data=optimized_graph, input_tensors=self._input_tensors, output_tensors=self._output_tensors, **converter_kwargs) else: result = _toco_convert_graph_def( input_data=optimized_graph, input_arrays_with_shape=self._input_arrays_with_shape, output_arrays=self._output_arrays, **converter_kwargs) if calibrate_quantize: result = self._calibrate_quantize_model(result, **flags) if self.experimental_new_converter or self.experimental_new_quantizer: flags_modify_model_io_type = quant_mode.flags_modify_model_io_type( self.inference_input_type, self.inference_output_type) if flags_modify_model_io_type: result = _modify_model_io_type(result, **flags_modify_model_io_type) if self._sparsify_model(): result = _mlir_sparsify(result) return result def get_input_arrays(self): """Returns a list of the names of the input tensors. Returns: List of strings. """ if self._has_valid_tensors(): return [_get_tensor_name(tensor) for tensor in self._input_tensors] else: return [name for name, _ in self._input_arrays_with_shape] def _has_valid_tensors(self): """Checks if the input and output tensors have been initialized. Returns: Bool. """ return self._input_tensors is not None and self._output_tensors def _set_batch_size(self, batch_size): """Sets the first dimension of the input tensor to `batch_size`. Args: batch_size: Batch size for the model. Replaces the first dimension of an input size array if undefined. (default 1) Raises: ValueError: input_tensor is not defined. """ if not self._has_valid_tensors(): raise ValueError("The batch size cannot be set for this model. Please " "use input_shapes parameter.") for tensor in self._input_tensors: shape = tensor.shape.as_list() if shape[0] is None: shape[0] = batch_size tensor.set_shape(shape) def _is_unknown_shapes_allowed(self): # Ophint Converted nodes will need the shapes to be known. if _is_ophint_converted(self._graph_def): return False if not super(TFLiteConverterBaseV1, self)._is_unknown_shapes_allowed(): return False # `conversion_summary_dir` calls TOCO. Unknown shapes are only supported by # the MLIR converter. if self.conversion_summary_dir: logging.warning( "`conversion_summary_dir` does not work with unknown shapes. " "Graphs with unknown shapes might be different than when this flag " "is disabled.") return False return True def _save_conversion_params_metric(self, converter_params): converter_kwargs = converter_params.copy() converter_kwargs.update({ "output_format": self.output_format, "default_ranges_stats": self.default_ranges_stats, "drop_control_dependency": self.drop_control_dependency, "reorder_across_fake_quant": self.reorder_across_fake_quant, "change_concat_input_ranges": self.change_concat_input_ranges, "dump_graphviz_dir": self.dump_graphviz_dir, "dump_graphviz_video": self.dump_graphviz_video, "conversion_summary_dir": self.conversion_summary_dir, "api_version": 1, }) super(TFLiteConverterBaseV1, self)._save_conversion_params_metric(converter_kwargs, self._graph_def, self.inference_type, self.inference_input_type) class TFLiteSavedModelConverter(TFLiteConverterBaseV1): """Converts the given SavedModel into TensorFlow Lite model. Attributes: saved_model_dir: Directory of the SavedModel. """ def __init__(self, saved_model_dir, saved_model_tags, saved_model_exported_names, experimental_debug_info_func=None): """Constructor for TFLiteConverter. Args: saved_model_dir: Directory of the SavedModel. saved_model_tags: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default {tf.saved_model.SERVING}). saved_model_exported_names: Names to be exported when the saved model import path is on. experimental_debug_info_func: An experimental function to retrieve the graph debug info for a set of nodes from the `graph_def`. Raises: ValueError: Invalid arguments. """ super(TFLiteSavedModelConverter, self).__init__(experimental_debug_info_func) self.saved_model_dir = saved_model_dir self._saved_model_tags = saved_model_tags self._saved_model_exported_names = saved_model_exported_names signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY if len(self._saved_model_exported_names) != 1: raise ValueError("Only support a single signature key.") signature_key = self._saved_model_exported_names[0] result = _freeze_saved_model(self.saved_model_dir, None, None, None, self._saved_model_tags, signature_key) self._graph_def = result[0] self._input_tensors = result[1] self._output_tensors = result[2] self._parse_saved_model_args() def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. Raises: ValueError: Input shape is not specified. None value for dimension in input_tensor. """ self._increase_conversion_attempt_metric() self._save_conversion_params_metric({}) result = super(TFLiteSavedModelConverter, self).convert() self._increase_conversion_success_metric(result) return result class TFLiteKerasModelConverter(TFLiteConverterBaseV1): """Converts the given SavedModel into TensorFlow Lite model.""" def __init__(self, model_file, input_arrays=None, input_shapes=None, output_arrays=None, custom_objects=None): """Constructor for TFLiteConverter. Args: model_file: Full filepath of HDF5 file containing the tf.keras model. input_arrays: List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. (default None) input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) output_arrays: List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. (default None) custom_objects: Dict mapping names (strings) to custom classes or functions to be considered during model deserialization. (default None) Raises: ValueError: Invalid arguments. """ super(TFLiteKerasModelConverter, self).__init__(experimental_debug_info_func=None) # Handles Keras when Eager mode is enabled. if context.executing_eagerly(): if input_arrays or output_arrays: raise ValueError("`input_arrays` and `output_arrays` are unsupported " "with Eager mode. If your model requires any of these " "parameters, please use disable_eager_execution().") keras_model = keras_deps.get_load_model_function()(model_file, custom_objects) function = _trace_model_call(keras_model) concrete_func = function.get_concrete_function() frozen_func = _convert_to_constants.convert_variables_to_constants_v2( concrete_func, lower_control_flow=False) _set_tensor_shapes(frozen_func.inputs, input_shapes) self._keras_model = keras_model self._graph_def = frozen_func.graph.as_graph_def() self._input_tensors = frozen_func.inputs self._output_tensors = frozen_func.outputs self._debug_info_func = _build_debug_info_func(frozen_func.graph) return # Handles Keras when Eager mode is disabled. keras_deps.get_clear_session_function()() keras_model = keras_deps.get_load_model_function()(model_file, custom_objects) sess = keras_deps.get_get_session_function()() # Get input and output tensors. if input_arrays: input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays) else: input_tensors = keras_model.inputs if output_arrays: output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays) else: output_tensors = keras_model.outputs _set_tensor_shapes(input_tensors, input_shapes) graph_def = _freeze_graph(sess, input_tensors, output_tensors) self._keras_model = keras_model self._graph_def = graph_def self._input_tensors = input_tensors self._output_tensors = output_tensors self._debug_info_func = _build_debug_info_func(sess.graph) def _convert_as_saved_model(self): """Converts a Keras model as a saved model. Returns: The converted data in serialized format. """ temp_dir = tempfile.mkdtemp() try: try: self._keras_model.save(temp_dir, save_format="tf") except Exception: # pylint: disable=broad-except # When storing the given keras model to a saved model is failed, let's # use original keras model conversion pipeline. return None tag_set = set([_tag_constants.SERVING]) signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY result = _freeze_saved_model(temp_dir, None, None, None, tag_set, signature_key) self.saved_model_dir = temp_dir self._saved_model_tags = tag_set self._saved_model_exported_names = [signature_key] self._parse_saved_model_args() if self.saved_model_dir: self._graph_def = result[0] self._input_tensors = result[1] self._output_tensors = result[2] self._debug_info_func = _build_debug_info_func(result[3]) # Update conversion params with graph_def. self._save_conversion_params_metric({}) return super(TFLiteKerasModelConverter, self).convert() finally: shutil.rmtree(temp_dir, True) def convert(self): """Converts a Keras model based on instance variables. Returns: The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. Raises: ValueError: Input shape is not specified. None value for dimension in input_tensor. """ self._increase_conversion_attempt_metric() self._save_conversion_params_metric({}) saved_model_convert_result = self._convert_as_saved_model() if saved_model_convert_result: self._increase_conversion_success_metric(saved_model_convert_result) return saved_model_convert_result result = super(TFLiteKerasModelConverter, self).convert() self._increase_conversion_success_metric(result) return result class TFLiteFrozenGraphConverter(TFLiteConverterBaseV1): """Converts the given frozen graph def into TensorFlow Lite model.""" def __init__(self, graph_def, input_tensors, output_tensors, input_arrays_with_shape=None, output_arrays=None, experimental_debug_info_func=None): """Constructor for TFLiteConverter. Args: graph_def: Frozen TensorFlow GraphDef. input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). input_arrays_with_shape: Tuple of strings representing input tensor names and list of integers representing input shapes (e.g., [("foo", [1, 16, 16, 3])]). Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) output_arrays: List of output tensors to freeze graph with. Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) experimental_debug_info_func: An experimental function to retrieve the graph debug info for a set of nodes from the `graph_def`. Raises: ValueError: Invalid arguments. """ super(TFLiteFrozenGraphConverter, self).__init__(experimental_debug_info_func) self._graph_def = graph_def self._input_tensors = input_tensors self._output_tensors = output_tensors # Attributes are used by models that cannot be loaded into TensorFlow. if not self._has_valid_tensors(): if not input_arrays_with_shape or not output_arrays: raise ValueError( "If input_tensors and output_tensors are None, both " "input_arrays_with_shape and output_arrays must be defined.") self._input_arrays_with_shape = input_arrays_with_shape self._output_arrays = output_arrays if input_tensors is not None and input_arrays_with_shape is not None: logging.warning("input_arrays_with_shape will be ignored when both the " "given input_tensors and input_arrays_with_shape are not " "None.") if output_tensors is not None and output_arrays is not None: logging.warning("output_arrays will be ignored when both the given " "output_tensors and output_arrays are not None.") def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. Raises: ValueError: Input shape is not specified. None value for dimension in input_tensor. """ self._increase_conversion_attempt_metric() self._save_conversion_params_metric({}) result = super(TFLiteFrozenGraphConverter, self).convert() self._increase_conversion_success_metric(result) return result @_tf_export(v1=["lite.TFLiteConverter"]) class TFLiteConverter(TFLiteFrozenGraphConverter): """Convert a TensorFlow model into `output_format`. This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras model into either a TFLite FlatBuffer or graph visualization. Attributes: optimizations: Experimental flag, subject to change. Set of optimizations to apply. e.g {tf.lite.Optimize.DEFAULT}. (default None, must be None or a set of values of type `tf.lite.Optimize`) representative_dataset: A generator function used for integer quantization where each generated sample has the same order, type and shape as the inputs to the model. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. This is an optional attribute, but required for full integer quantization, i.e, if `tf.int8` is the only supported type in `target_spec.supported_types`. Refer to `tf.lite.RepresentativeDataset`. (default None) target_spec: Experimental flag, subject to change. Specifications of target device, including supported ops set, supported types and a set of user's defined TensorFlow operators required in the TensorFlow Lite runtime. Refer to `tf.lite.TargetSpec`. inference_type: Data type of numeric arrays, excluding the input layer. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) inference_input_type: Data type of the numeric arrays in the input layer. If `inference_input_type` is in {tf.int8, tf.uint8}, then `quantized_input_stats` must be provided. (default is the value assigned to `inference_type`, must be in {tf.float32, tf.int8, tf.uint8}) inference_output_type: Data type of the numeric arrays in the output layer. (default is the value assigned to `inference_type`, must be in {tf.float32, tf.int8, tf.uint8}) quantized_input_stats: Map of input tensor names to a tuple of floats representing the mean and standard deviation of the training data. (e.g., {"foo" : (0., 1.)}). Required if `inference_input_type` is tf.int8 or tf.uint8. (default None) default_ranges_stats: Tuple of integers (min, max) representing range values for all numeric arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None) allow_custom_ops: Boolean indicating whether to allow custom operations. When False any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer will need to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) drop_control_dependency: Boolean indicating whether to drop control dependencies silently. This is due to TFLite not supporting control dependencies. (default True) reorder_across_fake_quant: Boolean indicating whether to reorder FakeQuant nodes in unexpected locations. Used when the location of the FakeQuant nodes is preventing graph transformations necessary to convert the graph. Results in a graph that differs from the quantized training graph, potentially causing differing arithmetic behavior. (default False) change_concat_input_ranges: Boolean to change behavior of min/max ranges for inputs and outputs of the concat operator for quantized models. Changes the ranges of concat operator overlap when true. (default False) output_format: Output file format. (default tf.compat.v1.lite.constants.TFLITE, must be in {tf.compat.v1.lite.constants.TFLITE, tf.compat.v1.lite.constants.GRAPHVIZ_DOT}) dump_graphviz_dir: Full filepath of folder to dump the graphs at various stages of processing GraphViz .dot files. Preferred over `output_format=tf.compat.v1.lite.constants.GRAPHVIZ_DOT` in order to keep the requirements of the output file. (default None) dump_graphviz_video: Boolean indicating whether to dump the GraphViz .dot files after every graph transformation. Requires the `dump_graphviz_dir` flag to be specified. (default False) conversion_summary_dir: Full path of the directory to store conversion logs. (default None) target_ops: Deprecated. Please use `target_spec.supported_ops` instead. post_training_quantize: Deprecated. Please use `optimizations` instead and set it to `{tf.lite.Optimize.DEFAULT}`. (default False) experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True) experimental_new_quantizer: Experimental flag, subject to change. Enables MLIR-based quantization conversion instead of Flatbuffer-based conversion. (default True) Example usage: ```python # Converting a GraphDef from session. converter = tf.compat.v1.lite.TFLiteConverter.from_session( sess, in_tensors, out_tensors) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a GraphDef from file. converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph( graph_def_file, input_arrays, output_arrays) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a SavedModel. converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model( saved_model_dir) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a tf.keras model. converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file( keras_model) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) ``` """ # pylint: disable=useless-super-delegation def __init__(self, graph_def, input_tensors, output_tensors, input_arrays_with_shape=None, output_arrays=None, experimental_debug_info_func=None): """Constructor for TFLiteConverter. Args: graph_def: Frozen TensorFlow GraphDef. input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). input_arrays_with_shape: Tuple of strings representing input tensor names and list of integers representing input shapes (e.g., [("foo" : [1, 16, 16, 3])]). Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) output_arrays: List of output tensors to freeze graph with. Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) experimental_debug_info_func: An experimental function to retrieve the graph debug info for a set of nodes from the `graph_def`. Raises: ValueError: Invalid arguments. """ super(TFLiteConverter, self).__init__(graph_def, input_tensors, output_tensors, input_arrays_with_shape, output_arrays, experimental_debug_info_func) @classmethod def from_session(cls, sess, input_tensors, output_tensors): """Creates a TFLiteConverter class from a TensorFlow Session. Args: sess: TensorFlow Session. input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). Returns: TFLiteConverter class. """ graph_def = _freeze_graph(sess, input_tensors, output_tensors) return cls( graph_def, input_tensors, output_tensors, experimental_debug_info_func=_build_debug_info_func(sess.graph)) @classmethod def from_frozen_graph(cls, graph_def_file, input_arrays, output_arrays, input_shapes=None): """Creates a TFLiteConverter class from a file containing a frozen GraphDef. Args: graph_def_file: Full filepath of file containing frozen GraphDef. input_arrays: List of input tensors to freeze graph with. output_arrays: List of output tensors to freeze graph with. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) Returns: TFLiteConverter class. Raises: IOError: File not found. Unable to parse input file. ValueError: The graph is not frozen. input_arrays or output_arrays contains an invalid tensor name. input_shapes is not correctly defined when required """ with _ops.Graph().as_default(): with _session.Session() as sess: # Read GraphDef from file. if not _file_io.file_exists(graph_def_file): raise IOError("File '{0}' does not exist.".format(graph_def_file)) with _file_io.FileIO(graph_def_file, "rb") as f: file_content = f.read() try: graph_def = _graph_pb2.GraphDef() graph_def.ParseFromString(file_content) except (_text_format.ParseError, DecodeError): try: print("Ignore 'tcmalloc: large alloc' warnings.") if not isinstance(file_content, str): if PY2: file_content = six.ensure_binary(file_content, "utf-8") else: file_content = six.ensure_text(file_content, "utf-8") graph_def = _graph_pb2.GraphDef() _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): raise IOError( "Unable to parse input file '{}'.".format(graph_def_file)) # Handles models with custom TFLite ops that cannot be resolved in # TensorFlow. load_model_in_session = True try: _import_graph_def(graph_def, name="") except _NotFoundError: load_model_in_session = False if load_model_in_session: # Check if graph is frozen. if not _is_frozen_graph(sess): raise ValueError("Please freeze the graph using freeze_graph.py.") # Get input and output tensors. input_tensors = _get_tensors_from_tensor_names( sess.graph, input_arrays) output_tensors = _get_tensors_from_tensor_names( sess.graph, output_arrays) _set_tensor_shapes(input_tensors, input_shapes) return cls(sess.graph_def, input_tensors, output_tensors) else: if not input_shapes: raise ValueError("input_shapes must be defined for this model.") if set(input_arrays) != set(input_shapes.keys()): raise ValueError("input_shapes must contain a value for each item " "in input_array.") input_arrays_with_shape = [ (name, input_shapes[name]) for name in input_arrays ] return cls( graph_def, input_tensors=None, output_tensors=None, input_arrays_with_shape=input_arrays_with_shape, output_arrays=output_arrays) @classmethod def from_saved_model(cls, saved_model_dir, input_arrays=None, input_shapes=None, output_arrays=None, tag_set=None, signature_key=None): """Creates a TFLiteConverter class from a SavedModel. Args: saved_model_dir: SavedModel directory to convert. input_arrays: List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. (default None) input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) output_arrays: List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. (default None) tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default {tf.saved_model.SERVING}) signature_key: Key identifying SignatureDef containing inputs and outputs. (default tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY) Returns: TFLiteConverter class. """ if tag_set is None: tag_set = set([_tag_constants.SERVING]) if signature_key is None: signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY saved_model_converter = TFLiteSavedModelConverter(saved_model_dir, tag_set, [signature_key]) if saved_model_converter.saved_model_dir: return saved_model_converter result = _freeze_saved_model(saved_model_dir, input_arrays, input_shapes, output_arrays, tag_set, signature_key) return cls( graph_def=result[0], input_tensors=result[1], output_tensors=result[2], experimental_debug_info_func=_build_debug_info_func(result[3])) @classmethod def from_keras_model_file(cls, model_file, input_arrays=None, input_shapes=None, output_arrays=None, custom_objects=None): """Creates a TFLiteConverter class from a tf.keras model file. Args: model_file: Full filepath of HDF5 file containing the tf.keras model. input_arrays: List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. (default None) input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) output_arrays: List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. (default None) custom_objects: Dict mapping names (strings) to custom classes or functions to be considered during model deserialization. (default None) Returns: TFLiteConverter class. """ return TFLiteKerasModelConverter(model_file, input_arrays, input_shapes, output_arrays, custom_objects) # pylint: disable=useless-super-delegation def convert(self): """Converts a TensorFlow GraphDef based on instance variables. Returns: The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. Raises: ValueError: Input shape is not specified. None value for dimension in input_tensor. """ return super(TFLiteConverter, self).convert() @_tf_export(v1=["lite.TocoConverter"]) class TocoConverter(object): """Convert a TensorFlow model into `output_format` using TOCO. This class has been deprecated. Please use `lite.TFLiteConverter` instead. """ @classmethod @_deprecation.deprecated(None, "Use `lite.TFLiteConverter.from_session` instead.") def from_session(cls, sess, input_tensors, output_tensors): """Creates a TocoConverter class from a TensorFlow Session.""" return TFLiteConverter.from_session(sess, input_tensors, output_tensors) @classmethod @_deprecation.deprecated( None, "Use `lite.TFLiteConverter.from_frozen_graph` instead.") def from_frozen_graph(cls, graph_def_file, input_arrays, output_arrays, input_shapes=None): """Creates a TocoConverter class from a file containing a frozen graph.""" return TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays, input_shapes) @classmethod @_deprecation.deprecated( None, "Use `lite.TFLiteConverter.from_saved_model` instead.") def from_saved_model(cls, saved_model_dir, input_arrays=None, input_shapes=None, output_arrays=None, tag_set=None, signature_key=None): """Creates a TocoConverter class from a SavedModel.""" return TFLiteConverter.from_saved_model(saved_model_dir, input_arrays, input_shapes, output_arrays, tag_set, signature_key) @classmethod @_deprecation.deprecated( None, "Use `lite.TFLiteConverter.from_keras_model_file` instead.") def from_keras_model_file(cls, model_file, input_arrays=None, input_shapes=None, output_arrays=None): """Creates a TocoConverter class from a tf.keras model file.""" return TFLiteConverter.from_keras_model_file(model_file, input_arrays, input_shapes, output_arrays)

42.150175

125

0.702745

c74f62328b4f6dca2b8b49f8233d22e7fa310dab

913

py

Python

object.py

ZhongXinWang/python

4cf3ecdc9d9e811e777c6d8408a8319097cfdec3

[ "Apache-2.0" ]

null

object.py

ZhongXinWang/python

4cf3ecdc9d9e811e777c6d8408a8319097cfdec3

[ "Apache-2.0" ]

null

object.py

ZhongXinWang/python

4cf3ecdc9d9e811e777c6d8408a8319097cfdec3

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- #Author:Winston.Wang print('---------------面向对象------------------') class Person(object): def __init__(self,name,score): self.name=name self.__score = score def toString(self): return self.name,":",self.__score def setScore(self,score): self.__score = score class Student(object): def __init__(self, name, gender): self.__name = name self.__gender = gender def getGender(self): return self.__gender def setGender(self,gender): self.__gender = gender if __name__ == '__main__': p = Person('张三',90) print(p.toString()) p.name='修改为李四' #变量已经被设置为私有，p.__score=40无法修改，两个_ p.setScore(40) print(p.toString()) # 测试: bart = Student('Bart', 'male') if bart.getGender() != 'male': print('测试失败!') else: bart.setGender('female') if bart.getGender() != 'female': print('测试失败!') else: print('测试成功!')

21.232558

46

0.619934

f6fa341dbe765b78addfacf64b3a9795c95f2eff

6,973

py

Python

sqlserver/tests/test_connection.py

ganeshkumarsv/integrations-core

55cce423d171a65f87760ff903214af7a0e2a4e4

[ "BSD-3-Clause" ]

null

sqlserver/tests/test_connection.py

ganeshkumarsv/integrations-core

55cce423d171a65f87760ff903214af7a0e2a4e4

[ "BSD-3-Clause" ]

null

sqlserver/tests/test_connection.py

ganeshkumarsv/integrations-core

55cce423d171a65f87760ff903214af7a0e2a4e4

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2020-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import re import mock import pyodbc import pytest from datadog_checks.base import ConfigurationError from datadog_checks.sqlserver import SQLServer from datadog_checks.sqlserver.connection import Connection from .common import CHECK_NAME from .utils import not_windows_ci, windows_ci pytestmark = pytest.mark.unit @pytest.mark.parametrize( 'connector, param', [ pytest.param('odbc', 'adoprovider', id='Provider is ignored when using odbc'), pytest.param('adodbapi', 'dsn', id='DSN is ignored when using adodbapi'), pytest.param('adodbapi', 'driver', id='Driver is ignored when using adodbapi'), ], ) def test_will_warn_parameters_for_the_wrong_connection(instance_sql_defaults, connector, param): instance_sql_defaults.update({'connector': connector, param: 'foo'}) connection = Connection({}, instance_sql_defaults, None) connection.log = mock.MagicMock() connection._connection_options_validation('somekey', 'somedb') connection.log.warning.assert_called_once_with( "%s option will be ignored since %s connection is used", param, connector ) @pytest.mark.parametrize( 'connector, cs, param', [ pytest.param('odbc', 'DSN', 'dsn', id='Cannot define DSN twice'), pytest.param('odbc', 'DRIVER', 'driver', id='Cannot define DRIVER twice'), pytest.param('odbc', 'SERVER', 'host', id='Cannot define DRIVER twice'), pytest.param('odbc', 'UID', 'username', id='Cannot define UID twice'), pytest.param('odbc', 'PWD', 'password', id='Cannot define PWD twice'), pytest.param('adodbapi', 'PROVIDER', 'adoprovider', id='Cannot define PROVIDER twice'), pytest.param('adodbapi', 'Data Source', 'host', id='Cannot define Data Source twice'), pytest.param('adodbapi', 'User ID', 'username', id='Cannot define User ID twice'), pytest.param('adodbapi', 'Password', 'password', id='Cannot define Password twice'), ], ) def test_will_fail_for_duplicate_parameters(instance_sql_defaults, connector, cs, param): instance_sql_defaults.update({'connector': connector, param: 'foo', 'connection_string': cs + "=foo"}) connection = Connection({}, instance_sql_defaults, None) match = ( "%s has been provided both in the connection string and as a configuration option (%s), " "please specify it only once" % (cs, param) ) with pytest.raises(ConfigurationError, match=re.escape(match)): connection._connection_options_validation('somekey', 'somedb') @pytest.mark.parametrize( 'connector, cs', [ pytest.param('adodbapi', 'DSN', id='Cannot define DSN for adodbapi'), pytest.param('adodbapi', 'DRIVER', id='Cannot define DRIVER for adodbapi'), pytest.param('adodbapi', 'SERVER', id='Cannot define DRIVER for adodbapi'), pytest.param('adodbapi', 'UID', id='Cannot define UID for adodbapi'), pytest.param('adodbapi', 'PWD', id='Cannot define PWD for adodbapi'), pytest.param('odbc', 'PROVIDER', id='Cannot define PROVIDER for odbc'), pytest.param('odbc', 'Data Source', id='Cannot define Data source for odbc'), pytest.param('odbc', 'User ID', id='Cannot define User ID for odbc'), pytest.param('odbc', 'Password', id='Cannot define Password for odbc'), ], ) def test_will_fail_for_wrong_parameters_in_the_connection_string(instance_sql_defaults, connector, cs): instance_sql_defaults.update({'connector': connector, 'connection_string': cs + '=foo'}) other_connector = 'odbc' if connector != 'odbc' else 'adodbapi' connection = Connection({}, instance_sql_defaults, None) match = ( "%s has been provided in the connection string. " "This option is only available for %s connections, however %s has been selected" % (cs, other_connector, connector) ) with pytest.raises(ConfigurationError, match=re.escape(match)): connection._connection_options_validation('somekey', 'somedb') @not_windows_ci @pytest.mark.integration @pytest.mark.usefixtures('dd_environment') def test_query_timeout(aggregator, dd_run_check, instance_docker): _run_test_query_timeout(aggregator, dd_run_check, instance_docker) @windows_ci @pytest.mark.integration def test_query_timeout_windows(aggregator, dd_run_check, instance_sql_msoledb): _run_test_query_timeout(aggregator, dd_run_check, instance_sql_msoledb) def _run_test_query_timeout(aggregator, dd_run_check, instance): instance['command_timeout'] = 1 check = SQLServer(CHECK_NAME, {}, [instance]) check.initialize_connection() with check.connection.open_managed_default_connection(): with check.connection.get_managed_cursor() as cursor: # should complete quickly cursor.execute("select 1") assert cursor.fetchall(), "should have a result here" with pytest.raises(Exception) as e: cursor.execute("waitfor delay '00:00:02'") if isinstance(e, pyodbc.OperationalError): assert 'timeout' in "".join(e.args).lower(), "must be a timeout" else: import adodbapi assert type(e) == adodbapi.apibase.DatabaseError assert 'timeout' in "".join(e.args).lower(), "must be a timeout" @not_windows_ci @pytest.mark.integration @pytest.mark.usefixtures('dd_environment') def test_connection_cleanup(instance_docker): check = SQLServer(CHECK_NAME, {}, [instance_docker]) check.initialize_connection() # regular operation with check.connection.open_managed_default_connection(): assert len(check.connection._conns) == 1 with check.connection.get_managed_cursor() as cursor: cursor.execute("select 1") assert len(check.connection._conns) == 1 assert len(check.connection._conns) == 0, "connection should have been closed" # db exception with pytest.raises(Exception) as e: with check.connection.open_managed_default_connection(): assert len(check.connection._conns) == 1 with check.connection.get_managed_cursor() as cursor: assert len(check.connection._conns) == 1 cursor.execute("gimme some data") assert "incorrect syntax" in str(e).lower() assert len(check.connection._conns) == 0, "connection should have been closed" # application exception with pytest.raises(Exception) as e: with check.connection.open_managed_default_connection(): assert len(check.connection._conns) == 1 with check.connection.get_managed_cursor(): assert len(check.connection._conns) == 1 raise Exception("oops") assert "oops" in str(e) assert len(check.connection._conns) == 0, "connection should have been closed"

43.855346

106

0.687222

980ad55bf6fe6c9339d537913ea75a0ff7664e85

36

py

Python

tests/__init__.py

AaronLaw/crypto

92fb012daf1a770a8481522ef3c683494696f227

[ "MIT" ]

null

tests/__init__.py

AaronLaw/crypto

92fb012daf1a770a8481522ef3c683494696f227

[ "MIT" ]

null

tests/__init__.py

AaronLaw/crypto

92fb012daf1a770a8481522ef3c683494696f227

[ "MIT" ]

null

"""Unit test package for crypto."""

18

35

0.666667

61603c37f778f56256190b193c8f02b80e005c6b

85,116

py

Python

brian2/spatialneuron/morphology.py

Kyzarok/SNNProject

14b555e221dbdd5100cb4f6333e49030423462ea

[ "BSD-2-Clause" ]

2

2020-03-20T13:30:19.000Z

2020-03-20T13:30:57.000Z

brian2/spatialneuron/morphology.py

deep-introspection/brian2

6c50e3a22d0e83a2b75c5bcf6f7c1d889311fca1

[ "BSD-2-Clause" ]

null

brian2/spatialneuron/morphology.py

deep-introspection/brian2

6c50e3a22d0e83a2b75c5bcf6f7c1d889311fca1

[ "BSD-2-Clause" ]

null

''' Neuronal morphology module. This module defines classes to load and build neuronal morphologies. ''' import abc import numbers from abc import abstractmethod from collections import OrderedDict, defaultdict, namedtuple import os from brian2.units.allunits import meter from brian2.utils.logger import get_logger from brian2.units.stdunits import um from brian2.units.fundamentalunits import (have_same_dimensions, Quantity, check_units, DimensionMismatchError) from brian2 import numpy as np logger = get_logger(__name__) __all__ = ['Morphology', 'Section', 'Cylinder', 'Soma'] Node = namedtuple('Node', field_names='index,comp_name,x,y,z,diameter,parent,children') def _to_meters(value): ''' Helper function to convert a floating point value (or array) to a `Quantity` in units of "meter", but also allow for ``None`` and return it as it is. ''' if value is None: return None else: return Quantity(value, dim=meter.dim) def _from_morphology(variable, i, j): ''' Helper function to return coordinates from a main morphology (used by `SubMorphology`), dealing with ``None``. ''' if variable is None: return None return variable[i:j] class MorphologyIndexWrapper(object): ''' A simpler version of `~brian2.groups.group.IndexWrapper`, not allowing for string indexing (`Morphology` is not a `Group`). It allows to use ``morphology.indices[...]`` instead of ``morphology[...]._indices()``. ''' def __init__(self, morphology): self.morphology = morphology def __getitem__(self, item): if isinstance(item, basestring): raise NotImplementedError(('Morphologies do not support string ' 'indexing')) assert isinstance(self.morphology, (SubMorphology, Morphology)) return self.morphology._indices(item) def _calc_start_idx(section): ''' Calculate the absolute start index that will be used by a flattened representation. ''' # calculate the absolute start index of this section # 1. find the root of the tree root = section while root._parent is not None: root = root._parent # 2. go down from the root and advance the indices until we find # the current section start_idx, found = _find_start_index(root, section) assert found return start_idx def _find_start_index(current, target_section, index=0): if current == target_section: return index, True index += current.n for child in current.children: if child == target_section: return index, True else: index, found = _find_start_index(child, target_section, index) if found: return index, True return index, False class Topology(object): ''' A representation of the topology of a `Morphology`. Has a useful string representation, inspired by NEURON's ``topology`` function. ''' def __init__(self, morphology): self.morphology = morphology def __str__(self): # TODO: Make sure that the shown compartments do not get out of hand divisor = 1 return Topology._str_topology(self.morphology, compartments_divisor=divisor) @staticmethod def _str_topology(morphology, indent=0, named_path='', compartments_divisor=1, parent=None): ''' A simple string-based representation of a morphology. Inspired by NEURON's ``topology`` function. ''' description = ' '*indent length = max([1, morphology.n//compartments_divisor]) if parent is not None: description += '`' if isinstance(morphology, Soma): description += '( )' else: description += '-' * length description += '|' if len(named_path) == 0: description += ' [root] \n' else: description += ' ' + named_path + '\n' for child in morphology.children: name = morphology.children.name(child) description += Topology._str_topology(child, indent=indent+2+length, named_path=named_path+'.'+name, compartments_divisor=compartments_divisor, parent=morphology) return description __repr__ = __str__ def _rotate(vec, axis, angle): ''' Rotate a vector around an arbitrary axis. Parameters ---------- vec : `ndarray` The vector to rotate. axis : `ndarray` The axis around which the vector should be rotated. angle : float The rotation angle (in radians). Returns ------- rotated : `ndarray` The rotated vector. ''' return (vec*np.cos(angle) - np.cross(axis, vec)*np.sin(angle) + axis*np.dot(axis, vec)*(1 - np.cos(angle))) def _perturb(vec, sigma): if sigma == 0: return vec # Get an arbitrary orthogonal vector if vec[1] != 0 or vec[0] != 0: orthogonal = np.hstack([vec[1], vec[0], 0]) else: # special case for the [0, 0, 1] vector orthogonal = np.array([1, 0, 0]) # Rotate the orthogonal vector orthogonal = _rotate(orthogonal, vec, np.random.rand()*np.pi*2) # Use an exponentially distributed angle for the perturbation perturbation = np.random.exponential(sigma, 1) return _rotate(vec, orthogonal, perturbation) def _add_coordinates(orig_morphology, root=None, parent=None, name=None, section_randomness=0.0, compartment_randomness=0.0, n_th_child=0, total_children=0, overwrite_existing=False): # Note that in the following, all values are without physical units # The new direction is based on the direction of the parent section if parent is None: section_dir = np.array([0, 0, 0]) else: section_dir = np.hstack([np.asarray(parent.end_x[-1] - parent.start_x[0]), np.asarray(parent.end_y[-1] - parent.start_y[0]), np.asarray(parent.end_z[-1] - parent.start_z[0])]) parent_dir_norm = np.sqrt(np.sum(section_dir**2)) if parent_dir_norm != 0: section_dir /= parent_dir_norm else: section_dir = np.array([0, 0, 0]) if not overwrite_existing and orig_morphology.x is not None: section = orig_morphology.copy_section() elif isinstance(orig_morphology, Soma): # No perturbation for the soma section = Soma(diameter=orig_morphology.diameter, x=section_dir[0]*meter, y=section_dir[1]*meter, z=section_dir[2]*meter) else: if np.sum(section_dir**2) == 0: # We don't have any direction to base this section on (most common # case is that the root section is a soma) # We stay in the x-y plane and distribute all children in a 360 degree # circle around (0, 0, 0) section_dir = np.array([1, 0, 0]) rotation_axis = np.array([0, 0, 1]) angle_increment = 2*np.pi/total_children rotation_angle = np.pi/2 + angle_increment * n_th_child section_dir = _rotate(section_dir, rotation_axis, rotation_angle) else: if section_randomness == 0 and section_dir[2] == 0: # If we are in the x-y plane, stay there rotation_axis = np.array([0, 0, 1]) else: rotation_axis = np.array([-section_dir[1], section_dir[2], 0]) if section_randomness == 0: angle_increment = np.pi/(total_children + 1) rotation_angle = -np.pi/2 + angle_increment * (n_th_child + 1) section_dir = _rotate(section_dir, rotation_axis, rotation_angle) if section_randomness > 0: # Rotate randomly section_dir = _perturb(section_dir, section_randomness) section_dir_norm = np.sqrt(np.sum(section_dir**2)) section_dir /= section_dir_norm # For a soma, we let child sections begin at the surface of the sphere if isinstance(parent, Soma): origin = parent.diameter/2*section_dir else: origin = (0, 0, 0)*um coordinates = np.zeros((orig_morphology.n + 1, 3))*meter start_coords = origin coordinates[0, :] = origin # Perturb individual compartments as well for idx, length in enumerate(orig_morphology.length): compartment_dir = _perturb(section_dir, compartment_randomness) compartment_dir_norm = np.sqrt(np.sum(compartment_dir**2)) compartment_dir /= compartment_dir_norm current_coords = start_coords + length*compartment_dir coordinates[idx + 1, :] = current_coords start_coords = current_coords if isinstance(orig_morphology, Cylinder) and compartment_randomness == 0: section = Cylinder(n=orig_morphology.n, diameter=orig_morphology.diameter[0], x=coordinates[[0, -1], 0], y=coordinates[[0, -1], 1], z=coordinates[[0, -1], 2], type=orig_morphology.type) elif isinstance(orig_morphology, Section): section = Section(n=orig_morphology.n, diameter=np.hstack([orig_morphology.start_diameter[0], orig_morphology.end_diameter])*meter, x=coordinates[:, 0], y=coordinates[:, 1], z=coordinates[:, 2], type=orig_morphology.type) else: raise NotImplementedError(('Do not know how to deal with section of ' 'type %s.' % type(orig_morphology))) if parent is None: root = section else: parent.children.add(name, section) for idx, child in enumerate(orig_morphology.children): _add_coordinates(child, root=root, parent=section, name=orig_morphology.children.name(child), n_th_child=idx, total_children=len(orig_morphology.children), section_randomness=section_randomness, compartment_randomness=compartment_randomness, overwrite_existing=overwrite_existing) return section class Children(object): ''' Helper class to represent the children (sub trees) of a section. Can be used like a dictionary (mapping names to `Morphology` objects), but iterates over the values (sub trees) instead of over the keys (names). ''' def __init__(self, owner): self._owner = owner self._counter = 0 self._children = [] self._named_children = {} self._given_name = defaultdict(lambda: None) def __iter__(self): return iter(self._children) def __len__(self): return len(self._children) def __contains__(self, item): return item in self._named_children def name(self, child): ''' Return the given name (i.e. not the automatic name such as ``1``) for a child subtree. Parameters ---------- child : `Morphology` Returns ------- name : str The given name for the ``child``. ''' return self._given_name[child] def __getitem__(self, item): if isinstance(item, basestring): return self._named_children[item] else: raise TypeError('Index has to be an integer or a string.') def add(self, name, subtree, automatic_name=False): ''' Add a new child to the morphology. Parameters ---------- name : str The name (e.g. ``"axon"``, ``"soma"``) to use for this sub tree. subtree : `Morphology` The subtree to link as a child. automatic_name : bool, optional Whether to chose a new name automatically, if a subtree of the same name already exists (uses e.g. ``"dend2"`` instead ``"dend"``). Defaults to ``False`` and will raise an error instead. ''' if (name in self._named_children and self._named_children[name] is not subtree): if automatic_name: basename = name counter = 1 while name in self._named_children: counter += 1 name = basename + str(counter) else: raise AttributeError('The name %s is already used for a ' 'subtree.' % name) if subtree not in self._children: self._counter += 1 self._children.append(subtree) self._named_children[str(self._counter)] = subtree self._given_name[subtree] = name if name is not None: self._named_children[name] = subtree subtree._parent = self._owner def remove(self, name): ''' Remove a subtree from this morphology. Parameters ---------- name : str The name of the sub tree to remove. ''' if name not in self: raise AttributeError('The subtree ' + name + ' does not exist') subtree = self._named_children[name] del self._named_children[name] self._children.remove(subtree) subtree._parent = None def __repr__(self): n = len(self._children) s = '<%d children' % n if n > 0: name_dict = {self.name(sec): sec for sec in self._children} s += ': %r' % name_dict return s + '>' class Morphology(object): ''' Neuronal morphology (tree structure). The data structure is a tree where each node is an un-branched section consisting of a number of connected compartments, each one defined by its geometrical properties (length, area, diameter, position). Notes ----- You cannot create objects of this class, create a `Soma`, a `Section`, or a `Cylinder` instead. ''' __metaclass__ = abc.ABCMeta @check_units(n=1) def __init__(self, n, type=None): if isinstance(n, basestring): raise TypeError('Need the number of compartments, not a string. ' 'If you want to load a morphology from a file, ' 'use Morphology.from_file instead.') self._n = int(n) if self._n != n: raise TypeError('The number of compartments n has to be an integer ' 'value.') if n <= 0: raise ValueError('The number of compartments n has to be at least 1.') self.type = type self._children = Children(self) self._parent = None self.indices = MorphologyIndexWrapper(self) def __getitem__(self, item): ''' Return the subtree with the given name/index. Ex.: ```neuron['axon']``` or ```neuron['11213']``` ```neuron[10*um:20*um]``` returns the subbranch from 10 um to 20 um. ```neuron[10*um]``` returns one compartment. ```neuron[5]``` returns compartment number 5. ''' if isinstance(item, slice): # neuron[10*um:20*um] or neuron[1:3] using_lengths = all([arg is None or have_same_dimensions(arg, meter) for arg in [item.start, item.stop]]) using_ints = all([arg is None or int(arg) == float(arg) for arg in [item.start, item.stop]]) if not (using_lengths or using_ints): raise TypeError('Index slice has to use lengths or integers') if using_lengths: if item.step is not None: raise TypeError(('Cannot provide a step argument when ' 'slicing with lengths')) l = np.cumsum(np.asarray(self.length)) # coordinate on the section # We use a special handling for values very close to the points # between the compartments to avoid non-intuitive rounding # effects: a point closer than 1e-12*length of section will be # considered to be within the following section (for a start # index), respectively within the previous section (for an end # index) if item.start is None: i = 0 else: diff = np.abs(float(item.start) - l) if min(diff) < 1e-12 * l[-1]: i = np.argmin(diff) + 1 else: i = np.searchsorted(l, item.start) if item.stop is None: j = len(l) else: diff = np.abs(float(item.stop) - l) if min(diff) < 1e-12 * l[-1]: j = np.argmin(diff) + 1 else: j = np.searchsorted(l, item.stop) + 1 else: # integers i, j, step = item.indices(self.n) if step != 1: raise TypeError('Can only slice a contiguous segment') elif isinstance(item, Quantity) and have_same_dimensions(item, meter): l = np.hstack([0, np.cumsum(np.asarray(self.length))]) # coordinate on the section if float(item) < 0 or float(item) > (1 + 1e-12) * l[-1]: raise IndexError(('Invalid index %s, has to be in the interval ' '[%s, %s].' % (item, 0*meter, l[-1]*meter))) diff = np.abs(float(item) - l) if min(diff) < 1e-12 * l[-1]: i = np.argmin(diff) else: i = np.searchsorted(l, item) - 1 j = i + 1 elif isinstance(item, numbers.Integral): # int: returns one compartment if item < 0: # allows e.g. to use -1 to get the last compartment item += self.n if item >= self.n: raise IndexError(('Invalid index %d ' 'for %d compartments') % (item, self.n)) i = item j = i + 1 elif isinstance(item, basestring): item = str(item) # convert int to string if (len(item) > 1) and all([c in 'LR123456789' for c in item]): # binary string of the form LLLRLR or 1213 (or mixed) return self._children[item[0]][item[1:]] elif item in self._children: return self._children[item] else: raise AttributeError('The subtree ' + item + ' does not exist') else: raise TypeError('Index of type %s not understood' % type(item)) return SubMorphology(self, i, j) def __setitem__(self, item, child): ''' Inserts the subtree and name it ``item``. Ex.: ``neuron['axon']`` or ``neuron['11213']`` ''' item = str(item) # convert int to string if (len(item) > 1) and all([c in 'LR123456789' for c in item]): # binary string of the form LLLRLR or 1213 (or mixed) self.children[item[0]][item[1:]] = child else: self.children.add(item, child) def __delitem__(self, item): ''' Remove the subtree ``item``. ''' item = str(item) # convert int to string if (len(item) > 1) and all([c in 'LR123456789' for c in item]): # binary string of the form LLLRLR or 1213 (or mixed) del self._children[item[0]][item[1:]] else: self._children.remove(item) def __getattr__(self, item): ''' Return the subtree named ``item``. Ex.: ``axon = neuron.axon`` ''' if item.startswith('_'): return super(object, self).__getattr__(item) else: return self[item] def __setattr__(self, item, child): ''' Attach a subtree and name it ``item``. Ex.: ``neuron.axon = Soma(diameter=10*um)`` ''' if isinstance(child, Morphology) and not item.startswith('_'): self[item] = child else: # If it is not a subtree, then it's a normal class attribute object.__setattr__(self, item, child) def __delattr__(self, item): ''' Remove the subtree ``item``. ''' del self[item] def _indices(self, item=None, index_var='_idx'): ''' Return compartment indices for the main section, relative to the original morphology. ''' if index_var != '_idx': raise AssertionError('Unexpected index %s' % index_var) if not (item is None or item == slice(None)): if isinstance(item, slice): # So that this always returns an array of values, even if it is # just a single value return self[item]._indices(slice(None)) else: return self[item]._indices(None) else: start_idx = _calc_start_idx(self) if self.n == 1 and item is None: return start_idx else: return np.arange(start_idx, start_idx + self.n) def topology(self): ''' Return a representation of the topology Returns ------- topology : `Topology` An object representing the topology (can be converted to a string by using ``str(...)`` or simply by printing it with `print`.) ''' return Topology(self) def generate_coordinates(self, section_randomness=0.0, compartment_randomness=0.0, overwrite_existing=False): r''' Create a new `Morphology`, with coordinates filled in place where the previous morphology did not have any. This is mostly useful for plotting a morphology, it does not affect its electrical properties. Parameters ---------- section_randomness : float, optional The randomness when deciding the direction vector for each new section. The given number is the :math:`\beta` parameter of an exponential distribution (in degrees) which will be used to determine the deviation from the direction of the parent section. If the given value equals 0 (the default), then a deterministic algorithm will be used instead. compartment_randomness : float, optional The randomness when deciding the direction vector for each compartment within a section. The given number is the :math:`\beta` parameter of an exponential distribution (in degrees) which will be used to determine the deviation from the main direction of the current section. If the given value equals 0 (the default), then all compartments will be along a straight line. overwrite_existing : bool, optional Whether to overwrite existing coordinates in the morphology. This is by default set to ``False``, meaning that only sections that do not currently have any coordinates set will get new coordinates. This allows to conveniently generate a morphology that can be plotted for a morphology that is based on points but also has artificially added sections (the most common case: an axon added to a reconstructed morphology). If set to ``True``, all sections will get new coordinates. This can be useful to either get a schematic representation of the morphology (with ``section_randomness`` and ``compartment_randomness`` both 0) or to simply generate a new random variation of a morphology (which will still be electrically equivalent, of course). Returns ------- morpho_with_coordinates : `Morphology` The same morphology, but with coordinates ''' # Convert to radians section_randomness *= np.pi/180 compartment_randomness *= np.pi/180 return _add_coordinates(self, section_randomness=section_randomness, compartment_randomness=compartment_randomness, overwrite_existing=overwrite_existing) @abstractmethod def copy_section(self): ''' Create a copy of the current section (attributes of this section only, not re-creating the parent/children relation) Returns ------- copy : `Morphology` A copy of this section (without the links to the parent/children) ''' raise NotImplementedError() @property def n(self): ''' The number of compartments in this section. ''' return self._n def __len__(self): ''' This is not well-defined, use `Morphology.n` or `Morphology.total_compartments` instead. ''' raise TypeError('The "length" of a Morphology is ambiguous, use its ' '"n" attribute for the number of compartments in this ' 'section or the "total_compartments" attribute for the ' 'total number of compartments in the whole sub-tree.') @property def total_compartments(self): ''' The total number of compartments in this subtree (i.e. the number of compartments in this section plus all the compartments in the sections deeper in the tree). ''' return self.n + sum(c.total_compartments for c in self.children) @property def total_sections(self): ''' The total number of sections in this subtree. ''' return 1 + sum(c.total_sections for c in self.children) @property def parent(self): ''' The parent section of this section. ''' return self._parent @property def children(self): ''' The children (as a `Children` object) of this section. ''' return self._children @abc.abstractproperty def end_distance(self): ''' The distance to the root of the morphology at the end of this section. ''' raise NotImplementedError() # Per-compartment attributes @abc.abstractproperty def area(self): ''' The membrane surface area of each compartment in this section. ''' raise NotImplementedError() @abc.abstractproperty def volume(self): ''' The volume of each compartment in this section. ''' raise NotImplementedError() @abc.abstractproperty def length(self): ''' The length of each compartment in this section. ''' raise NotImplementedError() @abc.abstractproperty def r_length_1(self): ''' The geometry-dependent term to calculate the conductance between the start and the midpoint of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' raise NotImplementedError() @abc.abstractproperty def r_length_2(self): ''' The geometry-dependent term to calculate the conductance between the midpoint and the end of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' raise NotImplementedError() # At-midpoint attributes @abc.abstractproperty def diameter(self): ''' The diameter at the middle of each compartment in this section. ''' raise NotImplementedError() @abc.abstractproperty def distance(self): ''' The total distance between the midpoint of each compartment and the root of the morphology. ''' raise NotImplementedError() @property def start_x(self): ''' The x coordinate at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_x_) @property def start_y(self): ''' The y coordinate at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_y_) @property def start_z(self): ''' The z coordinate at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_z_) @abc.abstractproperty def start_x_(self): ''' The x coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def start_y_(self): ''' The y coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def start_z_(self): ''' The z coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @property def x(self): ''' The x coordinate at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.x_) @property def y(self): ''' The y coordinate at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.y_) @property def z(self): ''' The y coordinate at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.z_) @abc.abstractproperty def x_(self): ''' The x coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def y_(self): ''' The y coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def z_(self): ''' The z coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @property def end_x(self): ''' The x coordinate at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_x_) @property def end_y(self): ''' The y coordinate at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_y_) @property def end_z(self): ''' The z coordinate at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_z_) @abc.abstractproperty def end_x_(self): ''' The x coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def end_y_(self): ''' The y coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @abc.abstractproperty def end_z_(self): ''' The z coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' raise NotImplementedError() @property def coordinates(self): r''' Array with all coordinates at the start- and end-points of each compartment in this section. The array has size :math:`(n+1) \times 3`, where :math:`n` is the number of compartments in this section. Each row is one point (start point of first compartment, end point of first compartment, end point of second compartment, ...), with the columns being the x, y, and z coordinates. Returns ``None`` for morphologies without coordinates. ''' if self.x_ is None: return None else: return Quantity(self.coordinates_, dim=meter.dim) @property def coordinates_(self): r''' Array with all coordinates (as unitless floating point numbers) at the start- and end-points of each compartment in this section. The array has size :math:`(n+1) \times 3`, where :math:`n` is the number of compartments in this section. Each row is one point (start point of first compartment, end point of first compartment, end point of second compartment, ...), with the columns being the x, y, and z coordinates. Returns ``None`` for morphologies without coordinates. ''' if self.x_ is None: return None else: return np.vstack([np.hstack([self.start_x_[0], self.end_x_[:]]), np.hstack([self.start_y_[0], self.end_y_[:]]), np.hstack([self.start_z_[0], self.end_z_[:]])]).T @staticmethod def _create_section(compartments, name, parent, sections, spherical_soma): if (spherical_soma and len(compartments) == 1 and compartments[0].comp_name == 'soma'): soma = compartments[0] section = Soma(diameter=soma.diameter * um, x=soma.x * um, y=soma.y * um, z=soma.z * um) else: sec_x, sec_y, sec_z, sec_diameter = zip(*[(c.x, c.y, c.z, c.diameter) for c in compartments]) # Add a point for the end of the parent_idx compartment if parent is not None: n = len(compartments) if (parent.comp_name is not None and parent.comp_name.lower() == 'soma'): # For a Soma, we don't use its diameter start_diameter = sec_diameter[0] else: start_diameter = parent.diameter # Use relative coordinates sec_x = np.array(sec_x) - parent.x sec_y = np.array(sec_y) - parent.y sec_z = np.array(sec_z) - parent.z start_x = start_y = start_z = 0. else: n = len(compartments) - 1 start_diameter = sec_diameter[0] sec_diameter = sec_diameter[1:] start_x = sec_x[0] start_y = sec_y[0] start_z = sec_z[0] sec_x = sec_x[1:] sec_y = sec_y[1:] sec_z = sec_z[1:] diameter = np.hstack([start_diameter, sec_diameter])*um x = np.hstack([start_x, sec_x])*um y = np.hstack([start_y, sec_y])*um z = np.hstack([start_z, sec_z])*um section = Section(n=n, diameter=diameter, x=x, y=y, z=z, type=name) # Add the section as a child to its parent if parent is not None: parent_sec = sections[parent.index] parent_sec.children.add(name, section, automatic_name=True) return section @staticmethod def _compartments_to_sections(compartment, spherical_soma, current_compartments=None, sections=None): # Merge all unbranched compartments of the same type into a single # section if sections is None: sections = OrderedDict() if current_compartments is None: current_compartments = [] current_compartments.append(compartment) # We have to create a new section, if we are either # 1. at a leaf of the tree or at a branching point, or # 2. if the compartment type changes if (len(compartment.children) != 1 or compartment.comp_name != compartment.children[0].comp_name): parent = current_compartments[0].parent section = Morphology._create_section(current_compartments, compartment.comp_name, parent=parent, sections=sections, spherical_soma=spherical_soma) sections[current_compartments[-1].index] = section # If we are at a branching point, recurse into all subtrees for child in compartment.children: Morphology._compartments_to_sections(child, spherical_soma=spherical_soma, current_compartments=None, sections=sections) else: # A single child of the same type, continue (recursive call) Morphology._compartments_to_sections(compartment.children[0], spherical_soma=spherical_soma, current_compartments=current_compartments, sections=sections) return sections @staticmethod def _replace_three_point_soma(compartment, all_compartments): # Replace a three-point/two-cylinder soma by a single spherical soma # if possible (see http://neuromorpho.org/SomaFormat.html for some # details) # We are looking for a node with two children of the soma type (and # other childen of other types), where the two children don't have any # children of their own soma_children = [c for c in compartment.children if c.comp_name == 'soma'] if (compartment.comp_name == 'soma' and len(soma_children) == 2 and all(len(c.children) == 0 for c in soma_children)): # We've found a 3-point soma to replace soma_c = [compartment] + soma_children if not all(abs(c.diameter - soma_c[0].diameter) < 1e-15 for c in soma_c): indices = ', '.join(str(c.index) for c in soma_c) raise ValueError('Found a "3-point-soma" (lines: %s), but not ' 'all the diameters are ' 'identical.' % indices) diameter = soma_c[0].diameter point_0 = np.array([soma_c[0].x, soma_c[0].y, soma_c[0].z]) point_1 = np.array([soma_c[1].x, soma_c[1].y, soma_c[1].z]) point_2 = np.array([soma_c[2].x, soma_c[2].y, soma_c[2].z]) length_1 = np.sqrt(np.sum((point_1 - point_0) ** 2)) length_2 = np.sqrt(np.sum((point_2 - point_0) ** 2)) if (np.abs(length_1 - diameter / 2) > 0.01 or np.abs(length_2 - diameter / 2) > 0.01): raise ValueError(('Cannot replace "3-point-soma" by a single ' 'point, the second and third points should ' 'be positioned one radius away from the ' 'first point. Distances are %.3fum and ' '%.3fum, respectively, while the ' 'radius is %.3fum.') % (length_1, length_2, diameter / 2)) children = [c for c in compartment.children if not c in soma_c] compartment = Node(index=compartment.index, comp_name='soma', x=point_0[0], y=point_0[1], z=point_0[2], diameter=diameter, parent=compartment.parent, children=children) all_compartments[compartment.index] = compartment del all_compartments[soma_children[0].index] del all_compartments[soma_children[1].index] # Recurse further down the tree all_compartments[compartment.index] = compartment for child in compartment.children: Morphology._replace_three_point_soma(child, all_compartments) @staticmethod def from_points(points, spherical_soma=True): ''' Create a morphology from a sequence of points (similar to the ``SWC`` format, see `Morphology.from_swc_file`). Each point has to be a 7-tuple: ``(index, name, x, y, z, diameter, parent)`` Note that the values should not use units, but are instead all taken to be in micrometers. Parameters ---------- points : sequence of 7-tuples The points of the morphology. spherical_soma : bool, optional Whether to model a soma as a sphere. Returns ------- morphology : `Morphology` Notes ----- This format closely follows the SWC format (see `Morphology.from_swc_file`) with two differences: the ``type`` should be a string (e.g. ``'soma'``) instead of an integer and the 6-th element should be the diameter and not the radius. ''' # First pass through all points to get the dependency structure compartments = OrderedDict() for counter, point in enumerate(points): if len(point) != 7: raise ValueError('Each point needs to be described by 7 ' 'values, got %d instead.' % len(point)) index, name, x, y, z, diameter, parent_idx = point if index in compartments: raise ValueError('Two compartments with index %d' % index) if parent_idx == index: raise ValueError('Compartment %d lists itself as the parent ' 'compartment.' % index) if counter == 0 and parent_idx == -1: parent = None # The first compartment does not have a parent elif parent_idx not in compartments: raise ValueError(('Did not find the compartment %d (parent ' 'compartment of compartment %d). Make sure ' 'that parent compartments are listed before ' 'their children.') % (parent_idx, index)) else: parent = compartments[parent_idx] children = [] node = Node(index, name, x, y, z, diameter, parent, children) compartments[index] = node if parent is not None: parent.children.append(node) if spherical_soma: Morphology._replace_three_point_soma(compartments.values()[0], compartments) sections = Morphology._compartments_to_sections(compartments.values()[0], spherical_soma) # Go through all the sections again and add standard names for all # sections (potentially in addition to the name they already have): # "L" + "R" for one or two children, "1", "2", "3", etc. otherwise children_counter = defaultdict(int) for section in sections.itervalues(): parent = section.parent if parent is not None: children_counter[parent] += 1 children = parent.children nth_child = children_counter[parent] if len(children) <= 2: name = 'L' if nth_child == 1 else 'R' else: name = '%d' % nth_child children.add(name, section) # There should only be one section without parents root = [sec for sec in sections.itervalues() if sec.parent is None] assert len(root) == 1 return root[0] @staticmethod def from_swc_file(filename, spherical_soma=True): ''' Load a morphology from a ``SWC`` file. A large database of morphologies in this format can be found at http://neuromorpho.org The format consists of an optional header of lines starting with ``#`` (ignored), followed by a sequence of points, each described in a line following the format:: index type x y z radius parent ``index`` is an integer label (starting at 1) that identifies the current point and increases by one each line. ``type`` is an integer representing the type of the neural segment. The only type that changes the interpretation by Brian is the type ``1`` which signals a soma. Types ``2`` (axon), ``3`` (dendrite), and ``4`` (apical dendrite) are used to give corresponding names to the respective sections. All other types are ignored. ``x``, ``y``, and ``z`` are the cartesian coordinates at each point and ``r`` is its radius. ``parent`` refers to the index of the parent point or is ``-1`` for the root point. Parameters ---------- filename : str The name of the ``SWC`` file. spherical_soma : bool, optional Whether to model the soma as a sphere. Returns ------- morpho : `Morphology` The morphology stored in the given file. ''' swc_types = defaultdict(lambda: None) # The following names will be translated into names, all other will be # ignored swc_types.update({'1': 'soma', '2': 'axon', '3': 'dend', '4': 'apic'}) with open(filename, 'r') as f: points = [] for line_no, line in enumerate(f): line = line.strip() if line.startswith('#') or len(line) == 0: # Ignore comments or empty lines continue splitted = line.split() if len(splitted) != 7: raise ValueError('Each line of an SWC file has to contain ' '7 space-separated entries, but line %d ' 'contains %d.' % (line_no + 1, len(splitted))) index, comp_type, x, y, z, radius, parent = splitted points.append((int(index), swc_types[comp_type], float(x), float(y), float(z), 2*float(radius), int(parent))) return Morphology.from_points(points, spherical_soma=spherical_soma) @staticmethod def from_file(filename, spherical_soma=True): ''' Convencience method to load a morphology from a given file. At the moment, only ``SWC`` files are supported, calling this function is therefore equivalent to calling `Morphology.from_swc_file` directly. Parameters ---------- filename : str The name of a file storing a morphology. spherical_soma : bool, optional Whether to model the soma as a sphere. Returns ------- morphology : `Morphology` The morphology stored in the given file. ''' _, ext = os.path.splitext(filename) if ext.lower() == '.swc': return Morphology.from_swc_file(filename, spherical_soma=spherical_soma) else: raise NotImplementedError('Currently, SWC is the only supported ' 'file format.') class SubMorphology(object): ''' A view on a subset of a section in a morphology. ''' def __init__(self, morphology, i, j): self._morphology = morphology self.indices = MorphologyIndexWrapper(self) self._i = i self._j = j def _indices(self, item=None): if not (item is None or item == slice(None)): raise IndexError('Cannot index a view on a subset of a section further') # Start index of the main section start_idx = _calc_start_idx(self._morphology) if item is None and self.n == 1: return start_idx + self._i else: return np.arange(start_idx + self._i, start_idx + self._j) @property def n(self): ''' The number of compartments in this sub-section. ''' return self._j - self._i def __len__(self): return self.n @property def n_sections(self): ''' The number of sections in this sub-section (always 1). ''' return 1 # Per-compartment attributes @property def area(self): ''' The membrane surface area of each compartment in this sub-section. ''' return self._morphology.area[self._i:self._j] @property def volume(self): ''' The volume of each compartment in this sub-section. ''' return self._morphology.volume[self._i:self._j] @property def length(self): ''' The length of each compartment in this sub-section. ''' return self._morphology.length[self._i:self._j] @property def r_length_1(self): ''' The geometry-dependent term to calculate the conductance between the start and the midpoint of each compartment in this sub-section. Dividing this value by the Intracellular resistivity gives the conductance. ''' return self._morphology.r_length_1[self._i:self._j] @property def r_length_2(self): ''' The geometry-dependent term to calculate the conductance between the midpoint and the end of each compartment in this sub-section. Dividing this value by the Intracellular resistivity gives the conductance. ''' return self._morphology.r_length_2[self._i:self._j] # At-midpoint attributes @property def diameter(self): ''' The diameter at the middle of each compartment in this sub-section. ''' return self._morphology.diameter[self._i:self._j] @property def distance(self): ''' The total distance between the midpoint of each compartment in this sub-section and the root of the morphology. ''' return self._morphology.distance[self._i:self._j] @property def start_x(self): ''' The x coordinate at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_x_) @property def start_y(self): ''' The y coordinate at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_y_) @property def start_z(self): ''' The x coordinate at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.start_z_) @property def start_x_(self): ''' The x coordinate (as a unitless floating point number) at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.start_x_, self._i, self._j) @property def start_y_(self): ''' The y coordinate (as a unitless floating point number) at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.start_y_, self._i, self._j) @property def start_z_(self): ''' The z coordinate (as a unitless floating point number) at the beginning of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.start_z_, self._i, self._j) @property def x(self): ''' The x coordinate at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.x_) @property def y(self): ''' The y coordinate at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.y_) @property def z(self): ''' The z coordinate at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.z_) @property def x_(self): ''' The x coordinate (as a unitless floating point number) at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.x_, self._i, self._j) @property def y_(self): ''' The y coordinate (as a unitless floating point number) at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.y_, self._i, self._j) @property def z_(self): ''' The z coordinate (as a unitless floating point number) at the midpoint of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.z_, self._i, self._j) @property def end_x(self): ''' The x coordinate at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_x_) @property def end_y(self): ''' The y coordinate at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_y_) @property def end_z(self): ''' The z coordinate at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _to_meters(self.end_z_) @property def end_x_(self): ''' The x coordinate (as a unitless floating point number) at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.end_x_, self._i, self._j) @property def end_y_(self): ''' The y coordinate (as a unitless floating point number) at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.end_y_, self._i, self._j) @property def end_z_(self): ''' The z coordinate (as a unitless floating point number) at the end of each compartment in this sub-section. Returns ``None`` for morphologies without coordinates. ''' return _from_morphology(self._morphology.end_z_, self._i, self._j) class Soma(Morphology): ''' A spherical, iso-potential soma. Parameters ---------- diameter : `Quantity` Diameter of the sphere. x : `Quantity`, optional The x coordinate of the position of the soma. y : `Quantity`, optional The y coordinate of the position of the soma. z : `Quantity`, optional The z coordinate of the position of the soma. type : str, optional The ``type`` of this section, defaults to ``'soma'``. ''' @check_units(diameter=meter, x=meter, y=meter, z=meter) def __init__(self, diameter, x=None, y=None, z=None, type='soma'): Morphology.__init__(self, n=1, type=type) if diameter.shape != () and len(diameter) != 1: raise TypeError('Diameter has to be a scalar value.') for coord in [x, y, z]: if coord is not None and coord.shape != () and len(coord) != 1: raise TypeError('Coordinates have to be scalar values.') self._diameter = np.ones(1) * diameter if any(coord is not None for coord in (x, y, z)): default_value = np.array([0.0]) else: default_value = None self._x = np.atleast_1d(np.asarray(x)) if x is not None else default_value self._y = np.atleast_1d(np.asarray(y)) if y is not None else default_value self._z = np.atleast_1d(np.asarray(z)) if z is not None else default_value def __repr__(self): s = '{klass}(diameter={diam!r}'.format(klass=self.__class__.__name__, diam=self.diameter[0]) if self._x is not None: s += ', x={x!r}, y={y!r}, z={z!r}'.format(x=self.x[0], y=self.y[0], z=self.z[0]) if self.type != 'soma': s += ', type={type!r}'.format(type=self.type) return s + ')' def copy_section(self): return Soma(self.diameter, x=self.x, y=self.y, z=self.z, type=self.type) # Note that the per-compartment properties should always return 1D arrays, # i.e. for the soma arrays of length 1 instead of scalar values @property def area(self): ''' The membrane surface area of this section (as an array of length 1). ''' return np.pi * self.diameter ** 2 @property def volume(self): ''' The volume of this section (as an array of length 1). ''' return (np.pi * self.diameter ** 3)/6 @property def length(self): ''' The "length" (equal to `diameter`) of this section (as an array of length 1). ''' return self.diameter @property def r_length_1(self): ''' The geometry-dependent term to calculate the conductance between the start and the midpoint of each compartment. Returns a fixed (high) value for a `Soma`, corresponding to a section with very low intracellular resistance. ''' return [1]*meter @property def r_length_2(self): ''' The geometry-dependent term to calculate the conductance between the midpoint and the end of each compartment. Returns a fixed (high) value for a `Soma`, corresponding to a section with very low intracellular resistance. ''' return [1]*meter @property def diameter(self): ''' The diameter of this section (as an array of length 1). ''' return self._diameter @property def distance(self): ''' The total distance between the midpoint of this section and the root of the morphology. The `Soma` is most likely the root of the morphology, and therefore the `distance` is 0. ''' dist = self._parent.distance[-1:] if self._parent is not None else [0]*um return dist @property def start_x_(self): ''' The x-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._x @property def start_y_(self): ''' The y-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._y @property def start_z_(self): ''' The z-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._z @property def x_(self): ''' The x-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._x @property def y_(self): ''' The y-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._y @property def z_(self): ''' The z-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._z @property def end_x_(self): ''' The x-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._x @property def end_y_(self): ''' The y-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._y @property def end_z_(self): ''' The z-coordinate of the current section (as an array of length 1). Note that a `Soma` is modelled as a "point" with finite surface/volume, equivalent to that of a sphere with the given `diameter`. It's start-, midpoint-, and end-coordinates are therefore identical. ''' return self._z @property def end_distance(self): ''' The distance to the root of the morphology at the end of this section. Note that since a `Soma` is modeled as a point (see docs of `x`, etc.), it does not add anything to the total distance, e.g. a section connecting to a `Soma` has a `distance` of 0 um at its start. ''' dist = self._parent.end_distance if self._parent is not None else 0 * um return dist class Section(Morphology): ''' A section (unbranched structure), described as a sequence of truncated cones with potentially varying diameters and lengths per compartment. Parameters ---------- diameter : `Quantity` Either a single value (the constant diameter along the whole section), or a value of length ``n+1``. When ``n+1`` values are given, they will be interpreted as the diameters at the start of the first compartment and the diameters at the end of each compartment (which is equivalent to: the diameter at the start of each compartment and the diameter at the end of the last compartment. n : int, optional The number of compartments in this section. Defaults to 1. length : `Quantity`, optional Either a single value (the total length of the section), or a value of length ``n``, the length of each individual compartment. Cannot be combined with the specification of coordinates. x : `Quantity`, optional ``n+1`` values, specifying the x coordinates of the start point of the first compartment and the end-points of all compartments (which is equivalent to: the start point of all compartments and the end point of the last compartment). The coordinates are interpreted as relative to the end point of the parent compartment (if any), so in most cases the start point should be ``0*um``. The common exception is a cylinder connecting to a `Soma`, here the start point can be used to make the cylinder start at the surface of the sphere instead of at its center. You can specify all of ``x``, ``y``, or ``z`` to specify a morphology in 3D, or only one or two out of them to specify a morphology in 1D or 2D. y : `Quantity`, optional See ``x`` z : `Quantity`, optional See ``x`` type : str, optional The type (e.g. ``"axon"``) of this `Section`. ''' @check_units(n=1, length=meter, diameter=meter, start_diameter=meter, x=meter, y=meter, z=meter) def __init__(self, diameter, n=1, length=None, x=None, y=None, z=None, start_diameter=None, origin=None, type=None): n = int(n) Morphology.__init__(self, n=n, type=type) if diameter.ndim != 1 or len(diameter) != n+1: raise TypeError('The diameter argument has to be a one-dimensional ' 'array of length %d' % (n + 1)) self._diameter = Quantity(diameter, copy=True).reshape((n+1, )) if ((x is not None or y is not None or z is not None) and length is not None): raise TypeError('Cannot specify coordinates and length at the same ' 'time.') if length is not None: # Length if length.ndim != 1 or len(length) != n: raise TypeError('The length argument has to be a ' 'one-dimensional array of length %d' % n) self._length = Quantity(length, copy=True).reshape((n, )) self._x = self._y = self._z = None else: # Coordinates if x is None and y is None and z is None: raise TypeError('No length specified, need to specify at least ' 'one out of x, y, or z.') for name, value in [('x', x), ('y', y), ('z', z)]: if value is not None and (value.ndim != 1 or len(value) != n + 1): raise TypeError(('%s needs to be a 1-dimensional array ' 'of length %d.') % (name, n + 1)) self._x = np.asarray(x).reshape((n+1, )) if x is not None else np.zeros(n + 1) self._y = np.asarray(y).reshape((n+1, )) if y is not None else np.zeros(n + 1) self._z = np.asarray(z).reshape((n+1, )) if z is not None else np.zeros(n + 1) length = np.sqrt((self.end_x - self.start_x) ** 2 + (self.end_y - self.start_y) ** 2 + (self.end_z - self.start_z) ** 2) self._length = length def __repr__(self): if all(np.abs(self.end_diameter - self.end_diameter[0]) < self.end_diameter[0]*1e-12): # Constant diameter diam = self.end_diameter[0] else: diam = np.hstack([np.asarray(self.start_diameter[0]), np.asarray(self.end_diameter)])*meter s = '{klass}(diameter={diam!r}'.format(klass=self.__class__.__name__, diam=diam) if self.n != 1: s += ', n={n}'.format(n=self.n) if self._x is not None: s += ', x={x!r}, y={y!r}, z={z!r}'.format(x=self._x, y=self._y, z=self._z) else: s += ', length={length!r}'.format(length=sum(self._length)) if self.type is not None: s += ', type={type!r}'.format(type=self.type) return s + ')' def copy_section(self): if self.x is None: x, y, z = None, None, None length = self.length else: x, y, z = self._x*meter, self._y*meter, self._z*meter length = None return Section(diameter=self._diameter, n=self.n, x=x, y=y, z=z, length=length, type=self.type) @property def area(self): r''' The membrane surface area of each compartment in this section. The surface area of each compartment is calculated as :math:`\frac{\pi}{2}(d_1 + d_2)\sqrt{\frac{(d_1 - d_2)^2}{4} + l^2)}`, where :math:`l` is the length of the compartment, and :math:`d_1` and :math:`d_2` are the diameter at the start and end of the compartment, respectively. Note that this surface area does not contain the area of the two disks at the two sides of the truncated cone. ''' d_1 = self.start_diameter d_2 = self.end_diameter return np.pi/2*(d_1 + d_2)*np.sqrt(((d_1 - d_2)**2)/4 + self._length**2) @property def volume(self): r''' The volume of each compartment in this section. The volume of each compartment is calculated as :math:`\frac{\pi}{12} l (d_1^2 + d_1 d_2 + d_2^2)`, where :math:`l` is the length of the compartment, and :math:`d_1` and :math:`d_2` are the diameter at the start and end of the compartment, respectively. ''' d_1 = self.start_diameter d_2 = self.end_diameter return np.pi * self._length * (d_1**2 + d_1*d_2 + d_2**2)/12 @property def length(self): ''' The length of each compartment in this section. ''' return self._length @property def start_diameter(self): ''' The diameter at the start of each compartment in this section. ''' return Quantity(self._diameter[:-1], copy=True) @property def end_diameter(self): ''' The diameter at the end of each compartment in this section. ''' return Quantity(self._diameter[1:], copy=True) @property def diameter(self): ''' The diameter at the middle of each compartment in this section. ''' d_1 = self.start_diameter d_2 = self.end_diameter # Diameter at the center return 0.5*(d_1 + d_2) @property def distance(self): ''' The total distance between the midpoint of each compartment and the root of the morphology. ''' dist = self._parent.end_distance if self._parent is not None else 0 * um return dist + np.cumsum(self.length) - 0.5 * self.length @property def end_distance(self): ''' The distance to the root of the morphology at the end of this section. ''' return self.distance[-1] + 0.5 * self.length[-1] @property def r_length_1(self): ''' The geometry-dependent term to calculate the conductance between the start and the midpoint of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' d_1 = self.start_diameter d_2 = (self.start_diameter + self.end_diameter)*0.5 return np.pi/2 * (d_1 * d_2)/self._length @property def r_length_2(self): ''' The geometry-dependent term to calculate the conductance between the midpoint and the end of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' d_1 = (self.start_diameter + self.end_diameter)*0.5 d_2 = self.end_diameter return np.pi/2 * (d_1 * d_2)/self._length @property def start_x_(self): ''' The x coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._x is None: return None parent_end_x = self.parent.end_x_ if self.parent is not None else None if parent_end_x is not None: return parent_end_x[-1] + self._x[:-1] else: return self._x[:-1] @property def start_y_(self): ''' The y coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._y is None: return None parent_end_y = self.parent.end_y_ if self.parent is not None else None if parent_end_y is not None: return parent_end_y[-1] + self._y[:-1] else: return self._y[:-1] @property def start_z_(self): ''' The z coordinate (as a unitless floating point number) at the beginning of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._z is None: return None parent_end_z = self.parent.end_z_ if self.parent is not None else None if parent_end_z is not None: return parent_end_z[-1] + self._z[:-1] else: return self._z[:-1] @property def x_(self): ''' The x coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._x is None: return None start_x = self.start_x_ diff_x = (self.end_x_ - start_x) return start_x + 0.5*diff_x @property def y_(self): ''' The y coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._y is None: return None start_y = self.start_y_ diff_y = (self.end_y_ - start_y) return start_y + 0.5*diff_y @property def z_(self): ''' The z coordinate (as a unitless floating point number) at the midpoint of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._z is None: return None start_z = self.start_z_ diff_z = (self.end_z_ - start_z) return start_z + 0.5*diff_z @property def end_x_(self): ''' The x coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._x is None: return None parent_end_x = self.parent.end_x_ if self.parent is not None else None if parent_end_x is not None: return parent_end_x[-1] + self._x[1:] else: return self._x[1:] @property def end_y_(self): ''' The y coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._y is None: return None parent_end_y = self.parent.end_y_ if self.parent is not None else None if parent_end_y is not None: return parent_end_y[-1] + self._y[1:] else: return self._y[1:] @property def end_z_(self): ''' The z coordinate (as a unitless floating point number) at the end of each compartment. Returns ``None`` for morphologies without coordinates. ''' if self._z is None: return None parent_end_z = self.parent.end_z_ if self.parent is not None else None if parent_end_z is not None: return parent_end_z[-1] + self._z[1:] else: return self._z[1:] class Cylinder(Section): ''' A cylindrical section. For sections with more complex geometry (varying length and/or diameter of each compartment), use the `Section` class. Parameters ---------- diameter : `Quantity` The diameter of the cylinder. n : int, optional The number of compartments in this section. Defaults to 1. length : `Quantity`, optional The length of the cylinder. Cannot be combined with the specification of coordinates. x : `Quantity`, optional A sequence of two values, the start and the end point of the cylinder. The coordinates are interpreted as relative to the end point of the parent compartment (if any), so in most cases the start point should be ``0*um``. The common exception is a cylinder connecting to a `Soma`, here the start point can be used to make the cylinder start at the surface of the sphere instead of at its center. You can specify all of ``x``, ``y``, or ``z`` to specify a morphology in 3D, or only one or two out of them to specify a morphology in 1D or 2D. y : `Quantity`, optional See ``x`` z : `Quantity`, optional See ``x`` type : str, optional The type (e.g. ``"axon"``) of this `Cylinder`. ''' @check_units(n=1, length=meter, diameter=meter, x=meter, y=meter, z=meter) def __init__(self, diameter, n=1, length=None, x=None, y=None, z=None, type=None): n = int(n) Morphology.__init__(self, n=n, type=type) # Diameter if diameter.shape != () and (diameter.ndim > 1 or len(diameter) != 1): raise TypeError('The diameter argument has to be a single value.') diameter = np.ones(n) * diameter self._diameter = diameter if ((x is not None or y is not None or z is not None) and length is not None): raise TypeError('Cannot specify coordinates and length at the same ' 'time.') if length is not None: # Length if length.shape != () and (length.ndim > 1 or len(length) != 1): raise TypeError('The length argument has to be a single value.') self._length = np.ones(n) * (length/n) # length was total length self._x = self._y = self._z = None else: # Coordinates if x is None and y is None and z is None: raise TypeError('No length specified, need to specify at least ' 'one out of x, y, or z.') for name, value in [('x', x), ('y', y), ('z', z)]: if value is not None and (value.ndim != 1 or len(value) != 2): raise TypeError('%s needs to be a 1-dimensional array of ' 'length 2 (start and end point)' % name) self._x = np.asarray(np.linspace(x[0], x[1], n+1)) if x is not None else np.zeros(n+1) self._y = np.asarray(np.linspace(y[0], y[1], n+1)) if y is not None else np.zeros(n+1) self._z = np.asarray(np.linspace(z[0], z[1], n+1)) if z is not None else np.zeros(n+1) length = np.sqrt((self.end_x - self.start_x) ** 2 + (self.end_y - self.start_y) ** 2 + (self.end_z - self.start_z) ** 2) self._length = length def __repr__(self): s = '{klass}(diameter={diam!r}'.format(klass=self.__class__.__name__, diam=self.diameter[0]) if self.n != 1: s += ', n={n}'.format(n=self.n) if self._x is not None: s += ', x={x!r}, y={y!r}, z={z!r}'.format(x=self._x[[0, -1]], y=self._y[[0, -1]], z=self._z[[0, -1]]) else: s += ', length={length!r}'.format(length=sum(self._length)) if self.type is not None: s += ', type={type!r}'.format(type=self.type) return s + ')' def copy_section(self): if self.x is None: return Cylinder(self.diameter[0], n=self.n, length=self.length, type=self.type) else: return Cylinder(self.diameter[0], n=self.n, x=self._x[[0, -1]], y=self._y[[0, -1]], z=self._z[[0, -1]], type=self.type) # Overwrite the properties that differ from `Section` @property def area(self): r''' The membrane surface area of each compartment in this section. The surface area of each compartment is calculated as :math:`\pi d l`, where :math:`l` is the length of the compartment, and :math:`d` is its diameter. Note that this surface area does not contain the area of the two disks at the two sides of the cylinder. ''' return np.pi * self._diameter * self.length @property def start_diameter(self): ''' The diameter at the start of each compartment in this section. ''' return self._diameter @property def diameter(self): ''' The diameter at the middle of each compartment in this section. ''' return self._diameter @property def end_diameter(self): ''' The diameter at the end of each compartment in this section. ''' return self._diameter @property def volume(self): r''' The volume of each compartment in this section. The volume of each compartment is calculated as :math:`\pi \frac{d}{2}^2 l` , where :math:`l` is the length of the compartment, and :math:`d` is its diameter. ''' return np.pi * (self._diameter/2)**2 * self.length @property def r_length_1(self): ''' The geometry-dependent term to calculate the conductance between the start and the midpoint of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' return np.pi/2 * (self._diameter**2)/self.length @property def r_length_2(self): ''' The geometry-dependent term to calculate the conductance between the midpoint and the end of each compartment. Dividing this value by the Intracellular resistivity gives the conductance. ''' return np.pi/2 * (self._diameter**2)/self.length

38.323278

105

0.569423

5846619656455af3a1c66349f2a5d9d0ac0ea4e1

377

py

Python

modules/time/timedelta.py

PraghadeshManivannan/Python

7a42269c5f8cfd9178f1ed39ffc2afea1dd6c5aa

[ "MIT" ]

null

modules/time/timedelta.py

PraghadeshManivannan/Python

7a42269c5f8cfd9178f1ed39ffc2afea1dd6c5aa

[ "MIT" ]

null

modules/time/timedelta.py

PraghadeshManivannan/Python

7a42269c5f8cfd9178f1ed39ffc2afea1dd6c5aa

[ "MIT" ]

null

from datetime import time,date,timedelta time_1 = time() print('Time_1: {}'.format(time_1)) time_2 = time(hour=10,minute=45,second=47,microsecond=123458) print('Time_2: {}'.format(time_2)) today = date.today() outbreak = date(2002,11,10) daysSinceOutbreak = today - outbreak print(daysSinceOutbreak) tomorrow = today + timedelta(seconds=86400) print(tomorrow)

25.133333

62

0.724138

b4c657c572bc2f244c70fb665fc34dd450042729

185

py

Python

PythonMundoDois/ex061.py

HendrylNogueira/CursoPython3

c3d9d4e2a27312b83d744aaf0f8d01b26e6faf4f

[ "MIT" ]

null

PythonMundoDois/ex061.py

HendrylNogueira/CursoPython3

c3d9d4e2a27312b83d744aaf0f8d01b26e6faf4f

[ "MIT" ]

null

PythonMundoDois/ex061.py

HendrylNogueira/CursoPython3

c3d9d4e2a27312b83d744aaf0f8d01b26e6faf4f

[ "MIT" ]

null

termo = int(input('Digite o termo: ')) razao = int(input('Digite a razão: ')) cont = 0 while cont <= 10: print(f'{termo} -> ', end='') termo += razao cont +=1 print('Fim.')

20.555556

38

0.556757