PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

e4c4a1a9b4555cbf8478e7bf4e73c7328720aa90

530

py

Python

server/server.py

straayke/ClassReport

5c4fbb0422142c4784a6eba99bbebe8418444898

[ "MIT" ]

7

2018-11-25T12:47:04.000Z

2019-08-02T14:14:54.000Z

server/server.py

straayke/ClassReport

5c4fbb0422142c4784a6eba99bbebe8418444898

[ "MIT" ]

3

2020-09-06T18:23:54.000Z

2022-02-12T18:13:28.000Z

server/server.py

straayke/ClassReport

5c4fbb0422142c4784a6eba99bbebe8418444898

[ "MIT" ]

null

from flask import Flask, request from flask import jsonify import os from openpose import nb_raised from flask_cors import CORS app = Flask(__name__) CORS(app) @app.route('/hand-raised', methods=['POST']) def hand_raised(): file = request.files["file"] file.save(os.path.join("uploaded.jpg")) ret, people, points = nb_raised() hands = {"count":ret, "skeletonCount":people, "positions":points} response = jsonify(hands) return response if __name__ == '__main__': app.run(host='0.0.0.0', port=5000)

23.043478

69

0.69434

24f38975b2b1d1be7c6b11bf38071662038084ed

110

py

Python

comb/mq/mysql.py

nextoa/comb

9bddd6c7366bd71b06d0ad7c28188abec8a874b0

[ "MIT" ]

null

comb/mq/mysql.py

nextoa/comb

9bddd6c7366bd71b06d0ad7c28188abec8a874b0

[ "MIT" ]

2

2015-06-30T10:59:58.000Z

2016-01-14T07:15:15.000Z

comb/mq/mysql.py

kbonez/comb

9bddd6c7366bd71b06d0ad7c28188abec8a874b0

[ "MIT" ]

1

2019-11-09T20:34:54.000Z

# -*- coding: utf-8 -*- def token(handle): # @todo pass def release(handle): # @todo pass

9.166667

23

0.509091

22d9e7f02b2ae6267e6489c7aae8ca82ff5ab691

2,146

py

Python

fcoclient/api.py

b-77/cloudify-flexiant-plugin

72b8bd98995331972a404d4e22c2df415d9d9e9e

[ "Apache-2.0" ]

null

fcoclient/api.py

b-77/cloudify-flexiant-plugin

72b8bd98995331972a404d4e22c2df415d9d9e9e

[ "Apache-2.0" ]

null

fcoclient/api.py

b-77/cloudify-flexiant-plugin

72b8bd98995331972a404d4e22c2df415d9d9e9e

[ "Apache-2.0" ]

null

# coding=UTF-8 """Abstraction of FCO API in the form of a Python wrapper.""" import fcoclient.clients as clients import fcoclient.exceptions as exceptions import resttypes.endpoints as endpoints import json class REST(object): """FCO REST API Interface.""" def __init__(self, auth, logger): """Initialise FCP API Interface.""" self.client = clients.get_client(auth, logger=logger) self.logger = logger self.logger.debug('REST API initialised with auth: %s', auth) def __getattr__(self, item): """Get relevant Endpoint object when accessed.""" def wrapper(*args, **kwargs): self.logger.debug('REST API endpoint request: %s', item) return self.query(item, *args, **kwargs) return wrapper def query(self, endpoint, parameters=None, data=None, validate=False, **kwargs): """Perform an API query to the given endpoint.""" endpoint = endpoint[0].capitalize() + endpoint[1:] endpoint = getattr(endpoints, endpoint)(parameters, data, **kwargs) type_, url = endpoint.endpoint self.logger.info('%s', endpoint) payload = endpoint.untype() self.logger.info('%s', payload) if not len(payload): payload = None self.logger.debug('REST API generated endpoint:\nTYPE: %s\nURL: %s\n' 'DATA: %s', type_, url, payload) if type_ is endpoints.Verbs.PUT: fn = self.client.put elif type_ is endpoints.Verbs.GET: fn = self.client.get elif type_ is endpoints.Verbs.POST: fn = self.client.post if payload: payload = json.JSONEncoder().encode(payload) elif type_ is endpoints.Verbs.DELETE: fn = self.client.delete else: raise exceptions.NonRecoverableError('unsupported API verb') rv = fn(url, payload) self.logger.debug('REST API return value: %s', rv) if validate: return rv, endpoint.validate_return(rv) else: return endpoint.RETURNS.items()[0][1](rv)

32.515152

77

0.603448

ab9e1413030083c82cb05a882dd3b761c0a7b44a

517

py

Python

discordbot.py

shiitake08/discordpy-startup

becd187061d15b2fc8b9dc12f3e6fbb9e85ab8bd

[ "MIT" ]

null

discordbot.py

shiitake08/discordpy-startup

becd187061d15b2fc8b9dc12f3e6fbb9e85ab8bd

[ "MIT" ]

null

discordbot.py

shiitake08/discordpy-startup

becd187061d15b2fc8b9dc12f3e6fbb9e85ab8bd

[ "MIT" ]

null

from discord.ext import commands import os import traceback bot = commands.Bot(command_prefix='/') token = os.environ['DISCORD_BOT_TOKEN'] @bot.event async def on_command_error(ctx, error): orig_error = getattr(error, "original", error) error_msg = ''.join(traceback.TracebackException.from_exception(orig_error).format()) await ctx.send(error_msg) @bot.command() async def ping(ctx): await ctx.send('pong') @bot.command() async def kamaneko(ctx): await ctx.send('boko') bot.run(token)

20.68

89

0.72147

d281f4fa442827272cc456b77af21d3bcc763f22

2,769

py

Python

examples/src/main/python/ml/simple_text_classification_pipeline.py

liuhb86/spark

18f2098433e0bfef9497bacd601fdf098ed03eab

[ "Apache-2.0" ]

24

2017-10-11T02:59:45.000Z

2021-12-06T05:01:13.000Z

examples/src/main/python/ml/simple_text_classification_pipeline.py

liuhb86/spark

18f2098433e0bfef9497bacd601fdf098ed03eab

[ "Apache-2.0" ]

1

2022-03-21T18:44:10.000Z

examples/src/main/python/ml/simple_text_classification_pipeline.py

liuhb86/spark

18f2098433e0bfef9497bacd601fdf098ed03eab

[ "Apache-2.0" ]

18

2017-10-26T14:16:10.000Z

2022-03-30T02:18:14.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You 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 pyspark import SparkContext from pyspark.ml import Pipeline from pyspark.ml.classification import LogisticRegression from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.sql import Row, SQLContext """ A simple text classification pipeline that recognizes "spark" from input text. This is to show how to create and configure a Spark ML pipeline in Python. Run with: bin/spark-submit examples/src/main/python/ml/simple_text_classification_pipeline.py """ if __name__ == "__main__": sc = SparkContext(appName="SimpleTextClassificationPipeline") sqlCtx = SQLContext(sc) # Prepare training documents, which are labeled. LabeledDocument = Row("id", "text", "label") training = sc.parallelize([(0L, "a b c d e spark", 1.0), (1L, "b d", 0.0), (2L, "spark f g h", 1.0), (3L, "hadoop mapreduce", 0.0)]) \ .map(lambda x: LabeledDocument(*x)).toDF() # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. tokenizer = Tokenizer(inputCol="text", outputCol="words") hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") lr = LogisticRegression(maxIter=10, regParam=0.01) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) # Fit the pipeline to training documents. model = pipeline.fit(training) # Prepare test documents, which are unlabeled. Document = Row("id", "text") test = sc.parallelize([(4L, "spark i j k"), (5L, "l m n"), (6L, "mapreduce spark"), (7L, "apache hadoop")]) \ .map(lambda x: Document(*x)).toDF() # Make predictions on test documents and print columns of interest. prediction = model.transform(test) selected = prediction.select("id", "text", "prediction") for row in selected.collect(): print row sc.stop()

39.557143

92

0.675334

128461083691a6de3b31088e462523e87f8699e6

1,567

py

Python

confusion_test.py

cocoaaa/ml_gesture

a23dd7b9d13bbcb5a1ee049a7f1b026f81a4ba9d

[ "MIT" ]

null

confusion_test.py

cocoaaa/ml_gesture

a23dd7b9d13bbcb5a1ee049a7f1b026f81a4ba9d

[ "MIT" ]

null

confusion_test.py

cocoaaa/ml_gesture

a23dd7b9d13bbcb5a1ee049a7f1b026f81a4ba9d

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Jun 30 10:12:44 2015 Confusion matrix studies on the Iris data @author: LLP-admin """ import matplotlib.pyplot as plt import pandas as pd import numpy as np from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn import datasets df =datasets.load_iris() data_x = pd.DataFrame( df.data, columns = df.feature_names ) f0 = data_x[ data_x.columns[[0]] ]; f1 = data_x [data_x.columns[1]] f2 = data_x[ data_x.columns[2] ] data_y = df.target x_min = np.min(f0) - 0.5; x_max = np.max(f0) + 0.5; y_min = np.min(f1) - 0.5; y_max = np.max(f1) + 0.5; plt.figure(0,figsize = (8,6)); plt.clf() x = np.arange(0,11,step = 0.1); y = [el**2 for el in x] plt.scatter(f0, f1, c = data_y, cmap = plt.cm.Paired) plt.xlim = [x_min, x_max]; plt.xlabel('sepal length (cm)') plt.ylim = [y_min, y_max]; plt.ylabel('sepal width (cm) ') ##3d fig1 = plt.figure(1, figsize = (8,6)); ax1 = Axes3D(fig1) ax1.scatter(f0, f1, f2, c = data_y, cmap = plt.cm.Paired) ax1.set_xlabel (data_x.columns[0]); ax1.set_ylabel (data_x.columns[1]); ax1.set_zlabel(data_x.columns[2]); ax1.set_title('raw data plotted in 3D') ##pca PCA_data_x = PCA(n_components = 3).fit_transform(data_x) pc0 = PCA_data_x[:, 0]; pc1 = PCA_data_x[:, 1]; pc2 = PCA_data_x[:, 2]; fig2 = plt.figure(2, figsize = (8,6)); ax2 = Axes3D(fig2); ax2.scatter(pc0, pc1, pc2, c = data_y, cmap = plt.cm.Paired); ax2.set_title("First three Principle components"); ax2.set_xlabel('first pc'); ax2.set_ylabel("second pc"); ax2.set_zlabel("third pc") plt.show()

29.566038

106

0.681557

b6ca608f06b523d93f3ac2201c9bf0481993de52

4,321

py

Python

examples/image/resnet50.py

vsl9/NeMo

4137c2b4e3cba0ec5ca1da7b58b3ff97fdb25e50

[ "Apache-2.0" ]

2

2021-03-04T16:37:46.000Z

2021-03-04T16:40:22.000Z

examples/image/resnet50.py

vsl9/NeMo

4137c2b4e3cba0ec5ca1da7b58b3ff97fdb25e50

[ "Apache-2.0" ]

null

examples/image/resnet50.py

vsl9/NeMo

4137c2b4e3cba0ec5ca1da7b58b3ff97fdb25e50

[ "Apache-2.0" ]

null

# Copyright (c) 2019 NVIDIA Corporation import argparse import os import sys from tensorboardX import SummaryWriter import nemo from nemo.backends.pytorch.torchvision.helpers import compute_accuracy, eval_epochs_done_callback, eval_iter_callback from nemo.utils.lr_policies import SquareAnnealing sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) parser = argparse.ArgumentParser(description='ResNet50 on ImageNet') parser.add_argument("--local_rank", default=None, type=int) parser.add_argument("--batch_size", default=32, type=int) parser.add_argument("--num_epochs", default=100, type=int) parser.add_argument("--max_steps", default=None, type=int) parser.add_argument("--learning_rate", default=0.1, type=float) parser.add_argument("--weight_decay", default=0.0001, type=float) parser.add_argument("--momentum", default=0.91, type=float) parser.add_argument("--num_gpus", default=1, type=int) parser.add_argument("--data_root", default=None, type=str) parser.add_argument("--tb_folder", default=None, type=str) args = parser.parse_args() if args.local_rank is not None: device = nemo.core.DeviceType.AllGpu else: device = nemo.core.DeviceType.GPU batch_size = args.batch_size num_epochs = args.num_epochs learning_rate = args.learning_rate max_steps = args.max_steps weight_decay = args.weight_decay momentum = args.momentum num_gpus = args.num_gpus if args.tb_folder is None: tb_folder = 'resnet50_fp32' else: tb_folder = args.tb_folder tb_writer = SummaryWriter(tb_folder) # instantiate Neural Factory with supported backend neural_factory = nemo.core.NeuralModuleFactory( backend=nemo.core.Backend.PyTorch, local_rank=args.local_rank, # Set this to nemo.core.Optimization.mxprO1 # if you have Volta or Turing GPU optimization_level=nemo.core.Optimization.mxprO0, ) resnet = neural_factory.get_module( name="resnet50", params={"placement": device}, collection="torchvision", pretrained=False, ) dl_train = neural_factory.get_module( name="ImageFolderDataLayer", collection="torchvision", params={ "batch_size": batch_size, "input_size": resnet.inputs["x"].axis2type[2].dim, "shuffle": True, "path": args.data_root + "train", # "path": "/mnt/D1/Data/ImageNet/ImageFolder/train", "placement": device, }, ) L_train = neural_factory.get_module(name="CrossEntropyLoss", collection="toys", params={"placement": device}) dl_eval = neural_factory.get_module( name="ImageFolderDataLayer", collection="torchvision", params={ "batch_size": batch_size, "input_size": resnet.inputs["x"].axis2type[2].dim, "shuffle": False, "is_eval": True, "path": args.data_root + "val", # "path": "/mnt/D1/Data/ImageNet/ImageFolder/val", # "path": "/raid/okuchaiev/Data/ImageNet/ImageFolder/val", "placement": device, }, ) L_eval = neural_factory.get_module(name="CrossEntropyLoss", collection="toys", params={"placement": device}) step_per_epoch = int(len(dl_train) / (batch_size * num_gpus)) images, labels = dl_train() outputs = resnet(x=images) train_loss = L_train(predictions=outputs, labels=labels) e_images, e_labels = dl_eval() e_outputs = resnet(x=e_images) e_loss = L_eval(predictions=e_outputs, labels=e_labels) callback = nemo.core.SimpleLossLoggerCallback( step_freq=50, tb_writer=tb_writer, tensor_list2str=lambda x: str(x[0].item()), tensor_list2str_evl=lambda x: compute_accuracy(x), ) callback_eval = nemo.core.EvaluatorCallback( eval_tensors=[e_loss, e_outputs, e_labels], user_iter_callback=eval_iter_callback, user_epochs_done_callback=eval_epochs_done_callback, eval_step=10000, tb_writer=tb_writer, ) # Instantiate an optimizer to perform `train` action optimizer = neural_factory.get_trainer( params={ "optimization_params": { "num_epochs": num_epochs, "lr": learning_rate, "max_steps": max_steps, "weight_decay": weight_decay, "momentum": momentum, } } ) optimizer.train( tensors_to_optimize=[train_loss], tensors_to_evaluate=[outputs, labels], callbacks=[callback, callback_eval], lr_policy=SquareAnnealing(num_epochs * step_per_epoch), )

31.540146

117

0.722055

87ae0ffa42dc7530b60e89e4df7abeed9e43a735

821

py

Python

pytorch-SAC/Hyperparameters.py

Fable67/Soft-Actor-Critic-Pytorch

034c5cc37904f568773cdf3c25caf1a5d28a6cee

[ "MIT" ]

4

2019-05-08T23:18:26.000Z

2019-09-05T19:59:47.000Z

pytorch-SAC/Hyperparameters.py

Fable67/Soft-Actor-Critic-Pytorch

034c5cc37904f568773cdf3c25caf1a5d28a6cee

[ "MIT" ]

null

pytorch-SAC/Hyperparameters.py

Fable67/Soft-Actor-Critic-Pytorch

034c5cc37904f568773cdf3c25caf1a5d28a6cee

[ "MIT" ]

1

2022-01-19T06:47:56.000Z

import torch from ReplayBuffer import ReplayBuffer from CombinedReplayBuffer import CombinedReplayBuffer import torch.optim as optim from ranger import Ranger DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' POLICY_LEARNING_RATE = 3e-4 Q_LEARNING_RATE = 3e-4 ALPHA_LEARNING_RATE = 3e-4 POLICY_OPTIM = optim.Adam # Ranger Q_OPTIM = optim.Adam # Ranger ALPHA_OPTIM = optim.Adam # Ranger GAMMA = 0.99 TAU = 0.005 LOGSTD_MIN = -20 LOGSTD_MAX = 2 INITIAL_REPLAY_SIZE = 10000 REPLAY_SIZE = 1000000 REPLAY_BUFFER = ReplayBuffer HIDDEN_SIZE = 256 BATCH_SIZE = 256 NUM_ITERATIONS = 10000000 EVAL_FREQ = 5000 NUM_EVAL_GAMES = 10 SUMMARY_FREQ = 1000 SAVE_FREQ = 500000 MAX_STEPS = 1000 NUM_TRAINS_PER_TRAIN_LOOP = 1000 NUM_EXPL_STEPS_PER_TRAIN_LOOP = 1000 MUNCHAUSEN = False M_ALPHA = 0.9 M_TAU = 0.03 M_L0 = -1

20.02439

55

0.784409

eecd58e988f0a937e5c4cc571a3f68a1d15835b6

2,057

py

Python

src/connectedvmware/azext_connectedvmware/vendored_sdks/models/tracked_resource.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

207

2017-11-29T06:59:41.000Z

2022-03-31T10:00:53.000Z

src/connectedvmware/azext_connectedvmware/vendored_sdks/models/tracked_resource.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

4,061

2017-10-27T23:19:56.000Z

2022-03-31T23:18:30.000Z

src/connectedvmware/azext_connectedvmware/vendored_sdks/models/tracked_resource.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

802

2017-10-11T17:36:26.000Z

2022-03-31T22:24:32.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .resource import Resource class TrackedResource(Resource): """The resource model definition for an Azure Resource Manager tracked top level resource. Variables are only populated by the server, and will be ignored when sending a request. All required parameters must be populated in order to send to Azure. :ivar id: Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName} :vartype id: str :ivar name: The name of the resource :vartype name: str :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" :vartype type: str :param tags: Resource tags. :type tags: dict[str, str] :param location: Required. The geo-location where the resource lives :type location: str """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, 'location': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '{str}'}, 'location': {'key': 'location', 'type': 'str'}, } def __init__(self, **kwargs): super(TrackedResource, self).__init__(**kwargs) self.tags = kwargs.get('tags', None) self.location = kwargs.get('location', None)

36.087719

139

0.601361

5a42bc515555091b62a329de7b2e4547c65b0aee

703

py

Python

server/catalog/management/commands/update_mapping.py

icapora/django-elasticsearch

4ae00c84b66a927c33eaffcdb86fedb0e100728d

[ "MIT" ]

null

server/catalog/management/commands/update_mapping.py

icapora/django-elasticsearch

4ae00c84b66a927c33eaffcdb86fedb0e100728d

[ "MIT" ]

null

server/catalog/management/commands/update_mapping.py

icapora/django-elasticsearch

4ae00c84b66a927c33eaffcdb86fedb0e100728d

[ "MIT" ]

null

from django.core.management.base import BaseCommand, CommandError from elasticsearch_dsl import connections from catalog.constants import ES_INDEX, ES_MAPPING class Command(BaseCommand): help = 'Updates a mapping on an Elasticsearch index.' def handle(self, *args, **kwargs): self.stdout.write(f'Updating mapping on "{ES_INDEX}" index...') connection = connections.get_connection() if connection.indices.exists(index=ES_INDEX): connection.indices.put_mapping(index=ES_INDEX, body=ES_MAPPING) self.stdout.write(f'Updated mapping on "{ES_INDEX}" successfully') else: raise CommandError(f'Index "{ES_INDEX}" does not exist')

37

78

0.709815

76a8732468efb01e91b3adb0789286ba6392b6ad

6,281

py

Python

tests/test_dpkg_evironment.py

adelton/swidGenerator

0d0941d261925cc01638e88df748c0f2c4395131

[ "MIT" ]

13

2015-10-16T18:28:32.000Z

2021-08-29T09:36:08.000Z

tests/test_dpkg_evironment.py

adelton/swidGenerator

0d0941d261925cc01638e88df748c0f2c4395131

[ "MIT" ]

11

2018-07-03T13:34:08.000Z

2019-04-10T10:29:29.000Z

tests/test_dpkg_evironment.py

adelton/swidGenerator

0d0941d261925cc01638e88df748c0f2c4395131

[ "MIT" ]

12

2017-02-22T14:51:10.000Z

2022-03-23T16:55:20.000Z

# -*- coding: utf-8 -*- from __future__ import print_function, division, absolute_import, unicode_literals import os import unittest from swid_generator.command_manager import CommandManager from tests.fixtures.command_manager_mock import CommandManagerMock from swid_generator.environments.dpkg_environment import DpkgEnvironment from swid_generator.package_info import PackageInfo, FileInfo from swid_generator.environments.common import CommonEnvironment from mock import patch class DpkgEnvironmentTests(unittest.TestCase): def setUp(self): self.command_manager_run_check_output_patch = patch.object(CommandManager, 'run_command_check_output') self.command_manager_run_command_patch = patch.object(CommandManager, 'run_command') self.common_environment_is_file_patch = patch.object(CommonEnvironment, '_is_file') self.os_path_getsize_patch = patch.object(os.path, 'getsize') self.command_manager_run_check_output_mock = self.command_manager_run_check_output_patch.start() self.common_environment_is_file_mock = self.common_environment_is_file_patch.start() self.os_path_getsize_mock = self.os_path_getsize_patch.start() self.command_manager_run_command_mock = self.command_manager_run_command_patch.start() self.command_manager_run_check_output_mock.side_effect = CommandManagerMock.run_command_check_output self.command_manager_run_command_mock.side_effect = CommandManagerMock.run_command self.common_environment_is_file_mock.return_value = True self.os_path_getsize_mock.return_value = 1 self.dpkg_environment = DpkgEnvironment() def tearDown(self): self.command_manager_run_check_output_patch.stop() self.command_manager_run_command_patch.stop() self.common_environment_is_file_patch.stop() self.os_path_getsize_patch.stop() def test_get_package_list(self): result_list = self.dpkg_environment.get_package_list() expected_package_list = list() expected_package_list.append(PackageInfo(package="adduser", version="3.113+nmu3ubuntu4")) expected_package_list.append(PackageInfo(package="apt", version="1.2.19")) expected_package_list.append(PackageInfo(package="base-files", version="9.4ubuntu4.4")) for index, result_package in enumerate(result_list): print(result_package.package) print(result_package.version) assert result_package.package == expected_package_list[index].package assert result_package.version == expected_package_list[index].version def test_get_package_arch_list(self): result_list = self.dpkg_environment.get_package_list({ "dpkg_include_package_arch": True }) expected_package_list = list() expected_package_list.append(PackageInfo(package="adduser", version="3.113+nmu3ubuntu4.all")) expected_package_list.append(PackageInfo(package="apt", version="1.2.19.amd64")) expected_package_list.append(PackageInfo(package="base-files", version="9.4ubuntu4.4.amd64")) for index, result_package in enumerate(result_list): print(result_package.package) print(result_package.version) assert result_package.package == expected_package_list[index].package assert result_package.version == expected_package_list[index].version def test_get_files_for_package(self): package_info = PackageInfo(package="docker") result_list = self.dpkg_environment.get_files_for_package(package_info) expected_file_list = list() expected_file_list.append(FileInfo("/etc/apt/apt.conf.d/01autoremove")) expected_file_list.append(FileInfo("/etc/cron.daily/apt-compat")) expected_file_list.append(FileInfo("/etc/kernel/postinst.d/apt-auto-removal")) expected_file_list.append(FileInfo("/usr/share/doc/docker")) expected_file_list.append(FileInfo("/usr/share/doc/docker/changelog.Debian.gz")) expected_file_list.append(FileInfo("/usr/share/menu")) expected_file_list.append(FileInfo("/usr/share/menu/docker")) for index, result_file in enumerate(result_list): assert result_file.name == expected_file_list[index].name assert result_file.location == expected_file_list[index].location if index <= 2: # These are configuration-files assert result_file.mutable is True assert result_file.location == expected_file_list[index].location assert result_file.full_pathname == expected_file_list[index].full_pathname def test_get_packageinfo_from_packagefile(self): result_package = self.dpkg_environment.get_packageinfo_from_packagefile("/tmp/docker.pkg") print(result_package.package) assert result_package.package == 'docker' assert result_package.version == '1.0-5' def test_get_packageinfo_arch_from_packagefile(self): result_package = self.dpkg_environment.get_packageinfo_from_packagefile("/tmp/docker.pkg", { "dpkg_include_package_arch": True }) print(result_package.package) assert result_package.package == 'docker' assert result_package.version == '1.0-5.amd64' def test_get_files_from_packagefile(self): all_files = self.dpkg_environment.get_files_from_packagefile("/tmp/docker.pkg") for f in all_files: print(f.full_pathname) expected_file_list = list() expected_file_list.append(FileInfo("/usr/share/bug/docker-bin/control")) expected_file_list.append(FileInfo("/usr/share/bug/docker/control")) expected_file_list.append(FileInfo("/usr/share/doc/docker/README.backtrace")) expected_file_list.append(FileInfo("/usr/share/man/man8/docker.8.gz")) expected_file_list.append(FileInfo("/usr/share/man/man8/dockerctl.8.gz")) for index, result_file in enumerate(all_files): assert result_file.name == expected_file_list[index].name assert result_file.location == expected_file_list[index].location assert result_file.location == expected_file_list[index].location assert result_file.full_pathname == expected_file_list[index].full_pathname

48.689922

137

0.741124

31ada8c9a12ca55750ecb63f23f6f61d3d33db48

623

py

Python

AutoLog/setup.py

gouzil/Learn-DeepFM

138971145617ace4c8bb5ff153a2c38723e181f7

[ "Apache-2.0" ]

1

2022-02-24T10:20:06.000Z

AutoLog/setup.py

gouzil/Learn-DeepFM

138971145617ace4c8bb5ff153a2c38723e181f7

[ "Apache-2.0" ]

null

AutoLog/setup.py

gouzil/Learn-DeepFM

138971145617ace4c8bb5ff153a2c38723e181f7

[ "Apache-2.0" ]

1

2022-02-24T10:20:08.000Z

from setuptools import setup # python3.7 setup.py bdist_wheel with open('requirements.txt', encoding="utf-8-sig") as f: requirements = f.readlines() setup(name='auto_log', version='1.0.0', install_requires=requirements, license='Apache License 2.0', keywords='auto log', description="The AutoLog Contains automatic timing, statistics on CPU memory, GPU memory and other information, since generating logs and other functions.", url='https://github.com/LDOUBLEV/AutoLog', author='DoubleV', author_email='liuvv0203@gmail.com', packages=['auto_log'], )

32.789474

162

0.682183

a7b903257023fa9f3ce89c0dfbfd044f2c12c7e1

49,450

py

Python

nova/api/openstack/compute/volumes.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/api/openstack/compute/volumes.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/api/openstack/compute/volumes.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

begin_unit comment|'# Copyright 2011 Justin Santa Barbara' nl|'\n' comment|'# All Rights Reserved.' nl|'\n' comment|'#' nl|'\n' comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl|'\n' comment|'# not use this file except in compliance with the License. You may obtain' nl|'\n' comment|'# a copy of the License at' nl|'\n' comment|'#' nl|'\n' comment|'# http://www.apache.org/licenses/LICENSE-2.0' nl|'\n' comment|'#' nl|'\n' comment|'# Unless required by applicable law or agreed to in writing, software' nl|'\n' comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl|'\n' comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl|'\n' comment|'# License for the specific language governing permissions and limitations' nl|'\n' comment|'# under the License.' nl|'\n' nl|'\n' string|'"""The volumes extension."""' newline|'\n' nl|'\n' name|'from' name|'oslo_utils' name|'import' name|'strutils' newline|'\n' name|'from' name|'webob' name|'import' name|'exc' newline|'\n' nl|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'openstack' name|'import' name|'api_version_request' newline|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'openstack' name|'import' name|'common' newline|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'openstack' op|'.' name|'compute' op|'.' name|'schemas' name|'import' name|'volumes' name|'as' name|'volumes_schema' newline|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'openstack' name|'import' name|'extensions' newline|'\n' name|'from' name|'nova' op|'.' name|'api' op|'.' name|'openstack' name|'import' name|'wsgi' newline|'\n' name|'from' name|'nova' op|'.' name|'api' name|'import' name|'validation' newline|'\n' name|'from' name|'nova' name|'import' name|'compute' newline|'\n' name|'from' name|'nova' op|'.' name|'compute' name|'import' name|'vm_states' newline|'\n' name|'from' name|'nova' name|'import' name|'exception' newline|'\n' name|'from' name|'nova' op|'.' name|'i18n' name|'import' name|'_' newline|'\n' name|'from' name|'nova' name|'import' name|'objects' newline|'\n' name|'from' name|'nova' name|'import' name|'volume' newline|'\n' nl|'\n' DECL|variable|ALIAS name|'ALIAS' op|'=' string|'"os-volumes"' newline|'\n' DECL|variable|authorize name|'authorize' op|'=' name|'extensions' op|'.' name|'os_compute_authorizer' op|'(' name|'ALIAS' op|')' newline|'\n' DECL|variable|authorize_attach name|'authorize_attach' op|'=' name|'extensions' op|'.' name|'os_compute_authorizer' op|'(' string|"'os-volumes-attachments'" op|')' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_volume_detail_view name|'def' name|'_translate_volume_detail_view' op|'(' name|'context' op|',' name|'vol' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for volumes details view."""' newline|'\n' nl|'\n' name|'d' op|'=' name|'_translate_volume_summary_view' op|'(' name|'context' op|',' name|'vol' op|')' newline|'\n' nl|'\n' comment|'# No additional data / lookups at the moment' nl|'\n' nl|'\n' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_volume_summary_view dedent|'' name|'def' name|'_translate_volume_summary_view' op|'(' name|'context' op|',' name|'vol' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for volumes summary view."""' newline|'\n' name|'d' op|'=' op|'{' op|'}' newline|'\n' nl|'\n' name|'d' op|'[' string|"'id'" op|']' op|'=' name|'vol' op|'[' string|"'id'" op|']' newline|'\n' name|'d' op|'[' string|"'status'" op|']' op|'=' name|'vol' op|'[' string|"'status'" op|']' newline|'\n' name|'d' op|'[' string|"'size'" op|']' op|'=' name|'vol' op|'[' string|"'size'" op|']' newline|'\n' name|'d' op|'[' string|"'availabilityZone'" op|']' op|'=' name|'vol' op|'[' string|"'availability_zone'" op|']' newline|'\n' name|'d' op|'[' string|"'createdAt'" op|']' op|'=' name|'vol' op|'[' string|"'created_at'" op|']' newline|'\n' nl|'\n' name|'if' name|'vol' op|'[' string|"'attach_status'" op|']' op|'==' string|"'attached'" op|':' newline|'\n' comment|'# NOTE(ildikov): The attachments field in the volume info that' nl|'\n' comment|'# Cinder sends is converted to an OrderedDict with the' nl|'\n' comment|'# instance_uuid as key to make it easier for the multiattach' nl|'\n' comment|'# feature to check the required information. Multiattach will' nl|'\n' comment|'# be enable in the Nova API in Newton.' nl|'\n' comment|'# The format looks like the following:' nl|'\n' comment|"# attachments = {'instance_uuid': {" nl|'\n' comment|"# 'attachment_id': 'attachment_uuid'," nl|'\n' comment|"# 'mountpoint': '/dev/sda/" nl|'\n' comment|'# }' nl|'\n' comment|'# }' nl|'\n' indent|' ' name|'attachment' op|'=' name|'vol' op|'[' string|"'attachments'" op|']' op|'.' name|'items' op|'(' op|')' op|'[' number|'0' op|']' newline|'\n' name|'d' op|'[' string|"'attachments'" op|']' op|'=' op|'[' name|'_translate_attachment_detail_view' op|'(' name|'vol' op|'[' string|"'id'" op|']' op|',' nl|'\n' name|'attachment' op|'[' number|'0' op|']' op|',' nl|'\n' name|'attachment' op|'[' number|'1' op|']' op|'.' name|'get' op|'(' string|"'mountpoint'" op|')' op|')' op|']' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'attachments'" op|']' op|'=' op|'[' op|'{' op|'}' op|']' newline|'\n' nl|'\n' dedent|'' name|'d' op|'[' string|"'displayName'" op|']' op|'=' name|'vol' op|'[' string|"'display_name'" op|']' newline|'\n' name|'d' op|'[' string|"'displayDescription'" op|']' op|'=' name|'vol' op|'[' string|"'display_description'" op|']' newline|'\n' nl|'\n' name|'if' name|'vol' op|'[' string|"'volume_type_id'" op|']' name|'and' name|'vol' op|'.' name|'get' op|'(' string|"'volume_type'" op|')' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'volumeType'" op|']' op|'=' name|'vol' op|'[' string|"'volume_type'" op|']' op|'[' string|"'name'" op|']' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'volumeType'" op|']' op|'=' name|'vol' op|'[' string|"'volume_type_id'" op|']' newline|'\n' nl|'\n' dedent|'' name|'d' op|'[' string|"'snapshotId'" op|']' op|'=' name|'vol' op|'[' string|"'snapshot_id'" op|']' newline|'\n' nl|'\n' name|'if' name|'vol' op|'.' name|'get' op|'(' string|"'volume_metadata'" op|')' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'metadata'" op|']' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'volume_metadata'" op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'metadata'" op|']' op|'=' op|'{' op|'}' newline|'\n' nl|'\n' dedent|'' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|class|VolumeController dedent|'' name|'class' name|'VolumeController' op|'(' name|'wsgi' op|'.' name|'Controller' op|')' op|':' newline|'\n' indent|' ' string|'"""The Volumes API controller for the OpenStack API."""' newline|'\n' nl|'\n' DECL|member|__init__ name|'def' name|'__init__' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'volume_api' op|'=' name|'volume' op|'.' name|'API' op|'(' op|')' newline|'\n' name|'super' op|'(' name|'VolumeController' op|',' name|'self' op|')' op|'.' name|'__init__' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|show name|'def' name|'show' op|'(' name|'self' op|',' name|'req' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Return data about the given volume."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'vol' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get' op|'(' name|'context' op|',' name|'id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' name|'return' op|'{' string|"'volume'" op|':' name|'_translate_volume_detail_view' op|'(' name|'context' op|',' name|'vol' op|')' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'wsgi' op|'.' name|'response' op|'(' number|'202' op|')' newline|'\n' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|delete name|'def' name|'delete' op|'(' name|'self' op|',' name|'req' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Delete a volume."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'volume_api' op|'.' name|'delete' op|'(' name|'context' op|',' name|'id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' op|')' op|')' newline|'\n' DECL|member|index name|'def' name|'index' op|'(' name|'self' op|',' name|'req' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a summary list of volumes."""' newline|'\n' name|'return' name|'self' op|'.' name|'_items' op|'(' name|'req' op|',' name|'entity_maker' op|'=' name|'_translate_volume_summary_view' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' op|')' op|')' newline|'\n' DECL|member|detail name|'def' name|'detail' op|'(' name|'self' op|',' name|'req' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a detailed list of volumes."""' newline|'\n' name|'return' name|'self' op|'.' name|'_items' op|'(' name|'req' op|',' name|'entity_maker' op|'=' name|'_translate_volume_detail_view' op|')' newline|'\n' nl|'\n' DECL|member|_items dedent|'' name|'def' name|'_items' op|'(' name|'self' op|',' name|'req' op|',' name|'entity_maker' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a list of volumes, transformed through entity_maker."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'volumes' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get_all' op|'(' name|'context' op|')' newline|'\n' name|'limited_list' op|'=' name|'common' op|'.' name|'limited' op|'(' name|'volumes' op|',' name|'req' op|')' newline|'\n' name|'res' op|'=' op|'[' name|'entity_maker' op|'(' name|'context' op|',' name|'vol' op|')' name|'for' name|'vol' name|'in' name|'limited_list' op|']' newline|'\n' name|'return' op|'{' string|"'volumes'" op|':' name|'res' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' number|'400' op|',' number|'403' op|',' number|'404' op|')' op|')' newline|'\n' op|'@' name|'validation' op|'.' name|'schema' op|'(' name|'volumes_schema' op|'.' name|'create' op|')' newline|'\n' DECL|member|create name|'def' name|'create' op|'(' name|'self' op|',' name|'req' op|',' name|'body' op|')' op|':' newline|'\n' indent|' ' string|'"""Creates a new volume."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'vol' op|'=' name|'body' op|'[' string|"'volume'" op|']' newline|'\n' nl|'\n' name|'vol_type' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'volume_type'" op|')' newline|'\n' name|'metadata' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'metadata'" op|')' newline|'\n' name|'snapshot_id' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'snapshot_id'" op|',' name|'None' op|')' newline|'\n' nl|'\n' name|'if' name|'snapshot_id' name|'is' name|'not' name|'None' op|':' newline|'\n' indent|' ' name|'try' op|':' newline|'\n' indent|' ' name|'snapshot' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get_snapshot' op|'(' name|'context' op|',' name|'snapshot_id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'SnapshotNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'snapshot' op|'=' name|'None' newline|'\n' nl|'\n' dedent|'' name|'size' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'size'" op|',' name|'None' op|')' newline|'\n' name|'if' name|'size' name|'is' name|'None' name|'and' name|'snapshot' name|'is' name|'not' name|'None' op|':' newline|'\n' indent|' ' name|'size' op|'=' name|'snapshot' op|'[' string|"'volume_size'" op|']' newline|'\n' nl|'\n' dedent|'' name|'availability_zone' op|'=' name|'vol' op|'.' name|'get' op|'(' string|"'availability_zone'" op|')' newline|'\n' nl|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'new_volume' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'create' op|'(' nl|'\n' name|'context' op|',' nl|'\n' name|'size' op|',' nl|'\n' name|'vol' op|'.' name|'get' op|'(' string|"'display_name'" op|')' op|',' nl|'\n' name|'vol' op|'.' name|'get' op|'(' string|"'display_description'" op|')' op|',' nl|'\n' name|'snapshot' op|'=' name|'snapshot' op|',' nl|'\n' name|'volume_type' op|'=' name|'vol_type' op|',' nl|'\n' name|'metadata' op|'=' name|'metadata' op|',' nl|'\n' name|'availability_zone' op|'=' name|'availability_zone' nl|'\n' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InvalidInput' name|'as' name|'err' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPBadRequest' op|'(' name|'explanation' op|'=' name|'err' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'OverQuota' name|'as' name|'err' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPForbidden' op|'(' name|'explanation' op|'=' name|'err' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' comment|'# TODO(vish): Instance should be None at db layer instead of' nl|'\n' comment|'# trying to lazy load, but for now we turn it into' nl|'\n' comment|'# a dict to avoid an error.' nl|'\n' dedent|'' name|'retval' op|'=' name|'_translate_volume_detail_view' op|'(' name|'context' op|',' name|'dict' op|'(' name|'new_volume' op|')' op|')' newline|'\n' name|'result' op|'=' op|'{' string|"'volume'" op|':' name|'retval' op|'}' newline|'\n' nl|'\n' name|'location' op|'=' string|"'%s/%s'" op|'%' op|'(' name|'req' op|'.' name|'url' op|',' name|'new_volume' op|'[' string|"'id'" op|']' op|')' newline|'\n' nl|'\n' name|'return' name|'wsgi' op|'.' name|'ResponseObject' op|'(' name|'result' op|',' name|'headers' op|'=' name|'dict' op|'(' name|'location' op|'=' name|'location' op|')' op|')' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_attachment_detail_view dedent|'' dedent|'' name|'def' name|'_translate_attachment_detail_view' op|'(' name|'volume_id' op|',' name|'instance_uuid' op|',' name|'mountpoint' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for attachment details view."""' newline|'\n' nl|'\n' name|'d' op|'=' name|'_translate_attachment_summary_view' op|'(' name|'volume_id' op|',' nl|'\n' name|'instance_uuid' op|',' nl|'\n' name|'mountpoint' op|')' newline|'\n' nl|'\n' comment|'# No additional data / lookups at the moment' nl|'\n' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_attachment_summary_view dedent|'' name|'def' name|'_translate_attachment_summary_view' op|'(' name|'volume_id' op|',' name|'instance_uuid' op|',' name|'mountpoint' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for attachment summary view."""' newline|'\n' name|'d' op|'=' op|'{' op|'}' newline|'\n' nl|'\n' comment|'# NOTE(justinsb): We use the volume id as the id of the attachment object' nl|'\n' name|'d' op|'[' string|"'id'" op|']' op|'=' name|'volume_id' newline|'\n' nl|'\n' name|'d' op|'[' string|"'volumeId'" op|']' op|'=' name|'volume_id' newline|'\n' nl|'\n' name|'d' op|'[' string|"'serverId'" op|']' op|'=' name|'instance_uuid' newline|'\n' name|'if' name|'mountpoint' op|':' newline|'\n' indent|' ' name|'d' op|'[' string|"'device'" op|']' op|'=' name|'mountpoint' newline|'\n' nl|'\n' dedent|'' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|function|_check_request_version dedent|'' name|'def' name|'_check_request_version' op|'(' name|'req' op|',' name|'min_version' op|',' name|'method' op|',' name|'server_id' op|',' name|'server_state' op|')' op|':' newline|'\n' indent|' ' name|'if' name|'not' name|'api_version_request' op|'.' name|'is_supported' op|'(' name|'req' op|',' name|'min_version' op|'=' name|'min_version' op|')' op|':' newline|'\n' indent|' ' name|'exc_inv' op|'=' name|'exception' op|'.' name|'InstanceInvalidState' op|'(' nl|'\n' name|'attr' op|'=' string|"'vm_state'" op|',' nl|'\n' name|'instance_uuid' op|'=' name|'server_id' op|',' nl|'\n' name|'state' op|'=' name|'server_state' op|',' nl|'\n' name|'method' op|'=' name|'method' op|')' newline|'\n' name|'common' op|'.' name|'raise_http_conflict_for_instance_invalid_state' op|'(' nl|'\n' name|'exc_inv' op|',' nl|'\n' name|'method' op|',' nl|'\n' name|'server_id' op|')' newline|'\n' nl|'\n' nl|'\n' DECL|class|VolumeAttachmentController dedent|'' dedent|'' name|'class' name|'VolumeAttachmentController' op|'(' name|'wsgi' op|'.' name|'Controller' op|')' op|':' newline|'\n' indent|' ' string|'"""The volume attachment API controller for the OpenStack API.\n\n A child resource of the server. Note that we use the volume id\n as the ID of the attachment (though this is not guaranteed externally)\n\n """' newline|'\n' nl|'\n' DECL|member|__init__ name|'def' name|'__init__' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'compute_api' op|'=' name|'compute' op|'.' name|'API' op|'(' name|'skip_policy_check' op|'=' name|'True' op|')' newline|'\n' name|'self' op|'.' name|'volume_api' op|'=' name|'volume' op|'.' name|'API' op|'(' op|')' newline|'\n' name|'super' op|'(' name|'VolumeAttachmentController' op|',' name|'self' op|')' op|'.' name|'__init__' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|index name|'def' name|'index' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns the list of volume attachments for a given instance."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize_attach' op|'(' name|'context' op|',' name|'action' op|'=' string|"'index'" op|')' newline|'\n' name|'return' name|'self' op|'.' name|'_items' op|'(' name|'req' op|',' name|'server_id' op|',' nl|'\n' name|'entity_maker' op|'=' name|'_translate_attachment_summary_view' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|show name|'def' name|'show' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Return data about the given volume attachment."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' name|'authorize_attach' op|'(' name|'context' op|',' name|'action' op|'=' string|"'show'" op|')' newline|'\n' nl|'\n' name|'volume_id' op|'=' name|'id' newline|'\n' name|'instance' op|'=' name|'common' op|'.' name|'get_instance' op|'(' name|'self' op|'.' name|'compute_api' op|',' name|'context' op|',' name|'server_id' op|')' newline|'\n' nl|'\n' name|'bdms' op|'=' name|'objects' op|'.' name|'BlockDeviceMappingList' op|'.' name|'get_by_instance_uuid' op|'(' nl|'\n' name|'context' op|',' name|'instance' op|'.' name|'uuid' op|')' newline|'\n' nl|'\n' name|'if' name|'not' name|'bdms' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"Instance %s is not attached."' op|')' op|'%' name|'server_id' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' nl|'\n' dedent|'' name|'assigned_mountpoint' op|'=' name|'None' newline|'\n' nl|'\n' name|'for' name|'bdm' name|'in' name|'bdms' op|':' newline|'\n' indent|' ' name|'if' name|'bdm' op|'.' name|'volume_id' op|'==' name|'volume_id' op|':' newline|'\n' indent|' ' name|'assigned_mountpoint' op|'=' name|'bdm' op|'.' name|'device_name' newline|'\n' name|'break' newline|'\n' nl|'\n' dedent|'' dedent|'' name|'if' name|'assigned_mountpoint' name|'is' name|'None' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"volume_id not found: %s"' op|')' op|'%' name|'volume_id' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' nl|'\n' dedent|'' name|'return' op|'{' string|"'volumeAttachment'" op|':' name|'_translate_attachment_detail_view' op|'(' nl|'\n' name|'volume_id' op|',' nl|'\n' name|'instance' op|'.' name|'uuid' op|',' nl|'\n' name|'assigned_mountpoint' op|')' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' number|'400' op|',' number|'404' op|',' number|'409' op|')' op|')' newline|'\n' op|'@' name|'validation' op|'.' name|'schema' op|'(' name|'volumes_schema' op|'.' name|'create_volume_attachment' op|')' newline|'\n' DECL|member|create name|'def' name|'create' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|',' name|'body' op|')' op|':' newline|'\n' indent|' ' string|'"""Attach a volume to an instance."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' name|'authorize_attach' op|'(' name|'context' op|',' name|'action' op|'=' string|"'create'" op|')' newline|'\n' nl|'\n' name|'volume_id' op|'=' name|'body' op|'[' string|"'volumeAttachment'" op|']' op|'[' string|"'volumeId'" op|']' newline|'\n' name|'device' op|'=' name|'body' op|'[' string|"'volumeAttachment'" op|']' op|'.' name|'get' op|'(' string|"'device'" op|')' newline|'\n' nl|'\n' name|'instance' op|'=' name|'common' op|'.' name|'get_instance' op|'(' name|'self' op|'.' name|'compute_api' op|',' name|'context' op|',' name|'server_id' op|')' newline|'\n' nl|'\n' name|'if' name|'instance' op|'.' name|'vm_state' name|'in' op|'(' name|'vm_states' op|'.' name|'SHELVED' op|',' nl|'\n' name|'vm_states' op|'.' name|'SHELVED_OFFLOADED' op|')' op|':' newline|'\n' indent|' ' name|'_check_request_version' op|'(' name|'req' op|',' string|"'2.20'" op|',' string|"'attach_volume'" op|',' nl|'\n' name|'server_id' op|',' name|'instance' op|'.' name|'vm_state' op|')' newline|'\n' nl|'\n' dedent|'' name|'try' op|':' newline|'\n' indent|' ' name|'device' op|'=' name|'self' op|'.' name|'compute_api' op|'.' name|'attach_volume' op|'(' name|'context' op|',' name|'instance' op|',' nl|'\n' name|'volume_id' op|',' name|'device' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceUnknownCell' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceIsLocked' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPConflict' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceInvalidState' name|'as' name|'state_error' op|':' newline|'\n' indent|' ' name|'common' op|'.' name|'raise_http_conflict_for_instance_invalid_state' op|'(' name|'state_error' op|',' nl|'\n' string|"'attach_volume'" op|',' name|'server_id' op|')' newline|'\n' dedent|'' name|'except' op|'(' name|'exception' op|'.' name|'InvalidVolume' op|',' nl|'\n' name|'exception' op|'.' name|'InvalidDevicePath' op|')' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPBadRequest' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' comment|'# The attach is async' nl|'\n' dedent|'' name|'attachment' op|'=' op|'{' op|'}' newline|'\n' name|'attachment' op|'[' string|"'id'" op|']' op|'=' name|'volume_id' newline|'\n' name|'attachment' op|'[' string|"'serverId'" op|']' op|'=' name|'server_id' newline|'\n' name|'attachment' op|'[' string|"'volumeId'" op|']' op|'=' name|'volume_id' newline|'\n' name|'attachment' op|'[' string|"'device'" op|']' op|'=' name|'device' newline|'\n' nl|'\n' comment|'# NOTE(justinsb): And now, we have a problem...' nl|'\n' comment|"# The attach is async, so there's a window in which we don't see" nl|'\n' comment|'# the attachment (until the attachment completes). We could also' nl|'\n' comment|'# get problems with concurrent requests. I think we need an' nl|'\n' comment|"# attachment state, and to write to the DB here, but that's a bigger" nl|'\n' comment|'# change.' nl|'\n' comment|"# For now, we'll probably have to rely on libraries being smart" nl|'\n' nl|'\n' comment|'# TODO(justinsb): How do I return "accepted" here?' nl|'\n' name|'return' op|'{' string|"'volumeAttachment'" op|':' name|'attachment' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'wsgi' op|'.' name|'response' op|'(' number|'202' op|')' newline|'\n' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' number|'400' op|',' number|'404' op|',' number|'409' op|')' op|')' newline|'\n' op|'@' name|'validation' op|'.' name|'schema' op|'(' name|'volumes_schema' op|'.' name|'update_volume_attachment' op|')' newline|'\n' DECL|member|update name|'def' name|'update' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|',' name|'id' op|',' name|'body' op|')' op|':' newline|'\n' indent|' ' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' name|'authorize_attach' op|'(' name|'context' op|',' name|'action' op|'=' string|"'update'" op|')' newline|'\n' nl|'\n' name|'old_volume_id' op|'=' name|'id' newline|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'old_volume' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get' op|'(' name|'context' op|',' name|'old_volume_id' op|')' newline|'\n' nl|'\n' name|'new_volume_id' op|'=' name|'body' op|'[' string|"'volumeAttachment'" op|']' op|'[' string|"'volumeId'" op|']' newline|'\n' name|'new_volume' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get' op|'(' name|'context' op|',' name|'new_volume_id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' name|'instance' op|'=' name|'common' op|'.' name|'get_instance' op|'(' name|'self' op|'.' name|'compute_api' op|',' name|'context' op|',' name|'server_id' op|')' newline|'\n' nl|'\n' name|'bdms' op|'=' name|'objects' op|'.' name|'BlockDeviceMappingList' op|'.' name|'get_by_instance_uuid' op|'(' nl|'\n' name|'context' op|',' name|'instance' op|'.' name|'uuid' op|')' newline|'\n' name|'found' op|'=' name|'False' newline|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'for' name|'bdm' name|'in' name|'bdms' op|':' newline|'\n' indent|' ' name|'if' name|'bdm' op|'.' name|'volume_id' op|'!=' name|'old_volume_id' op|':' newline|'\n' indent|' ' name|'continue' newline|'\n' dedent|'' name|'try' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'compute_api' op|'.' name|'swap_volume' op|'(' name|'context' op|',' name|'instance' op|',' name|'old_volume' op|',' nl|'\n' name|'new_volume' op|')' newline|'\n' name|'found' op|'=' name|'True' newline|'\n' name|'break' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeUnattached' op|':' newline|'\n' comment|'# The volume is not attached. Treat it as NotFound' nl|'\n' comment|'# by falling through.' nl|'\n' indent|' ' name|'pass' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InvalidVolume' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPBadRequest' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' dedent|'' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceIsLocked' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPConflict' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceInvalidState' name|'as' name|'state_error' op|':' newline|'\n' indent|' ' name|'common' op|'.' name|'raise_http_conflict_for_instance_invalid_state' op|'(' name|'state_error' op|',' nl|'\n' string|"'swap_volume'" op|',' name|'server_id' op|')' newline|'\n' nl|'\n' dedent|'' name|'if' name|'not' name|'found' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"The volume was either invalid or not attached to the "' nl|'\n' string|'"instance."' op|')' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'wsgi' op|'.' name|'response' op|'(' number|'202' op|')' newline|'\n' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' number|'400' op|',' number|'403' op|',' number|'404' op|',' number|'409' op|')' op|')' newline|'\n' DECL|member|delete name|'def' name|'delete' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Detach a volume from an instance."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' name|'authorize_attach' op|'(' name|'context' op|',' name|'action' op|'=' string|"'delete'" op|')' newline|'\n' nl|'\n' name|'volume_id' op|'=' name|'id' newline|'\n' nl|'\n' name|'instance' op|'=' name|'common' op|'.' name|'get_instance' op|'(' name|'self' op|'.' name|'compute_api' op|',' name|'context' op|',' name|'server_id' op|')' newline|'\n' name|'if' name|'instance' op|'.' name|'vm_state' name|'in' op|'(' name|'vm_states' op|'.' name|'SHELVED' op|',' nl|'\n' name|'vm_states' op|'.' name|'SHELVED_OFFLOADED' op|')' op|':' newline|'\n' indent|' ' name|'_check_request_version' op|'(' name|'req' op|',' string|"'2.20'" op|',' string|"'detach_volume'" op|',' nl|'\n' name|'server_id' op|',' name|'instance' op|'.' name|'vm_state' op|')' newline|'\n' dedent|'' name|'try' op|':' newline|'\n' indent|' ' name|'volume' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get' op|'(' name|'context' op|',' name|'volume_id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' name|'bdms' op|'=' name|'objects' op|'.' name|'BlockDeviceMappingList' op|'.' name|'get_by_instance_uuid' op|'(' nl|'\n' name|'context' op|',' name|'instance' op|'.' name|'uuid' op|')' newline|'\n' name|'if' name|'not' name|'bdms' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"Instance %s is not attached."' op|')' op|'%' name|'server_id' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' nl|'\n' dedent|'' name|'found' op|'=' name|'False' newline|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'for' name|'bdm' name|'in' name|'bdms' op|':' newline|'\n' indent|' ' name|'if' name|'bdm' op|'.' name|'volume_id' op|'!=' name|'volume_id' op|':' newline|'\n' indent|' ' name|'continue' newline|'\n' dedent|'' name|'if' name|'bdm' op|'.' name|'is_root' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"Can\'t detach root device volume"' op|')' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPForbidden' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' dedent|'' name|'try' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'compute_api' op|'.' name|'detach_volume' op|'(' name|'context' op|',' name|'instance' op|',' name|'volume' op|')' newline|'\n' name|'found' op|'=' name|'True' newline|'\n' name|'break' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'VolumeUnattached' op|':' newline|'\n' comment|'# The volume is not attached. Treat it as NotFound' nl|'\n' comment|'# by falling through.' nl|'\n' indent|' ' name|'pass' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InvalidVolume' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPBadRequest' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceUnknownCell' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InvalidInput' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPBadRequest' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceIsLocked' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPConflict' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'InstanceInvalidState' name|'as' name|'state_error' op|':' newline|'\n' indent|' ' name|'common' op|'.' name|'raise_http_conflict_for_instance_invalid_state' op|'(' name|'state_error' op|',' nl|'\n' string|"'detach_volume'" op|',' name|'server_id' op|')' newline|'\n' nl|'\n' dedent|'' name|'if' name|'not' name|'found' op|':' newline|'\n' indent|' ' name|'msg' op|'=' name|'_' op|'(' string|'"volume_id not found: %s"' op|')' op|'%' name|'volume_id' newline|'\n' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'msg' op|')' newline|'\n' nl|'\n' DECL|member|_items dedent|'' dedent|'' name|'def' name|'_items' op|'(' name|'self' op|',' name|'req' op|',' name|'server_id' op|',' name|'entity_maker' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a list of attachments, transformed through entity_maker."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'instance' op|'=' name|'common' op|'.' name|'get_instance' op|'(' name|'self' op|'.' name|'compute_api' op|',' name|'context' op|',' name|'server_id' op|')' newline|'\n' nl|'\n' name|'bdms' op|'=' name|'objects' op|'.' name|'BlockDeviceMappingList' op|'.' name|'get_by_instance_uuid' op|'(' nl|'\n' name|'context' op|',' name|'instance' op|'.' name|'uuid' op|')' newline|'\n' name|'limited_list' op|'=' name|'common' op|'.' name|'limited' op|'(' name|'bdms' op|',' name|'req' op|')' newline|'\n' name|'results' op|'=' op|'[' op|']' newline|'\n' nl|'\n' name|'for' name|'bdm' name|'in' name|'limited_list' op|':' newline|'\n' indent|' ' name|'if' name|'bdm' op|'.' name|'volume_id' op|':' newline|'\n' indent|' ' name|'results' op|'.' name|'append' op|'(' name|'entity_maker' op|'(' name|'bdm' op|'.' name|'volume_id' op|',' nl|'\n' name|'bdm' op|'.' name|'instance_uuid' op|',' nl|'\n' name|'bdm' op|'.' name|'device_name' op|')' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' name|'return' op|'{' string|"'volumeAttachments'" op|':' name|'results' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_snapshot_detail_view dedent|'' dedent|'' name|'def' name|'_translate_snapshot_detail_view' op|'(' name|'context' op|',' name|'vol' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for snapshots details view."""' newline|'\n' nl|'\n' name|'d' op|'=' name|'_translate_snapshot_summary_view' op|'(' name|'context' op|',' name|'vol' op|')' newline|'\n' nl|'\n' comment|'# NOTE(gagupta): No additional data / lookups at the moment' nl|'\n' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|function|_translate_snapshot_summary_view dedent|'' name|'def' name|'_translate_snapshot_summary_view' op|'(' name|'context' op|',' name|'vol' op|')' op|':' newline|'\n' indent|' ' string|'"""Maps keys for snapshots summary view."""' newline|'\n' name|'d' op|'=' op|'{' op|'}' newline|'\n' nl|'\n' name|'d' op|'[' string|"'id'" op|']' op|'=' name|'vol' op|'[' string|"'id'" op|']' newline|'\n' name|'d' op|'[' string|"'volumeId'" op|']' op|'=' name|'vol' op|'[' string|"'volume_id'" op|']' newline|'\n' name|'d' op|'[' string|"'status'" op|']' op|'=' name|'vol' op|'[' string|"'status'" op|']' newline|'\n' comment|'# NOTE(gagupta): We map volume_size as the snapshot size' nl|'\n' name|'d' op|'[' string|"'size'" op|']' op|'=' name|'vol' op|'[' string|"'volume_size'" op|']' newline|'\n' name|'d' op|'[' string|"'createdAt'" op|']' op|'=' name|'vol' op|'[' string|"'created_at'" op|']' newline|'\n' name|'d' op|'[' string|"'displayName'" op|']' op|'=' name|'vol' op|'[' string|"'display_name'" op|']' newline|'\n' name|'d' op|'[' string|"'displayDescription'" op|']' op|'=' name|'vol' op|'[' string|"'display_description'" op|']' newline|'\n' name|'return' name|'d' newline|'\n' nl|'\n' nl|'\n' DECL|class|SnapshotController dedent|'' name|'class' name|'SnapshotController' op|'(' name|'wsgi' op|'.' name|'Controller' op|')' op|':' newline|'\n' indent|' ' string|'"""The Snapshots API controller for the OpenStack API."""' newline|'\n' nl|'\n' DECL|member|__init__ name|'def' name|'__init__' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'volume_api' op|'=' name|'volume' op|'.' name|'API' op|'(' op|')' newline|'\n' name|'super' op|'(' name|'SnapshotController' op|',' name|'self' op|')' op|'.' name|'__init__' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|show name|'def' name|'show' op|'(' name|'self' op|',' name|'req' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Return data about the given snapshot."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'vol' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get_snapshot' op|'(' name|'context' op|',' name|'id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'SnapshotNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' name|'return' op|'{' string|"'snapshot'" op|':' name|'_translate_snapshot_detail_view' op|'(' name|'context' op|',' name|'vol' op|')' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'wsgi' op|'.' name|'response' op|'(' number|'202' op|')' newline|'\n' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' number|'404' op|')' newline|'\n' DECL|member|delete name|'def' name|'delete' op|'(' name|'self' op|',' name|'req' op|',' name|'id' op|')' op|':' newline|'\n' indent|' ' string|'"""Delete a snapshot."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'try' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'volume_api' op|'.' name|'delete_snapshot' op|'(' name|'context' op|',' name|'id' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'SnapshotNotFound' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPNotFound' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' op|')' op|')' newline|'\n' DECL|member|index name|'def' name|'index' op|'(' name|'self' op|',' name|'req' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a summary list of snapshots."""' newline|'\n' name|'return' name|'self' op|'.' name|'_items' op|'(' name|'req' op|',' name|'entity_maker' op|'=' name|'_translate_snapshot_summary_view' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' op|')' op|')' newline|'\n' DECL|member|detail name|'def' name|'detail' op|'(' name|'self' op|',' name|'req' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a detailed list of snapshots."""' newline|'\n' name|'return' name|'self' op|'.' name|'_items' op|'(' name|'req' op|',' name|'entity_maker' op|'=' name|'_translate_snapshot_detail_view' op|')' newline|'\n' nl|'\n' DECL|member|_items dedent|'' name|'def' name|'_items' op|'(' name|'self' op|',' name|'req' op|',' name|'entity_maker' op|')' op|':' newline|'\n' indent|' ' string|'"""Returns a list of snapshots, transformed through entity_maker."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'snapshots' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'get_all_snapshots' op|'(' name|'context' op|')' newline|'\n' name|'limited_list' op|'=' name|'common' op|'.' name|'limited' op|'(' name|'snapshots' op|',' name|'req' op|')' newline|'\n' name|'res' op|'=' op|'[' name|'entity_maker' op|'(' name|'context' op|',' name|'snapshot' op|')' name|'for' name|'snapshot' name|'in' name|'limited_list' op|']' newline|'\n' name|'return' op|'{' string|"'snapshots'" op|':' name|'res' op|'}' newline|'\n' nl|'\n' dedent|'' op|'@' name|'extensions' op|'.' name|'expected_errors' op|'(' op|'(' number|'400' op|',' number|'403' op|')' op|')' newline|'\n' op|'@' name|'validation' op|'.' name|'schema' op|'(' name|'volumes_schema' op|'.' name|'snapshot_create' op|')' newline|'\n' DECL|member|create name|'def' name|'create' op|'(' name|'self' op|',' name|'req' op|',' name|'body' op|')' op|':' newline|'\n' indent|' ' string|'"""Creates a new snapshot."""' newline|'\n' name|'context' op|'=' name|'req' op|'.' name|'environ' op|'[' string|"'nova.context'" op|']' newline|'\n' name|'authorize' op|'(' name|'context' op|')' newline|'\n' nl|'\n' name|'snapshot' op|'=' name|'body' op|'[' string|"'snapshot'" op|']' newline|'\n' name|'volume_id' op|'=' name|'snapshot' op|'[' string|"'volume_id'" op|']' newline|'\n' nl|'\n' name|'force' op|'=' name|'snapshot' op|'.' name|'get' op|'(' string|"'force'" op|',' name|'False' op|')' newline|'\n' name|'force' op|'=' name|'strutils' op|'.' name|'bool_from_string' op|'(' name|'force' op|',' name|'strict' op|'=' name|'True' op|')' newline|'\n' name|'if' name|'force' op|':' newline|'\n' indent|' ' name|'create_func' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'create_snapshot_force' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'create_func' op|'=' name|'self' op|'.' name|'volume_api' op|'.' name|'create_snapshot' newline|'\n' nl|'\n' dedent|'' name|'try' op|':' newline|'\n' indent|' ' name|'new_snapshot' op|'=' name|'create_func' op|'(' name|'context' op|',' name|'volume_id' op|',' nl|'\n' name|'snapshot' op|'.' name|'get' op|'(' string|"'display_name'" op|')' op|',' nl|'\n' name|'snapshot' op|'.' name|'get' op|'(' string|"'display_description'" op|')' op|')' newline|'\n' dedent|'' name|'except' name|'exception' op|'.' name|'OverQuota' name|'as' name|'e' op|':' newline|'\n' indent|' ' name|'raise' name|'exc' op|'.' name|'HTTPForbidden' op|'(' name|'explanation' op|'=' name|'e' op|'.' name|'format_message' op|'(' op|')' op|')' newline|'\n' nl|'\n' dedent|'' name|'retval' op|'=' name|'_translate_snapshot_detail_view' op|'(' name|'context' op|',' name|'new_snapshot' op|')' newline|'\n' name|'return' op|'{' string|"'snapshot'" op|':' name|'retval' op|'}' newline|'\n' nl|'\n' nl|'\n' DECL|class|Volumes dedent|'' dedent|'' name|'class' name|'Volumes' op|'(' name|'extensions' op|'.' name|'V21APIExtensionBase' op|')' op|':' newline|'\n' indent|' ' string|'"""Volumes support."""' newline|'\n' nl|'\n' DECL|variable|name name|'name' op|'=' string|'"Volumes"' newline|'\n' DECL|variable|alias name|'alias' op|'=' name|'ALIAS' newline|'\n' DECL|variable|version name|'version' op|'=' number|'1' newline|'\n' nl|'\n' DECL|member|get_resources name|'def' name|'get_resources' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'resources' op|'=' op|'[' op|']' newline|'\n' nl|'\n' name|'res' op|'=' name|'extensions' op|'.' name|'ResourceExtension' op|'(' nl|'\n' name|'ALIAS' op|',' name|'VolumeController' op|'(' op|')' op|',' name|'collection_actions' op|'=' op|'{' string|"'detail'" op|':' string|"'GET'" op|'}' op|')' newline|'\n' name|'resources' op|'.' name|'append' op|'(' name|'res' op|')' newline|'\n' nl|'\n' name|'res' op|'=' name|'extensions' op|'.' name|'ResourceExtension' op|'(' string|"'os-volumes_boot'" op|',' nl|'\n' name|'inherits' op|'=' string|"'servers'" op|')' newline|'\n' name|'resources' op|'.' name|'append' op|'(' name|'res' op|')' newline|'\n' nl|'\n' name|'res' op|'=' name|'extensions' op|'.' name|'ResourceExtension' op|'(' string|"'os-volume_attachments'" op|',' nl|'\n' name|'VolumeAttachmentController' op|'(' op|')' op|',' nl|'\n' name|'parent' op|'=' name|'dict' op|'(' nl|'\n' name|'member_name' op|'=' string|"'server'" op|',' nl|'\n' name|'collection_name' op|'=' string|"'servers'" op|')' op|')' newline|'\n' name|'resources' op|'.' name|'append' op|'(' name|'res' op|')' newline|'\n' nl|'\n' name|'res' op|'=' name|'extensions' op|'.' name|'ResourceExtension' op|'(' nl|'\n' string|"'os-snapshots'" op|',' name|'SnapshotController' op|'(' op|')' op|',' nl|'\n' name|'collection_actions' op|'=' op|'{' string|"'detail'" op|':' string|"'GET'" op|'}' op|')' newline|'\n' name|'resources' op|'.' name|'append' op|'(' name|'res' op|')' newline|'\n' nl|'\n' name|'return' name|'resources' newline|'\n' nl|'\n' DECL|member|get_controller_extensions dedent|'' name|'def' name|'get_controller_extensions' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'return' op|'[' op|']' newline|'\n' dedent|'' dedent|'' endmarker|'' end_unit

12.751418

229

0.596704

7e3e8356438dcf119531f901f7966495b18a8b87

4,682

py

Python

rl_trainer/episode_serializer/proto/episode_pb2.py

Roboy/nips-2018-ai-for-prosthetics

acb69f267a0cc852842828edbbfb47d1840c0a17

[ "BSD-3-Clause" ]

3

2018-08-31T15:04:53.000Z

2019-07-13T01:11:10.000Z

rl_trainer/episode_serializer/proto/episode_pb2.py

Roboy/nips-2018-ai-for-prosthetics

acb69f267a0cc852842828edbbfb47d1840c0a17

[ "BSD-3-Clause" ]

null

rl_trainer/episode_serializer/proto/episode_pb2.py

Roboy/nips-2018-ai-for-prosthetics

acb69f267a0cc852842828edbbfb47d1840c0a17

[ "BSD-3-Clause" ]

null

# Generated by the protocol buffer compiler. DO NOT EDIT! # source: episode.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='episode.proto', package='rl_trainer', syntax='proto3', serialized_pb=_b('\n\repisode.proto\x12\nrl_trainer\"p\n\x0f\x45xperienceTuple\x12\x0f\n\x07state_1\x18\x01 \x03(\x01\x12\x0e\n\x06\x61\x63tion\x18\x02 \x03(\x01\x12\x0e\n\x06reward\x18\x03 \x01(\x01\x12\x0f\n\x07state_2\x18\x04 \x03(\x01\x12\x1b\n\x13state_2_is_terminal\x18\x05 \x01(\x08\"A\n\x07\x45pisode\x12\x36\n\x11\x65xperience_tuples\x18\x01 \x03(\x0b\x32\x1b.rl_trainer.ExperienceTupleb\x06proto3') ) _sym_db.RegisterFileDescriptor(DESCRIPTOR) _EXPERIENCETUPLE = _descriptor.Descriptor( name='ExperienceTuple', full_name='rl_trainer.ExperienceTuple', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='state_1', full_name='rl_trainer.ExperienceTuple.state_1', index=0, number=1, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='action', full_name='rl_trainer.ExperienceTuple.action', index=1, number=2, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='reward', full_name='rl_trainer.ExperienceTuple.reward', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='state_2', full_name='rl_trainer.ExperienceTuple.state_2', index=3, number=4, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='state_2_is_terminal', full_name='rl_trainer.ExperienceTuple.state_2_is_terminal', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=29, serialized_end=141, ) _EPISODE = _descriptor.Descriptor( name='Episode', full_name='rl_trainer.Episode', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='experience_tuples', full_name='rl_trainer.Episode.experience_tuples', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=143, serialized_end=208, ) _EPISODE.fields_by_name['experience_tuples'].message_type = _EXPERIENCETUPLE DESCRIPTOR.message_types_by_name['ExperienceTuple'] = _EXPERIENCETUPLE DESCRIPTOR.message_types_by_name['Episode'] = _EPISODE ExperienceTuple = _reflection.GeneratedProtocolMessageType('ExperienceTuple', (_message.Message,), dict( DESCRIPTOR = _EXPERIENCETUPLE, __module__ = 'episode_pb2' # @@protoc_insertion_point(class_scope:rl_trainer.ExperienceTuple) )) _sym_db.RegisterMessage(ExperienceTuple) Episode = _reflection.GeneratedProtocolMessageType('Episode', (_message.Message,), dict( DESCRIPTOR = _EPISODE, __module__ = 'episode_pb2' # @@protoc_insertion_point(class_scope:rl_trainer.Episode) )) _sym_db.RegisterMessage(Episode) # @@protoc_insertion_point(module_scope)

33.927536

410

0.745622

ad10d97006a669a48e3800d706022f0230c55e8b

5,767

py

Python

install_viewer.py

gruzzlymug/ddg-2018

76f598f7548ad51b126ec9efb7da0fd0d4a306c2

[ "MIT" ]

1

2018-02-11T03:32:22.000Z

install_viewer.py

gruzzlymug/ddg-2018

76f598f7548ad51b126ec9efb7da0fd0d4a306c2

[ "MIT" ]

null

install_viewer.py

gruzzlymug/ddg-2018

76f598f7548ad51b126ec9efb7da0fd0d4a306c2

[ "MIT" ]

null

import os import json import zipfile import shutil import sys from urllib.request import urlretrieve, urlopen #def downloadFile(url, outFile): # urlretrieve(url, outFile) def isNewer(new, original): majorMult, minorMult, patchMult = 10000,100,1 major1, minor1, patch1 = original.split('.') major2, minor2, patch2 = new.split('.') return majorMult * major2 + minorMult * minor2 + patchMult * minor2 > majorMult * major1 + minorMult * minor1 + patchMult * minor1 def downloadProgress(count, blockSize, totalSize): if count % 1000 == 0: percentDone = float(count) * blockSize / totalSize print("%4.2f%%" % percentDone,end='\b\b\b\b\b\b',flush=True) def main(): currentInstallFileName = "viewer_currentInstall.json" versionAndChangeLogUrl = "http://s3.amazonaws.com/battlecode-2018/viewer/" versionFileName = "version.txt" changelogFileName = "changelog.json" baseUrl = "http://s3.amazonaws.com/battlecode-2018/viewer/" directory = os.path.dirname(os.path.realpath(__file__)) zipFileName = "viewer_latest.zip" viewerDirectory = "viewer_latest/" currentInfoFileLocation = os.path.join(directory, currentInstallFileName) if os.path.exists(currentInfoFileLocation): currentInfoFile = open(currentInfoFileLocation) currentInfo = json.load(currentInfoFile) currentInfoFile.close() else: possibleSystems = [ ("1", "Windows (64-bit)", "Win64"), ("2", "Windows (32-bit)", "Win32"), ("3", "Linux (64-bit)", "Linux64"), ("4", "Linux (32-bit)", "Linux32"), ("5", "Mac OS X", "Mac") ] print("It looks like this is your first time installing the viewer. What system are you using?") for optionNum, optionName, actualName in possibleSystems: print("%s) %s" % (optionNum, optionName)) systemInp = input("> ") try: systemInp = int(systemInp) if systemInp <= 0 or systemInp > len(possibleSystems): raise Exception() currentInfo = { 'version': '0.0.0', 'system': possibleSystems[systemInp - 1][2] } print("Done setup! You've selected the system %s. \nIf you ever want to change this setup, delete the file %s " % (possibleSystems[systemInp-1][1], currentInstallFileName)) except: print("Invalid input. Exiting..") sys.exit(1) versionFileUrl = versionAndChangeLogUrl + versionFileName latestVersion = urlopen(versionFileUrl).read().decode() print("Checking for updates...") if isNewer(latestVersion, currentInfo['version']): print("There is a newer version available.\nCurrent version is: %s. The new version is %s." % (currentInfo['version'], latestVersion)) shouldDownload = input("Download? (Y/N) > ").lower() == "y" if shouldDownload: newestUrl = baseUrl + ("%s/%s.zip" % (latestVersion, currentInfo['system'])) downloadLocation = os.path.join(directory, zipFileName) if os.path.exists(downloadLocation): print("Removing previous archive...") os.remove(downloadLocation) print("Deleted old archive.") print("Downloading new client... This could take a while.") urlretrieve(newestUrl, downloadLocation, downloadProgress) print() print("Successfully downloaded files. ") outputDirectory = os.path.join(directory, viewerDirectory) if os.path.exists(outputDirectory): print("Removing previous client") shutil.rmtree(outputDirectory, True) print("Successfully removed previous client.") print("Extracting from archive...") zip_ref = zipfile.ZipFile(downloadLocation, "r") zip_ref.extractall(outputDirectory) zip_ref.close() print("Extracted fully!") if os.path.exists(downloadLocation): print("Cleaning up downloads...") os.remove(downloadLocation) print("Cleaned up") try: if currentInfo['system'] == 'Linux32': print("Fixing permissions...You'll need to provide elevated privileges for this to work.") os.system("sudo chmod 777 viewer_latest/Linux32/battleclient18.x86") print("Done fixing permissions!") elif currentInfo['system'] == 'Linux64': print("Fixing permissions...You'll need to provide elevated privileges for this to work.") os.system("sudo chmod 777 viewer_latest/Linux64/battleclient18.x86_64") print("Done fixing permissions!") if currentInfo['system'] == 'Mac': print("Fixing permissions...You'll need to provide elevated privileges for this to work.") os.system("sudo chmod -R 777 viewer_latest/Mac/battleclient18.app") print("Done fixing permissions!") except: pass print("Updating current version number...") newInfo = {} newInfo['version'] = latestVersion newInfo['system'] = currentInfo['system'] currentInfoFile = open(currentInfoFileLocation, "w") currentInfo = json.dump(newInfo, currentInfoFile) currentInfoFile.close() print("All set! The viewer is in: %s" % outputDirectory) else: print("Not downloading - your system has not been changed.") else: print("No updates!") if __name__ == "__main__": main()

42.718519

184

0.601873

ac91986cfa31d1b802c5b1e44e1b4aad9d0a55de

253

py

Python

app/recipe/serializers.py

NicolefAvella/api-maquina

2f8301d364a57baf16c92cdff734b9a43b676289

[ "MIT" ]

null

app/recipe/serializers.py

NicolefAvella/api-maquina

2f8301d364a57baf16c92cdff734b9a43b676289

[ "MIT" ]

null

app/recipe/serializers.py

NicolefAvella/api-maquina

2f8301d364a57baf16c92cdff734b9a43b676289

[ "MIT" ]

null

from rest_framework import serializers from core.models import Tag class TagSerializer(serializers.ModelSerializer): """Serializer para tag """ class Meta: model = Tag fields = ('id','name') read_only_fields = ('id',)

21.083333

49

0.656126

499d1be71a050c1d83ce97089f15d280acc05bf2

11,654

py

Python

ppcls/modeling/architectures/xception.py

vslyu/PaddleClas

1b6799cf508ec48a8b76da202f22fb7961f52ee3

[ "Apache-2.0" ]

null

ppcls/modeling/architectures/xception.py

vslyu/PaddleClas

1b6799cf508ec48a8b76da202f22fb7961f52ee3

[ "Apache-2.0" ]

null

ppcls/modeling/architectures/xception.py

vslyu/PaddleClas

1b6799cf508ec48a8b76da202f22fb7961f52ee3

[ "Apache-2.0" ]

null

import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math __all__ = ['Xception41', 'Xception65', 'Xception71'] class ConvBNLayer(nn.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act=None, name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, weight_attr=ParamAttr(name=name + "_weights"), bias_attr=False) bn_name = "bn_" + name self._batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(name=bn_name + "_scale"), bias_attr=ParamAttr(name=bn_name + "_offset"), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') def forward(self, inputs): y = self._conv(inputs) y = self._batch_norm(y) return y class SeparableConv(nn.Layer): def __init__(self, input_channels, output_channels, stride=1, name=None): super(SeparableConv, self).__init__() self._pointwise_conv = ConvBNLayer( input_channels, output_channels, 1, name=name + "_sep") self._depthwise_conv = ConvBNLayer( output_channels, output_channels, 3, stride=stride, groups=output_channels, name=name + "_dw") def forward(self, inputs): x = self._pointwise_conv(inputs) x = self._depthwise_conv(x) return x class EntryFlowBottleneckBlock(nn.Layer): def __init__(self, input_channels, output_channels, stride=2, name=None, relu_first=False): super(EntryFlowBottleneckBlock, self).__init__() self.relu_first = relu_first self._short = Conv2D( in_channels=input_channels, out_channels=output_channels, kernel_size=1, stride=stride, padding=0, weight_attr=ParamAttr(name + "_branch1_weights"), bias_attr=False) self._conv1 = SeparableConv( input_channels, output_channels, stride=1, name=name + "_branch2a_weights") self._conv2 = SeparableConv( output_channels, output_channels, stride=1, name=name + "_branch2b_weights") self._pool = MaxPool2D(kernel_size=3, stride=stride, padding=1) def forward(self, inputs): conv0 = inputs short = self._short(inputs) if self.relu_first: conv0 = F.relu(conv0) conv1 = self._conv1(conv0) conv2 = F.relu(conv1) conv2 = self._conv2(conv2) pool = self._pool(conv2) return paddle.add(x=short, y=pool) class EntryFlow(nn.Layer): def __init__(self, block_num=3): super(EntryFlow, self).__init__() name = "entry_flow" self.block_num = block_num self._conv1 = ConvBNLayer( 3, 32, 3, stride=2, act="relu", name=name + "_conv1") self._conv2 = ConvBNLayer(32, 64, 3, act="relu", name=name + "_conv2") if block_num == 3: self._conv_0 = EntryFlowBottleneckBlock( 64, 128, stride=2, name=name + "_0", relu_first=False) self._conv_1 = EntryFlowBottleneckBlock( 128, 256, stride=2, name=name + "_1", relu_first=True) self._conv_2 = EntryFlowBottleneckBlock( 256, 728, stride=2, name=name + "_2", relu_first=True) elif block_num == 5: self._conv_0 = EntryFlowBottleneckBlock( 64, 128, stride=2, name=name + "_0", relu_first=False) self._conv_1 = EntryFlowBottleneckBlock( 128, 256, stride=1, name=name + "_1", relu_first=True) self._conv_2 = EntryFlowBottleneckBlock( 256, 256, stride=2, name=name + "_2", relu_first=True) self._conv_3 = EntryFlowBottleneckBlock( 256, 728, stride=1, name=name + "_3", relu_first=True) self._conv_4 = EntryFlowBottleneckBlock( 728, 728, stride=2, name=name + "_4", relu_first=True) else: sys.exit(-1) def forward(self, inputs): x = self._conv1(inputs) x = self._conv2(x) if self.block_num == 3: x = self._conv_0(x) x = self._conv_1(x) x = self._conv_2(x) elif self.block_num == 5: x = self._conv_0(x) x = self._conv_1(x) x = self._conv_2(x) x = self._conv_3(x) x = self._conv_4(x) return x class MiddleFlowBottleneckBlock(nn.Layer): def __init__(self, input_channels, output_channels, name): super(MiddleFlowBottleneckBlock, self).__init__() self._conv_0 = SeparableConv( input_channels, output_channels, stride=1, name=name + "_branch2a_weights") self._conv_1 = SeparableConv( output_channels, output_channels, stride=1, name=name + "_branch2b_weights") self._conv_2 = SeparableConv( output_channels, output_channels, stride=1, name=name + "_branch2c_weights") def forward(self, inputs): conv0 = F.relu(inputs) conv0 = self._conv_0(conv0) conv1 = F.relu(conv0) conv1 = self._conv_1(conv1) conv2 = F.relu(conv1) conv2 = self._conv_2(conv2) return paddle.add(x=inputs, y=conv2) class MiddleFlow(nn.Layer): def __init__(self, block_num=8): super(MiddleFlow, self).__init__() self.block_num = block_num self._conv_0 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_0") self._conv_1 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_1") self._conv_2 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_2") self._conv_3 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_3") self._conv_4 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_4") self._conv_5 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_5") self._conv_6 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_6") self._conv_7 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_7") if block_num == 16: self._conv_8 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_8") self._conv_9 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_9") self._conv_10 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_10") self._conv_11 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_11") self._conv_12 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_12") self._conv_13 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_13") self._conv_14 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_14") self._conv_15 = MiddleFlowBottleneckBlock( 728, 728, name="middle_flow_15") def forward(self, inputs): x = self._conv_0(inputs) x = self._conv_1(x) x = self._conv_2(x) x = self._conv_3(x) x = self._conv_4(x) x = self._conv_5(x) x = self._conv_6(x) x = self._conv_7(x) if self.block_num == 16: x = self._conv_8(x) x = self._conv_9(x) x = self._conv_10(x) x = self._conv_11(x) x = self._conv_12(x) x = self._conv_13(x) x = self._conv_14(x) x = self._conv_15(x) return x class ExitFlowBottleneckBlock(nn.Layer): def __init__(self, input_channels, output_channels1, output_channels2, name): super(ExitFlowBottleneckBlock, self).__init__() self._short = Conv2D( in_channels=input_channels, out_channels=output_channels2, kernel_size=1, stride=2, padding=0, weight_attr=ParamAttr(name + "_branch1_weights"), bias_attr=False) self._conv_1 = SeparableConv( input_channels, output_channels1, stride=1, name=name + "_branch2a_weights") self._conv_2 = SeparableConv( output_channels1, output_channels2, stride=1, name=name + "_branch2b_weights") self._pool = MaxPool2D(kernel_size=3, stride=2, padding=1) def forward(self, inputs): short = self._short(inputs) conv0 = F.relu(inputs) conv1 = self._conv_1(conv0) conv2 = F.relu(conv1) conv2 = self._conv_2(conv2) pool = self._pool(conv2) return paddle.add(x=short, y=pool) class ExitFlow(nn.Layer): def __init__(self, class_dim): super(ExitFlow, self).__init__() name = "exit_flow" self._conv_0 = ExitFlowBottleneckBlock( 728, 728, 1024, name=name + "_1") self._conv_1 = SeparableConv(1024, 1536, stride=1, name=name + "_2") self._conv_2 = SeparableConv(1536, 2048, stride=1, name=name + "_3") self._pool = AdaptiveAvgPool2D(1) stdv = 1.0 / math.sqrt(2048 * 1.0) self._out = Linear( 2048, class_dim, weight_attr=ParamAttr( name="fc_weights", initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(name="fc_offset")) def forward(self, inputs): conv0 = self._conv_0(inputs) conv1 = self._conv_1(conv0) conv1 = F.relu(conv1) conv2 = self._conv_2(conv1) conv2 = F.relu(conv2) pool = self._pool(conv2) pool = paddle.flatten(pool, start_axis=1, stop_axis=-1) out = self._out(pool) return out class Xception(nn.Layer): def __init__(self, entry_flow_block_num=3, middle_flow_block_num=8, class_dim=1000): super(Xception, self).__init__() self.entry_flow_block_num = entry_flow_block_num self.middle_flow_block_num = middle_flow_block_num self._entry_flow = EntryFlow(entry_flow_block_num) self._middle_flow = MiddleFlow(middle_flow_block_num) self._exit_flow = ExitFlow(class_dim) def forward(self, inputs): x = self._entry_flow(inputs) x = self._middle_flow(x) x = self._exit_flow(x) return x def Xception41(**args): model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **args) return model def Xception65(**args): model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **args) return model def Xception71(**args): model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **args) return model

33.77971

78

0.578514

8baae63de707231476240997e8146840c6816dce

3,912

py

Python

thrift/test/py/ForwardCompatibility.py

lucyge/FBThrift

2cb49e1c1ee1712416db9cc1f4b833382b04d8cd

[ "Apache-2.0" ]

1

2018-02-28T06:45:51.000Z

thrift/test/py/ForwardCompatibility.py

lucyge/FBThrift

2cb49e1c1ee1712416db9cc1f4b833382b04d8cd

[ "Apache-2.0" ]

null

thrift/test/py/ForwardCompatibility.py

lucyge/FBThrift

2cb49e1c1ee1712416db9cc1f4b833382b04d8cd

[ "Apache-2.0" ]

1

2018-02-28T06:45:18.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 from __future__ import unicode_literals import unittest from thrift.protocol import TBinaryProtocol, \ TCompactProtocol, TSimpleJSONProtocol from thrift.util import Serializer from ForwardCompatibility.ForwardCompatibility.ttypes import \ NewStructure, OldStructure, \ NewStructureNested, OldStructureNested class AbstractTest(): def _serialize(self, obj): return Serializer.serialize(self.protocol_factory, obj) def _deserialize(self, objtype, data): return Serializer.deserialize(self.protocol_factory, data, objtype()) class TestForwardCompatibilityAbstract(AbstractTest): def assertFeaturesAlmostEqual(self, a, b): self.assertTrue(abs(a - b) < 1e-3) def testPrimitiveType(self): old = OldStructure() old.features = {} old.features[1] = 100.1 old.features[217] = 314.5 sOld = self._serialize(old) new = self._deserialize(NewStructure, sOld) self.assertFeaturesAlmostEqual(new.features[1], 100.1) self.assertFeaturesAlmostEqual(new.features[217], 314.5) sNew = self._serialize(new) new2 = self._deserialize(NewStructure, sNew) self.assertFeaturesAlmostEqual(new2.features[1], 100.1) self.assertFeaturesAlmostEqual(new2.features[217], 314.5) def testNested(self): old = OldStructureNested() old.features = [{}] old.features[0][1] = 100.1 old.features[0][217] = 314.5 sOld = self._serialize(old) new = self._deserialize(NewStructureNested, sOld) self.assertFeaturesAlmostEqual(new.features[0][1], 100.1) self.assertFeaturesAlmostEqual(new.features[0][217], 314.5) sNew = self._serialize(new) new2 = self._deserialize(NewStructureNested, sNew) self.assertFeaturesAlmostEqual(new2.features[0][1], 100.1) self.assertFeaturesAlmostEqual(new2.features[0][217], 314.5) class TestForwardCompatibilityBinary(TestForwardCompatibilityAbstract, unittest.TestCase): protocol_factory = TBinaryProtocol.TBinaryProtocolFactory() class TestForwardCompatibilityCompact(TestForwardCompatibilityAbstract, unittest.TestCase): protocol_factory = TCompactProtocol.TCompactProtocolFactory() class TestForwardCompatibilityBinaryAccelerated(TestForwardCompatibilityAbstract, unittest.TestCase): protocol_factory = TBinaryProtocol.TBinaryProtocolAcceleratedFactory() class TestForwardCompatibilityCompactAccelerated(TestForwardCompatibilityAbstract, unittest.TestCase): protocol_factory = TCompactProtocol.TCompactProtocolAcceleratedFactory() class TestForwardCompatibilityJSON(TestForwardCompatibilityAbstract, unittest.TestCase): protocol_factory = TSimpleJSONProtocol.TSimpleJSONProtocolFactory() if __name__ == "__main__": unittest.main()

37.980583

82

0.716002

c974364539921c1e2ea1ca130c4cc03e817cf818

7,192

py

Python

login_sonicwall.py

NathanLundner/Sonic-Wall-Login

c57608ddc6fbc9030c184caf459e11892cebb8d3

[ "BSD-3-Clause" ]

null

login_sonicwall.py

NathanLundner/Sonic-Wall-Login

c57608ddc6fbc9030c184caf459e11892cebb8d3

[ "BSD-3-Clause" ]

null

login_sonicwall.py

NathanLundner/Sonic-Wall-Login

c57608ddc6fbc9030c184caf459e11892cebb8d3

[ "BSD-3-Clause" ]

null

# from def_funtions import (setup_session, login, persist, update_rem_time,keep_alive) '''I have modified this code for my schools use. The code did not function as advertised so I changed a few funtions and how the main executes so it provides constant access to wifi as long as the program is open. BSD 3-Clause License Copyright (c) 2017, Shubham Sopan Dighe All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * 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. * Neither the name of the copyright holder 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. ''' import time import re from hashlib import md5 import requests from html.parser import HTMLParser import os import sys import logging import json import getpass import errno from string import digits from hashlib import md5 import random import urllib3 # Removes insecure https connection error urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) # Reads file with credentials and processes them for login file = open("credentials.txt", "r") cred_unproc = '' for word in file.readlines(): cred_unproc += word.strip("\n") file.close() cred = cred_unproc.split(" ") UNAME = cred[0] PASSWORD = cred[1] print(f"[+] Logging In as: {UNAME}") BEAT_INTERVAL = 10 MIN_RELOGIN = 10 DOMAIN = 'https://10.20.51.1/' def snooze(factor): ONE_MINUTE = 30 time.sleep(ONE_MINUTE * factor) def generate_cookie(): seed = ''.join(random.choice(digits) for _ in range(16)) value = md5(seed.encode()).hexdigest() return value def is_logged_in(response): if "refresh=true" in response.text: return False return True def remaining_time(response): time = 0 pos = response.text.find("remTime=") if pos != -1: time = response.text[pos+8:pos+11] time = time.split(';')[0] try: time = int(time) except ValueError: time = 0; return time def set_cookies(session): domain = '10.20.51.1' session.cookies.set(name='SessId', value=generate_cookie().upper(), domain=domain) session.cookies.set(name='PageSeed', value=generate_cookie(), domain=domain) def read_credentials(): print("\n[+] Reading credentials ...") creds = {} creds['uName'] = [UNAME] creds['pass'] = [PASSWORD] return creds def login(session): payload = read_credentials() print("[+] Authenticating with SonicWall ...") login_attempt = 6 while login_attempt > 0: t = session.get(DOMAIN + 'auth1.html') t = session.post(DOMAIN +'auth.cgi', data=payload) session.get(DOMAIN + "loginStatusTop(eng).html") t = session.post(DOMAIN + "usrHeartbeat.cgi", verify=False) if is_logged_in(t): print("[+] Logged in successfully !! :)") current_time = time.strftime("%H:%M:%S %d-%m-%Y", time.localtime()) print("[+] Login time :- %s " % current_time) print("[+] (Keep this window open for a persistent connection and minimize it.)") return True else: login_attempt -= 1 print("[-] Login failed !! :( \n") return False def persist(session): logged_in = True while logged_in: try: t = session.post(DOMAIN + "usrHeartbeat.cgi", verify=False) logged_in = is_logged_in(t) rem_time = remaining_time(t) if rem_time <= 30: print("\n[*] Session will expire soon. Logging in again ...") set_cookies(session) logged_in = login(session) else: snooze(1) except (requests.exceptions.ConnectionError): snooze(1) print("[-] Seems like something went wrong !!") print("[-] You have been logged out of SonicWall Wifi portal.") def setup_session(): s = requests.Session() http_adapter = requests.adapters.HTTPAdapter(max_retries=6) https_adapter = requests.adapters.HTTPAdapter(max_retries=6) s.mount('http://', http_adapter) s.mount('https://', https_adapter) s.verify = False set_cookies(s) return s def keep_alive(session): logged_in = True while logged_in: try: t = session.post(DOMAIN + "usrHeartbeat.cgi", verify=False) logged_in = is_logged_in(t) if logged_in: snooze(1) except (requests.exceptions.ConnectionError): snooze(1) print("[+] You have been logged out of Dell SonicWall") def update_rem_time(session, rem_time): if rem_time <=0: rem_time = 1 payload = {'maxSessionTime': rem_time} t = session.post(DOMAIN + 'userSettings.cgi', data=payload) session.post(DOMAIN + "usrHeartbeat.cgi", verify=False) def main(): while True: try: print( "[+] Logging in for 85 min. You will be automtaically relogged in at the end of the 85 min. To disconnect, Exit this window.") print( "[*] By running this program, you agree that the author will not be held responsible if there is a malfunction (in middle of a zoom class).") session = setup_session() login_time = 85 if login(session): try: if login_time: update_rem_time(session, login_time) keep_alive(session) else: persist(session) print("[+] Setting up Session") except KeyboardInterrupt: print("\n[-] Exiting ...\n") exit() except KeyboardInterrupt: print("\n[-] Exiting ...\n") exit() main()

32.990826

158

0.631257

66dc7694c677d3671bdfe011b1fe3fd503d0fcb7

16,885

py

Python

portfolyo/core/pfline/tests/test_interop.py

rwijtvliet/portfolyo

b22948fbc55264ec5d69824e791ca7ef45c6e49c

[ "BSD-3-Clause" ]

null

portfolyo/core/pfline/tests/test_interop.py

rwijtvliet/portfolyo

b22948fbc55264ec5d69824e791ca7ef45c6e49c

[ "BSD-3-Clause" ]

null

portfolyo/core/pfline/tests/test_interop.py

rwijtvliet/portfolyo

b22948fbc55264ec5d69824e791ca7ef45c6e49c

[ "BSD-3-Clause" ]

null

from typing import Dict from pint import DimensionalityError, UndefinedUnitError from portfolyo.core.pfline import interop as io from portfolyo.tools.nits import Q_ import pandas as pd import numpy as np import pytest idx1 = pd.date_range("2020", freq="MS", periods=12) val1 = 100 + 20 * np.random.random(len(idx1)) s1 = pd.Series(val1, idx1) idx2 = pd.date_range("2020-08", freq="MS", periods=12) val2 = 200 + 50 * np.random.random(len(idx2)) s2 = pd.Series(val2, idx2) idx_i = idx1.intersection(idx2).sort_values() s1_i = s1.loc[idx_i] s2_i = s2.loc[idx_i] idx_u = idx1.union(idx2).sort_values() s1_u = pd.Series((s1.get(i) for i in idx_u), idx_u) s2_u = pd.Series((s2.get(i) for i in idx_u), idx_u) def id_fn(data): if isinstance(data, Dict): return str({key: id_fn(val) for key, val in data.items()}) if isinstance(data, pd.Series): if isinstance(data.index, pd.DatetimeIndex): return "ts" else: return f"series (idx: {''.join(str(i) for i in data.index)})" if isinstance(data, pd.DataFrame): return f"df (columns: {''.join(str(c) for c in data.columns)})" if isinstance(data, io.InOp): return "" return str(data) @pytest.mark.parametrize( ("data_in", "expected_io", "expected_io2"), [ # One value # . unit-agnostic ( 23.0, io.InOp(agn=23.0), ValueError, ), # . unitless ( Q_(23.0, ""), io.InOp(nodim=23.0), ValueError, ), # . known unit ( Q_(-120.0, "MW"), io.InOp(w=-120), ValueError, ), ( Q_(120e-3, "GW"), io.InOp(w=120), ValueError, ), ( Q_(432e9, "J/h"), io.InOp(w=120), ValueError, ), ( Q_(90_000.0, "MWh"), io.InOp(q=90_000), ValueError, ), ( Q_(90.0, "GWh"), io.InOp(q=90_000), ValueError, ), ( Q_(50.0, "Eur/MWh"), io.InOp(p=50), ValueError, ), ( Q_(5.0, "ctEur/kWh"), io.InOp(p=50), ValueError, ), ( Q_(4_500_000.0, "Eur"), io.InOp(r=4_500_000), ValueError, ), ( Q_(4.5, "MEur"), io.InOp(r=4_500_000), ValueError, ), # . unknown unit ( Q_(4.5, "MWh/Eur"), UndefinedUnitError, None, ), # One or several values # . name but no unit ( {"nodim": 120.0}, io.InOp(nodim=120), ValueError, ), ( pd.Series({"nodim": 120.0}), io.InOp(nodim=120), ValueError, ), ( {"w": 120.0}, io.InOp(w=120), ValueError, ), ( pd.Series({"w": 120.0}), io.InOp(w=120), ValueError, ), ( {"q": -90_000.0}, io.InOp(q=-90_000), ValueError, ), ( pd.Series({"q": -90_000.0}), io.InOp(q=-90_000), ValueError, ), ( {"p": 50.0}, io.InOp(p=50), ValueError, ), ( pd.Series({"p": 50.0}), io.InOp(p=50), ValueError, ), ( {"r": 4.5e6}, io.InOp(r=4_500_000), ValueError, ), ( pd.Series({"r": 4.5e6}), io.InOp(r=4_500_000), ValueError, ), ( {"w": 120.0, "q": -90_000}, io.InOp(w=120, q=-90_000), ValueError, ), ( pd.Series({"w": 120.0, "q": -90_000}), io.InOp(w=120.0, q=-90_000), ValueError, ), ( {"w": 120.0, "p": 50}, io.InOp(w=120.0, p=50), ValueError, ), ( pd.Series({"w": 120.0, "p": 50}), io.InOp(w=120.0, p=50), ValueError, ), ( {"w": 120.0, "p": 50.0, "r": 4.5e6}, io.InOp(w=120.0, p=50.0, r=4.5e6), ValueError, ), ( pd.Series({"w": 120.0, "p": 50.0, "r": 4.5e6}), io.InOp(w=120.0, p=50.0, r=4.5e6), ValueError, ), ( {"w": 120.0, "p": 50.0, "r": 4.5e6}, io.InOp(w=120.0, p=50.0, r=4.5e6), ValueError, ), ( pd.Series({"w": 120.0, "p": 50.0, "r": 4.5e6}), io.InOp(w=120.0, p=50.0, r=4.5e6), ValueError, ), # . name and correct unit ( {"p": Q_(50.0, "Eur/MWh")}, io.InOp(p=50), ValueError, ), ( pd.Series({"p": Q_(50.0, "Eur/MWh")}), io.InOp(p=50), ValueError, ), ( pd.Series({"p": 50}).astype("pint[Eur/MWh]"), io.InOp(p=50), ValueError, ), ( {"r": Q_(4.5, "MEur")}, io.InOp(r=4_500_000), ValueError, ), ( pd.Series({"r": Q_(4.5, "MEur")}), io.InOp(r=4_500_000), ValueError, ), ( pd.Series({"r": 4.5}).astype("pint[MEur]"), io.InOp(r=4_500_000), ValueError, ), ( {"w": 120.0, "q": Q_(-90_000.0, "MWh")}, io.InOp(w=120.0, q=-90_000), ValueError, ), ( pd.Series({"w": 120.0, "q": Q_(-90_000.0, "MWh")}), io.InOp(w=120.0, q=-90_000), ValueError, ), ( pd.Series({"w": 120.0, "q": Q_(-90.0, "GWh")}), io.InOp(w=120.0, q=-90_000), ValueError, ), # . unknown name -> KeyError ( {"z": 28.0}, KeyError, None, ), ( pd.Series({"z": 28.0}), KeyError, None, ), ( {"z": Q_(120.0, "MWh")}, KeyError, None, ), ( pd.Series({"z": Q_(120.0, "MWh")}), KeyError, None, ), # . mix of know and unknown names -> KeyError ( {"w": 120.0, "z": 28.0}, KeyError, None, ), ( pd.Series({"w": 120.0, "z": 28.0}), KeyError, None, ), ( {"w": 120.0, "p": 50.0, "z": 28.0}, KeyError, None, ), ( pd.Series({"w": 120.0, "p": 50.0, "z": 28.0}), KeyError, None, ), # . combination of name with incorrect unit -> error ( {"w": Q_(90.0, "MWh")}, DimensionalityError, None, ), ( pd.Series({"w": Q_(90.0, "MWh")}), DimensionalityError, None, ), ( pd.Series({"w": 90}).astype("pint[MWh]"), DimensionalityError, None, ), ( {"p": Q_(90.0, "MWh")}, DimensionalityError, None, ), ( pd.Series({"p": Q_(90.0, "MWh")}), DimensionalityError, None, ), ( {"p": 50.0, "w": Q_(90.0, "MWh")}, DimensionalityError, None, ), ( pd.Series({"p": 50.0, "w": Q_(90.0, "MWh")}), DimensionalityError, None, ), # One timeseries # . unit-agnostic ( s1, io.InOp(agn=s1), io.InOp(agn=s1), ), # . unitless # (s1.astype("pint[dimensionless]"), io.InterOp(nodim=s1)), # TODO: fix # . known unit ( s1.astype("pint[MW]"), io.InOp(w=s1), io.InOp(w=s1), ), ( (s1 / 1000).astype("pint[GW]"), # series with pint unit io.InOp(w=s1), io.InOp(w=s1), ), ( pd.Series([Q_(v, "MW") for v in val1], idx1), # series of Quantities io.InOp(w=s1), io.InOp(w=s1), ), ( s1.astype("pint[GWh]"), io.InOp(q=s1 * 1000), io.InOp(q=s1 * 1000), ), ( s1.astype("pint[Eur/MWh]"), io.InOp(p=s1), io.InOp(p=s1), ), ( s1.astype("pint[MEur]"), io.InOp(r=s1 * 1e6), io.InOp(r=s1 * 1e6), ), # . unknown unit ( s1.astype("pint[Wh/MEur]"), UndefinedUnitError, None, ), # One or several timeseries # . name but no unit ( {"w": s1}, io.InOp(w=s1), io.InOp(w=s1), ), ( pd.DataFrame({"w": s1}), io.InOp(w=s1), io.InOp(w=s1), ), ( {"q": -s1}, io.InOp(q=-s1), io.InOp(q=-s1), ), ( pd.DataFrame({"q": -s1}), io.InOp(q=-s1), io.InOp(q=-s1), ), ( {"r": s1}, io.InOp(r=s1), io.InOp(r=s1), ), ( pd.DataFrame({"r": s1}), io.InOp(r=s1), io.InOp(r=s1), ), ( {"w": s1, "q": -s2}, io.InOp(w=s1, q=-s2), io.InOp(w=s1_i, q=-s2_i), ), ( pd.DataFrame({"w": s1, "q": -s2}), io.InOp(w=s1_u, q=-s2_u), io.InOp(w=s1_u, q=-s2_u), ), ( {"w": s1, "p": s2, "r": s1 * 4}, io.InOp(w=s1, p=s2, r=s1 * 4), io.InOp(w=s1_i, p=s2_i, r=s1_i * 4), ), ( pd.DataFrame({"w": s1, "p": s2, "r": s1 * 4}), io.InOp(w=s1_u, p=s2_u, r=s1_u * 4), io.InOp(w=s1_u, p=s2_u, r=s1_u * 4), ), # . name and correct unit ( {"p": s1.astype("pint[Eur/MWh]")}, io.InOp(p=s1), io.InOp(p=s1), ), ( pd.DataFrame({"p": s1.astype("pint[Eur/MWh]")}), io.InOp(p=s1), io.InOp(p=s1), ), ( pd.DataFrame({"p": [Q_(v, "Eur/MWh") for v in val1]}, idx1), io.InOp(p=s1), io.InOp(p=s1), ), ( {"r": s1.astype("pint[MEur]")}, io.InOp(r=s1 * 1e6), io.InOp(r=s1 * 1e6), ), ( pd.DataFrame({"r": s1.astype("pint[MEur]")}), io.InOp(r=s1 * 1e6), io.InOp(r=s1 * 1e6), ), ( {"w": s1.astype("pint[MW]"), "q": s2.astype("pint[MWh]")}, io.InOp(w=s1, q=s2), io.InOp(w=s1_i, q=s2_i), ), ( pd.DataFrame({"w": s1.astype("pint[MW]"), "q": s2.astype("pint[MWh]")}), io.InOp(w=s1_u, q=s2_u), io.InOp(w=s1_u, q=s2_u), ), ( {"w": s1.astype("pint[MW]"), "q": s2.astype("pint[GWh]")}, io.InOp(w=s1, q=s2 * 1000), io.InOp(w=s1_i, q=s2_i * 1000), ), ( pd.DataFrame({"w": s1.astype("pint[MW]"), "q": s2.astype("pint[GWh]")}), io.InOp(w=s1_u, q=s2_u * 1000), io.InOp(w=s1_u, q=s2_u * 1000), ), # . unknown name -> KeyError ( {"z": s1}, KeyError, None, ), ( pd.DataFrame({"z": s1}), KeyError, None, ), ( {"z": s1.astype("pint[MW]")}, KeyError, None, ), ( pd.DataFrame({"z": s1.astype("pint[MW]")}), KeyError, None, ), # . mix of know and unknown names -> KeyError ( {"w": s1, "z": s2}, KeyError, None, ), ( pd.DataFrame({"w": s1, "z": s2}), KeyError, None, ), ( {"w": s1, "p": s2 * 10, "z": s2}, KeyError, None, ), ( pd.DataFrame({"w": s1, "p": s2 * 10, "z": s2}), KeyError, None, ), ( pd.DataFrame({"w": s2.astype("pint[GW]"), "p": s2 * 10, "z": s2}), KeyError, None, ), # . combination of name with incorrect unit -> error ( {"w": s1.astype("pint[MWh]")}, DimensionalityError, None, ), ( pd.DataFrame({"w": s1.astype("pint[MWh]")}), DimensionalityError, None, ), ( {"p": s1.astype("pint[MWh]")}, DimensionalityError, None, ), ( pd.DataFrame({"p": s1.astype("pint[MWh]")}), DimensionalityError, None, ), ( {"p": s2, "w": s1.astype("pint[MWh]")}, DimensionalityError, None, ), ( pd.DataFrame({"p": s2, "w": s1.astype("pint[MWh]")}), DimensionalityError, None, ), # Combinations of value(s) and timeseries. # . name but no unit ( {"w": s1, "p": 50.0}, io.InOp(w=s1, p=50), io.InOp(w=s1, p=pd.Series(50, idx1)), ), ( {"q": -s1, "p": 50.0, "r": s2}, io.InOp(q=-s1, p=50, r=s2), io.InOp(q=-s1_i, r=s2_i, p=pd.Series(50, idx_i)), ), # . name and correct unit ( {"w": s1.astype("pint[MW]"), "p": 50.0}, io.InOp(w=s1, p=50), io.InOp(w=s1, p=pd.Series(50, idx1)), ), ( {"w": s1.astype("pint[MW]"), "q": s2.astype("pint[MWh ]"), "p": 50}, io.InOp(w=s1, q=s2, p=50), io.InOp(w=s1_i, q=s2_i, p=pd.Series(50, idx_i)), ), ( {"r": s1.astype("pint[MEur]"), "p": 50.0, "q": 90_000}, io.InOp(r=s1 * 1e6, p=50, q=90_000), io.InOp(r=s1 * 1e6, p=pd.Series(50, idx1), q=pd.Series(90_000, idx1)), ), # . unknown name -> KeyError ( {"z": s1, "xy": 50}, KeyError, None, ), # . mix of know and unknown names -> KeyError ( {"z": s1, "p": 50.0}, KeyError, None, ), ( {"z": s1.astype("pint[MW]"), "p": s2}, KeyError, None, ), ( {"w": s1.astype("pint[GW]"), "z": 28}, KeyError, None, ), ( {"w": s1, "p": s2 * 10, "z": 50}, KeyError, None, ), # ( # exclude: not a valid dataframe contructor # pd.DataFrame({"w": s1, "p": Q_(5.0, "ctEur/kWh"), "z": s2}), # io.InterOp(w=s1, p=50, rest=({"z": s2},)), # ), ( pd.DataFrame({"w": s1.astype("pint[GW]"), "p": 50.0, "z": s2}), KeyError, None, ), # . combination of name with incorrect unit -> error ( {"w": s1.astype("pint[MWh]"), "p": Q_(50.0, "MW")}, DimensionalityError, None, ), ( {"p": s1.astype("pint[MWh]"), "w": 120.0}, DimensionalityError, None, ), ( {"z": 23.0, "p": s2, "w": s1.astype("pint[MWh]")}, KeyError, None, ), ], ids=id_fn, ) def test_interop(data_in, expected_io, expected_io2): """Test if random data creates the expected InterOp object.""" if type(expected_io) is type and issubclass(expected_io, Exception): with pytest.raises(expected_io): _ = io.InOp.from_data(data_in) return result_io = io.InOp.from_data(data_in) assert result_io == expected_io if type(expected_io2) is type and issubclass(expected_io2, Exception): with pytest.raises(expected_io2): _ = result_io.to_timeseries() return result_io2 = result_io.to_timeseries() assert result_io2 == expected_io2 result_io3 = result_io2.to_timeseries() assert result_io3 == result_io2 # repeated application of intersection does nothing

26.057099

88

0.375659

46915a4ede9c51565edfbd0a439dd6467f9b8985

17,916

py

Python

PZR_bubblegeneration_Fin/SAC_Discrete.py

LeeDaeil/CNS_Autonomous

2ae3688cfd654b9669893e3cdf4cdf1ac0748b9f

[ "Apache-2.0" ]

2

2020-03-22T14:35:00.000Z

2020-05-26T05:06:41.000Z

PZR_bubblegeneration_Fin/SAC_Discrete.py

LeeDaeil/CNS_Autonomous

2ae3688cfd654b9669893e3cdf4cdf1ac0748b9f

[ "Apache-2.0" ]

null

PZR_bubblegeneration_Fin/SAC_Discrete.py

LeeDaeil/CNS_Autonomous

2ae3688cfd654b9669893e3cdf4cdf1ac0748b9f

[ "Apache-2.0" ]

null

""" Builder: Daeil Lee 2021-01-03 Ref-Code: - https://github.com/ku2482/sac-discrete.pytorch - """ import torch import torch.optim as opt import torch.nn.functional as F import numpy as np import asyncio from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, wait from datetime import datetime from PZR_bubblegeneration_Fin.Memory import ReplayBuffer from PZR_bubblegeneration_Fin.SAC_Network import ActorNet, CriticNet from PZR_bubblegeneration_Fin.CNS_PZR import ENVCNS from torch.utils.tensorboard import SummaryWriter WRITER = SummaryWriter('./TFBoard') class SAC: def __init__(self, # info net_type='DNN', lr=0.0003, alpha=1, gamma=0.99, tau=0.005, # mem_info capacity=1e6, seq_len=2, # Agent Run info max_episodes=1e6, max_steps=1e6, interval_steps=15, target_update_interval=15, batch_size=128, ): # ----------------------------------------------------------------------------------------- self.alpha = alpha self.gamma = gamma self.tau = tau self.interval_steps = interval_steps self.target_update_interval = target_update_interval # ----------------------------------------------------------------------------------------- self._log_set() # ----------------------------------------------------------------------------------------- # Call ENV self.envs, self.agent_n, self.a_dim, self.s_dim = self._call_env() # make Thread Pool self.pool = ThreadPoolExecutor(len(self.envs)) # self.a_dim = 3 # Define Memory self.replay_buffer = ReplayBuffer(capacity, net_type, seq_len) # Define Networks self.Actor_Policy_Nets = [ActorNet(nub_a=self.a_dim, nub_s=self.s_dim, net_type=net_type) for _ in self.envs] self.Critic_Q_Net1s = [CriticNet(nub_a=self.a_dim, nub_s=self.s_dim, net_type=net_type) for _ in self.envs] self.Critic_Q_Net2s = [CriticNet(nub_a=self.a_dim, nub_s=self.s_dim, net_type=net_type) for _ in self.envs] self.Critic_Q_Target_Net1s = [CriticNet(nub_a=self.a_dim, nub_s=self.s_dim, net_type=net_type) for _ in self.envs] self.Critic_Q_Target_Net2s = [CriticNet(nub_a=self.a_dim, nub_s=self.s_dim, net_type=net_type) for _ in self.envs] # Copy parameters from Critic_Q_Nets to Critoc_Q_Target_Nets for critic_q_target, critic_q, i in zip(self.Critic_Q_Target_Net1s, self.Critic_Q_Net1s, range(len(self.envs))): critic_q_target.load_state_dict(critic_q.state_dict()) for critic_q_target_para in critic_q_target.parameters(): critic_q_target_para.requires_grad = False critic_q.save(path=f'./Model/Critic_Q_net1_{i}') for critic_q_target, critic_q, i in zip(self.Critic_Q_Target_Net2s, self.Critic_Q_Net2s, range(len(self.envs))): critic_q_target.load_state_dict(critic_q.state_dict()) for critic_q_target_para in critic_q_target.parameters(): critic_q_target_para.requires_grad = False critic_q.save(path=f'./Model/Critic_Q_net2_{i}') # Save Models policy for poliy, i in zip(self.Actor_Policy_Nets, range(len(self.envs))): poliy.save(path=f'./Model/Actor_policy_net_{i}') # Define Optimizer self.Actor_Policy_Net_Opts = [opt.Adam(poliy_net.parameters(), lr=lr) for poliy_net in self.Actor_Policy_Nets] self.Critic_Q_Net1_Opts = [opt.Adam(critic_q_net1.parameters(), lr=lr) for critic_q_net1 in self.Critic_Q_Net1s] self.Critic_Q_Net2_Opts = [opt.Adam(critic_q_net2.parameters(), lr=lr) for critic_q_net2 in self.Critic_Q_Net2s] # Agent info ------------------------------------------------------------------------------ print(f'{self.Actor_Policy_Nets}\n{self.Critic_Q_Net1s}\n{self.Critic_Q_Net2s}\n' f'{self.Critic_Q_Target_Net1s}\n{self.Critic_Q_Target_Net2s}') for i in range(self.agent_n): print(f'Agent {i}|' f'ReplayBuffer {self.replay_buffer}|MonitoringMem {0}|' f'ENV CNSIP{self.envs[i].CNS_ip}-CNSPort{self.envs[i].CNS_port}-' f'ComIP{self.envs[i].Remote_ip}-ComPort{self.envs[i].Remote_port}') # Agent Run ------------------------------------------------------------------------------- self._run(self.envs, self.replay_buffer, max_episodes, max_steps, interval_steps, target_update_interval, batch_size) def _log_set(self): with open('Debug_logger.txt', 'w') as f: f.write(f'[{datetime.now()}]\n') def _log(self, txt): with open('Debug_logger.txt', 'a') as f: f.write(f'[{datetime.now()}]\t{txt}\n') def _call_env(self): _CNS_info = { 0: ['192.168.0.211', 7101, False], #CNS1 1: ['192.168.0.211', 7102, False], 2: ['192.168.0.211', 7103, False], 3: ['192.168.0.211', 7104, False], 4: ['192.168.0.211', 7105, False], # 5: ['192.168.0.212', 7201, False], #CNS2 6: ['192.168.0.212', 7202, False], 7: ['192.168.0.212', 7203, False], 8: ['192.168.0.212', 7204, False], 9: ['192.168.0.212', 7205, False], # 10: ['192.168.0.213', 7301, False], #CNS3 11: ['192.168.0.213', 7302, False], 12: ['192.168.0.213', 7303, False], 13: ['192.168.0.213', 7304, False], 14: ['192.168.0.213', 7305, False], } # Set CNS envs = [ENVCNS(Name=i, IP=_CNS_info[i][0], PORT=_CNS_info[i][1]) for i in range(len(_CNS_info))] return envs, len(_CNS_info), envs[0].action_space, envs[0].observation_space def _update(self, mini_batch, i, target_update): self._log(txt=f'call_update_{i}'+'='*50) s, a, r, s_next, d = mini_batch # print('_update_mini_batch:\n', s, s_next, a, r, d) s = torch.FloatTensor(s) s_next = torch.FloatTensor(s_next) a = torch.FloatTensor(a) r = torch.FloatTensor(r).unsqueeze(1) d = torch.FloatTensor(np.float32(d)).unsqueeze(1) # print('_update:\n', s, s_next, a, r, d) # ------------------------------------------------------------------------------------- # Update the Q-function or Critic network's parameters q1, q2 = self._update_cal_q(s, a, i) target_q = self._update_cal_target_q(r, s_next, d, i) Critic_Q1_loss = 0.5 * F.mse_loss(q1, target_q.detach()) Critic_Q2_loss = 0.5 * F.mse_loss(q2, target_q.detach()) self._log(txt=f'q1_{q1}_{target_q}') self._log(txt=f'q1_{q2}_{target_q}') Critic_Q1_loss_mean = torch.mean(Critic_Q1_loss) Critic_Q2_loss_mean = torch.mean(Critic_Q2_loss) # print(f'_Critic_loss_sum:\n{q1}\n{target_q}\n{Critic_Q1_loss}\n{Critic_Q2_loss}\n{Critic_Q1_loss_mean}') # print(f'_Critic_Q1_loss_mean:\n{Critic_Q1_loss_mean}') self.Critic_Q_Net1_Opts[i].zero_grad() Critic_Q1_loss_mean.backward() self.Critic_Q_Net1_Opts[i].step() self.Critic_Q_Net2_Opts[i].zero_grad() Critic_Q2_loss_mean.backward() self.Critic_Q_Net2_Opts[i].step() # ------------------------------------------------------------------------------------- # Update the Actor_policy's parameters entropies, expect_q = self._update_cal_policy_entropy(s, i) Actor_policy_loss = entropies - expect_q Actor_policy_loss_mean = torch.mean(Actor_policy_loss) # print(f'Actor_policy_loss_mean:\n{Actor_policy_loss_mean}') self.Actor_Policy_Net_Opts[i].zero_grad() Actor_policy_loss_mean.backward() self.Actor_Policy_Net_Opts[i].step() # ------------------------------------------------------------------------------------- # Log net calculation process ep = self.Wd[i]['ep'] with open(f'./DB_ep_net/{ep}.txt', 'a') as f_net_: f_net_.write(f'{q1.data.tolist()}|{q2.data.tolist()}|{target_q.data.tolist()}|' f'{Critic_Q1_loss_mean.data.tolist()}|{Critic_Q2_loss_mean.data.tolist()}|' f'{entropies.data.tolist()}|{expect_q.data.tolist()}|' f'{Actor_policy_loss.data.tolist()}|{Actor_policy_loss_mean.data.tolist()}|' f'\n') # ------------------------------------------------------------------------------------- # Update the Target Q network: soft-Q update if target_update: self._log(txt='target_update') Q_nets = [self.Critic_Q_Net1s[i], self.Critic_Q_Net2s[i]] Q_target_nets = [self.Critic_Q_Target_Net1s[i], self.Critic_Q_Target_Net2s[i]] for Q_net_, Q_target_net_ in zip(Q_nets, Q_target_nets): for Q_net_para_, Q_target_net_para_ in zip(Q_net_.parameters(), Q_target_net_.parameters()): Q_target_net_para_.data.copy_(self.tau * Q_net_para_.data + (1 - self.tau) * Q_target_net_para_.data) return Critic_Q1_loss_mean.detach().cpu().numpy(), Critic_Q2_loss_mean.detach().cpu().numpy(), Actor_policy_loss_mean.detach().cpu().numpy() def _update_cal_q(self, s, a, i): q1 = self.Critic_Q_Net1s[i](s) q2 = self.Critic_Q_Net2s[i](s) q1 = q1.gather(1, a.long()) q2 = q2.gather(1, a.long()) return q1, q2 def _update_cal_target_q(self, r, s_next, d, i): with torch.no_grad(): Actor_s_next_out = self.Actor_Policy_Nets[i].sample(s_next) # print('_Actor_Policy_Net_next_Out:\n', Actor_s_next_out) action_next_, action_probs_next, log_probs_next = Actor_s_next_out q1_target = self.Critic_Q_Target_Net1s[i](s_next) q2_target = self.Critic_Q_Target_Net2s[i](s_next) min_q1_q2_target = torch.min(q1_target, q2_target) target_V = min_q1_q2_target - self.alpha * log_probs_next target_V_prob = action_probs_next * target_V target_V_sum = target_V_prob.sum(dim=1, keepdim=True) # print(f'_target_V:\n{min_q1_q2_target}\n{log_probs_next}\n{target_V}\n{action_probs_next}' # f'\n{target_V_prob}\n{target_V_sum}') target_Q = r + self.gamma * (1 - d) * target_V_sum # print(f'_target_Q:\n{target_Q}') return target_Q def _update_cal_policy_entropy(self, s, i): Actor_s_out = self.Actor_Policy_Nets[i].sample(s) # print('_Actor_Policy_Net_Out:\n', Actor_s_out) action_, action_probs, log_probs = Actor_s_out with torch.no_grad(): q1_ = self.Critic_Q_Net1s[i](s) q2_ = self.Critic_Q_Net2s[i](s) min_q1_q2 = torch.min(q1_, q2_) entropies = torch.sum(action_probs * self.alpha * log_probs, dim=1, keepdim=True) # print(f'_Actor_policy_entropies\n{action_probs}\n{log_probs}\n{entropies}') expect_q = torch.sum(action_probs * min_q1_q2, dim=1, keepdim=True) # print(f'_Actor_policy_expect_q\n{action_probs}\n{min_q1_q2}\n{expect_q}') return entropies, expect_q def _pool_one_step(self, envs, actions): def __pool_one_step(env, a): next_s, r, d, _ = env.step(a) return next_s, r, d, _ futures = [self.pool.submit(__pool_one_step, env_, a) for env_, a in zip(envs, actions)] wait(futures) out = [pack_out.result() for pack_out in futures] next_s = [out[_][0].tolist() for _ in range(self.agent_n)] r = [out[_][1] for _ in range(self.agent_n)] d = [out[_][2] for _ in range(self.agent_n)] a = [out[_][3] for _ in range(self.agent_n)] return next_s, r, d, a def _pool_reset(self, envs): def __pool_reset(env, ep): env.reset(file_name=f'{ep}') calculate_ep = [] for i in range(self.agent_n): self.Wd[i]['ep'] = self.episode calculate_ep.append(self.episode) self.episode += 1 futures = [self.pool.submit(__pool_reset, env_, ep_) for env_, ep_ in zip(envs, calculate_ep)] wait(futures) print('All Env Reset !!') def _pool_done_reset(self, envs, dones): done_envs = [] done_envs_ep = [] for i in range(self.agent_n): if dones[i]: self.Wd[i]['ep'] = self.episode done_envs.append(envs[i]) done_envs_ep.append(self.episode) self.episode += 1 def __pool_done_reset(env, ep): env.reset(file_name=f'{ep}') futures = [self.pool.submit(__pool_done_reset, env_, ep_) for env_, ep_ in zip(done_envs, done_envs_ep)] wait(futures) def _run_exploit(self, net, s): with torch.no_grad(): a = net.get_act(s) return a.item() def _run_explore(self, net, s): with torch.no_grad(): a, _, _ = net.sample(s) return a.item() def _run_learn(self, steps, interval_steps): return steps % interval_steps == 0 def _run_update_target(self): return def _run(self, envs, replay_buffer, max_episodes, max_steps, interval_steps, target_update_interval, batch_size): print('Run' + '=' * 50) steps = 0 self.episode = 0 self.writer_ep = 0 # Worker mem self.Wd = {i: {'ep_acur': 0, 'ep_q1': 0, 'ep_q2': 0, 'ep_p': 0, 'ep':0} for i in range(self.agent_n)} self._pool_reset(envs) next_s, r, d, _ = self._pool_one_step(envs, actions=[[0] for _ in range(self.agent_n)]) s = next_s while steps < max_steps and self.episode < max_episodes: print(f'Time:[{datetime.now().minute}:{datetime.now().second}]' f'Global_info:[{self.episode}/{max_episodes}][{steps}/{max_steps}]' f'Env_info: {[env_.ENVStep for env_ in envs]}') # s 에대한 a 예측 # a = [self.Actor_Policy_Nets[i].get_act(s[i]) for i in range(self.agent_n)] # a[0] tensor([[0]]) a = [[self._run_exploit(self.Actor_Policy_Nets[i], s[i])] for i in range(self.agent_n)] # a[0] [0] # CNS Step <- next_s, r, d, _ = self._pool_one_step(envs, a) # Log for i in range(self.agent_n): with open(f'./DB_ep_srd/{self.Wd[i]["ep"]}.txt', 'a') as f_ep_srd: f_ep_srd.write(f"{s[i]},{r[i]},{d[i]}\n") # Buffer <- for s_, a_, r_, next_s_, d_, id in zip(s, a, r, next_s, d, range(self.agent_n)): self.Wd[id]['ep_acur'] += r_ replay_buffer.push(s_, a_, r_, next_s_, d_) # s <- next_s s = next_s # learn if replay_buffer.get_length() > batch_size and self._run_learn(steps=steps, interval_steps=interval_steps): target_update = True if steps % target_update_interval == 0 else False for i in range(self.agent_n): mini_batch = replay_buffer.sample(batch_size, per=False) q1_loss, q2_loss, p_loss = self._update(mini_batch, i, target_update) with open(f'./DB_ep/{self.Wd[i]["ep"]}.txt', 'a') as f: f.write(f"{q1_loss},{q2_loss},{p_loss}\n") self.Wd[i]['ep_q1'] += q1_loss self.Wd[i]['ep_q2'] += q2_loss self.Wd[i]['ep_p'] += p_loss # Done ep ?? for d_, id in zip(d, range(self.agent_n)): if d_: print(f"{self.Wd[id]['ep_q1']},{self.Wd[id]['ep_q1']/envs[id].ENVStep}," f"{self.Wd[id]['ep_q2']},{self.Wd[id]['ep_q2']/envs[id].ENVStep}," f"{self.Wd[id]['ep_p']},{self.Wd[id]['ep_p']/envs[id].ENVStep}," f"{self.Wd[id]['ep_acur']},{self.Wd[id]['ep_acur']/envs[id].ENVStep}\n") with open(f'./DB_ep/tot.txt', 'a') as f: f.write(f"{self.Wd[id]['ep']}," f"{self.Wd[id]['ep_q1']},{self.Wd[id]['ep_q1']/envs[id].ENVStep}," f"{self.Wd[id]['ep_q2']},{self.Wd[id]['ep_q2']/envs[id].ENVStep}," f"{self.Wd[id]['ep_p']},{self.Wd[id]['ep_p']/envs[id].ENVStep}," f"{self.Wd[id]['ep_acur']},{self.Wd[id]['ep_acur']/envs[id].ENVStep}\n") self.writer_ep += 1 WRITER.add_scalar('Loss/q1', self.Wd[id]['ep_q1'], self.writer_ep) WRITER.add_scalar('Loss/q2', self.Wd[id]['ep_q2'], self.writer_ep) WRITER.add_scalar('Loss/p', self.Wd[id]['ep_p'], self.writer_ep) WRITER.add_scalar('Loss/r', self.Wd[id]['ep_acur'], self.writer_ep) WRITER.add_scalar('Loss-av/q1-av', self.Wd[id]['ep_q1']/envs[id].ENVStep, self.writer_ep) WRITER.add_scalar('Loss-av/q2-av', self.Wd[id]['ep_q2']/envs[id].ENVStep, self.writer_ep) WRITER.add_scalar('Loss-av/p-av', self.Wd[id]['ep_p']/envs[id].ENVStep, self.writer_ep) WRITER.add_scalar('Loss-av/r-av', self.Wd[id]['ep_acur']/envs[id].ENVStep, self.writer_ep) for _ in self.Wd[id].keys(): self.Wd[id][_] = 0 self._pool_done_reset(envs, d) steps += self.agent_n # End print(f'Done Training:' f'[{self.episode}/{max_episodes}]' f'[{steps}/{max_steps}]' + '=' * 50) if __name__ == '__main__': _ = SAC()

44.567164

148

0.558439

dcf8abe258e05bdf4a36697c49e573eb5ef7cea8

7,007

py

Python

keystone/tests/unit/backend/role/test_ldap.py

maestro-hybrid-cloud/keystone

a597a86b854215835a4d54885daeb161d7b0efb8

[ "Apache-2.0" ]

null

keystone/tests/unit/backend/role/test_ldap.py

maestro-hybrid-cloud/keystone

a597a86b854215835a4d54885daeb161d7b0efb8

[ "Apache-2.0" ]

null

keystone/tests/unit/backend/role/test_ldap.py

maestro-hybrid-cloud/keystone

a597a86b854215835a4d54885daeb161d7b0efb8

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # 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 uuid from oslo_config import cfg from keystone import exception from keystone.tests import unit from keystone.tests.unit.backend import core_ldap from keystone.tests.unit.backend.role import core as core_role from keystone.tests.unit import default_fixtures CONF = cfg.CONF class LdapRoleCommon(core_ldap.BaseBackendLdapCommon, core_role.RoleTests): """Tests that should be run in every LDAP configuration. Include additional tests that are unique to LDAP (or need to be overridden) which should be run for all the various LDAP configurations we test. """ pass class LdapRole(LdapRoleCommon, core_ldap.BaseBackendLdap, unit.TestCase): """Test in an all-LDAP configuration. Include additional tests that are unique to LDAP (or need to be overridden) which only need to be run in a basic LDAP configurations. """ def test_configurable_allowed_role_actions(self): role = {'id': u'fäké1', 'name': u'fäké1'} self.role_api.create_role(u'fäké1', role) role_ref = self.role_api.get_role(u'fäké1') self.assertEqual(u'fäké1', role_ref['id']) role['name'] = u'fäké2' self.role_api.update_role(u'fäké1', role) self.role_api.delete_role(u'fäké1') self.assertRaises(exception.RoleNotFound, self.role_api.get_role, u'fäké1') def test_configurable_forbidden_role_actions(self): self.config_fixture.config( group='ldap', role_allow_create=False, role_allow_update=False, role_allow_delete=False) self.load_backends() role = {'id': uuid.uuid4().hex, 'name': uuid.uuid4().hex} self.assertRaises(exception.ForbiddenAction, self.role_api.create_role, role['id'], role) self.role_member['name'] = uuid.uuid4().hex self.assertRaises(exception.ForbiddenAction, self.role_api.update_role, self.role_member['id'], self.role_member) self.assertRaises(exception.ForbiddenAction, self.role_api.delete_role, self.role_member['id']) def test_role_filter(self): role_ref = self.role_api.get_role(self.role_member['id']) self.assertDictEqual(self.role_member, role_ref) self.config_fixture.config(group='ldap', role_filter='(CN=DOES_NOT_MATCH)') self.load_backends() # NOTE(morganfainberg): CONF.ldap.role_filter will not be # dynamically changed at runtime. This invalidate is a work-around for # the expectation that it is safe to change config values in tests that # could affect what the drivers would return up to the manager. This # solves this assumption when working with aggressive (on-create) # cache population. self.role_api.get_role.invalidate(self.role_api, self.role_member['id']) self.assertRaises(exception.RoleNotFound, self.role_api.get_role, self.role_member['id']) def test_role_attribute_mapping(self): self.config_fixture.config(group='ldap', role_name_attribute='ou') self.clear_database() self.load_backends() self.load_fixtures(default_fixtures) # NOTE(morganfainberg): CONF.ldap.role_name_attribute will not be # dynamically changed at runtime. This invalidate is a work-around for # the expectation that it is safe to change config values in tests that # could affect what the drivers would return up to the manager. This # solves this assumption when working with aggressive (on-create) # cache population. self.role_api.get_role.invalidate(self.role_api, self.role_member['id']) role_ref = self.role_api.get_role(self.role_member['id']) self.assertEqual(self.role_member['id'], role_ref['id']) self.assertEqual(self.role_member['name'], role_ref['name']) self.config_fixture.config(group='ldap', role_name_attribute='sn') self.load_backends() # NOTE(morganfainberg): CONF.ldap.role_name_attribute will not be # dynamically changed at runtime. This invalidate is a work-around for # the expectation that it is safe to change config values in tests that # could affect what the drivers would return up to the manager. This # solves this assumption when working with aggressive (on-create) # cache population. self.role_api.get_role.invalidate(self.role_api, self.role_member['id']) role_ref = self.role_api.get_role(self.role_member['id']) self.assertEqual(self.role_member['id'], role_ref['id']) self.assertNotIn('name', role_ref) def test_role_attribute_ignore(self): self.config_fixture.config(group='ldap', role_attribute_ignore=['name']) self.clear_database() self.load_backends() self.load_fixtures(default_fixtures) # NOTE(morganfainberg): CONF.ldap.role_attribute_ignore will not be # dynamically changed at runtime. This invalidate is a work-around for # the expectation that it is safe to change config values in tests that # could affect what the drivers would return up to the manager. This # solves this assumption when working with aggressive (on-create) # cache population. self.role_api.get_role.invalidate(self.role_api, self.role_member['id']) role_ref = self.role_api.get_role(self.role_member['id']) self.assertEqual(self.role_member['id'], role_ref['id']) self.assertNotIn('name', role_ref) class LdapIdentitySqlEverythingElseRole( core_ldap.BaseBackendLdapIdentitySqlEverythingElse, LdapRoleCommon, unit.TestCase): """Test Identity in LDAP, Everything else in SQL.""" pass class LdapIdentitySqlEverythingElseWithMappingRole( LdapIdentitySqlEverythingElseRole, core_ldap.BaseBackendLdapIdentitySqlEverythingElseWithMapping): """Test ID mapping of default LDAP backend.""" pass

43.253086

79

0.6572

cbab3a357e4ad69576afc85b4060587fbf96f82b

3,329

py

Python

code/main.py

bnesposito/zika-detection

62d5f962e71af54d9dc51eb91b62329d84735e68

[ "Apache-2.0" ]

null

code/main.py

bnesposito/zika-detection

62d5f962e71af54d9dc51eb91b62329d84735e68

[ "Apache-2.0" ]

null

code/main.py

bnesposito/zika-detection

62d5f962e71af54d9dc51eb91b62329d84735e68

[ "Apache-2.0" ]

null

import numpy as np import pandas as pd import time from sklearn.ensemble import VotingClassifier import config import process import models def main(): LOGGER_LEVEL = 10 RAW_DATA_PATH = './data/raw/' RAW_CSV_NAME = 'raw_data.csv' t0 = time.time() logger = config.config_logger(__name__, LOGGER_LEVEL) pd.set_option('display.float_format', lambda x: '{0:.2f}'.format(x)) logger.info('Beginning execution: zika dataset') logger.info('Logger configured - level {0}'.format(LOGGER_LEVEL)) logger.info('Opening CSV: {0}{1}'.format(RAW_DATA_PATH, RAW_CSV_NAME)) raw_data = pd.read_csv(RAW_DATA_PATH + RAW_CSV_NAME) logger.info('Raw dataset description:') process.basic_descriptives(raw_data) raw_data = process.preprocess(raw_data) #print(raw_data.describe().transpose().to_string()) #print(raw_data.head().to_string()) #print(raw_data.info().to_string()) y_dengue = raw_data['dengue_pcr'] y_zika = raw_data['zika_pcr'] y_chik = raw_data['chik_pcr'] diseases = [y_dengue, y_zika, y_chik] # Check process code for further explanation of select_disease function. # code: 1. Dengue, 2. Zika, 3. Chik, 4. Any # only_one: if True, input np.nan to patients with another disease. y = process.select_disease(diseases, code=1, only_one=False) logger.info('Target var frequency: \n{0}'.format(y.value_counts())) logger.info('Total obs: {0}'.format(y.value_counts().sum())) remove_list = ['id', 'centro_pob', 'name', 'dep', 'prov', 'dist', 'serotipo1', 'serotipo2', 'serotipo3', 'serotipo4', 'dengue_pcr', 'zika_pcr', 'chik_pcr'] X = process.remove_vars(raw_data, remove_list) X = process.keep_non_nan(X, y) y = y.dropna() logger.info('Features dataset') process.basic_descriptives(X) logger.info('Split train test') X_train, X_test, y_train, y_test = models.split_data(X, y, proportion=0.4) logger.info('Estimating models') logger.info('GBM') grid_gbm = models.gbm_grid(X_train, y_train, n_cv=5) logger.info(grid_gbm.best_params_) logger.info('Train score: {0}'.format(grid_gbm.best_score_)) logger.info('Test score: {0}'.format(grid_gbm.score(X_test, y_test))) logger.info('Logit') grid_logit = models.logit_grid(X_train, y_train, n_cv=5) logger.info(grid_logit.best_params_) logger.info('Train score: {0}'.format(grid_logit.best_score_)) logger.info('Test score: {0}'.format(grid_logit.score(X_test, y_test))) logger.info('AdaBoost') grid_adaboost = models.adaboost_grid(X_train, y_train, n_cv=5) logger.info(grid_adaboost.best_params_) logger.info('Train score: {0}'.format(grid_adaboost.best_score_)) logger.info('Test score: {0}'.format(grid_adaboost.score(X_test, y_test))) logger.info('Soft Voting') eclf = VotingClassifier(estimators=[('gbm', grid_gbm), ('logit', grid_logit), ('ada', grid_adaboost)], voting='soft') eclf.fit(X_train, y_train) y_pred = eclf.predict_proba(X_test) print(y_pred[:5,:]) logger.info('Train score: {0}'.format(eclf.score(X_train, y_train))) logger.info('Test score: {0}'.format(eclf.score(X_test, y_test))) config.time_taken_display(t0) if __name__ == '__main__': main()

36.582418

81

0.676479

d00423c680ad949ad979bd13fbaf3a375a88ee47

1,196

py

Python

tests/testapp/migrations/0001_initial.py

garyd203/django-lifecycle

f60a1394b3fb44b84c9c997ac87c2edc7b7a7f55

[ "MIT" ]

null

tests/testapp/migrations/0001_initial.py

garyd203/django-lifecycle

f60a1394b3fb44b84c9c997ac87c2edc7b7a7f55

[ "MIT" ]

null

tests/testapp/migrations/0001_initial.py

garyd203/django-lifecycle

f60a1394b3fb44b84c9c997ac87c2edc7b7a7f55

[ "MIT" ]

null

# Generated by Django 2.0.3 on 2018-03-23 05:44 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='UserAccount', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('username', models.CharField(max_length=100)), ('first_name', models.CharField(max_length=100)), ('last_name', models.CharField(max_length=100)), ('password', models.CharField(max_length=200)), ('email', models.FileField(null=True, upload_to='')), ('password_updated_at', models.DateTimeField(null=True)), ('joined_at', models.DateTimeField(null=True)), ('has_trial', models.BooleanField(default=False)), ('status', models.CharField(choices=[('active', 'Active'), ('banned', 'Banned'), ('inactive', 'Inactive')], default='active', max_length=30)), ], options={ 'abstract': False, }, ), ]

36.242424

158

0.560201

4b99949609a4b6ed5e86b2396aae2366d75f8167

555

py

Python

1-100/17/17.py

Thomaw/Project-Euler

bcad5d8a1fd3ebaa06fa52d92d286607e9372a8d

[ "MIT" ]

null

1-100/17/17.py

Thomaw/Project-Euler

bcad5d8a1fd3ebaa06fa52d92d286607e9372a8d

[ "MIT" ]

null

1-100/17/17.py

Thomaw/Project-Euler

bcad5d8a1fd3ebaa06fa52d92d286607e9372a8d

[ "MIT" ]

null

s={0:"",1:"one",2:"two",3:"three",4:"four",5:"five",6:"six"/ ,7:"seven",8:"eight",9:"nine",10:"ten",11:"eleven"/ ,12:"twelve",13:"thirteen",14:"fourteen",15:"fifteen"/ ,16:"sixteen",17:"seventeen",18:"eighteen",19:"nineteen"/ ,20:"twenty",30:"thirty",40:"forty",50:"fifty"/ ,60:"sixty",70:"seventy",80:"eighty",90:"ninety"} for i in range(1,1000): if(not i in s.keys()): if(i<100): s[i]=s[i/10*10]+s[i%10] else: s[i]=s[i/100]+"hundred" if(i%100): s[i]+="and"+s[i%100] s[1000]="onethousand" total=0; for i in s.values(): total+=len(i)

27.75

60

0.583784

0954f281c8639b3673eb8cae034b02aa05706ce5

1,961

py

Python

omtk/__init__.py

renaudll/omtk

a7740d53a5587529773594bfd7c37e553787028f

[ "MIT" ]

20

2015-09-30T16:07:02.000Z

2022-03-12T06:57:59.000Z

omtk/__init__.py

nilouco/omtk

a7740d53a5587529773594bfd7c37e553787028f

[ "MIT" ]

23

2015-12-22T15:41:02.000Z

2018-04-13T02:52:41.000Z

omtk/__init__.py

nilouco/omtk

a7740d53a5587529773594bfd7c37e553787028f

[ "MIT" ]

13

2015-07-10T16:06:26.000Z

2021-08-21T20:09:41.000Z

import sys from .core import * import pymel.core as pymel __dependencies__ = [ ('deps',) ] current_dir = os.path.dirname(os.path.realpath(__file__)) for dependency in __dependencies__: path = os.path.realpath(os.path.join(current_dir, *dependency)) sys.path.append(path) # HACK: Load matrixNodes.dll pymel.loadPlugin('matrixNodes', quiet=True) def _reload(kill_ui=True): """ Reload all module in their respective order. """ import core reload(core) core._reload() import libs reload(libs) libs._reload() from omtk.core import plugin_manager reload(plugin_manager) plugin_manager.plugin_manager.reload_all() import ui_shared reload(ui_shared) from ui import pluginmanager_window reload(pluginmanager_window) from ui import preferences_window reload(preferences_window) from ui import widget_list_influences reload(widget_list_influences) from ui import widget_list_modules reload(widget_list_modules) from ui import widget_list_meshes reload(widget_list_meshes) from ui import widget_logger reload(widget_logger) import widget_list_influences reload(widget_list_influences) import widget_list_modules reload(widget_list_modules) import widget_list_meshes reload(widget_list_meshes) import widget_logger reload(widget_logger) from ui import main_window reload(main_window) import preferences_window reload(preferences_window) import pluginmanager_window reload(pluginmanager_window) import main_window reload(main_window) if kill_ui: # Try to kill the window to prevent any close event error try: pymel.deleteUI('OpenRiggingToolkit') except: pass reload(main_window) def show(): """ Show a simple gui. Note that PySide or PyQt4 is needed. """ import main_window main_window.show()

20.642105

67

0.711882

41a07abad738f41570fda5fe865b70918bfc53bd

1,917

py

Python

Web/4/example_12.py

mabdelaal86/python-courses

5e2be0df3c00eb084ec39d49402be38fac635097

[ "MIT" ]

1

2020-03-10T15:40:22.000Z

Web/4/example_12.py

mabdelaal86/python-courses

5e2be0df3c00eb084ec39d49402be38fac635097

[ "MIT" ]

null

Web/4/example_12.py

mabdelaal86/python-courses

5e2be0df3c00eb084ec39d49402be38fac635097

[ "MIT" ]

null

#!/usr/bin/env python3 from flask import Flask, request, jsonify, abort app = Flask(__name__) students = { 105: {"id": 105, "name": "Ibrahim Fatti", "gender": "Male", "birth_date": "1995-12-13", "address": "Giza", "class": 3, "group": "A"}, 109: {"id": 109, "name": "Shady Hamdy", "gender": "Male", "birth_date": "1995-10-22", "address": "Giza", "class": 3, "group": "B"}, 115: {"id": 115, "name": "Amani Fahmy", "gender": "Female", "birth_date": "1996-05-12", "address": "Cairo", "class": 2, "group": "A"}, 122: {"id": 122, "name": "Kareem Ahmad", "gender": "Male", "birth_date": "1997-09-14", "address": "Cairo", "class": 1, "group": "C"} } @app.route("/students/", methods=['GET']) def read_all(): return jsonify(list(students.values())) @app.route("/students/<int:student_id>/", methods=['GET']) def read(student_id): student = students.get(student_id) if student is None: abort(404, "Student not found") return jsonify(student) @app.route("/students/", methods=['POST']) def create(): data = request.get_json() student_id = data['id'] if student_id in students: abort(400, "Duplicated ID") students[student_id] = data return jsonify(data), 201 @app.route("/students/<int:student_id>/", methods=['PUT']) def update(student_id): if student_id not in students: abort(404, "Student not found") data = request.get_json() students[student_id] = data return "", 204 @app.route("/students/<int:student_id>/", methods=['DELETE']) def delete(student_id): if student_id not in students: abort(404, "Student not found") del students[student_id] return "", 204 @app.errorhandler(404) @app.errorhandler(400) def on_error(error): return jsonify({"status": error.code, "title": error.description}), error.code if __name__ == "__main__": app.run(host="0.0.0.0", port=5000, debug=True)

27.385714

138

0.624413

45ac478bcc60a60ba1fd6d251de2e254b76ada60

6,164

py

Python

xblog/tests/test_mt.py

rubeon/django-xblog

1709a3c2f6c1901231f817f9adeb189b0be6251e

[ "BSD-2-Clause" ]

null

xblog/tests/test_mt.py

rubeon/django-xblog

1709a3c2f6c1901231f817f9adeb189b0be6251e

[ "BSD-2-Clause" ]

null

xblog/tests/test_mt.py

rubeon/django-xblog

1709a3c2f6c1901231f817f9adeb189b0be6251e

[ "BSD-2-Clause" ]

null

""" test case for mt. xmlrpc methods """ from django.test import TestCase from django.core.exceptions import PermissionDenied from django.contrib.auth.models import User from django.contrib.sites.models import Site from django.test.utils import override_settings from django.test.client import Client from django.conf import settings from xblog.models import Post from xblog.models import Blog from xblog.models import Author from xblog.models import Category from xblog.models import Link from xblog.models import Tag from xblog.models import LinkCategory from xblog.models import FILTERS from datetime import datetime try: from xmlrpc.client import Binary from xmlrpc.client import Fault from xmlrpc.client import ServerProxy except ImportError: # Python 2 from xmlrpclib import Binary from xmlrpclib import Fault from xmlrpclib import ServerProxy from .utils import TestTransport post_content = { 'title':'This is a test title', 'description': "This is the post content. Hey-ooooo!", 'post_type': 'post', 'dateCreated': datetime.now(), 'date_created_gmt': datetime.now(), 'categories': [], 'mt_keywords': ['tag1','tag2','tag3'], 'mt_excerpt': "This is the...", 'mt_text_more': "Hey-oooooO!", 'mt_allow_comments':True, 'mt_allow_pings': True, 'wp_slug': 'this-is-the-test-title', 'wp_password': 'mypassword', # 'wp_author_id': '' # 'wp_author_display_name': 'post_status':'publish', 'wp_post_format': 'Post', 'sticky': False, 'custom_fields':[], 'enclosure':{}, } @override_settings( ROOT_URLCONF='xblog.tests.conf.urls' ) class MtTestCase(TestCase): """ Test Cases for the wp.* XMLRPC API calls """ def setUp(self): """ Bring up the test environment """ # create our test user self.test_user1 = User.objects.create( username="test_user1", first_name="Test", last_name="User2", email="testuser@example.com", password="MyTestPass1", is_staff=False, is_superuser=False ) # self.test_user2 = User.objects.create( username="test_user2", first_name="Test", last_name="User2", email="testuser2@example.com", password="MyTestPass1", is_staff=False, is_superuser=False ) self.rogue_user = User.objects.create( username="rogue_user", first_name="Rogue", last_name="User", email="testuser2@example.com", password="MyTestPass1", is_staff=False, is_superuser=False ) self.test_admin = User.objects.create( username="admin", first_name="Admin", last_name="User", email="admin@example.com", password="MyAdminPass1", is_staff=True, is_superuser=True ) self.test_blog = Blog.objects.create( title="Test User 1's Space", description="A blog for Test User 1. Slippery when wet!", owner = User.objects.get(username="test_user1"), site = Site.objects.get_current() ) self.test_category1 = Category.objects.create( title="Test Category 1", description="Category mean namely for testing", blog = self.test_blog ) self.post = Post.objects.create( title = "Test User 1 Post", body = "This is some stuff.\n\nSome stuff, you know.", blog = self.test_blog, author = self.test_user1.author, status = 'publish' ) self.post.save() self.draft = Post.objects.create( title = "Test User 1 Post", body = "This is some stuff.\n\nSome stuff, you know.", blog = self.test_blog, author = self.test_user1.author, status = 'draft' ) # enable remote access for test_user1 self.test_user1.author.remote_access_enabled = True self.test_user1.author.save() # disable remote access for test_user2 self.test_user2.author.remote_access_enabled = False self.test_user2.author.save() self.rogue_user.author.remote_access_enabled = True self.rogue_user.author.save() self.test_admin.author.remote_access_enabled = True self.test_admin.author.save() self.s = ServerProxy('http://localhost:8000/xmlrpc/', transport=TestTransport(), verbose=0) def test_mt_set_post_categories(self): """ make sure that categories can be set """ postid = self.post.id username = self.test_user1.username password = self.test_user1.author.remote_access_key cat = self.test_category1 categories = [{ 'categoryId': cat.id, 'isPrimary': True },] res = self.s.mt.setPostCategories(postid, username, password, categories) # smoke check self.assertTrue(res) p = self.post for category in categories: c = Category.objects.get(pk=category['categoryId']) self.assertIn(c, p.categories.all()) def test_mt_get_post_categories(self): postid = self.post.id username = self.test_user1.username password = self.test_user1.author.remote_access_key categories = self.s.mt.getPostCategories(postid, username, password) for category in categories: c = Category.objects.get(pk=categories['categoryId']) self.assertIn(c, p.categories.all()) def test_mt_publish_post(self): postid = self.draft.id username = self.test_user1.username password = self.test_user1.author.remote_access_key self.assertTrue(self.draft.status=="draft") res = self.s.mt.publishPost(postid, username, password) self.assertTrue(res) post = Post.objects.get(pk=postid) self.assertTrue(post.status=='publish')

30.364532

99

0.614049

40055188cda07e9016356349c69a69d50848fabb

11,502

py

Python

trainer.py

zedoggo/ThesisBinus

1132330cd221677a4e7abe27ff0637642ee02872

[ "MIT" ]

2

2020-11-08T15:39:10.000Z

2021-02-25T08:07:55.000Z

trainer.py

zedoggo/ThesisBinus

1132330cd221677a4e7abe27ff0637642ee02872

[ "MIT" ]

null

trainer.py

zedoggo/ThesisBinus

1132330cd221677a4e7abe27ff0637642ee02872

[ "MIT" ]

1

2020-12-13T13:40:34.000Z

import numpy as np import torch from torch import optim from torch.autograd import Variable from torch.optim.lr_scheduler import StepLR from models.CC import CrowdCounter from config import cfg from misc.utils import * import pdb import csv class Trainer(): def __init__(self, dataloader, cfg_data, pwd): self.cfg_data = cfg_data self.data_mode = cfg.DATASET self.exp_name = cfg.EXP_NAME self.exp_path = cfg.EXP_PATH self.pwd = pwd self.net_name = cfg.NET self.net = CrowdCounter(cfg.GPU_ID,self.net_name).cuda() self.optimizer = optim.Adam(self.net.CCN.parameters(), lr=cfg.LR, weight_decay=1e-4) # self.optimizer = optim.SGD(self.net.parameters(), cfg.LR, momentum=0.95,weight_decay=5e-4) self.scheduler = StepLR(self.optimizer, step_size=cfg.NUM_EPOCH_LR_DECAY, gamma=cfg.LR_DECAY) self.train_record = {'best_mae': 1e20, 'best_mse':1e20, 'best_model_name': ''} self.timer = {'iter time' : Timer(),'train time' : Timer(),'val time' : Timer()} self.epoch = 0 self.i_tb = 0 if cfg.PRE_GCC: self.net.load_state_dict(torch.load(cfg.PRE_GCC_MODEL)) self.train_loader, self.val_loader, self.restore_transform = dataloader() if cfg.RESUME: latest_state = torch.load(cfg.RESUME_PATH) self.net.load_state_dict(latest_state['net']) self.optimizer.load_state_dict(latest_state['optimizer']) self.scheduler.load_state_dict(latest_state['scheduler']) self.epoch = latest_state['epoch'] + 1 self.i_tb = latest_state['i_tb'] self.train_record = latest_state['train_record'] self.exp_path = latest_state['exp_path'] self.exp_name = latest_state['exp_name'] self.writer, self.log_txt = logger(self.exp_path, self.exp_name, self.pwd, 'exp', resume=cfg.RESUME) def forward(self): # self.validate_V3() for epoch in range(self.epoch,cfg.MAX_EPOCH): self.epoch = epoch if epoch > cfg.LR_DECAY_START: self.scheduler.step() # training self.timer['train time'].tic() self.train() self.timer['train time'].toc(average=False) print( 'train time: {:.2f}s'.format(self.timer['train time'].diff) ) print( '='*20 ) # validation if epoch%cfg.VAL_FREQ==0 or epoch>cfg.VAL_DENSE_START: self.timer['val time'].tic() if self.data_mode in ['SHHA', 'SHHB', 'QNRF', 'UCF50']: self.validate_V1() elif self.data_mode is 'WE': self.validate_V2() elif self.data_mode is 'GCC': self.validate_V3() self.timer['val time'].toc(average=False) print( 'val time: {:.2f}s'.format(self.timer['val time'].diff) ) def train(self): # training for all datasets self.net.train() for i, data in enumerate(self.train_loader, 0): self.timer['iter time'].tic() img, gt_map = data img = Variable(img).cuda() gt_map = Variable(gt_map).cuda() self.optimizer.zero_grad() pred_map = self.net(img, gt_map) loss = self.net.loss loss.backward() self.optimizer.step() if (i + 1) % cfg.PRINT_FREQ == 0: self.i_tb += 1 self.writer.add_scalar('train_loss', loss.item(), self.i_tb) self.timer['iter time'].toc(average=False) print( '[ep %d][it %d][loss %.4f][lr %.4f][%.2fs]' % \ (self.epoch + 1, i + 1, loss.item(), self.optimizer.param_groups[0]['lr']*10000, self.timer['iter time'].diff) ) print( ' [cnt: gt: %.1f pred: %.2f]' % (gt_map[0].sum().data/self.cfg_data.LOG_PARA, pred_map[0].sum().data/self.cfg_data.LOG_PARA) ) # nge write ke .csv file inline (ada 2, 1 training, 1 validasi dibawah ) # nge write epoch, iter, loss, waktu(time) juga training_iter_time = self.timer['iter time'].diff csvRow = ['number_of_epoch', 'iteration', 'training_loss', 'iteration_time'] csvFile = "training_result.csv" with open(csvFile, 'a') as fp: wr = csv.writer(fp, dialect='excel') wr.writerow(csvRow) wr.writerow([self.epoch + 1, i + 1, loss.item(), training_iter_time]) def validate_V1(self):# validate_V1 for SHHA, SHHB, UCF-QNRF, UCF50 self.net.eval() losses = AverageMeter() maes = AverageMeter() mses = AverageMeter() for vi, data in enumerate(self.val_loader, 0): img, gt_map = data with torch.no_grad(): img = Variable(img).cuda() gt_map = Variable(gt_map).cuda() pred_map = self.net.forward(img,gt_map) pred_map = pred_map.data.cpu().numpy() gt_map = gt_map.data.cpu().numpy() for i_img in range(pred_map.shape[0]): pred_cnt = np.sum(pred_map[i_img])/self.cfg_data.LOG_PARA gt_count = np.sum(gt_map[i_img])/self.cfg_data.LOG_PARA losses.update(self.net.loss.item()) maes.update(abs(gt_count-pred_cnt)) mses.update((gt_count-pred_cnt)*(gt_count-pred_cnt)) if vi==0: vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map) mae = maes.avg mse = np.sqrt(mses.avg) loss = losses.avg self.writer.add_scalar('val_loss', loss, self.epoch + 1) self.writer.add_scalar('mae', mae, self.epoch + 1) self.writer.add_scalar('mse', mse, self.epoch + 1) self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \ [mae, mse, loss],self.train_record,self.log_txt) print_summary(self.exp_name,[mae, mse, loss],self.train_record) # loss, mae, mse csvRow = ['mae_value', 'mse_value', 'validation_loss'] csvFile = "validation_result.csv" with open(csvFile, 'a') as fp: wr = csv.writer(fp, dialect='excel') wr.writerow(csvRow) wr.writerow([mae, mse, loss]) def validate_V2(self):# validate_V2 for WE self.net.eval() losses = AverageCategoryMeter(5) maes = AverageCategoryMeter(5) roi_mask = [] from datasets.WE.setting import cfg_data from scipy import io as sio for val_folder in cfg_data.VAL_FOLDER: roi_mask.append(sio.loadmat(os.path.join(cfg_data.DATA_PATH,'test',val_folder + '_roi.mat'))['BW']) for i_sub,i_loader in enumerate(self.val_loader,0): mask = roi_mask[i_sub] for vi, data in enumerate(i_loader, 0): img, gt_map = data with torch.no_grad(): img = Variable(img).cuda() gt_map = Variable(gt_map).cuda() pred_map = self.net.forward(img,gt_map) pred_map = pred_map.data.cpu().numpy() gt_map = gt_map.data.cpu().numpy() for i_img in range(pred_map.shape[0]): pred_cnt = np.sum(pred_map[i_img])/self.cfg_data.LOG_PARA gt_count = np.sum(gt_map[i_img])/self.cfg_data.LOG_PARA losses.update(self.net.loss.item(),i_sub) maes.update(abs(gt_count-pred_cnt),i_sub) if vi==0: vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map) mae = np.average(maes.avg) loss = np.average(losses.avg) self.writer.add_scalar('val_loss', loss, self.epoch + 1) self.writer.add_scalar('mae', mae, self.epoch + 1) self.writer.add_scalar('mae_s1', maes.avg[0], self.epoch + 1) self.writer.add_scalar('mae_s2', maes.avg[1], self.epoch + 1) self.writer.add_scalar('mae_s3', maes.avg[2], self.epoch + 1) self.writer.add_scalar('mae_s4', maes.avg[3], self.epoch + 1) self.writer.add_scalar('mae_s5', maes.avg[4], self.epoch + 1) self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \ [mae, 0, loss],self.train_record,self.log_txt) print_WE_summary(self.log_txt,self.epoch,[mae, 0, loss],self.train_record,maes) def validate_V3(self):# validate_V3 for GCC self.net.eval() losses = AverageMeter() maes = AverageMeter() mses = AverageMeter() c_maes = {'level':AverageCategoryMeter(9), 'time':AverageCategoryMeter(8),'weather':AverageCategoryMeter(7)} c_mses = {'level':AverageCategoryMeter(9), 'time':AverageCategoryMeter(8),'weather':AverageCategoryMeter(7)} for vi, data in enumerate(self.val_loader, 0): img, gt_map, attributes_pt = data with torch.no_grad(): img = Variable(img).cuda() gt_map = Variable(gt_map).cuda() pred_map = self.net.forward(img,gt_map) pred_map = pred_map.data.cpu().numpy() gt_map = gt_map.data.cpu().numpy() for i_img in range(pred_map.shape[0]): pred_cnt = np.sum(pred_map[i_img])/self.cfg_data.LOG_PARA gt_count = np.sum(gt_map[i_img])/self.cfg_data.LOG_PARA s_mae = abs(gt_count-pred_cnt) s_mse = (gt_count-pred_cnt)*(gt_count-pred_cnt) losses.update(self.net.loss.item()) maes.update(s_mae) mses.update(s_mse) attributes_pt = attributes_pt.squeeze() c_maes['level'].update(s_mae,attributes_pt[i_img][0]) c_mses['level'].update(s_mse,attributes_pt[i_img][0]) c_maes['time'].update(s_mae,attributes_pt[i_img][1]/3) c_mses['time'].update(s_mse,attributes_pt[i_img][1]/3) c_maes['weather'].update(s_mae,attributes_pt[i_img][2]) c_mses['weather'].update(s_mse,attributes_pt[i_img][2]) if vi==0: vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map) loss = losses.avg mae = maes.avg mse = np.sqrt(mses.avg) self.writer.add_scalar('val_loss', loss, self.epoch + 1) self.writer.add_scalar('mae', mae, self.epoch + 1) self.writer.add_scalar('mse', mse, self.epoch + 1) self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \ [mae, mse, loss],self.train_record,self.log_txt) print_GCC_summary(self.log_txt,self.epoch,[mae, mse, loss],self.train_record,c_maes,c_mses)

39.662069

167

0.565467

9ea8d9ccbfd0def3d7a84712fe1828ccb9d69b0d

235

py

Python

tests/packages/tree/tree-package2/setup.py

sbg/dante

104543c3ccb5e762d3e9cd6e8fa04c5fa91e2227

[ "Apache-2.0" ]

9

2017-11-03T15:53:01.000Z

2019-10-01T14:09:56.000Z

tests/packages/tree/tree-package2/setup.py

sbg/dante

104543c3ccb5e762d3e9cd6e8fa04c5fa91e2227

[ "Apache-2.0" ]

4

2019-10-01T12:53:58.000Z

2021-04-26T15:39:16.000Z

tests/packages/tree/tree-package2/setup.py

sbg/dante

104543c3ccb5e762d3e9cd6e8fa04c5fa91e2227

[ "Apache-2.0" ]

5

2017-11-03T15:50:40.000Z

2021-09-13T08:50:45.000Z

from setuptools import setup, find_packages install_requires = [ 'tree-package3', 'tree-package7' ] setup( name='tree-package2', version='1.0.0', install_requires=install_requires, packages=find_packages(), )

16.785714

43

0.689362

501b2784e981c8e2b766beeac3ba2218b9884d98

2,916

py

Python

tests/test_reduce_max.py

yanndupis/tf-encrypted

cfaea3ba87520f73979ed4e4f397eba3beb0a535

[ "Apache-2.0" ]

null

tests/test_reduce_max.py

yanndupis/tf-encrypted

cfaea3ba87520f73979ed4e4f397eba3beb0a535

[ "Apache-2.0" ]

null

tests/test_reduce_max.py

yanndupis/tf-encrypted

cfaea3ba87520f73979ed4e4f397eba3beb0a535

[ "Apache-2.0" ]

null

import unittest import numpy as np import tensorflow as tf import tf_encrypted as tfe import pytest @pytest.mark.slow class TestReduceMax(unittest.TestCase): def setUp(self): tf.reset_default_graph() def tearDown(self): tf.reset_default_graph() def test_reduce_max_1d(self): t = np.array([1, 2, 3, 4]).astype(float) with tf.Session() as sess: out_tf = tf.reduce_max(t) expected = sess.run(out_tf) with tfe.protocol.SecureNN() as prot: b = prot.define_private_variable(tf.constant(t)) out_tfe = prot.reduce_max(b) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2): actual = sess.run(out_tfe.reveal(), tag='test_1d') np.testing.assert_array_equal(actual, expected) def test_reduce_max_2d_axis0(self): t = np.array([1, 2, 3, 4, 5, 6, 7, 8]).reshape(2, 4).astype(float) with tf.Session() as sess: out_tf = tf.reduce_max(t, axis=0) expected = sess.run(out_tf) with tfe.protocol.SecureNN() as prot: b = prot.define_private_variable(tf.constant(t)) out_tfe = prot.reduce_max(b, axis=0) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2): actual = sess.run(out_tfe.reveal(), tag='test_2d_axis0') np.testing.assert_array_equal(actual, expected) def test_reduce_max_2d_axis1(self): t = np.array([1, 2, 3, 4, 5, 6, 7, 8]).reshape(2, 4).astype(float) with tf.Session() as sess: out_tf = tf.reduce_max(t, axis=1) expected = sess.run(out_tf) with tfe.protocol.SecureNN() as prot: b = prot.define_private_variable(tf.constant(t)) out_tfe = prot.reduce_max(b, axis=1) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2): actual = sess.run(out_tfe.reveal(), tag='test_2d_axis1') np.testing.assert_array_equal(actual, expected) def test_reduce_max_3d_axis0(self): t = np.array([1, 2, 3, 4, 5, 6, 7, 8]).reshape(2, 2, 2) with tf.Session() as sess: out = tf.reduce_max(t, axis=0) expected = sess.run(out) with tfe.protocol.SecureNN() as prot: b = prot.define_private_variable(tf.constant(t)) out_tfe = prot.reduce_max(b, axis=0) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2): actual = sess.run(out_tfe.reveal(), tag='test_3d_axis0') np.testing.assert_array_equal(actual, expected) if __name__ == '__main__': unittest.main()

30.061856

76

0.580247

a70be78f82e9b079a603298a6da542545d6fc4ce

2,277

py

Python

doc/scripts/new_kernel.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

doc/scripts/new_kernel.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

doc/scripts/new_kernel.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

from radical.ensemblemd.kernel_plugins.kernel_base import KernelBase # ------------------------------------------------------------------------------ # _KERNEL_INFO = { "name": "sleep", # Mandatory "description": "sleeping kernel", # Optional "arguments": { # Mandatory "--interval=": { "mandatory": True, # Mandatory argument? True or False "description": "Number of seconds to do nothing." }, }, "machine_configs": # Use a dictionary with keys as { # resource names and values specific "local.localhost": # to the resource { "environment" : None, # dict or None, can be used to set env variables "pre_exec" : None, # list or None, can be used to load modules "executable" : ["/bin/sleep"], # specify the executable to be used "uses_mpi" : False # mpi-enabled? True or False }, } } # ------------------------------------------------------------------------------ # class MyUserDefinedKernel(KernelBase): def __init__(self): super(MyUserDefinedKernel, self).__init__(_KERNEL_INFO) """Le constructor.""" # -------------------------------------------------------------------------- # @staticmethod def get_name(): return _KERNEL_INFO["name"] def _bind_to_resource(self, resource_key): """This function binds the Kernel to a specific resource defined in "resource_key". """ arguments = ['{0}'.format(self.get_arg("--interval="))] self._executable = _KERNEL_INFO["machine_configs"][resource_key]["executable"] self._arguments = arguments self._environment = _KERNEL_INFO["machine_configs"][resource_key]["environment"] self._uses_mpi = _KERNEL_INFO["machine_configs"][resource_key]["uses_mpi"] self._pre_exec = _KERNEL_INFO["machine_configs"][resource_key]["pre_exec"] # ------------------------------------------------------------------------------

41.4

94

0.467721

36fd7c4e13cfcdedbc820127300c3083245cb73f

1,079

py

Python

checkov/kubernetes/checks/resource/k8s/MemoryRequests.py

vangundy-jason-pfg/checkov

2fb50908f62390c98dda665f1fa94fe24806b654

[ "Apache-2.0" ]

null

checkov/kubernetes/checks/resource/k8s/MemoryRequests.py

vangundy-jason-pfg/checkov

2fb50908f62390c98dda665f1fa94fe24806b654

[ "Apache-2.0" ]

null

checkov/kubernetes/checks/resource/k8s/MemoryRequests.py

vangundy-jason-pfg/checkov

2fb50908f62390c98dda665f1fa94fe24806b654

[ "Apache-2.0" ]

null

from checkov.common.models.enums import CheckCategories, CheckResult from checkov.kubernetes.checks.resource.base_spec_check import BaseK8Check class MemoryRequests(BaseK8Check): def __init__(self): name = "Memory requests should be set" id = "CKV_K8S_12" # Location: container .resources.requests.memory supported_kind = ['containers', 'initContainers'] categories = [CheckCategories.KUBERNETES] super().__init__(name=name, id=id, categories=categories, supported_entities=supported_kind) def get_resource_id(self, conf): return f'{conf["parent"]} - {conf["name"]}' if conf.get('name') else conf["parent"] def scan_spec_conf(self, conf): if conf.get("resources"): if "requests" in conf["resources"]: if "memory" not in conf["resources"]["requests"]: return CheckResult.FAILED else: return CheckResult.FAILED else: return CheckResult.FAILED return CheckResult.PASSED check = MemoryRequests()

34.806452

100

0.650602

2b85296d381b7291d7f65939352149c99b60e83c

2,866

py

Python

demo/demo.py

mcstro/natural-neighbor-interpolation

76ba7bb50c84aef35e993902c46824e5991df45d

[ "MIT" ]

64

2017-09-17T00:37:20.000Z

2022-02-03T20:16:54.000Z

demo/demo.py

mcstro/natural-neighbor-interpolation

76ba7bb50c84aef35e993902c46824e5991df45d

[ "MIT" ]

5

2018-07-27T16:31:35.000Z

2020-06-15T02:53:48.000Z

demo/demo.py

mcstro/natural-neighbor-interpolation

76ba7bb50c84aef35e993902c46824e5991df45d

[ "MIT" ]

13

2018-06-06T18:51:50.000Z

2021-12-26T02:47:05.000Z

''' Comparison of natural neighbor and linear barycentric interpolation. ''' import numpy as np import scipy.interpolate import matplotlib as mpl mpl.use('Agg') # so it can run on Travis without a display import matplotlib.pyplot as plt import naturalneighbor def error_str(errors): numerical_error = errors[~np.isnan(errors)] mean_err = np.mean(numerical_error) std_err = np.std(numerical_error) max_err = np.max(numerical_error) return "(Mean={:.2f}, Std={:.2f} Max={:.2f})".format(mean_err, std_err, max_err) def compare_interp_for_func(func, func_as_string, image_name): coord_max = 60 xmax = coord_max ymax = coord_max zmax = coord_max final_shape = (xmax, ymax, zmax) num_known_points = 100 known_points = np.round(np.random.rand(num_known_points, 3) * np.min([xmax, ymax, zmax])) grid_ranges = [ [0, xmax, 1], [0, ymax, 1], [0, zmax, 1], ] grid = np.mgrid[0:xmax:1, 0:ymax:1, 0:zmax:1] known_values = np.array([func(*point) for point in known_points], dtype=np.float64) true_values = np.reshape([func(x, y, z) for x, y, z in zip(*grid)], final_shape) linear_interp = scipy.interpolate.griddata(known_points, known_values, tuple(grid), method='linear') nn_interp = naturalneighbor.griddata(known_points, known_values, grid_ranges) nn_interp[np.isnan(linear_interp)] = float('nan') nn_interp_slice = nn_interp[:, :, 20] linear_interp_slice = linear_interp[:, :, 20] true_values_slice = true_values[:, :, 20] nn_interp_err = np.abs(nn_interp_slice - true_values_slice) linear_interp_err = np.abs(linear_interp_slice - true_values_slice) fig = plt.figure(figsize=(16, 10)) ax1 = fig.add_subplot(2, 3, 1) ax1.imshow(true_values_slice) ax1.set_title("True Values\n{}".format(func_as_string)) ax2 = fig.add_subplot(2, 3, 2) ax2.imshow(nn_interp_err) nn_error_str = error_str(nn_interp_err) ax2.set_title("Natural Neighbor Abs Error\n{}".format(nn_error_str)) ax3 = fig.add_subplot(2, 3, 3) ax3.imshow(linear_interp_err) linear_error_str = error_str(linear_interp_err) ax3.set_title("Linear Barycentric Abs Error\n{}".format(linear_error_str)) ax5 = fig.add_subplot(2, 3, 5) ax5.imshow(nn_interp_slice) ax5.set_title("Natural Neighbor Values") ax6 = fig.add_subplot(2, 3, 6) ax6.imshow(linear_interp_slice) ax6.set_title("Linear Barycentric Values") plt.savefig(image_name, dpi=100) if __name__ == '__main__': np.random.seed(100) compare_interp_for_func( (lambda x, y, z: np.sin(y / 10) + np.sin(x / 10)), 'sin(y/10) + sin(x/10)', 'sin_sin_comparison.png', ) compare_interp_for_func( (lambda x, y, z: x + np.sin(x / 10) / 10), 'x + sin(x/10)/10', 'linear_comparison.png', )

30.168421

104

0.671668

d765dc90842041b034636bca6e35759d3c8fac58

4,267

py

Python

kubernetes/client/models/extensions_v1beta1_deployment_strategy.py

L3T/python

b6e4ae81a2afb49f668a142eb7d1c6e2571ef478

[ "Apache-2.0" ]

2

2020-06-21T08:03:18.000Z

2020-06-21T09:53:29.000Z

kubernetes/client/models/extensions_v1beta1_deployment_strategy.py

L3T/python

b6e4ae81a2afb49f668a142eb7d1c6e2571ef478

[ "Apache-2.0" ]

null

kubernetes/client/models/extensions_v1beta1_deployment_strategy.py

L3T/python

b6e4ae81a2afb49f668a142eb7d1c6e2571ef478

[ "Apache-2.0" ]

1

2020-12-10T07:28:08.000Z

# coding: utf-8 """ Kubernetes No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 OpenAPI spec version: release-1.16 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six class ExtensionsV1beta1DeploymentStrategy(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 = { 'rolling_update': 'ExtensionsV1beta1RollingUpdateDeployment', 'type': 'str' } attribute_map = { 'rolling_update': 'rollingUpdate', 'type': 'type' } def __init__(self, rolling_update=None, type=None): # noqa: E501 """ExtensionsV1beta1DeploymentStrategy - a model defined in OpenAPI""" # noqa: E501 self._rolling_update = None self._type = None self.discriminator = None if rolling_update is not None: self.rolling_update = rolling_update if type is not None: self.type = type @property def rolling_update(self): """Gets the rolling_update of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 :return: The rolling_update of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 :rtype: ExtensionsV1beta1RollingUpdateDeployment """ return self._rolling_update @rolling_update.setter def rolling_update(self, rolling_update): """Sets the rolling_update of this ExtensionsV1beta1DeploymentStrategy. :param rolling_update: The rolling_update of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 :type: ExtensionsV1beta1RollingUpdateDeployment """ self._rolling_update = rolling_update @property def type(self): """Gets the type of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 Type of deployment. Can be \"Recreate\" or \"RollingUpdate\". Default is RollingUpdate. # noqa: E501 :return: The type of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 :rtype: str """ return self._type @type.setter def type(self, type): """Sets the type of this ExtensionsV1beta1DeploymentStrategy. Type of deployment. Can be \"Recreate\" or \"RollingUpdate\". Default is RollingUpdate. # noqa: E501 :param type: The type of this ExtensionsV1beta1DeploymentStrategy. # noqa: E501 :type: str """ self._type = type 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, ExtensionsV1beta1DeploymentStrategy): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

30.262411

124

0.609562

4c31f4bb76ebaf032fe58c1ed8efbd00da52a9c2

2,438

py

Python

day22/sol.py

samstronghammer/adventofcode2020

a03098ce886bbf011e01f5897461e7caac468202

[ "MIT" ]

null

day22/sol.py

samstronghammer/adventofcode2020

a03098ce886bbf011e01f5897461e7caac468202

[ "MIT" ]

null

day22/sol.py

samstronghammer/adventofcode2020

a03098ce886bbf011e01f5897461e7caac468202

[ "MIT" ]

null

#!/usr/bin/python3 import sys import os sys.path.append(f"{os.path.dirname(__file__)}/..") import util import math # Implementation of the classic "war" card game with a recursive # twist at the end. Very fun :) For part 1 and 2 I used lists # where the end of the list is the top of the deck. It seemed # easier. def deck_score(deck): ans = 0 for i, val in enumerate(deck): ans += ((i + 1) * val) return ans def combat(p1_original_deck, p2_original_deck): p1_deck = p1_original_deck.copy() p2_deck = p2_original_deck.copy() while len(p1_deck) > 0 and len(p2_deck) > 0: top_p1 = p1_deck.pop() top_p2 = p2_deck.pop() if top_p1 > top_p2: p1_deck = [top_p2, top_p1] + p1_deck else: p2_deck = [top_p1, top_p2] + p2_deck return deck_score(p1_deck if len(p1_deck) > 0 else p2_deck) # Returns a pair of values. A boolean representing the winner (T => p1 and F => p2) # and the deck score of the winner. def recursive_combat(p1_original_deck, p2_original_deck): p1_deck = p1_original_deck.copy() p2_deck = p2_original_deck.copy() prev_states = set() while True: # End conditions if len(p1_deck) == 0: return False, deck_score(p2_deck) if len(p2_deck) == 0: return True, deck_score(p1_deck) game_state = (tuple(p1_deck), tuple(p2_deck)) if game_state in prev_states: return True, deck_score(p1_deck) # If didn't terminate, add to set of game states prev_states.add(game_state) top_p1 = p1_deck.pop() top_p2 = p2_deck.pop() winner = False if top_p1 <= len(p1_deck) and top_p2 <= len(p2_deck): winner = recursive_combat(p1_deck[-top_p1:], p2_deck[-top_p2:])[0] else: winner = top_p1 > top_p2 if winner: p1_deck = [top_p2, top_p1] + p1_deck else: p2_deck = [top_p1, top_p2] + p2_deck fn = f"{os.path.dirname(__file__)}/in.txt" l = util.filetolist(fn) p1 = [] p2 = [] on_p1 = True for line in l: if line: try: val = int(line) if on_p1: p1.insert(0, val) else: p2.insert(0, val) except: pass else: on_p1 = not on_p1 print("Part 1 Solution:") print(combat(p1, p2)) print("Part 2 Solution:") print(recursive_combat(p1, p2)[1])

28.022989

83

0.597621

a717eed88b103ba28b6c3cb1a7d7262f9fa49bc8

246

py

Python

deposito/api/viewsets.py

TooDoo-BlastOff-Desafios/Python-Squad1

e5e9523baf20b770aeb6682abb09522a8402ae27

[ "MIT" ]

null

deposito/api/viewsets.py

TooDoo-BlastOff-Desafios/Python-Squad1

e5e9523baf20b770aeb6682abb09522a8402ae27

[ "MIT" ]

null

deposito/api/viewsets.py

TooDoo-BlastOff-Desafios/Python-Squad1

e5e9523baf20b770aeb6682abb09522a8402ae27

[ "MIT" ]

null

from rest_framework import viewsets from deposito.api import serializers from deposito import models class DepositoViewset(viewsets.ModelViewSet): serializer_class = serializers.DepositoSerializer queryset = models.Deposito.objects.all()

35.142857

53

0.833333

533694e6de300a9c4fe71fca6cd42c1ea2334e52

831

py

Python

Growth_calc.py

Samosborne94/FinanceModel

a8faff18538a080ead42602c2b96c61bd8a13021

[ "Apache-2.0" ]

null

Growth_calc.py

Samosborne94/FinanceModel

a8faff18538a080ead42602c2b96c61bd8a13021

[ "Apache-2.0" ]

null

Growth_calc.py

Samosborne94/FinanceModel

a8faff18538a080ead42602c2b96c61bd8a13021

[ "Apache-2.0" ]

1

2021-09-26T03:57:42.000Z

#Portfolio Growth Calculator (incorporating asset allocation decisions) #User Inputs: num1 = input('Waiting Time (Years): ') num2 = input('Equity Allocation (Dollars): ') num3 = input('Bond Allocation (Dollars): ') #Transform string inputs into floats and integers for calculation: a = int(float(num1)) b = int(float(num2)) c = int(float(num3)) #Calculate the customer's asset allocation: d = b/(b+c) e = c/(b+c) #Alternatively, customer can specify these expected returns. We fix them for simplicity: stock = 0.08 bond = 0.03 #Compound Interest print ("Projected Net Worth (Dollars):", int(d*b*(1+stock)**a + (e*c*(1+bond)**a))) #Doubling Time Calculator (CAGR) projected_worth = int(d*b*(1+stock)**a + (e*c*(1+bond)**a)) multiple = projected_worth / (b+c) print("Doubling Time (Years):", int(72/(100*(multiple**(1/a)-1))))

29.678571

88

0.695548

ce545c49f8f43be4b066813a2f1e86a586b88553

40,315

py

Python

pygments/lexers/asm.py

eerimoq/pygments

3cd60987c27d2228ac46bfa2648e280aaaf61fc1

[ "BSD-2-Clause" ]

1

2021-12-27T22:40:31.000Z

pygments/lexers/asm.py

eerimoq/pygments

3cd60987c27d2228ac46bfa2648e280aaaf61fc1

[ "BSD-2-Clause" ]

null

pygments/lexers/asm.py

eerimoq/pygments

3cd60987c27d2228ac46bfa2648e280aaaf61fc1

[ "BSD-2-Clause" ]

null

""" pygments.lexers.asm ~~~~~~~~~~~~~~~~~~~ Lexers for assembly languages. :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS. :license: BSD, see LICENSE for details. """ import re from pygments.lexer import RegexLexer, include, bygroups, using, words, \ DelegatingLexer, default from pygments.lexers.c_cpp import CppLexer, CLexer from pygments.lexers.d import DLexer from pygments.token import Text, Name, Number, String, Comment, Punctuation, \ Other, Keyword, Operator, Literal __all__ = ['GasLexer', 'ObjdumpLexer', 'DObjdumpLexer', 'CppObjdumpLexer', 'CObjdumpLexer', 'HsailLexer', 'LlvmLexer', 'LlvmMirBodyLexer', 'LlvmMirLexer', 'NasmLexer', 'NasmObjdumpLexer', 'TasmLexer', 'Ca65Lexer', 'Dasm16Lexer'] class GasLexer(RegexLexer): """ For Gas (AT&T) assembly code. """ name = 'GAS' aliases = ['gas', 'asm'] filenames = ['*.s', '*.S'] mimetypes = ['text/x-gas'] #: optional Comment or Whitespace string = r'"(\\"|[^"])*"' char = r'[\w$.@-]' identifier = r'(?:[a-zA-Z$_]' + char + r'*|\.' + char + '+)' number = r'(?:0[xX][a-fA-F0-9]+|#?-?\d+)' register = '%' + identifier tokens = { 'root': [ include('whitespace'), (identifier + ':', Name.Label), (r'\.' + identifier, Name.Attribute, 'directive-args'), (r'lock|rep(n?z)?|data\d+', Name.Attribute), (identifier, Name.Function, 'instruction-args'), (r'[\r\n]+', Text) ], 'directive-args': [ (identifier, Name.Constant), (string, String), ('@' + identifier, Name.Attribute), (number, Number.Integer), (register, Name.Variable), (r'[\r\n]+', Text, '#pop'), (r'([;#]|//).*?\n', Comment.Single, '#pop'), (r'/[*].*?[*]/', Comment.Multiline), (r'/[*].*?\n[\w\W]*?[*]/', Comment.Multiline, '#pop'), include('punctuation'), include('whitespace') ], 'instruction-args': [ # For objdump-disassembled code, shouldn't occur in # actual assembler input ('([a-z0-9]+)( )(<)('+identifier+')(>)', bygroups(Number.Hex, Text, Punctuation, Name.Constant, Punctuation)), ('([a-z0-9]+)( )(<)('+identifier+')([-+])('+number+')(>)', bygroups(Number.Hex, Text, Punctuation, Name.Constant, Punctuation, Number.Integer, Punctuation)), # Address constants (identifier, Name.Constant), (number, Number.Integer), # Registers (register, Name.Variable), # Numeric constants ('$'+number, Number.Integer), (r"$'(.|\\')'", String.Char), (r'[\r\n]+', Text, '#pop'), (r'([;#]|//).*?\n', Comment.Single, '#pop'), (r'/[*].*?[*]/', Comment.Multiline), (r'/[*].*?\n[\w\W]*?[*]/', Comment.Multiline, '#pop'), include('punctuation'), include('whitespace') ], 'whitespace': [ (r'\n', Text), (r'\s+', Text), (r'([;#]|//).*?\n', Comment.Single), (r'/[*][\w\W]*?[*]/', Comment.Multiline) ], 'punctuation': [ (r'[-*,.()\[\]!:]+', Punctuation) ] } def analyse_text(text): if re.search(r'^\.(text|data|section)', text, re.M): return True elif re.search(r'^\.\w+', text, re.M): return 0.1 def _objdump_lexer_tokens(asm_lexer): """ Common objdump lexer tokens to wrap an ASM lexer. """ hex_re = r'[0-9A-Za-z]' return { 'root': [ # File name & format: ('(.*?)(:)( +file format )(.*?)$', bygroups(Name.Label, Punctuation, Text, String)), # Section header ('(Disassembly of section )(.*?)(:)$', bygroups(Text, Name.Label, Punctuation)), # Function labels # (With offset) ('('+hex_re+'+)( )(<)(.*?)([-+])(0[xX][A-Za-z0-9]+)(>:)$', bygroups(Number.Hex, Text, Punctuation, Name.Function, Punctuation, Number.Hex, Punctuation)), # (Without offset) ('('+hex_re+'+)( )(<)(.*?)(>:)$', bygroups(Number.Hex, Text, Punctuation, Name.Function, Punctuation)), # Code line with disassembled instructions ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)( *\t)([a-zA-Z].*?)$', bygroups(Text, Name.Label, Text, Number.Hex, Text, using(asm_lexer))), # Code line with ascii ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)( *)(.*?)$', bygroups(Text, Name.Label, Text, Number.Hex, Text, String)), # Continued code line, only raw opcodes without disassembled # instruction ('( *)('+hex_re+r'+:)(\t)((?:'+hex_re+hex_re+' )+)$', bygroups(Text, Name.Label, Text, Number.Hex)), # Skipped a few bytes (r'\t\.\.\.$', Text), # Relocation line # (With offset) (r'(\t\t\t)('+hex_re+r'+:)( )([^\t]+)(\t)(.*?)([-+])(0x'+hex_re+'+)$', bygroups(Text, Name.Label, Text, Name.Property, Text, Name.Constant, Punctuation, Number.Hex)), # (Without offset) (r'(\t\t\t)('+hex_re+r'+:)( )([^\t]+)(\t)(.*?)$', bygroups(Text, Name.Label, Text, Name.Property, Text, Name.Constant)), (r'[^\n]+\n', Other) ] } class ObjdumpLexer(RegexLexer): """ For the output of ``objdump -dr``. """ name = 'objdump' aliases = ['objdump'] filenames = ['*.objdump'] mimetypes = ['text/x-objdump'] tokens = _objdump_lexer_tokens(GasLexer) class DObjdumpLexer(DelegatingLexer): """ For the output of ``objdump -Sr`` on compiled D files. """ name = 'd-objdump' aliases = ['d-objdump'] filenames = ['*.d-objdump'] mimetypes = ['text/x-d-objdump'] def __init__(self, **options): super().__init__(DLexer, ObjdumpLexer, **options) class CppObjdumpLexer(DelegatingLexer): """ For the output of ``objdump -Sr`` on compiled C++ files. """ name = 'cpp-objdump' aliases = ['cpp-objdump', 'c++-objdumb', 'cxx-objdump'] filenames = ['*.cpp-objdump', '*.c++-objdump', '*.cxx-objdump'] mimetypes = ['text/x-cpp-objdump'] def __init__(self, **options): super().__init__(CppLexer, ObjdumpLexer, **options) class CObjdumpLexer(DelegatingLexer): """ For the output of ``objdump -Sr`` on compiled C files. """ name = 'c-objdump' aliases = ['c-objdump'] filenames = ['*.c-objdump'] mimetypes = ['text/x-c-objdump'] def __init__(self, **options): super().__init__(CLexer, ObjdumpLexer, **options) class HsailLexer(RegexLexer): """ For HSAIL assembly code. .. versionadded:: 2.2 """ name = 'HSAIL' aliases = ['hsail', 'hsa'] filenames = ['*.hsail'] mimetypes = ['text/x-hsail'] string = r'"[^"]*?"' identifier = r'[a-zA-Z_][\w.]*' # Registers register_number = r'[0-9]+' register = r'(\$(c|s|d|q)' + register_number + ')' # Qualifiers alignQual = r'(align$\d+$)' widthQual = r'(width$(\d+|all)$)' allocQual = r'(alloc$agent$)' # Instruction Modifiers roundingMod = (r'((_ftz)?(_up|_down|_zero|_near))') datatypeMod = (r'_(' # packedTypes r'u8x4|s8x4|u16x2|s16x2|u8x8|s8x8|u16x4|s16x4|u32x2|s32x2|' r'u8x16|s8x16|u16x8|s16x8|u32x4|s32x4|u64x2|s64x2|' r'f16x2|f16x4|f16x8|f32x2|f32x4|f64x2|' # baseTypes r'u8|s8|u16|s16|u32|s32|u64|s64|' r'b128|b8|b16|b32|b64|b1|' r'f16|f32|f64|' # opaqueType r'roimg|woimg|rwimg|samp|sig32|sig64)') # Numeric Constant float = r'((\d+\.)|(\d*\.\d+))[eE][+-]?\d+' hexfloat = r'0[xX](([0-9a-fA-F]+\.[0-9a-fA-F]*)|([0-9a-fA-F]*\.[0-9a-fA-F]+))[pP][+-]?\d+' ieeefloat = r'0((h|H)[0-9a-fA-F]{4}|(f|F)[0-9a-fA-F]{8}|(d|D)[0-9a-fA-F]{16})' tokens = { 'root': [ include('whitespace'), include('comments'), (string, String), (r'@' + identifier + ':?', Name.Label), (register, Name.Variable.Anonymous), include('keyword'), (r'&' + identifier, Name.Variable.Global), (r'%' + identifier, Name.Variable), (hexfloat, Number.Hex), (r'0[xX][a-fA-F0-9]+', Number.Hex), (ieeefloat, Number.Float), (float, Number.Float), (r'\d+', Number.Integer), (r'[=<>{}\[\]()*.,:;!]|x\b', Punctuation) ], 'whitespace': [ (r'(\n|\s)+', Text), ], 'comments': [ (r'/\*.*?\*/', Comment.Multiline), (r'//.*?\n', Comment.Single), ], 'keyword': [ # Types (r'kernarg' + datatypeMod, Keyword.Type), # Regular keywords (r'\$(full|base|small|large|default|zero|near)', Keyword), (words(( 'module', 'extension', 'pragma', 'prog', 'indirect', 'signature', 'decl', 'kernel', 'function', 'enablebreakexceptions', 'enabledetectexceptions', 'maxdynamicgroupsize', 'maxflatgridsize', 'maxflatworkgroupsize', 'requireddim', 'requiredgridsize', 'requiredworkgroupsize', 'requirenopartialworkgroups'), suffix=r'\b'), Keyword), # instructions (roundingMod, Keyword), (datatypeMod, Keyword), (r'_(' + alignQual + '|' + widthQual + ')', Keyword), (r'_kernarg', Keyword), (r'(nop|imagefence)\b', Keyword), (words(( 'cleardetectexcept', 'clock', 'cuid', 'debugtrap', 'dim', 'getdetectexcept', 'groupbaseptr', 'kernargbaseptr', 'laneid', 'maxcuid', 'maxwaveid', 'packetid', 'setdetectexcept', 'waveid', 'workitemflatabsid', 'workitemflatid', 'nullptr', 'abs', 'bitrev', 'currentworkgroupsize', 'currentworkitemflatid', 'fract', 'ncos', 'neg', 'nexp2', 'nlog2', 'nrcp', 'nrsqrt', 'nsin', 'nsqrt', 'gridgroups', 'gridsize', 'not', 'sqrt', 'workgroupid', 'workgroupsize', 'workitemabsid', 'workitemid', 'ceil', 'floor', 'rint', 'trunc', 'add', 'bitmask', 'borrow', 'carry', 'copysign', 'div', 'rem', 'sub', 'shl', 'shr', 'and', 'or', 'xor', 'unpackhi', 'unpacklo', 'max', 'min', 'fma', 'mad', 'bitextract', 'bitselect', 'shuffle', 'cmov', 'bitalign', 'bytealign', 'lerp', 'nfma', 'mul', 'mulhi', 'mul24hi', 'mul24', 'mad24', 'mad24hi', 'bitinsert', 'combine', 'expand', 'lda', 'mov', 'pack', 'unpack', 'packcvt', 'unpackcvt', 'sad', 'sementp', 'ftos', 'stof', 'cmp', 'ld', 'st', '_eq', '_ne', '_lt', '_le', '_gt', '_ge', '_equ', '_neu', '_ltu', '_leu', '_gtu', '_geu', '_num', '_nan', '_seq', '_sne', '_slt', '_sle', '_sgt', '_sge', '_snum', '_snan', '_sequ', '_sneu', '_sltu', '_sleu', '_sgtu', '_sgeu', 'atomic', '_ld', '_st', '_cas', '_add', '_and', '_exch', '_max', '_min', '_or', '_sub', '_wrapdec', '_wrapinc', '_xor', 'ret', 'cvt', '_readonly', '_kernarg', '_global', 'br', 'cbr', 'sbr', '_scacq', '_screl', '_scar', '_rlx', '_wave', '_wg', '_agent', '_system', 'ldimage', 'stimage', '_v2', '_v3', '_v4', '_1d', '_2d', '_3d', '_1da', '_2da', '_1db', '_2ddepth', '_2dadepth', '_width', '_height', '_depth', '_array', '_channelorder', '_channeltype', 'querysampler', '_coord', '_filter', '_addressing', 'barrier', 'wavebarrier', 'initfbar', 'joinfbar', 'waitfbar', 'arrivefbar', 'leavefbar', 'releasefbar', 'ldf', 'activelaneid', 'activelanecount', 'activelanemask', 'activelanepermute', 'call', 'scall', 'icall', 'alloca', 'packetcompletionsig', 'addqueuewriteindex', 'casqueuewriteindex', 'ldqueuereadindex', 'stqueuereadindex', 'readonly', 'global', 'private', 'group', 'spill', 'arg', '_upi', '_downi', '_zeroi', '_neari', '_upi_sat', '_downi_sat', '_zeroi_sat', '_neari_sat', '_supi', '_sdowni', '_szeroi', '_sneari', '_supi_sat', '_sdowni_sat', '_szeroi_sat', '_sneari_sat', '_pp', '_ps', '_sp', '_ss', '_s', '_p', '_pp_sat', '_ps_sat', '_sp_sat', '_ss_sat', '_s_sat', '_p_sat')), Keyword), # Integer types (r'i[1-9]\d*', Keyword) ] } class LlvmLexer(RegexLexer): """ For LLVM assembly code. """ name = 'LLVM' aliases = ['llvm'] filenames = ['*.ll'] mimetypes = ['text/x-llvm'] #: optional Comment or Whitespace string = r'"[^"]*?"' identifier = r'([-a-zA-Z$._][\w\-$.]*|' + string + ')' tokens = { 'root': [ include('whitespace'), # Before keywords, because keywords are valid label names :(... (identifier + r'\s*:', Name.Label), include('keyword'), (r'%' + identifier, Name.Variable), (r'@' + identifier, Name.Variable.Global), (r'%\d+', Name.Variable.Anonymous), (r'@\d+', Name.Variable.Global), (r'#\d+', Name.Variable.Global), (r'!' + identifier, Name.Variable), (r'!\d+', Name.Variable.Anonymous), (r'c?' + string, String), (r'0[xX][a-fA-F0-9]+', Number), (r'-?\d+(?:[.]\d+)?(?:[eE][-+]?\d+(?:[.]\d+)?)?', Number), (r'[=<>{}\[\]()*.,!]|x\b', Punctuation) ], 'whitespace': [ (r'(\n|\s)+', Text), (r';.*?\n', Comment) ], 'keyword': [ # Regular keywords (words(( 'aarch64_sve_vector_pcs', 'aarch64_vector_pcs', 'acq_rel', 'acquire', 'add', 'addrspace', 'addrspacecast', 'afn', 'alias', 'aliasee', 'align', 'alignLog2', 'alignstack', 'alloca', 'allocsize', 'allOnes', 'alwaysinline', 'alwaysInline', 'amdgpu_cs', 'amdgpu_es', 'amdgpu_gfx', 'amdgpu_gs', 'amdgpu_hs', 'amdgpu_kernel', 'amdgpu_ls', 'amdgpu_ps', 'amdgpu_vs', 'and', 'any', 'anyregcc', 'appending', 'arcp', 'argmemonly', 'args', 'arm_aapcs_vfpcc', 'arm_aapcscc', 'arm_apcscc', 'ashr', 'asm', 'atomic', 'atomicrmw', 'attributes', 'available_externally', 'avr_intrcc', 'avr_signalcc', 'bit', 'bitcast', 'bitMask', 'blockaddress', 'blockcount', 'br', 'branchFunnel', 'builtin', 'byArg', 'byref', 'byte', 'byteArray', 'byval', 'c', 'call', 'callbr', 'callee', 'caller', 'calls', 'canAutoHide', 'catch', 'catchpad', 'catchret', 'catchswitch', 'cc', 'ccc', 'cfguard_checkcc', 'cleanup', 'cleanuppad', 'cleanupret', 'cmpxchg', 'cold', 'coldcc', 'comdat', 'common', 'constant', 'contract', 'convergent', 'critical', 'cxx_fast_tlscc', 'datalayout', 'declare', 'default', 'define', 'deplibs', 'dereferenceable', 'dereferenceable_or_null', 'distinct', 'dllexport', 'dllimport', 'dso_local', 'dso_local_equivalent', 'dso_preemptable', 'dsoLocal', 'eq', 'exact', 'exactmatch', 'extern_weak', 'external', 'externally_initialized', 'extractelement', 'extractvalue', 'fadd', 'false', 'fast', 'fastcc', 'fcmp', 'fdiv', 'fence', 'filter', 'flags', 'fmul', 'fneg', 'fpext', 'fptosi', 'fptoui', 'fptrunc', 'freeze', 'frem', 'from', 'fsub', 'funcFlags', 'function', 'gc', 'getelementptr', 'ghccc', 'global', 'guid', 'gv', 'hash', 'hhvm_ccc', 'hhvmcc', 'hidden', 'hot', 'hotness', 'icmp', 'ifunc', 'inaccessiblemem_or_argmemonly', 'inaccessiblememonly', 'inalloca', 'inbounds', 'indir', 'indirectbr', 'info', 'initialexec', 'inline', 'inlineBits', 'inlinehint', 'inrange', 'inreg', 'insertelement', 'insertvalue', 'insts', 'intel_ocl_bicc', 'inteldialect', 'internal', 'inttoptr', 'invoke', 'jumptable', 'kind', 'landingpad', 'largest', 'linkage', 'linkonce', 'linkonce_odr', 'live', 'load', 'local_unnamed_addr', 'localdynamic', 'localexec', 'lshr', 'max', 'metadata', 'min', 'minsize', 'module', 'monotonic', 'msp430_intrcc', 'mul', 'mustprogress', 'musttail', 'naked', 'name', 'nand', 'ne', 'nest', 'ninf', 'nnan', 'noalias', 'nobuiltin', 'nocallback', 'nocapture', 'nocf_check', 'noduplicate', 'noduplicates', 'nofree', 'noimplicitfloat', 'noinline', 'noInline', 'nomerge', 'none', 'nonlazybind', 'nonnull', 'noprofile', 'norecurse', 'noRecurse', 'noredzone', 'noreturn', 'nosync', 'notail', 'notEligibleToImport', 'noundef', 'nounwind', 'nsw', 'nsz', 'null', 'null_pointer_is_valid', 'nuw', 'oeq', 'offset', 'oge', 'ogt', 'ole', 'olt', 'one', 'opaque', 'optforfuzzing', 'optnone', 'optsize', 'or', 'ord', 'param', 'params', 'partition', 'path', 'personality', 'phi', 'poison', 'preallocated', 'prefix', 'preserve_allcc', 'preserve_mostcc', 'private', 'prologue', 'protected', 'ptrtoint', 'ptx_device', 'ptx_kernel', 'readnone', 'readNone', 'readonly', 'readOnly', 'reassoc', 'refs', 'relbf', 'release', 'resByArg', 'resume', 'ret', 'returnDoesNotAlias', 'returned', 'returns_twice', 'safestack', 'samesize', 'sanitize_address', 'sanitize_hwaddress', 'sanitize_memory', 'sanitize_memtag', 'sanitize_thread', 'sdiv', 'section', 'select', 'seq_cst', 'sext', 'sge', 'sgt', 'shadowcallstack', 'shl', 'shufflevector', 'sideeffect', 'signext', 'single', 'singleImpl', 'singleImplName', 'sitofp', 'sizeM1', 'sizeM1BitWidth', 'sle', 'slt', 'source_filename', 'speculatable', 'speculative_load_hardening', 'spir_func', 'spir_kernel', 'srem', 'sret', 'ssp', 'sspreq', 'sspstrong', 'store', 'strictfp', 'sub', 'summaries', 'summary', 'swiftcc', 'swifterror', 'swiftself', 'switch', 'syncscope', 'tail', 'tailcc', 'target', 'thread_local', 'to', 'token', 'triple', 'true', 'trunc', 'type', 'typeCheckedLoadConstVCalls', 'typeCheckedLoadVCalls', 'typeid', 'typeidCompatibleVTable', 'typeIdInfo', 'typeTestAssumeConstVCalls', 'typeTestAssumeVCalls', 'typeTestRes', 'typeTests', 'udiv', 'ueq', 'uge', 'ugt', 'uitofp', 'ule', 'ult', 'umax', 'umin', 'undef', 'une', 'uniformRetVal', 'uniqueRetVal', 'unknown', 'unnamed_addr', 'uno', 'unordered', 'unreachable', 'unsat', 'unwind', 'urem', 'uselistorder', 'uselistorder_bb', 'uwtable', 'va_arg', 'varFlags', 'variable', 'vcall_visibility', 'vFuncId', 'virtFunc', 'virtualConstProp', 'void', 'volatile', 'vscale', 'vTableFuncs', 'weak', 'weak_odr', 'webkit_jscc', 'win64cc', 'within', 'wpdRes', 'wpdResolutions', 'writeonly', 'x', 'x86_64_sysvcc', 'x86_fastcallcc', 'x86_intrcc', 'x86_mmx', 'x86_regcallcc', 'x86_stdcallcc', 'x86_thiscallcc', 'x86_vectorcallcc', 'xchg', 'xor', 'zeroext', 'zeroinitializer', 'zext', 'immarg', 'willreturn'), suffix=r'\b'), Keyword), # Types (words(('void', 'half', 'bfloat', 'float', 'double', 'fp128', 'x86_fp80', 'ppc_fp128', 'label', 'metadata', 'x86_mmx', 'x86_amx', 'token')), Keyword.Type), # Integer types (r'i[1-9]\d*', Keyword.Type) ] } class LlvmMirBodyLexer(RegexLexer): """ For LLVM MIR examples without the YAML wrapper. For more information on LLVM MIR see https://llvm.org/docs/MIRLangRef.html. .. versionadded:: 2.6 """ name = 'LLVM-MIR Body' aliases = ['llvm-mir-body'] filenames = [] mimetypes = [] tokens = { 'root': [ # Attributes on basic blocks (words(('liveins', 'successors'), suffix=':'), Keyword), # Basic Block Labels (r'bb\.[0-9]+(\.[a-zA-Z0-9_.-]+)?( $address-taken$)?:', Name.Label), (r'bb\.[0-9]+ $%[a-zA-Z0-9_.-]+$( $address-taken$)?:', Name.Label), (r'%bb\.[0-9]+(\.\w+)?', Name.Label), # Stack references (r'%stack\.[0-9]+(\.\w+\.addr)?', Name), # Subreg indices (r'%subreg\.\w+', Name), # Virtual registers (r'%[a-zA-Z0-9_]+ *', Name.Variable, 'vreg'), # Reference to LLVM-IR global include('global'), # Reference to Intrinsic (r'intrinsic$\@[a-zA-Z0-9_.]+$', Name.Variable.Global), # Comparison predicates (words(('eq', 'ne', 'sgt', 'sge', 'slt', 'sle', 'ugt', 'uge', 'ult', 'ule'), prefix=r'intpred$', suffix=r'$'), Name.Builtin), (words(('oeq', 'one', 'ogt', 'oge', 'olt', 'ole', 'ugt', 'uge', 'ult', 'ule'), prefix=r'floatpred$', suffix=r'$'), Name.Builtin), # Physical registers (r'\$\w+', String.Single), # Assignment operator (r'=', Operator), # gMIR Opcodes (r'(G_ANYEXT|G_[SZ]EXT|G_SEXT_INREG|G_TRUNC|G_IMPLICIT_DEF|G_PHI|' r'G_FRAME_INDEX|G_GLOBAL_VALUE|G_INTTOPTR|G_PTRTOINT|G_BITCAST|' r'G_CONSTANT|G_FCONSTANT|G_VASTART|G_VAARG|G_CTLZ|G_CTLZ_ZERO_UNDEF|' r'G_CTTZ|G_CTTZ_ZERO_UNDEF|G_CTPOP|G_BSWAP|G_BITREVERSE|' r'G_ADDRSPACE_CAST|G_BLOCK_ADDR|G_JUMP_TABLE|G_DYN_STACKALLOC|' r'G_ADD|G_SUB|G_MUL|G_[SU]DIV|G_[SU]REM|G_AND|G_OR|G_XOR|G_SHL|' r'G_[LA]SHR|G_[IF]CMP|G_SELECT|G_GEP|G_PTR_MASK|G_SMIN|G_SMAX|' r'G_UMIN|G_UMAX|G_[US]ADDO|G_[US]ADDE|G_[US]SUBO|G_[US]SUBE|' r'G_[US]MULO|G_[US]MULH|G_FNEG|G_FPEXT|G_FPTRUNC|G_FPTO[US]I|' r'G_[US]ITOFP|G_FABS|G_FCOPYSIGN|G_FCANONICALIZE|G_FMINNUM|' r'G_FMAXNUM|G_FMINNUM_IEEE|G_FMAXNUM_IEEE|G_FMINIMUM|G_FMAXIMUM|' r'G_FADD|G_FSUB|G_FMUL|G_FMA|G_FMAD|G_FDIV|G_FREM|G_FPOW|G_FEXP|' r'G_FEXP2|G_FLOG|G_FLOG2|G_FLOG10|G_FCEIL|G_FCOS|G_FSIN|G_FSQRT|' r'G_FFLOOR|G_FRINT|G_FNEARBYINT|G_INTRINSIC_TRUNC|' r'G_INTRINSIC_ROUND|G_LOAD|G_[ZS]EXTLOAD|G_INDEXED_LOAD|' r'G_INDEXED_[ZS]EXTLOAD|G_STORE|G_INDEXED_STORE|' r'G_ATOMIC_CMPXCHG_WITH_SUCCESS|G_ATOMIC_CMPXCHG|' r'G_ATOMICRMW_(XCHG|ADD|SUB|AND|NAND|OR|XOR|MAX|MIN|UMAX|UMIN|FADD|' r'FSUB)' r'|G_FENCE|G_EXTRACT|G_UNMERGE_VALUES|G_INSERT|G_MERGE_VALUES|' r'G_BUILD_VECTOR|G_BUILD_VECTOR_TRUNC|G_CONCAT_VECTORS|' r'G_INTRINSIC|G_INTRINSIC_W_SIDE_EFFECTS|G_BR|G_BRCOND|' r'G_BRINDIRECT|G_BRJT|G_INSERT_VECTOR_ELT|G_EXTRACT_VECTOR_ELT|' r'G_SHUFFLE_VECTOR)\b', Name.Builtin), # Target independent opcodes (r'(COPY|PHI|INSERT_SUBREG|EXTRACT_SUBREG|REG_SEQUENCE)\b', Name.Builtin), # Flags (words(('killed', 'implicit')), Keyword), # ConstantInt values (r'i[0-9]+ +', Keyword.Type, 'constantint'), # ConstantFloat values (r'(half|float|double) +', Keyword.Type, 'constantfloat'), # Bare immediates include('integer'), # MMO's (r':: *', Operator, 'mmo'), # MIR Comments (r';.*', Comment), # If we get here, assume it's a target instruction (r'[a-zA-Z0-9_]+', Name), # Everything else that isn't highlighted (r'[(), \n]+', Text), ], # The integer constant from a ConstantInt value 'constantint': [ include('integer'), (r'(?=.)', Text, '#pop'), ], # The floating point constant from a ConstantFloat value 'constantfloat': [ include('float'), (r'(?=.)', Text, '#pop'), ], 'vreg': [ # The bank or class if there is one (r' *:(?!:)', Keyword, ('#pop', 'vreg_bank_or_class')), # The LLT if there is one (r' *$', Text, 'vreg_type'), (r'(?=.)', Text, '#pop'), ], 'vreg_bank_or_class': [ # The unassigned bank/class (r' *_', Name.Variable.Magic), (r' *[a-zA-Z0-9_]+', Name.Variable), # The LLT if there is one (r' *\(', Text, 'vreg_type'), (r'(?=.)', Text, '#pop'), ], 'vreg_type': [ # Scalar and pointer types (r' *[sp][0-9]+', Keyword.Type), (r' *<[0-9]+ *x *[sp][0-9]+>', Keyword.Type), (r'$', Text, '#pop'), (r'(?=.)', Text, '#pop'), ], 'mmo': [ (r'$', Text), (r' +', Text), (words(('load', 'store', 'on', 'into', 'from', 'align', 'monotonic', 'acquire', 'release', 'acq_rel', 'seq_cst')), Keyword), # IR references (r'%ir\.[a-zA-Z0-9_.-]+', Name), (r'%ir-block\.[a-zA-Z0-9_.-]+', Name), (r'[-+]', Operator), include('integer'), include('global'), (r',', Punctuation), (r'$, $', Text), (r'$', Text, '#pop'), ], 'integer': [(r'-?[0-9]+', Number.Integer),], 'float': [(r'-?[0-9]+\.[0-9]+(e[+-][0-9]+)?', Number.Float)], 'global': [(r'\@[a-zA-Z0-9_.]+', Name.Variable.Global)], } class LlvmMirLexer(RegexLexer): """ Lexer for the overall LLVM MIR document format. MIR is a human readable serialization format that's used to represent LLVM's machine specific intermediate representation. It allows LLVM's developers to see the state of the compilation process at various points, as well as test individual pieces of the compiler. For more information on LLVM MIR see https://llvm.org/docs/MIRLangRef.html. .. versionadded:: 2.6 """ name = 'LLVM-MIR' aliases = ['llvm-mir'] filenames = ['*.mir'] tokens = { 'root': [ # Comments are hashes at the YAML level (r'#.*', Comment), # Documents starting with | are LLVM-IR (r'--- \|$', Keyword, 'llvm_ir'), # Other documents are MIR (r'---', Keyword, 'llvm_mir'), # Consume everything else in one token for efficiency (r'[^-#]+|.', Text), ], 'llvm_ir': [ # Documents end with '...' or '---' (r'(\.\.\.|(?=---))', Keyword, '#pop'), # Delegate to the LlvmLexer (r'((?:.|\n)+?)(?=(\.\.\.|---))', bygroups(using(LlvmLexer))), ], 'llvm_mir': [ # Comments are hashes at the YAML level (r'#.*', Comment), # Documents end with '...' or '---' (r'(\.\.\.|(?=---))', Keyword, '#pop'), # Handle the simple attributes (r'name:', Keyword, 'name'), (words(('alignment', ), suffix=':'), Keyword, 'number'), (words(('legalized', 'regBankSelected', 'tracksRegLiveness', 'selected', 'exposesReturnsTwice'), suffix=':'), Keyword, 'boolean'), # Handle the attributes don't highlight inside (words(('registers', 'stack', 'fixedStack', 'liveins', 'frameInfo', 'machineFunctionInfo'), suffix=':'), Keyword), # Delegate the body block to the LlvmMirBodyLexer (r'body: *\|', Keyword, 'llvm_mir_body'), # Consume everything else (r'.+', Text), (r'\n', Text), ], 'name': [ (r'[^\n]+', Name), default('#pop'), ], 'boolean': [ (r' *(true|false)', Name.Builtin), default('#pop'), ], 'number': [ (r' *[0-9]+', Number), default('#pop'), ], 'llvm_mir_body': [ # Documents end with '...' or '---'. # We have to pop llvm_mir_body and llvm_mir (r'(\.\.\.|(?=---))', Keyword, '#pop:2'), # Delegate the body block to the LlvmMirBodyLexer (r'((?:.|\n)+?)(?=\.\.\.|---)', bygroups(using(LlvmMirBodyLexer))), # The '...' is optional. If we didn't already find it then it isn't # there. There might be a '---' instead though. (r'(?!\.\.\.|---)((?:.|\n)+)', bygroups(using(LlvmMirBodyLexer))), ], } class NasmLexer(RegexLexer): """ For Nasm (Intel) assembly code. """ name = 'NASM' aliases = ['nasm'] filenames = ['*.asm', '*.ASM'] mimetypes = ['text/x-nasm'] # Tasm uses the same file endings, but TASM is not as common as NASM, so # we prioritize NASM higher by default priority = 1.0 identifier = r'[a-z$._?][\w$.?#@~]*' hexn = r'(?:0x[0-9a-f]+|$0[0-9a-f]*|[0-9]+[0-9a-f]*h)' octn = r'[0-7]+q' binn = r'[01]+b' decn = r'[0-9]+' floatn = decn + r'\.e?' + decn string = r'"(\\"|[^"\n])*"|' + r"'(\\'|[^'\n])*'|" + r"`(\\`|[^`\n])*`" declkw = r'(?:res|d)[bwdqt]|times' register = (r'r[0-9][0-5]?[bwd]?|' r'[a-d][lh]|[er]?[a-d]x|[er]?[sb]p|[er]?[sd]i|[c-gs]s|st[0-7]|' r'mm[0-7]|cr[0-4]|dr[0-367]|tr[3-7]') wordop = r'seg|wrt|strict' type = r'byte|[dq]?word' # Directives must be followed by whitespace, otherwise CPU will match # cpuid for instance. directives = (r'(?:BITS|USE16|USE32|SECTION|SEGMENT|ABSOLUTE|EXTERN|GLOBAL|' r'ORG|ALIGN|STRUC|ENDSTRUC|COMMON|CPU|GROUP|UPPERCASE|IMPORT|' r'EXPORT|LIBRARY|MODULE)\s+') flags = re.IGNORECASE | re.MULTILINE tokens = { 'root': [ (r'^\s*%', Comment.Preproc, 'preproc'), include('whitespace'), (identifier + ':', Name.Label), (r'(%s)(\s+)(equ)' % identifier, bygroups(Name.Constant, Keyword.Declaration, Keyword.Declaration), 'instruction-args'), (directives, Keyword, 'instruction-args'), (declkw, Keyword.Declaration, 'instruction-args'), (identifier, Name.Function, 'instruction-args'), (r'[\r\n]+', Text) ], 'instruction-args': [ (string, String), (hexn, Number.Hex), (octn, Number.Oct), (binn, Number.Bin), (floatn, Number.Float), (decn, Number.Integer), include('punctuation'), (register, Name.Builtin), (identifier, Name.Variable), (r'[\r\n]+', Text, '#pop'), include('whitespace') ], 'preproc': [ (r'[^;\n]+', Comment.Preproc), (r';.*?\n', Comment.Single, '#pop'), (r'\n', Comment.Preproc, '#pop'), ], 'whitespace': [ (r'\n', Text), (r'[ \t]+', Text), (r';.*', Comment.Single) ], 'punctuation': [ (r'[,():\[\]]+', Punctuation), (r'[&|^<>+*/%~-]+', Operator), (r'[$]+', Keyword.Constant), (wordop, Operator.Word), (type, Keyword.Type) ], } def analyse_text(text): # Probably TASM if re.match(r'PROC', text, re.IGNORECASE): return False class NasmObjdumpLexer(ObjdumpLexer): """ For the output of ``objdump -d -M intel``. .. versionadded:: 2.0 """ name = 'objdump-nasm' aliases = ['objdump-nasm'] filenames = ['*.objdump-intel'] mimetypes = ['text/x-nasm-objdump'] tokens = _objdump_lexer_tokens(NasmLexer) class TasmLexer(RegexLexer): """ For Tasm (Turbo Assembler) assembly code. """ name = 'TASM' aliases = ['tasm'] filenames = ['*.asm', '*.ASM', '*.tasm'] mimetypes = ['text/x-tasm'] identifier = r'[@a-z$._?][\w$.?#@~]*' hexn = r'(?:0x[0-9a-f]+|$0[0-9a-f]*|[0-9]+[0-9a-f]*h)' octn = r'[0-7]+q' binn = r'[01]+b' decn = r'[0-9]+' floatn = decn + r'\.e?' + decn string = r'"(\\"|[^"\n])*"|' + r"'(\\'|[^'\n])*'|" + r"`(\\`|[^`\n])*`" declkw = r'(?:res|d)[bwdqt]|times' register = (r'r[0-9][0-5]?[bwd]|' r'[a-d][lh]|[er]?[a-d]x|[er]?[sb]p|[er]?[sd]i|[c-gs]s|st[0-7]|' r'mm[0-7]|cr[0-4]|dr[0-367]|tr[3-7]') wordop = r'seg|wrt|strict' type = r'byte|[dq]?word' directives = (r'BITS|USE16|USE32|SECTION|SEGMENT|ABSOLUTE|EXTERN|GLOBAL|' r'ORG|ALIGN|STRUC|ENDSTRUC|ENDS|COMMON|CPU|GROUP|UPPERCASE|INCLUDE|' r'EXPORT|LIBRARY|MODULE|PROC|ENDP|USES|ARG|DATASEG|UDATASEG|END|IDEAL|' r'P386|MODEL|ASSUME|CODESEG|SIZE') # T[A-Z][a-z] is more of a convention. Lexer should filter out STRUC definitions # and then 'add' them to datatype somehow. datatype = (r'db|dd|dw|T[A-Z][a-z]+') flags = re.IGNORECASE | re.MULTILINE tokens = { 'root': [ (r'^\s*%', Comment.Preproc, 'preproc'), include('whitespace'), (identifier + ':', Name.Label), (directives, Keyword, 'instruction-args'), (r'(%s)(\s+)(%s)' % (identifier, datatype), bygroups(Name.Constant, Keyword.Declaration, Keyword.Declaration), 'instruction-args'), (declkw, Keyword.Declaration, 'instruction-args'), (identifier, Name.Function, 'instruction-args'), (r'[\r\n]+', Text) ], 'instruction-args': [ (string, String), (hexn, Number.Hex), (octn, Number.Oct), (binn, Number.Bin), (floatn, Number.Float), (decn, Number.Integer), include('punctuation'), (register, Name.Builtin), (identifier, Name.Variable), # Do not match newline when it's preceeded by a backslash (r'(\\\s*)(;.*)([\r\n])', bygroups(Text, Comment.Single, Text)), (r'[\r\n]+', Text, '#pop'), include('whitespace') ], 'preproc': [ (r'[^;\n]+', Comment.Preproc), (r';.*?\n', Comment.Single, '#pop'), (r'\n', Comment.Preproc, '#pop'), ], 'whitespace': [ (r'[\n\r]', Text), (r'\\[\n\r]', Text), (r'[ \t]+', Text), (r';.*', Comment.Single) ], 'punctuation': [ (r'[,():\[\]]+', Punctuation), (r'[&|^<>+*=/%~-]+', Operator), (r'[$]+', Keyword.Constant), (wordop, Operator.Word), (type, Keyword.Type) ], } def analyse_text(text): # See above if re.match(r'PROC', text, re.I): return True class Ca65Lexer(RegexLexer): """ For ca65 assembler sources. .. versionadded:: 1.6 """ name = 'ca65 assembler' aliases = ['ca65'] filenames = ['*.s'] flags = re.IGNORECASE tokens = { 'root': [ (r';.*', Comment.Single), (r'\s+', Text), (r'[a-z_.@$][\w.@$]*:', Name.Label), (r'((ld|st)[axy]|(in|de)[cxy]|asl|lsr|ro[lr]|adc|sbc|cmp|cp[xy]' r'|cl[cvdi]|se[cdi]|jmp|jsr|bne|beq|bpl|bmi|bvc|bvs|bcc|bcs' r'|p[lh][ap]|rt[is]|brk|nop|ta[xy]|t[xy]a|txs|tsx|and|ora|eor' r'|bit)\b', Keyword), (r'\.\w+', Keyword.Pseudo), (r'[-+~*/^&|!<>=]', Operator), (r'"[^"\n]*.', String), (r"'[^'\n]*.", String.Char), (r'\$[0-9a-f]+|[0-9a-f]+h\b', Number.Hex), (r'\d+', Number.Integer), (r'%[01]+', Number.Bin), (r'[#,.:()=\[\]]', Punctuation), (r'[a-z_.@$][\w.@$]*', Name), ] } def analyse_text(self, text): # comments in GAS start with "#" if re.search(r'^\s*;', text, re.MULTILINE): return 0.9 class Dasm16Lexer(RegexLexer): """ For DCPU-16 Assembly. Check http://0x10c.com/doc/dcpu-16.txt .. versionadded:: 2.4 """ name = 'DASM16' aliases = ['dasm16'] filenames = ['*.dasm16', '*.dasm'] mimetypes = ['text/x-dasm16'] INSTRUCTIONS = [ 'SET', 'ADD', 'SUB', 'MUL', 'MLI', 'DIV', 'DVI', 'MOD', 'MDI', 'AND', 'BOR', 'XOR', 'SHR', 'ASR', 'SHL', 'IFB', 'IFC', 'IFE', 'IFN', 'IFG', 'IFA', 'IFL', 'IFU', 'ADX', 'SBX', 'STI', 'STD', 'JSR', 'INT', 'IAG', 'IAS', 'RFI', 'IAQ', 'HWN', 'HWQ', 'HWI', ] REGISTERS = [ 'A', 'B', 'C', 'X', 'Y', 'Z', 'I', 'J', 'SP', 'PC', 'EX', 'POP', 'PEEK', 'PUSH' ] # Regexes yo char = r'[a-zA-Z0-9_$@.]' identifier = r'(?:[a-zA-Z$_]' + char + r'*|\.' + char + '+)' number = r'[+-]?(?:0[xX][a-zA-Z0-9]+|\d+)' binary_number = r'0b[01_]+' instruction = r'(?i)(' + '|'.join(INSTRUCTIONS) + ')' single_char = r"'\\?" + char + "'" string = r'"(\\"|[^"])*"' def guess_identifier(lexer, match): ident = match.group(0) klass = Name.Variable if ident.upper() in lexer.REGISTERS else Name.Label yield match.start(), klass, ident tokens = { 'root': [ include('whitespace'), (':' + identifier, Name.Label), (identifier + ':', Name.Label), (instruction, Name.Function, 'instruction-args'), (r'\.' + identifier, Name.Function, 'data-args'), (r'[\r\n]+', Text) ], 'numeric' : [ (binary_number, Number.Integer), (number, Number.Integer), (single_char, String), ], 'arg' : [ (identifier, guess_identifier), include('numeric') ], 'deref' : [ (r'\+', Punctuation), (r'\]', Punctuation, '#pop'), include('arg'), include('whitespace') ], 'instruction-line' : [ (r'[\r\n]+', Text, '#pop'), (r';.*?$', Comment, '#pop'), include('whitespace') ], 'instruction-args': [ (r',', Punctuation), (r'\[', Punctuation, 'deref'), include('arg'), include('instruction-line') ], 'data-args' : [ (r',', Punctuation), include('numeric'), (string, String), include('instruction-line') ], 'whitespace': [ (r'\n', Text), (r'\s+', Text), (r';.*?\n', Comment) ], }

39.064922

94

0.479598

f2ed06d019ed23f69b7810bb3bb85c4c4d21fe4b

3,750

py

Python

labcontrol/gui/handlers/process_handlers/test/test_sequencing_process.py

jdereus/LabControl

9c1867dc8047075f1f3e505a2f4c3479ee6388cc

[ "BSD-3-Clause" ]

3

2018-01-21T05:24:32.000Z

2019-07-12T21:49:02.000Z

labcontrol/gui/handlers/process_handlers/test/test_sequencing_process.py

jdereus/labman

9c1867dc8047075f1f3e505a2f4c3479ee6388cc

[ "BSD-3-Clause" ]

465

2017-05-25T01:33:29.000Z

2019-07-12T21:47:59.000Z

labcontrol/gui/handlers/process_handlers/test/test_sequencing_process.py

biocore/LabControl

9c1867dc8047075f1f3e505a2f4c3479ee6388cc

[ "BSD-3-Clause" ]

16

2017-05-12T21:39:18.000Z

2019-04-03T16:19:21.000Z

# ---------------------------------------------------------------------------- # Copyright (c) 2017-, LabControl development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- import zipfile from io import BytesIO from unittest import main from tornado.escape import json_encode, json_decode from labcontrol.gui.testing import TestHandlerBase import logging class TestSequencingProcessHandler(TestHandlerBase): def test_get_sequencing_process_handler_pool_type(self): response = self.get('/process/sequencing/somepooltype/') self.assertEqual(response.code, 200) self.assertNotEqual(response.body, '') def test_post_sequencing_process_handler(self): data = {'pools': json_encode([1, 2]), 'run_name': 'test_run', 'experiment': 'test_experiment', 'sequencer': 19, 'fwd_cycles': 150, 'rev_cycles': 150, 'principal_investigator': 'admin@foo.bar', 'additional_contacts': json_encode( ['demo@microbio.me', 'shared@foo.bar'])} response = self.post('/process/sequencing/couldbeanything/', data) self.assertEqual(response.code, 200) self.assertCountEqual(json_decode(response.body), ['process']) def test_get_download_sample_sheet_handler(self): # amplicon sequencing process logging.debug("in test_get_download_sample_sheet_handler") response = self.get('/process/sequencing/1/sample_sheet') self.assertNotEqual(response.body, '') self.assertEqual(response.code, 200) self.assertTrue(response.body.startswith(b'# PI,Dude,test@foo.bar\n')) logging.debug(response.headers['Content-Disposition']) s = "attachment; filename=2017-10-25_samplesheet_Test_Run.1.csv" self.assertEqual(response.headers['Content-Disposition'], s) # shotgun sequencing process response = self.get('/process/sequencing/2/sample_sheet') self.assertNotEqual(response.body, '') self.assertEqual(response.code, 200) self.assertTrue(response.body.startswith(b'# PI,Dude,test@foo.bar\n')) self.assertEqual(response.headers['Content-Disposition'], "attachment; filename=2017-10-25_samplesheet_" "TestShotgunRun1_TestExperimentShotgun1.csv") def test_get_download_preparation_sheet_handler(self): response = self.get('/process/sequencing/1/preparation_sheets') self.assertNotEqual(response.body, '') self.assertEqual(response.code, 200) self.assertEqual(response.headers['Content-Type'], 'application/zip') self.assertEqual(response.headers['Expires'], '0') self.assertEqual(response.headers['Cache-Control'], 'no-cache') self.assertEqual(response.headers['Content-Disposition'], 'attachment; filename=2017-10-25_preps' '_Test_Run.1.zip') expected_files = ['2017-10-25_prep_Test_Run.1.txt'] archive = zipfile.ZipFile(BytesIO(response.body), 'r') # NB: Apparently order of namelist results is not stable, hence # the need to call sorted() self.assertEqual(sorted(archive.namelist()), expected_files) # NB: All the below does is test that the files in the archive have # SOME non-empty content--it doesn't check what that content IS. for curr_file_name in expected_files: contents = archive.open(curr_file_name).read() self.assertNotEqual(contents, '') if __name__ == '__main__': main()

44.642857

78

0.645867

15992f14f25e2e2945c556dffe9d2a2bc89bacf0

2,158

py

Python

templates/app.py

brix4dayz/TRiCAM2.0

716f154403c8c0aa903d7391bf4c14d45c778a22

[ "MIT" ]

1

2015-08-11T20:50:36.000Z

templates/app.py

brix4dayz/TRiCAM2.0

716f154403c8c0aa903d7391bf4c14d45c778a22

[ "MIT" ]

null

templates/app.py

brix4dayz/TRiCAM2.0

716f154403c8c0aa903d7391bf4c14d45c778a22

[ "MIT" ]

null

import os from flask import Flask, render_template, request, redirect, url_for, send_from_directory from werkzeug import secure_filename # Initialize the Flask application app = Flask(__name__) # This is the path to the upload directory app.config['UPLOAD_FOLDER'] = 'uploads/' # These are the extension that we are accepting to be uploaded app.config['ALLOWED_EXTENSIONS'] = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif']) # For a given file, return whether it's an allowed type or not def allowed_file(filename): return '.' in filename and \ filename.rsplit('.', 1)[1] in app.config['ALLOWED_EXTENSIONS'] # This route will show a form to perform an AJAX request # jQuery is loaded to execute the request and update the # value of the operation @app.route('/') def index(): return render_template('index.html') # Route that will process the file upload @app.route('/upload', methods=['POST']) def upload(): # Get the name of the uploaded file file = request.files['file'] # Check if the file is one of the allowed types/extensions if file and allowed_file(file.filename): # Make the filename safe, remove unsupported chars filename = secure_filename(file.filename) # Move the file form the temporal folder to # the upload folder we setup file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) # Redirect the user to the uploaded_file route, which # will basicaly show on the browser the uploaded file return redirect(url_for('uploaded_file', filename=filename)) # This route is expecting a parameter containing the name # of a file. Then it will locate that file on the upload # directory and show it on the browser, so if the user uploads # an image, that image is going to be show after the upload @app.route('/uploads/<filename>') def uploaded_file(filename): return send_from_directory(app.config['UPLOAD_FOLDER'], filename) if __name__ == '__main__': app.run( host="0.0.0.0", port=int("80"), debug=True )

36.576271

89

0.673772

ef7304c1f55547f8e355782f15d2423ae2e2cdf5

4,057

py

Python

docs/conf.py

f4nu/vpype

2328ce3fb0bef60aeaf3556d2c47d0dc882d5daf

[ "MIT" ]

null

docs/conf.py

f4nu/vpype

2328ce3fb0bef60aeaf3556d2c47d0dc882d5daf

[ "MIT" ]

46

2021-01-26T01:09:10.000Z

2022-03-25T06:22:02.000Z

docs/conf.py

str4w/vpype

c649445b8fec56b4ce9a436a7b8741c5fec1d640

[ "MIT" ]

null

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- Project information ----------------------------------------------------- # noinspection PyPackageRequirements from recommonmark.parser import CommonMarkParser project = "vpype" # noinspection PyShadowingBuiltins copyright = "2020, Antoine Beyeler" author = "Antoine Beyeler" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.autosummary", "sphinx.ext.napoleon", "sphinx_click.ext", "sphinx_autodoc_typehints", # "recommonmark", # NOTE: see workaround below # "alabaster", # 'autoapi.extension', ] # -- Autoapi configuration ------------------------------------------------ # autoapi_dirs = ['../vpype'] # autoapi_options = ['members', 'undoc-members', 'show-inheritance'] # autoapi_generate_api_docs = False autosummary_generate = True add_module_names = False autosummary_imported_members = True # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "venv", ".*"] # -- Global options ---------------------------------------------------------- # Don't mess with double-dash used in CLI options smartquotes_action = "qe" # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "sphinx_rtd_theme" # html_theme = "alabaster" # html_theme_path = [alabaster.get_path()] # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # -- Intersphinx options intersphinx_mapping = { "shapely": ("https://shapely.readthedocs.io/en/latest/", None), "click": ("https://click.palletsprojects.com/en/7.x/", None), "python": ("https://docs.python.org/3/", None), "Pillow": ("https://pillow.readthedocs.io/en/stable/", None), } # -- Napoleon options napoleon_include_init_with_doc = True # noinspection PyUnusedLocal def autodoc_skip_member(app, what, name, obj, skip, options): exclusions = ( # vpype/model.py "VectorData", # vpype/utils.py "PAGE_FORMATS", "convert", "convert_page_format", "Length", # vpype_cli/debug.py "DebugData", # private attribute "__dict__", "__doc__", "__module__", "__weakref__", ) exclude = name in exclusions return skip or exclude # RECOMMONMARK WORKAROUND # see https://github.com/readthedocs/recommonmark/issues/177 class CustomCommonMarkParser(CommonMarkParser): def visit_document(self, node): pass def setup(app): app.connect("autodoc-skip-member", autodoc_skip_member) # recommonmark workaround app.add_source_suffix(".md", "markdown") app.add_source_parser(CustomCommonMarkParser)

31.449612

79

0.653439

4448a6544487d434df7489d374fd3456107f8879

647

py

Python

__primeNumberLessThanGivenNumber.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__primeNumberLessThanGivenNumber.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__primeNumberLessThanGivenNumber.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

''' Write a Python program to print the number of prime numbers which are less than or equal to a given integer. Input: n (1 <= n <= 999,999) Input the number(n): 35 Number of prime numbers which are less than or equal to n.: 11 ''' primes = [1] * 500000 primes[0] = 0 for i in range (3, 1000, 2): if primes[i // 2]: primes [(i * i) // 2 :: i] = [0] * len (primes[(i * i) // 2::i]) print("Input the number(n):") n = int (input ()) if n < 4: print("Number of prime numbers which are less than or equal to n.:", n - 1) else: print("Number of prime numbers which are less than or equal to n.:",sum(primes[:(n + 1) // 2]) + 1)

29.409091

103

0.605873

c7b6c2a4ea7917ef12d1d36e6f7e6b0c7542cc6c

21,994

py

Python

django/conf/global_settings.py

Oktosha/django

b10a8bd90bb1087d7abcdda971d51269579aeaad

[ "PSF-2.0", "BSD-3-Clause" ]

5

2019-10-17T21:29:53.000Z

2021-06-23T16:27:02.000Z

django/conf/global_settings.py

Oktosha/django

b10a8bd90bb1087d7abcdda971d51269579aeaad

[ "PSF-2.0", "BSD-3-Clause" ]

2

2020-04-16T15:27:35.000Z

2020-06-28T00:42:50.000Z

django/conf/global_settings.py

Oktosha/django

b10a8bd90bb1087d7abcdda971d51269579aeaad

[ "PSF-2.0", "BSD-3-Clause" ]

11

2019-09-14T20:57:30.000Z

2022-01-19T17:59:26.000Z

""" Default Django settings. Override these with settings in the module pointed to by the DJANGO_SETTINGS_MODULE environment variable. """ # This is defined here as a do-nothing function because we can't import # django.utils.translation -- that module depends on the settings. def gettext_noop(s): return s #################### # CORE # #################### DEBUG = False # Whether the framework should propagate raw exceptions rather than catching # them. This is useful under some testing situations and should never be used # on a live site. DEBUG_PROPAGATE_EXCEPTIONS = False # People who get code error notifications. # In the format [('Full Name', 'email@example.com'), ('Full Name', 'anotheremail@example.com')] ADMINS = [] # List of IP addresses, as strings, that: # * See debug comments, when DEBUG is true # * Receive x-headers INTERNAL_IPS = [] # Hosts/domain names that are valid for this site. # "*" matches anything, ".example.com" matches example.com and all subdomains ALLOWED_HOSTS = [] # Local time zone for this installation. All choices can be found here: # https://en.wikipedia.org/wiki/List_of_tz_zones_by_name (although not all # systems may support all possibilities). When USE_TZ is True, this is # interpreted as the default user time zone. TIME_ZONE = 'America/Chicago' # If you set this to True, Django will use timezone-aware datetimes. USE_TZ = False # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' # Languages we provide translations for, out of the box. LANGUAGES = [ ('af', gettext_noop('Afrikaans')), ('ar', gettext_noop('Arabic')), ('ast', gettext_noop('Asturian')), ('az', gettext_noop('Azerbaijani')), ('bg', gettext_noop('Bulgarian')), ('be', gettext_noop('Belarusian')), ('bn', gettext_noop('Bengali')), ('br', gettext_noop('Breton')), ('bs', gettext_noop('Bosnian')), ('ca', gettext_noop('Catalan')), ('cs', gettext_noop('Czech')), ('cy', gettext_noop('Welsh')), ('da', gettext_noop('Danish')), ('de', gettext_noop('German')), ('dsb', gettext_noop('Lower Sorbian')), ('el', gettext_noop('Greek')), ('en', gettext_noop('English')), ('en-au', gettext_noop('Australian English')), ('en-gb', gettext_noop('British English')), ('eo', gettext_noop('Esperanto')), ('es', gettext_noop('Spanish')), ('es-ar', gettext_noop('Argentinian Spanish')), ('es-co', gettext_noop('Colombian Spanish')), ('es-mx', gettext_noop('Mexican Spanish')), ('es-ni', gettext_noop('Nicaraguan Spanish')), ('es-ve', gettext_noop('Venezuelan Spanish')), ('et', gettext_noop('Estonian')), ('eu', gettext_noop('Basque')), ('fa', gettext_noop('Persian')), ('fi', gettext_noop('Finnish')), ('fr', gettext_noop('French')), ('fy', gettext_noop('Frisian')), ('ga', gettext_noop('Irish')), ('gd', gettext_noop('Scottish Gaelic')), ('gl', gettext_noop('Galician')), ('he', gettext_noop('Hebrew')), ('hi', gettext_noop('Hindi')), ('hr', gettext_noop('Croatian')), ('hsb', gettext_noop('Upper Sorbian')), ('hu', gettext_noop('Hungarian')), ('hy', gettext_noop('Armenian')), ('ia', gettext_noop('Interlingua')), ('id', gettext_noop('Indonesian')), ('io', gettext_noop('Ido')), ('is', gettext_noop('Icelandic')), ('it', gettext_noop('Italian')), ('ja', gettext_noop('Japanese')), ('ka', gettext_noop('Georgian')), ('kab', gettext_noop('Kabyle')), ('kk', gettext_noop('Kazakh')), ('km', gettext_noop('Khmer')), ('kn', gettext_noop('Kannada')), ('ko', gettext_noop('Korean')), ('lb', gettext_noop('Luxembourgish')), ('lt', gettext_noop('Lithuanian')), ('lv', gettext_noop('Latvian')), ('mk', gettext_noop('Macedonian')), ('ml', gettext_noop('Malayalam')), ('mn', gettext_noop('Mongolian')), ('mr', gettext_noop('Marathi')), ('my', gettext_noop('Burmese')), ('nb', gettext_noop('Norwegian Bokmål')), ('ne', gettext_noop('Nepali')), ('nl', gettext_noop('Dutch')), ('nn', gettext_noop('Norwegian Nynorsk')), ('os', gettext_noop('Ossetic')), ('pa', gettext_noop('Punjabi')), ('pl', gettext_noop('Polish')), ('pt', gettext_noop('Portuguese')), ('pt-br', gettext_noop('Brazilian Portuguese')), ('ro', gettext_noop('Romanian')), ('ru', gettext_noop('Russian')), ('sk', gettext_noop('Slovak')), ('sl', gettext_noop('Slovenian')), ('sq', gettext_noop('Albanian')), ('sr', gettext_noop('Serbian')), ('sr-latn', gettext_noop('Serbian Latin')), ('sv', gettext_noop('Swedish')), ('sw', gettext_noop('Swahili')), ('ta', gettext_noop('Tamil')), ('te', gettext_noop('Telugu')), ('th', gettext_noop('Thai')), ('tr', gettext_noop('Turkish')), ('tt', gettext_noop('Tatar')), ('udm', gettext_noop('Udmurt')), ('uk', gettext_noop('Ukrainian')), ('ur', gettext_noop('Urdu')), ('vi', gettext_noop('Vietnamese')), ('zh-hans', gettext_noop('Simplified Chinese')), ('zh-hant', gettext_noop('Traditional Chinese')), ] # Languages using BiDi (right-to-left) layout LANGUAGES_BIDI = ["he", "ar", "fa", "ur"] # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True LOCALE_PATHS = [] # Settings for language cookie LANGUAGE_COOKIE_NAME = 'django_language' LANGUAGE_COOKIE_AGE = None LANGUAGE_COOKIE_DOMAIN = None LANGUAGE_COOKIE_PATH = '/' LANGUAGE_COOKIE_SECURE = False LANGUAGE_COOKIE_HTTPONLY = False LANGUAGE_COOKIE_SAMESITE = None # If you set this to True, Django will format dates, numbers and calendars # according to user current locale. USE_L10N = False # Not-necessarily-technical managers of the site. They get broken link # notifications and other various emails. MANAGERS = ADMINS # Default charset to use for all HttpResponse objects, if a MIME type isn't # manually specified. It's used to construct the Content-Type header. DEFAULT_CHARSET = 'utf-8' # Encoding of files read from disk (template and initial SQL files). FILE_CHARSET = 'utf-8' # Email address that error messages come from. SERVER_EMAIL = 'root@localhost' # Database connection info. If left empty, will default to the dummy backend. DATABASES = {} # Classes used to implement DB routing behavior. DATABASE_ROUTERS = [] # The email backend to use. For possible shortcuts see django.core.mail. # The default is to use the SMTP backend. # Third-party backends can be specified by providing a Python path # to a module that defines an EmailBackend class. EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # Host for sending email. EMAIL_HOST = 'localhost' # Port for sending email. EMAIL_PORT = 25 # Whether to send SMTP 'Date' header in the local time zone or in UTC. EMAIL_USE_LOCALTIME = False # Optional SMTP authentication information for EMAIL_HOST. EMAIL_HOST_USER = '' EMAIL_HOST_PASSWORD = '' EMAIL_USE_TLS = False EMAIL_USE_SSL = False EMAIL_SSL_CERTFILE = None EMAIL_SSL_KEYFILE = None EMAIL_TIMEOUT = None # List of strings representing installed apps. INSTALLED_APPS = [] TEMPLATES = [] # Default form rendering class. FORM_RENDERER = 'django.forms.renderers.DjangoTemplates' # Default email address to use for various automated correspondence from # the site managers. DEFAULT_FROM_EMAIL = 'webmaster@localhost' # Subject-line prefix for email messages send with django.core.mail.mail_admins # or ...mail_managers. Make sure to include the trailing space. EMAIL_SUBJECT_PREFIX = '[Django] ' # Whether to append trailing slashes to URLs. APPEND_SLASH = True # Whether to prepend the "www." subdomain to URLs that don't have it. PREPEND_WWW = False # Override the server-derived value of SCRIPT_NAME FORCE_SCRIPT_NAME = None # List of compiled regular expression objects representing User-Agent strings # that are not allowed to visit any page, systemwide. Use this for bad # robots/crawlers. Here are a few examples: # import re # DISALLOWED_USER_AGENTS = [ # re.compile(r'^NaverBot.*'), # re.compile(r'^EmailSiphon.*'), # re.compile(r'^SiteSucker.*'), # re.compile(r'^sohu-search'), # ] DISALLOWED_USER_AGENTS = [] ABSOLUTE_URL_OVERRIDES = {} # List of compiled regular expression objects representing URLs that need not # be reported by BrokenLinkEmailsMiddleware. Here are a few examples: # import re # IGNORABLE_404_URLS = [ # re.compile(r'^/apple-touch-icon.*\.png$'), # re.compile(r'^/favicon.ico$'), # re.compile(r'^/robots.txt$'), # re.compile(r'^/phpmyadmin/'), # re.compile(r'\.(cgi|php|pl)$'), # ] IGNORABLE_404_URLS = [] # A secret key for this particular Django installation. Used in secret-key # hashing algorithms. Set this in your settings, or Django will complain # loudly. SECRET_KEY = '' # Default file storage mechanism that holds media. DEFAULT_FILE_STORAGE = 'django.core.files.storage.FileSystemStorage' # Absolute filesystem path to the directory that will hold user-uploaded files. # Example: "/var/www/example.com/media/" MEDIA_ROOT = '' # URL that handles the media served from MEDIA_ROOT. # Examples: "http://example.com/media/", "http://media.example.com/" MEDIA_URL = '' # Absolute path to the directory static files should be collected to. # Example: "/var/www/example.com/static/" STATIC_ROOT = None # URL that handles the static files served from STATIC_ROOT. # Example: "http://example.com/static/", "http://static.example.com/" STATIC_URL = None # List of upload handler classes to be applied in order. FILE_UPLOAD_HANDLERS = [ 'django.core.files.uploadhandler.MemoryFileUploadHandler', 'django.core.files.uploadhandler.TemporaryFileUploadHandler', ] # Maximum size, in bytes, of a request before it will be streamed to the # file system instead of into memory. FILE_UPLOAD_MAX_MEMORY_SIZE = 2621440 # i.e. 2.5 MB # Maximum size in bytes of request data (excluding file uploads) that will be # read before a SuspiciousOperation (RequestDataTooBig) is raised. DATA_UPLOAD_MAX_MEMORY_SIZE = 2621440 # i.e. 2.5 MB # Maximum number of GET/POST parameters that will be read before a # SuspiciousOperation (TooManyFieldsSent) is raised. DATA_UPLOAD_MAX_NUMBER_FIELDS = 1000 # Directory in which upload streamed files will be temporarily saved. A value of # `None` will make Django use the operating system's default temporary directory # (i.e. "/tmp" on *nix systems). FILE_UPLOAD_TEMP_DIR = None # The numeric mode to set newly-uploaded files to. The value should be a mode # you'd pass directly to os.chmod; see https://docs.python.org/library/os.html#files-and-directories. FILE_UPLOAD_PERMISSIONS = 0o644 # The numeric mode to assign to newly-created directories, when uploading files. # The value should be a mode as you'd pass to os.chmod; # see https://docs.python.org/library/os.html#files-and-directories. FILE_UPLOAD_DIRECTORY_PERMISSIONS = None # Python module path where user will place custom format definition. # The directory where this setting is pointing should contain subdirectories # named as the locales, containing a formats.py file # (i.e. "myproject.locale" for myproject/locale/en/formats.py etc. use) FORMAT_MODULE_PATH = None # Default formatting for date objects. See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATE_FORMAT = 'N j, Y' # Default formatting for datetime objects. See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date DATETIME_FORMAT = 'N j, Y, P' # Default formatting for time objects. See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date TIME_FORMAT = 'P' # Default formatting for date objects when only the year and month are relevant. # See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date YEAR_MONTH_FORMAT = 'F Y' # Default formatting for date objects when only the month and day are relevant. # See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date MONTH_DAY_FORMAT = 'F j' # Default short formatting for date objects. See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATE_FORMAT = 'm/d/Y' # Default short formatting for datetime objects. # See all available format strings here: # https://docs.djangoproject.com/en/dev/ref/templates/builtins/#date SHORT_DATETIME_FORMAT = 'm/d/Y P' # Default formats to be used when parsing dates from input boxes, in order # See all available format string here: # https://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATE_INPUT_FORMATS = [ '%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06' '%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006' '%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006' '%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006' '%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006' ] # Default formats to be used when parsing times from input boxes, in order # See all available format string here: # https://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates TIME_INPUT_FORMATS = [ '%H:%M:%S', # '14:30:59' '%H:%M:%S.%f', # '14:30:59.000200' '%H:%M', # '14:30' ] # Default formats to be used when parsing dates and times from input boxes, # in order # See all available format string here: # https://docs.python.org/library/datetime.html#strftime-behavior # * Note that these format strings are different from the ones to display dates DATETIME_INPUT_FORMATS = [ '%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59' '%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200' '%Y-%m-%d %H:%M', # '2006-10-25 14:30' '%Y-%m-%d', # '2006-10-25' '%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59' '%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200' '%m/%d/%Y %H:%M', # '10/25/2006 14:30' '%m/%d/%Y', # '10/25/2006' '%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59' '%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200' '%m/%d/%y %H:%M', # '10/25/06 14:30' '%m/%d/%y', # '10/25/06' ] # First day of week, to be used on calendars # 0 means Sunday, 1 means Monday... FIRST_DAY_OF_WEEK = 0 # Decimal separator symbol DECIMAL_SEPARATOR = '.' # Boolean that sets whether to add thousand separator when formatting numbers USE_THOUSAND_SEPARATOR = False # Number of digits that will be together, when splitting them by # THOUSAND_SEPARATOR. 0 means no grouping, 3 means splitting by thousands... NUMBER_GROUPING = 0 # Thousand separator symbol THOUSAND_SEPARATOR = ',' # The tablespaces to use for each model when not specified otherwise. DEFAULT_TABLESPACE = '' DEFAULT_INDEX_TABLESPACE = '' # Default X-Frame-Options header value X_FRAME_OPTIONS = 'SAMEORIGIN' USE_X_FORWARDED_HOST = False USE_X_FORWARDED_PORT = False # The Python dotted path to the WSGI application that Django's internal server # (runserver) will use. If `None`, the return value of # 'django.core.wsgi.get_wsgi_application' is used, thus preserving the same # behavior as previous versions of Django. Otherwise this should point to an # actual WSGI application object. WSGI_APPLICATION = None # If your Django app is behind a proxy that sets a header to specify secure # connections, AND that proxy ensures that user-submitted headers with the # same name are ignored (so that people can't spoof it), set this value to # a tuple of (header_name, header_value). For any requests that come in with # that header/value, request.is_secure() will return True. # WARNING! Only set this if you fully understand what you're doing. Otherwise, # you may be opening yourself up to a security risk. SECURE_PROXY_SSL_HEADER = None ############## # MIDDLEWARE # ############## # List of middleware to use. Order is important; in the request phase, these # middleware will be applied in the order given, and in the response # phase the middleware will be applied in reverse order. MIDDLEWARE = [] ############ # SESSIONS # ############ # Cache to store session data if using the cache session backend. SESSION_CACHE_ALIAS = 'default' # Cookie name. This can be whatever you want. SESSION_COOKIE_NAME = 'sessionid' # Age of cookie, in seconds (default: 2 weeks). SESSION_COOKIE_AGE = 60 * 60 * 24 * 7 * 2 # A string like "example.com", or None for standard domain cookie. SESSION_COOKIE_DOMAIN = None # Whether the session cookie should be secure (https:// only). SESSION_COOKIE_SECURE = False # The path of the session cookie. SESSION_COOKIE_PATH = '/' # Whether to use the HttpOnly flag. SESSION_COOKIE_HTTPONLY = True # Whether to set the flag restricting cookie leaks on cross-site requests. # This can be 'Lax', 'Strict', or None to disable the flag. SESSION_COOKIE_SAMESITE = 'Lax' # Whether to save the session data on every request. SESSION_SAVE_EVERY_REQUEST = False # Whether a user's session cookie expires when the Web browser is closed. SESSION_EXPIRE_AT_BROWSER_CLOSE = False # The module to store session data SESSION_ENGINE = 'django.contrib.sessions.backends.db' # Directory to store session files if using the file session module. If None, # the backend will use a sensible default. SESSION_FILE_PATH = None # class to serialize session data SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer' ######### # CACHE # ######### # The cache backends to use. CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', } } CACHE_MIDDLEWARE_KEY_PREFIX = '' CACHE_MIDDLEWARE_SECONDS = 600 CACHE_MIDDLEWARE_ALIAS = 'default' ################## # AUTHENTICATION # ################## AUTH_USER_MODEL = 'auth.User' AUTHENTICATION_BACKENDS = ['django.contrib.auth.backends.ModelBackend'] LOGIN_URL = '/accounts/login/' LOGIN_REDIRECT_URL = '/accounts/profile/' LOGOUT_REDIRECT_URL = None # The number of days a password reset link is valid for PASSWORD_RESET_TIMEOUT_DAYS = 3 # the first hasher in this list is the preferred algorithm. any # password using different algorithms will be converted automatically # upon login PASSWORD_HASHERS = [ 'django.contrib.auth.hashers.PBKDF2PasswordHasher', 'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher', 'django.contrib.auth.hashers.Argon2PasswordHasher', 'django.contrib.auth.hashers.BCryptSHA256PasswordHasher', ] AUTH_PASSWORD_VALIDATORS = [] ########### # SIGNING # ########### SIGNING_BACKEND = 'django.core.signing.TimestampSigner' ######## # CSRF # ######## # Dotted path to callable to be used as view when a request is # rejected by the CSRF middleware. CSRF_FAILURE_VIEW = 'django.views.csrf.csrf_failure' # Settings for CSRF cookie. CSRF_COOKIE_NAME = 'csrftoken' CSRF_COOKIE_AGE = 60 * 60 * 24 * 7 * 52 CSRF_COOKIE_DOMAIN = None CSRF_COOKIE_PATH = '/' CSRF_COOKIE_SECURE = False CSRF_COOKIE_HTTPONLY = False CSRF_COOKIE_SAMESITE = 'Lax' CSRF_HEADER_NAME = 'HTTP_X_CSRFTOKEN' CSRF_TRUSTED_ORIGINS = [] CSRF_USE_SESSIONS = False ############ # MESSAGES # ############ # Class to use as messages backend MESSAGE_STORAGE = 'django.contrib.messages.storage.fallback.FallbackStorage' # Default values of MESSAGE_LEVEL and MESSAGE_TAGS are defined within # django.contrib.messages to avoid imports in this settings file. ########### # LOGGING # ########### # The callable to use to configure logging LOGGING_CONFIG = 'logging.config.dictConfig' # Custom logging configuration. LOGGING = {} # Default exception reporter filter class used in case none has been # specifically assigned to the HttpRequest instance. DEFAULT_EXCEPTION_REPORTER_FILTER = 'django.views.debug.SafeExceptionReporterFilter' ########### # TESTING # ########### # The name of the class to use to run the test suite TEST_RUNNER = 'django.test.runner.DiscoverRunner' # Apps that don't need to be serialized at test database creation time # (only apps with migrations are to start with) TEST_NON_SERIALIZED_APPS = [] ############ # FIXTURES # ############ # The list of directories to search for fixtures FIXTURE_DIRS = [] ############### # STATICFILES # ############### # A list of locations of additional static files STATICFILES_DIRS = [] # The default file storage backend used during the build process STATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage' # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = [ 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ] ############## # MIGRATIONS # ############## # Migration module overrides for apps, by app label. MIGRATION_MODULES = {} ################# # SYSTEM CHECKS # ################# # List of all issues generated by system checks that should be silenced. Light # issues like warnings, infos or debugs will not generate a message. Silencing # serious issues like errors and criticals does not result in hiding the # message, but Django will not stop you from e.g. running server. SILENCED_SYSTEM_CHECKS = [] ####################### # SECURITY MIDDLEWARE # ####################### SECURE_BROWSER_XSS_FILTER = False SECURE_CONTENT_TYPE_NOSNIFF = True SECURE_HSTS_INCLUDE_SUBDOMAINS = False SECURE_HSTS_PRELOAD = False SECURE_HSTS_SECONDS = 0 SECURE_REDIRECT_EXEMPT = [] SECURE_SSL_HOST = None SECURE_SSL_REDIRECT = False

34.473354

101

0.701873

8c0e0884748d6b991611ffe94a49130edfc61d72

902

py

Python

May_work/python/tkinter/quitBUtton.py

EricMorse/ECE434-Project

315b81003b49b51d4fc936b4826a4b70cb6b403d

[ "MIT" ]

null

May_work/python/tkinter/quitBUtton.py

EricMorse/ECE434-Project

315b81003b49b51d4fc936b4826a4b70cb6b403d

[ "MIT" ]

null

May_work/python/tkinter/quitBUtton.py

EricMorse/ECE434-Project

315b81003b49b51d4fc936b4826a4b70cb6b403d

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- """ ZetCode Tkinter tutorial This program creates a Quit button. When we press the button, the application terminates. Author: Jan Bodnar Last modified: July 2017 Website: www.zetcode.com """ from tkinter import Tk, BOTH from tkinter.ttk import Frame, Button, Style class Example(Frame): def __init__(self): super().__init__() self.initUI() def initUI(self): self.style = Style() self.style.theme_use("default") self.master.title("Quit button") self.pack(fill=BOTH, expand=1) quitButton = Button(self, text="Quit", command=self.quit) quitButton.place(x=50, y=50) def main(): root = Tk() root.geometry("250x150+300+300") app = Example() root.mainloop() if __name__ == '__main__': main()

18.04

46

0.590909

be97feb70d74485b6e5b7a64fe33d09d8372016a

7,667

py

Python

doc/conf.py

bioidiap/bob.pipelines

cbefdaf3b384ee11cb26a279281f007adc2d8f19

[ "BSD-3-Clause" ]

1

2020-10-13T19:58:44.000Z

doc/conf.py

bioidiap/bob.pipelines

cbefdaf3b384ee11cb26a279281f007adc2d8f19

[ "BSD-3-Clause" ]

null

doc/conf.py

bioidiap/bob.pipelines

cbefdaf3b384ee11cb26a279281f007adc2d8f19

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import time import pkg_resources import sphinx_rtd_theme # For inter-documentation mapping: from bob.extension.utils import link_documentation, load_requirements # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. needs_sphinx = "1.3" # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "sphinx.ext.todo", "sphinx.ext.coverage", "sphinx.ext.ifconfig", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.doctest", "sphinx.ext.graphviz", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.mathjax", # 'matplotlib.sphinxext.plot_directive', ] # Be picky about warnings nitpicky = True # Ignores stuff we can't easily resolve on other project's sphinx manuals nitpick_ignore = [] # Allows the user to override warnings from a separate file if os.path.exists("nitpick-exceptions.txt"): for line in open("nitpick-exceptions.txt"): if line.strip() == "" or line.startswith("#"): continue dtype, target = line.split(None, 1) target = target.strip() nitpick_ignore.append((dtype, target)) # Always includes todos todo_include_todos = True # Generates auto-summary automatically autosummary_generate = True # Create numbers on figures with captions numfig = False # If we are on OSX, the 'dvipng' path maybe different dvipng_osx = "/Library/TeX/texbin/dvipng" if os.path.exists(dvipng_osx): pngmath_dvipng = dvipng_osx # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix of source filenames. source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = "bob.pipelines" copyright = "%s, Idiap Research Institute" % time.strftime("%Y") # Grab the setup entry distribution = pkg_resources.require(project)[0] # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = distribution.version # The full version, including alpha/beta/rc tags. release = distribution.version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ["links.rst"] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # Some variables which are useful for generated material project_variable = project.replace(".", "_") short_description = "bob.pipelines" owner = ["Idiap Research Institute"] # -- Options for HTML output --------------------------------------------------- html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = project_variable # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "img/bob-logo.png" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "img/bob-favicon.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = project_variable + "_doc" # -- Post configuration -------------------------------------------------------- # Included after all input documents rst_epilog = """ .. |project| replace:: Bob .. |version| replace:: %s .. |current-year| date:: %%Y """ % ( version, ) # Default processing flags for sphinx autoclass_content = "class" autodoc_member_order = "bysource" autodoc_default_options = { "members": True, "undoc-members": True, "show-inheritance": True, } sphinx_requirements = "extra-intersphinx.txt" if os.path.exists(sphinx_requirements): intersphinx_mapping = link_documentation( additional_packages=["python", "numpy"] + load_requirements(sphinx_requirements) ) else: intersphinx_mapping = link_documentation() def setup(app): # Add `>>>` button to toggle visibility of prompts in code blocks. # see https://github.com/readthedocs/sphinx_rtd_theme/issues/167 and # https://raw.githubusercontent.com/python/python-docs-theme/master/python_docs_theme/static/copybutton.js app.add_js_file("copybutton.js")

31.040486

110

0.718925

52cef4c0e1c1086e90a051cf9971738bcaaeb805

321

py

Python

plaso/engine/logger.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

1,253

2015-01-02T13:58:02.000Z

2022-03-31T08:43:39.000Z

plaso/engine/logger.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

3,388

2015-01-02T11:17:58.000Z

2022-03-30T10:21:45.000Z

plaso/engine/logger.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

376

2015-01-20T07:04:54.000Z

2022-03-04T23:53:00.000Z

# -*- coding: utf-8 -*- """The engine sub module logger.""" import logging _logger = logging.getLogger('engine') # Mimic the logging module interface. critical = _logger.critical debug = _logger.debug error = _logger.error exception = _logger.exception info = _logger.info log = _logger.log warning = _logger.warning

18.882353

37

0.738318

62dc48021c7971c56b9f0f65a4bfbbc815d0315c

28,335

py

Python

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/operations/_images_operations.py

dubiety/azure-sdk-for-python

62ffa839f5d753594cf0fe63668f454a9d87a346

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/operations/_images_operations.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/operations/_images_operations.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union from msrest import Serializer from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') JSONType = Any ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_create_or_update_request_initial( resource_group_name: str, image_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2017-03-30") # type: str content_type = kwargs.pop('content_type', None) # type: Optional[str] accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "imageName": _SERIALIZER.url("image_name", image_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PUT", url=_url, params=_query_parameters, headers=_header_parameters, json=json, content=content, **kwargs ) def build_delete_request_initial( resource_group_name: str, image_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2017-03-30") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "imageName": _SERIALIZER.url("image_name", image_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="DELETE", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) def build_get_request( resource_group_name: str, image_name: str, subscription_id: str, *, expand: Optional[str] = None, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2017-03-30") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "imageName": _SERIALIZER.url("image_name", image_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if expand is not None: _query_parameters['$expand'] = _SERIALIZER.query("expand", expand, 'str') _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) def build_list_by_resource_group_request( resource_group_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2017-03-30") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images") # pylint: disable=line-too-long path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) def build_list_request( subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2017-03-30") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.Compute/images") path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) class ImagesOperations(object): """ImagesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.compute.v2017_03_30.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _create_or_update_initial( self, resource_group_name: str, image_name: str, parameters: "_models.Image", **kwargs: Any ) -> "_models.Image": cls = kwargs.pop('cls', None) # type: ClsType["_models.Image"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2017-03-30") # type: str content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(parameters, 'Image') request = build_create_or_update_request_initial( resource_group_name=resource_group_name, image_name=image_name, subscription_id=self._config.subscription_id, api_version=api_version, content_type=content_type, json=_json, template_url=self._create_or_update_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('Image', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('Image', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}"} # type: ignore @distributed_trace def begin_create_or_update( self, resource_group_name: str, image_name: str, parameters: "_models.Image", **kwargs: Any ) -> LROPoller["_models.Image"]: """Create or update an image. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param image_name: The name of the image. :type image_name: str :param parameters: Parameters supplied to the Create Image operation. :type parameters: ~azure.mgmt.compute.v2017_03_30.models.Image :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either Image or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.compute.v2017_03_30.models.Image] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2017-03-30") # type: str content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.Image"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._create_or_update_initial( resource_group_name=resource_group_name, image_name=image_name, parameters=parameters, api_version=api_version, content_type=content_type, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('Image', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}"} # type: ignore def _delete_initial( self, resource_group_name: str, image_name: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2017-03-30") # type: str request = build_delete_request_initial( resource_group_name=resource_group_name, image_name=image_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self._delete_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}"} # type: ignore @distributed_trace def begin_delete( self, resource_group_name: str, image_name: str, **kwargs: Any ) -> LROPoller["_models.OperationStatusResponse"]: """Deletes an Image. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param image_name: The name of the image. :type image_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2017-03-30") # type: str polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._delete_initial( resource_group_name=resource_group_name, image_name=image_name, api_version=api_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}"} # type: ignore @distributed_trace def get( self, resource_group_name: str, image_name: str, expand: Optional[str] = None, **kwargs: Any ) -> "_models.Image": """Gets an image. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param image_name: The name of the image. :type image_name: str :param expand: The expand expression to apply on the operation. Default value is None. :type expand: str :keyword callable cls: A custom type or function that will be passed the direct response :return: Image, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2017_03_30.models.Image :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.Image"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2017-03-30") # type: str request = build_get_request( resource_group_name=resource_group_name, image_name=image_name, subscription_id=self._config.subscription_id, api_version=api_version, expand=expand, template_url=self.get.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('Image', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images/{imageName}"} # type: ignore @distributed_trace def list_by_resource_group( self, resource_group_name: str, **kwargs: Any ) -> Iterable["_models.ImageListResult"]: """Gets the list of images under a resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ImageListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.compute.v2017_03_30.models.ImageListResult] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2017-03-30") # type: str cls = kwargs.pop('cls', None) # type: ClsType["_models.ImageListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_by_resource_group_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self.list_by_resource_group.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_by_resource_group_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request def extract_data(pipeline_response): deserialized = self._deserialize("ImageListResult", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_by_resource_group.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/images"} # type: ignore @distributed_trace def list( self, **kwargs: Any ) -> Iterable["_models.ImageListResult"]: """Gets the list of Images in the subscription. Use nextLink property in the response to get the next page of Images. Do this till nextLink is null to fetch all the Images. :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ImageListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.compute.v2017_03_30.models.ImageListResult] :raises: ~azure.core.exceptions.HttpResponseError """ api_version = kwargs.pop('api_version', "2017-03-30") # type: str cls = kwargs.pop('cls', None) # type: ClsType["_models.ImageListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_request( subscription_id=self._config.subscription_id, api_version=api_version, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_request( subscription_id=self._config.subscription_id, api_version=api_version, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request def extract_data(pipeline_response): deserialized = self._deserialize("ImageListResult", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.Compute/images"} # type: ignore

41.124819

187

0.654773

88d3a987205b05336482703559d27efc2269b815

6,387

py

Python

sentry_sdk/integrations/tornado.py

Siecje/sentry-python

d8405491c60c5b7c3d2ec3ed97ab4bea104f4e51

[ "BSD-2-Clause" ]

1

2019-04-15T03:36:19.000Z

sentry_sdk/integrations/tornado.py

Siecje/sentry-python

d8405491c60c5b7c3d2ec3ed97ab4bea104f4e51

[ "BSD-2-Clause" ]

null

sentry_sdk/integrations/tornado.py

Siecje/sentry-python

d8405491c60c5b7c3d2ec3ed97ab4bea104f4e51

[ "BSD-2-Clause" ]

null

import weakref from inspect import iscoroutinefunction from sentry_sdk.hub import Hub, _should_send_default_pii from sentry_sdk.utils import ( HAS_REAL_CONTEXTVARS, event_from_exception, capture_internal_exceptions, transaction_from_function, ) from sentry_sdk.integrations import Integration from sentry_sdk.integrations._wsgi_common import ( RequestExtractor, _filter_headers, _is_json_content_type, ) from sentry_sdk.integrations.logging import ignore_logger from tornado.web import RequestHandler, HTTPError # type: ignore from tornado.gen import coroutine # type: ignore if False: from typing import Any from typing import List from typing import Optional from typing import Dict from typing import Callable class TornadoIntegration(Integration): identifier = "tornado" @staticmethod def setup_once(): # type: () -> None import tornado # type: ignore tornado_version = getattr(tornado, "version_info", None) if tornado_version is None or tornado_version < (5, 0): raise RuntimeError("Tornado 5+ required") if not HAS_REAL_CONTEXTVARS: # Tornado is async. We better have contextvars or we're going to leak # state between requests. raise RuntimeError( "The tornado integration for Sentry requires Python 3.6+ or the aiocontextvars package" ) ignore_logger("tornado.application") ignore_logger("tornado.access") old_execute = RequestHandler._execute awaitable = iscoroutinefunction(old_execute) if awaitable: # Starting Tornado 6 RequestHandler._execute method is a standard Python coroutine (async/await) # In that case our method should be a coroutine function too async def sentry_execute_request_handler(self, *args, **kwargs): # type: (Any, *List, **Any) -> Any hub = Hub.current integration = hub.get_integration(TornadoIntegration) if integration is None: return await old_execute(self, *args, **kwargs) weak_handler = weakref.ref(self) with Hub(hub) as hub: with hub.configure_scope() as scope: scope.clear_breadcrumbs() scope.add_event_processor(_make_event_processor(weak_handler)) return await old_execute(self, *args, **kwargs) else: @coroutine # type: ignore def sentry_execute_request_handler(self, *args, **kwargs): hub = Hub.current integration = hub.get_integration(TornadoIntegration) if integration is None: return old_execute(self, *args, **kwargs) weak_handler = weakref.ref(self) with Hub(hub) as hub: with hub.configure_scope() as scope: scope.add_event_processor(_make_event_processor(weak_handler)) result = yield from old_execute(self, *args, **kwargs) return result RequestHandler._execute = sentry_execute_request_handler old_log_exception = RequestHandler.log_exception def sentry_log_exception(self, ty, value, tb, *args, **kwargs): # type: (Any, type, BaseException, Any, *Any, **Any) -> Optional[Any] _capture_exception(ty, value, tb) return old_log_exception(self, ty, value, tb, *args, **kwargs) RequestHandler.log_exception = sentry_log_exception def _capture_exception(ty, value, tb): # type: (type, BaseException, Any) -> None hub = Hub.current if hub.get_integration(TornadoIntegration) is None: return if isinstance(value, HTTPError): return event, hint = event_from_exception( (ty, value, tb), client_options=hub.client.options, mechanism={"type": "tornado", "handled": False}, ) hub.capture_event(event, hint=hint) def _make_event_processor(weak_handler): # type: (Callable[[], RequestHandler]) -> Callable def tornado_processor(event, hint): # type: (Dict[str, Any], Dict[str, Any]) -> Dict[str, Any] handler = weak_handler() if handler is None: return event request = handler.request with capture_internal_exceptions(): method = getattr(handler, handler.request.method.lower()) event["transaction"] = transaction_from_function(method) with capture_internal_exceptions(): extractor = TornadoRequestExtractor(request) extractor.extract_into_event(event) request_info = event["request"] request_info["url"] = "%s://%s%s" % ( request.protocol, request.host, request.path, ) request_info["query_string"] = request.query request_info["method"] = request.method request_info["env"] = {"REMOTE_ADDR": request.remote_ip} request_info["headers"] = _filter_headers(dict(request.headers)) with capture_internal_exceptions(): if handler.current_user and _should_send_default_pii(): event.setdefault("user", {})["is_authenticated"] = True return event return tornado_processor class TornadoRequestExtractor(RequestExtractor): def content_length(self): # type: () -> int if self.request.body is None: return 0 return len(self.request.body) def cookies(self): # type: () -> Dict return {k: v.value for k, v in self.request.cookies.items()} def raw_data(self): # type: () -> bytes return self.request.body def form(self): # type: () -> Optional[Any] return { k: [v.decode("latin1", "replace") for v in vs] for k, vs in self.request.body_arguments.items() } def is_json(self): # type: () -> bool return _is_json_content_type(self.request.headers.get("content-type")) def files(self): # type: () -> Dict return {k: v[0] for k, v in self.request.files.items() if v} def size_of_file(self, file): return len(file.body or ())

33.265625

108

0.617817

1b19a171614b14406a92a8b5831462d0ee184820

688

py

Python

OpenCV/Q2.py

fun-math/Autumn-of-Automation

08c04510f3500ac335f5c830ce3fbabb9c3fa05c

[ "MIT" ]

null

OpenCV/Q2.py

fun-math/Autumn-of-Automation

08c04510f3500ac335f5c830ce3fbabb9c3fa05c

[ "MIT" ]

null

OpenCV/Q2.py

fun-math/Autumn-of-Automation

08c04510f3500ac335f5c830ce3fbabb9c3fa05c

[ "MIT" ]

null

import cv2 import numpy as np import random img=cv2.imread("T.jpg",1) rows,cols,ch=img.shape M=np.float32([[1,0,0],[0,1,0]]) for i in range(8): x=random.randrange(40,80) y=random.randrange(40,80) sgnx=random.randrange(-1,2,2) sgny=random.randrange(-1,2,2) theta=random.randrange(0,360) M[0,2]=sgnx*x M[1,2]=sgny*y M_rot=cv2.getRotationMatrix2D((166,220),theta,1) img_new=cv2.warpAffine(img,M_rot,(cols,rows)) img_new=cv2.warpAffine(img_new,M,(cols,rows)) cv2.imshow(f"frame{i}",img_new) img_blur1=cv2.GaussianBlur(img,(5,5),0) cv2.imshow("frame8",img_blur1) img_blur2=cv2.bilateralFilter(img,9,75,75) cv2.imshow("frame9",img_blur2) cv2.waitKey(0) cv2.destroyAllWindows()

22.933333

49

0.728198

44fbfa2265f71f2f461f19fc0a07bc85c90a4609

1,643

py

Python

heap/k_largest_elements_immutable_max_heap.py

greyshell/ds_algorithm

6d61b56b5c91b8159b0705d1eb09718cc66b14f5

[ "MIT" ]

18

2020-04-09T02:53:55.000Z

2022-02-23T19:12:08.000Z

heap/k_largest_elements_immutable_max_heap.py

greyshell/ds_algorithm

6d61b56b5c91b8159b0705d1eb09718cc66b14f5

[ "MIT" ]

1

2020-06-22T00:35:30.000Z

2020-06-27T18:09:42.000Z

heap/k_largest_elements_immutable_max_heap.py

greyshell/ds_algorithm

6d61b56b5c91b8159b0705d1eb09718cc66b14f5

[ "MIT" ]

null

#!/usr/bin/env python3 # author: greyshell # description: find k largest elements from a immutable max heap from snowowl import Heap, HeapType class Node: def __init__(self, value, index): self.value = value self.index = index def __lt__(self, other): return self.value < other.value def __eq__(self, other): return self.value == other.value def get_k_largest_elements_immutable_max_heap(immutable_max_heap: Heap, k: int) -> list: """ time complexity: O(k * log k) space complexity: O(k) -> auxiliary max heap """ # create an auxiliary max heap auxiliary_max_heap = Heap([], heap_type=HeapType.MAX) # peek the min item from the immutable max heap # create a node obj with the value and index and push that object into auxiliary max heap node = Node(immutable_max_heap.peek(), 0) auxiliary_max_heap.insert(node) result = list() for i in range(0, k): node = auxiliary_max_heap.remove() result.append(node.value) index = node.index left_child_index = 2 * index + 1 if left_child_index < len(immutable_max_heap): left_child = immutable_max_heap.__getitem__(left_child_index) left_node = Node(left_child, left_child_index) auxiliary_max_heap.insert(left_node) right_child_index = 2 * index + 2 if right_child_index < len(immutable_max_heap): right_child = immutable_max_heap.__getitem__(right_child_index) right_node = Node(right_child, right_child_index) auxiliary_max_heap.insert(right_node) return result

29.339286

93

0.671942

3139657f501d1cb7dfb0c6da88355c321c47f1b8

335

py

Python

koku/api/settings/default_settings.py

rubik-ai/koku

3255d1c217b7b6685cb2e130bf4e025946e76fac

[ "Apache-2.0" ]

157

2018-04-30T16:27:53.000Z

2022-03-31T08:17:21.000Z

koku/api/settings/default_settings.py

rubik-ai/koku

3255d1c217b7b6685cb2e130bf4e025946e76fac

[ "Apache-2.0" ]

3,250

2018-04-26T14:14:25.000Z

2022-03-31T23:49:15.000Z

koku/api/settings/default_settings.py

rubik-ai/koku

3255d1c217b7b6685cb2e130bf4e025946e76fac

[ "Apache-2.0" ]

65

2018-05-10T14:11:50.000Z

2022-03-18T19:22:58.000Z

# # Copyright 2021 Red Hat Inc. # SPDX-License-Identifier: Apache-2.0 # """Set of default settings for the user_settings table jsonfield""" from koku.settings import KOKU_DEFAULT_COST_TYPE from koku.settings import KOKU_DEFAULT_CURRENCY DEFAULT_USER_SETTINGS = {"currency": KOKU_DEFAULT_CURRENCY, "cost_type": KOKU_DEFAULT_COST_TYPE}

33.5

96

0.81194

0718f4ea3eefec9642fd08e5fd4d109f052de471

872

py

Python

mockup page/plot.py

pohldavid/weather

880760a6840bfb2bca909e9ae3f06159107dba15

[ "CC0-1.0" ]

null

mockup page/plot.py

pohldavid/weather

880760a6840bfb2bca909e9ae3f06159107dba15

[ "CC0-1.0" ]

null

mockup page/plot.py

pohldavid/weather

880760a6840bfb2bca909e9ae3f06159107dba15

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 import matplotlib.pyplot as plt import csv def make_X_Axis_Label(): x_label = [] for h in range(8): x_label.append(str(h)+"AM") return x_label # x = ['Maths', 'Physics', 'Chemistry'] y1=[] y2=[] y3=[] with open('data.html','r') as csvfile: plot = csv.reader(csvfile, delimiter = '\t') for row in plot: y1.append(row[2]) y2.append(row[3]) y3.append(row[4]) print(y1) x = make_X_Axis_Label() # y1 = [95, 88, 45, 65, 87, 90, 46, 75] plt.plot(x, y1, label="Temperature \u00b0F") #y2 = [67, 45, 56, 55, 45, 56, 60, 62] plt.plot(x, y2, label="Humidity %") #y3 = [28.87, 29.92, 29.90, 29.91, 29.92, 29.92, 29.90, 28.90] plt.plot(x, y3, label="Pressure inHg") #plt.xlabel('Time') #plt.ylabel('Value') plt.title('BME_280 Humidity, Temperature, Pressure') plt.legend() plt.show()

17.44

62

0.59289

f0054ebfdbdcc3a45b0744f9a61f3289a685b6ef

181

py

Python

__Training__/Python - HackerRank/2. Basic Data Types/Tuples.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

__Training__/Python - HackerRank/2. Basic Data Types/Tuples.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

__Training__/Python - HackerRank/2. Basic Data Types/Tuples.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

# https://www.hackerrank.com/challenges/python-tuples/problem num = int(input()) # 2 int_list = tuple(map(int, input().split())) # 1 2 print(hash(int_list)) # 3713081631934410656

18.1

61

0.712707

88c68474d8821a923ee6073ff6b56471033a1a8f

862

py

Python

setup.py

smithblack-0/pygenetic

185e6b6f1a97e748094610cdf6557607024b4c8e

[ "MIT" ]

2

2020-05-30T05:13:37.000Z

2021-03-15T19:54:28.000Z

setup.py

smithblack-0/pygenetic

185e6b6f1a97e748094610cdf6557607024b4c8e

[ "MIT" ]

1

2021-06-19T20:30:25.000Z

setup.py

smithblack-0/pygenetic

185e6b6f1a97e748094610cdf6557607024b4c8e

[ "MIT" ]

2

2020-08-02T20:52:50.000Z

2021-02-07T15:52:15.000Z

import setuptools with open("README.md", "r") as f: long_description = f.read() setuptools.setup( name="pygenetic", version="1.0.2", author="Bharatraj S Telkar, Daniel Isaac, Shreyas V Patil", author_email="telkarraj@gmail.com, danielbcbs2@gmail.com, pshreyasv100@gmail.com", description="An Efficient Python Genetic Algorithm API", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/danny311296/pygenetic", packages=['pygenetic'], include_package_data=True, license='MIT', classifiers=( "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ), install_requires=[ 'rstr==2.2.6', 'numpy==1.15.4', 'matplotlib==2.2.2', ], )

30.785714

86

0.643852

06223891018246fe555c65b4ecd0acbf933cfc50

130

py

Python

remote-scripts/stop-server.py

stevecui/azure-linux-automation-1

ce5479f8ad229fb176c84b4ab924e78d30090e56

[ "Apache-2.0" ]

59

2015-01-21T17:04:14.000Z

2022-03-05T19:51:15.000Z

remote-scripts/stop-server.py

stevecui/azure-linux-automation-1

ce5479f8ad229fb176c84b4ab924e78d30090e56

[ "Apache-2.0" ]

24

2015-03-04T07:46:45.000Z

2018-10-11T07:32:55.000Z

remote-scripts/stop-server.py

stevecui/azure-linux-automation-1

ce5479f8ad229fb176c84b4ab924e78d30090e56

[ "Apache-2.0" ]

151

2015-01-16T06:51:57.000Z

2021-06-08T19:00:52.000Z

#!/usr/bin/python import subprocess import logging import string import os from azuremodules import * global op StopServer()

9.285714

26

0.776923

9a9223f8b1c7a88fe34c6ed2682b977c467f785b

9,303

py

Python

agent/vm_manager/vm_manager.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

21

2019-10-24T04:59:52.000Z

2021-05-11T12:47:17.000Z

agent/vm_manager/vm_manager.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

null

agent/vm_manager/vm_manager.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

9

2019-10-26T16:56:08.000Z

2021-03-15T14:10:21.000Z

import socket import sys import re import subprocess import time BPH_NAME = "[BLACKPHENIX]" def show_banner(): banner = \ """ -=[B L A C K P H E N I X]=- by Chris Navarrete @ FortiGuard Labs [VirtualMachine Server Manager] """ print(banner) class VBoxManager: def __init__(self): self.vm_manager = "C:\\Progra~1\\Oracle\\VirtualBox\\VBoxManage.exe" def run(self, vm_data, check_output=False): try: if check_output: print("{} | Checking Output".format(BPH_NAME)) return subprocess.check_output(vm_data) else: print("{} | Not-Checking Output".format(BPH_NAME)) subprocess.check_call(vm_data) except subprocess.CalledProcessError: return False else: return True class VBoxControl(VBoxManager): def __init__(self): super().__init__() self.nic_numbers = [] self.vm_running = False def __vm_cmd_output(self, cmd): try: output_data = self.run(cmd, check_output=True).decode('ascii').split('\n') if "not find a registered machine" in output_data: print("{} | Wrong machine name".format(BPH_NAME)) except AttributeError: print("{} | Error in the user input".format(BPH_NAME)) else: return output_data def __nic_counter(self, vm_id): cmd = [self.vm_manager, "showvminfo", vm_id] for data in self.__vm_cmd_output(cmd): if re.match(r'NIC\s\d+:\s+MAC', data): nic_found = re.search(r'(\d+)', data).group() print("{} | >> NIC Found: {}".format(BPH_NAME, nic_found)) if nic_found not in self.nic_numbers: self.nic_numbers.append(nic_found) print(self.nic_numbers) def __is_vm_running(self, vm_id): print("{} | Searching for running VMs".format(BPH_NAME)) status = None cmd = [self.vm_manager, "showvminfo", vm_id] for data in self.__vm_cmd_output(cmd): if "State: " in data: status = list([ status for status in data.split(' ') if len(status) != 0 ])[1] print("{} | Status Detected: {}".format(BPH_NAME, status)) if status is not None: if status == "restoring": print("{} | Restoring state detected. Waiting for a status change to avoid VM start-up problems...".format(BPH_NAME)) time.sleep(5) self.__is_vm_running(vm_id) # State: saved (since 2019-07-20T19:40:32.000000000) # State: restoring snapshot (since 2019-07-20T19:40:32.613000000) if status == "saved" or status == "running": print("{} | VM-ID:({}) Found".format(BPH_NAME, vm_id)) self.vm_running = True return True print("{} | VM-ID:({}) Not Found".format(BPH_NAME, vm_id)) self.vm_running = False return False def set_network(self, vm_data): print("{} | Setting up Network connection for the VM".format(BPH_NAME)) # Here, the network connection selected by the user will be activated. if self.__is_vm_running(vm_data['vm_id']): self.__nic_counter(vm_data['vm_id']) if len(self.nic_numbers) != 0: for nic_found in self.nic_numbers: # If nic is not the user's selected, then disable the rest. if vm_data['network_id'] != nic_found: cmd = [ self.vm_manager, "controlvm", vm_data['vm_id'], "setlinkstate{}".format(nic_found), "off" ] print(cmd) if self.run(cmd): print("{} | Deactivation of unused network interface was OK".format(BPH_NAME)) else: print("{} | Error when deactivating unused network interfaces".format(BPH_NAME)) # At this point all the network interfaces not-selected by the user # were turning off. Here the right one will be enabled. cmd = [ self.vm_manager, "controlvm", vm_data['vm_id'], "setlinkstate{}".format(vm_data['network_id']), "on" ] print(cmd) if self.run(cmd): print("{} | Network was set correctly".format(BPH_NAME)) else: print("{} | Network was not set".format(BPH_NAME)) def start(self, vm_data): print("{} | Starting VM".format(BPH_NAME)) cmd = [self.vm_manager, "startvm", vm_data['vm_id'], "--type", "gui"] print(cmd) if self.__is_vm_running(vm_data['vm_id']): # If VM is running, stop and restore. self.stop(vm_data) # Then restore and run. self.restore(vm_data) if self.run(cmd): print("{} | VM started correctly".format(BPH_NAME)) self.set_network(vm_data) return True return False def stop(self, vm_data): print("{} | Stopping VM".format(BPH_NAME)) cmd = [self.vm_manager, "controlvm", vm_data['vm_id'], "poweroff"] print(cmd) if self.__is_vm_running(vm_data['vm_id']): # If VM is running, stop it. if self.run(cmd): print("{} | VM stopped correctly".format(BPH_NAME)) return True return False def restore(self, vm_data): print("{} | Restoring VM".format(BPH_NAME)) cmd = [self.vm_manager, "snapshot", vm_data['vm_id'], "restore", vm_data['snapshot_id']] print(cmd) if self.__is_vm_running(vm_data['vm_id']): # If VM is running, stop and restore. self.stop(vm_data) if not self.vm_running: time.sleep(5) if self.run(cmd): print("{} | VM restoration OK".format(BPH_NAME)) return True return False def main(): show_banner() print("{} | Starting VM Control server...".format(BPH_NAME)) s = socket.socket() host = sys.argv[1] port = int(sys.argv[2]) s.bind((host, port)) s.listen(1) vbox = VBoxControl() while True: print("{} | Accepting connections".format(BPH_NAME)) try: client_socket, addr = s.accept() except KeyboardInterrupt: sys.exit() else: print('Receiving connection from:', addr) while True: print("{} | Waiting for data...".format(BPH_NAME)) data = client_socket.recv(512).decode('ascii') if data: if re.match(r'restart|restore|start|stop', data): print("{} | VM Command received: {}".format(BPH_NAME, data)) data = data.strip().split('|') vm_data = {} if len(data) == 4: print("{} | OK".format(BPH_NAME)) vm_data = {} vm_data['cmd'] = data[0] vm_data['vm_id'] = data[1] vm_data['snapshot_id'] = data[2] vm_data['network_id'] = data[3] print(vm_data) if vm_data['cmd'] == "start": if vbox.start(vm_data): client_socket.send(b'OK\n') else: client_socket.send(b'ERROR\n') elif vm_data['cmd'] == "stop": if vbox.stop(vm_data): client_socket.send(b'OK\n') else: client_socket.send(b'ERROR\n') else: print("{} | Unknown command: {}".format(BPH_NAME, data)) else: break if __name__ == '__main__': main()

37.361446

138

0.448565

54e04bb61b341ef9d3dad6089e5d30d4cc2e35ea

1,820

py

Python

voorbeelden/hardware/adc/example_arduino.py

ddland/TIS-TN-python

d1f7d864c09f0af907697e5d81d66a24c08814ad

[ "MIT" ]

3

2019-05-19T14:52:43.000Z

2020-09-24T07:54:29.000Z

voorbeelden/hardware/adc/example_arduino.py

ddland/TIS-TN-python

d1f7d864c09f0af907697e5d81d66a24c08814ad

[ "MIT" ]

1

2017-03-31T07:18:02.000Z

2017-05-03T20:21:20.000Z

voorbeelden/hardware/adc/example_arduino.py

ddland/TIS-TN-python

d1f7d864c09f0af907697e5d81d66a24c08814ad

[ "MIT" ]

4

2017-01-31T10:12:49.000Z

2021-11-18T07:47:16.000Z

import serial from TN_code.hardware import get_data from TN_code.hardware import write_data ser = serial.Serial('/dev/ttyACM0', 9600) # arduino """ AnalogReadSerial.ino -> 1 datapunt /* AnalogReadSerial Reads an analog input on pin 0, prints the result to the serial monitor. Attach the center pin of a potentiometer to pin A0, and the outside pins to +5V and ground. This example code is in the public domain. */ // the setup routine runs once when you press reset: void setup() { // initialize serial communication at 9600 bits per second: Serial.begin(9600); } // the loop routine runs over and over again forever: void loop() { // read the input on analog pin 0: int sensorValue = analogRead(A0); // print out the value you read: Serial.println(sensorValue); delay(1); // delay in between reads for stability } """ # data = get_data.readArduino(ser) """ AnalogReadSerial.ino -> 2 datapunten /* AnalogReadSerial Reads an analog input on pin 0, prints the result to the serial monitor. Attach the center pin of a potentiometer to pin A0, and the outside pins to +5V and ground. This example code is in the public domain. */ String semicolumn, values2; // the setup routine runs once when you press reset: void setup() { // initialize serial communication at 9600 bits per second: Serial.begin(9600); semicolumn = ";"; } // the loop routine runs over and over again forever: void loop() { // read the input on analog pin 0: int sensorValue1 = analogRead(A0); int sensorValue2 = analogRead(A1); // print out the value you read: values2 = sensorValue1 + semicolumn; values2 = values2 + sensorValue2; Serial.println(values2); delay(1); // delay in between reads for stability } """ data = get_data.readArduino(ser, Ndata=2) print(data)

24.931507

74

0.713736

b736d096ff0493d96a7dedcc3d8df9a646bf11f3

1,733

py

Python

utils/transforms.py

AndreRoelofs/Random-Erasing

2dd4c1ac82d27423fc16b450c8ea07a55cff7b9d

[ "Apache-2.0" ]

650

2017-09-15T09:01:45.000Z

2022-03-22T08:22:54.000Z

utils/transforms.py

AndreRoelofs/Random-Erasing

2dd4c1ac82d27423fc16b450c8ea07a55cff7b9d

[ "Apache-2.0" ]

18

2017-09-23T15:25:11.000Z

2022-03-09T13:23:00.000Z

utils/transforms.py

AndreRoelofs/Random-Erasing

2dd4c1ac82d27423fc16b450c8ea07a55cff7b9d

[ "Apache-2.0" ]

160

2017-10-19T08:22:53.000Z

2022-03-25T07:00:32.000Z

from __future__ import absolute_import from torchvision.transforms import * import numpy as np import torch class RandomErasing(object): def __init__(self, EPSILON = 0.5, sl = 0.02, sh = 0.4, r1 = 0.3, mean=[0.4914, 0.4822, 0.4465]): self.EPSILON = EPSILON self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 def __call__(self, img): if random.uniform(0, 1) > self.EPSILON: return img for attempt in range(100): area = img.size()[1] * img.size()[2] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1/self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < img.size()[2] and h < img.size()[1]: x1 = random.randint(0, img.size()[1] - h) y1 = random.randint(0, img.size()[2] - w) if img.size()[0] == 3: #img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1) #img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1) #img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1) img[0, x1:x1+h, y1:y1+w] = self.mean[0] img[1, x1:x1+h, y1:y1+w] = self.mean[1] img[2, x1:x1+h, y1:y1+w] = self.mean[2] #img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w)) else: img[0, x1:x1+h, y1:y1+w] = self.mean[1] # img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w)) return img return img

36.104167

100

0.491056

a2684607f62942c572a3f726e3f07004430e0d84

9,097

py

Python

EnterpriseAPT29Eval.py

0xF2EDCA5A/EnterpriseAPT29Eval

425a62ca34a9baba8283a97a4124cb79d0e662e3

[ "MIT" ]

null

EnterpriseAPT29Eval.py

0xF2EDCA5A/EnterpriseAPT29Eval

425a62ca34a9baba8283a97a4124cb79d0e662e3

[ "MIT" ]

null

EnterpriseAPT29Eval.py

0xF2EDCA5A/EnterpriseAPT29Eval

425a62ca34a9baba8283a97a4124cb79d0e662e3

[ "MIT" ]

null

from enum import Enum import pandas as pd import json import glob import os class EnterpriseAPT29Eval(): def __init__(self, filename): self._vendor = filename.split('/', 2)[2] self._vendor = self._vendor.split('.', 1)[0] with open(filename, 'r') as infile: data=infile.read() self._obj = json.loads(data)['Techniques'] self._df = pd.json_normalize(self._obj,'Steps', ['TechniqueId','TechniqueName', 'Tactics']) self._steps = None self._dfir = None self._mssp = None self._scores = None self._visibility = None self._correlated = None self._actionability = None self._alerts = None self._alerts_correlated = None self._uncorrelated_alert_steps = None # sort and reindex dataframe by substep def sortSubSteps(self, cleanup=False): ver = self._df['SubStep'].str.split('.', expand=True) self._df['Major'] = ver[0].astype(int) self._df['Alpha'] = ver[1] self._df['Minor'] = ver[2].astype(int) self._df.sort_values(by=['Major','Alpha','Minor'], inplace=True) self._df.reset_index(drop=True, inplace=True) if cleanup: self._df.drop(columns=['Major', 'Alpha', 'Minor'], inplace=True) # flatten Tactics json, throwing away id's since not sequential anyway def flattenTactics(self, inplace=False): self._df['Tactics' if inplace else 'Tactic'] = self._df['Tactics'].apply(lambda x: x[0]['TacticName'] if len(x)==1 else x[0]['TacticName'] + ', ' + x[1]['TacticName']) # row level operations when flattening detections def _flattenDetections(self, detections, confchange=False): ret, mods, mssp = 'None', [], False dt = Enum('DetectionTypes', 'None Telemetry General Tactic Technique') for detection in detections: # check if we're allowing conf change and there is one if not confchange: ischange = False for modifier in detection['Modifiers']: if modifier.startswith('Configuration Change'): ischange = True if ischange: continue if detection['DetectionType'] == 'N/A': ret = detection['DetectionType'] mods = detection['Modifiers'] break if detection['DetectionType'] == 'MSSP': mssp = True elif dt[ret].value < dt[detection['DetectionType']].value: ret = detection['DetectionType'] mods = detection['Modifiers'] return pd.Series([ret, sorted(mods), mssp]) def flattenDetections(self, inplace=False, confchange=False): detections = self._df['Detections'].apply(lambda x: self._flattenDetections(x, confchange)) self._df['Detections' if inplace else 'Detection'] = detections[0] self._df['Modifiers'] = detections[1] self._df['MSSP'] = detections[2] def get_steps(self): if self._steps == None: self.flattenDetections(confchange=True) removed = pd.value_counts(self._df['Detection'].values)['N/A'] self._steps = len(self._df.index) - removed return self._steps steps = property(get_steps) # This attempts to calculate the max visibility the product enables # when configured to see/detect everything as may be adventagous for # a digital forensics professional performing an incident response. def score_dfir(self): if self._steps == None: self.get_steps() if self._dfir == None: misses = pd.value_counts(self._df['Detection'].values)['None'] self._dfir = self._steps - misses def get_dfir(self): if self._dfir == None: self.score_dfir() return self._dfir dfir = property(get_dfir) # This is a straight count of the number of MSSP detections reported # by MITRE during the evaluation. This scoring was done under the # DFIR configuration during the eval and must be compared to that. def score_mssp(self): if self._dfir == None: self.score_dfir() if self._mssp == None: if True in self._df['MSSP'].values: self._mssp = pd.value_counts(self._df['MSSP'].values)[True] else: self._mssp = 0 def get_mssp(self): if self._mssp == None: self.score_mssp() return self._mssp mssp = property(get_mssp) def score_detections(self): self.sortSubSteps() if self._visibility == None: self.flattenDetections(confchange=False) misses = pd.value_counts(self._df['Detection'].values)['None'] self._visibility = self._steps - misses if self._correlated == None: self._correlated = 0 self._alerts = 0 self._alerts_correlated = 0 self._uncorrelated_alert_steps = 0 self._techniques = 0 arr = [] for index, row in self._df.iterrows(): if 'Correlated' in row['Modifiers']: self._correlated += 1 if 'Alert' in row['Modifiers']: self._alerts += 1 if 'Correlated' in row['Modifiers']: self._alerts_correlated += 1 elif row['Major'] not in arr: self._uncorrelated_alert_steps += 1 arr.append(row['Major']) if row['Detection'] == 'Technique': self._techniques += 1 if self._actionability == None: self._efficiency = 1 - (self._alerts/self._steps) if self._alerts > 0: self._quality = (self._alerts_correlated + self._uncorrelated_alert_steps + self._techniques)/(2 * self._alerts) else: self._quality = 0 self._actionability = self._efficiency * self._quality if self._scores == None: self._scores = {'vendor' : self._vendor, \ 'alerts' : self._alerts, \ 'visibility' : self._visibility/self._steps, \ 'correlation' : self._correlated/self._visibility, \ 'efficiency' : self._efficiency, \ 'quality' : self._quality, \ 'actionability': self._actionability} def get_scores(self): if self._scores == None: self.score_detections() return self._scores scores = property(get_scores) def get_actionability(self): if self._actionability == None: self.score_detections() return self._actionability actionability = property(get_actionability) def get_efficiency(self): if self._efficiency == None: self.score_detections() return self._efficiency efficiency = property(get_efficiency) def get_quality(self): if self._quality == None: self.score_detections() return self._quality quality = property(get_quality) def get_visibility(self): if self._visibility == None: self.score_detections() return self._visibility visibility = property(get_visibility) def get_correlated(self): if self._correlated == None: self.score_detections() return self._correlated correlated = property(get_correlated) def get_vendor(self): return self._vendor vendor = property(get_vendor) def get_alerts(self): if self._alerts == None: self.score_detections() return self._alerts alerts = property(get_alerts) def get_dataframe(self): return self._df df = property(get_dataframe) def readout(results): print(f'{results.vendor}\n---------------------------') if results.mssp > 0: print(f'The MSSP service was able to detect {results.mssp} of the {results.dfir} events the product was able') print(f'to detect under a dfir configuration, for an efficacy of {(results.mssp * 100)/results.dfir :.2f}%') else: print(f'The vendor doesn\'t appear to have been leveraging an MSSP service. It should') print(f'still be noted that a dfir configuration identified {results.dfir} events.') print(f'\nThe product provided visibility out of the box for {results.visibility} of {results.steps} steps, for an') print(f'efficacy of {(results.visibility * 100)/results.steps :.2f}%') print(f'\nThe product was able to correlate {results.correlated} of the {results.visibility} events it had visibility into') print(f'out of the box, for an efficacy of {(results.correlated * 100)/results.visibility :.2f}%\n') if results.alerts > 0: print(f'The product generated {results.alerts} distinct alerts for an efficiency of {results.efficiency * 100 :.2f}%, with an') print(f'alert quality of {results.quality * 100:.2f}%, for an overall alert actionability metric of {results.quality * results.efficiency * 100 :.2f}%\n') else: print(f'The product was unable to generate any alerts.\n') def write_xlsx(dfs, columns=['SubStep', 'Procedure', 'Tactic', 'TechniqueId', 'TechniqueName', 'Detection', 'Modifiers', 'MSSP']): writer = pd.ExcelWriter(f'apt29eval.xlsx', engine='xlsxwriter') results = pd.DataFrame(columns=['vendor', \ 'alerts', \ 'visibility', \ 'correlation', \ 'efficiency', \ 'quality', \ 'actionability']) # Write out results tab for vendor in dfs.keys(): results = results.append([dfs[vendor].scores], ignore_index=True) results.to_excel(writer, sheet_name='Results', index=False) # Write out individual vendor tabs for vendor in dfs.keys(): dfs[vendor].flattenTactics() dfs[vendor].sortSubSteps(cleanup=True) dfs[vendor].df.to_excel(writer, sheet_name=vendor, index=False, columns=columns) writer.save() if __name__ == '__main__': results = {} for infile in sorted(glob.glob(os.path.join('./data/', '*json'))): obj = EnterpriseAPT29Eval(infile) readout(obj) results.update({obj.vendor: obj}) write_xlsx(results)

30.62963

169

0.68682

e3da042c1f03d48514d0799410f5572c12656ce4

32,323

py

Python

numba/tests/test_array_reductions.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

76

2020-07-06T14:44:05.000Z

2022-02-14T15:30:21.000Z

numba/tests/test_array_reductions.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

11

2020-08-09T02:30:14.000Z

2022-03-12T00:50:14.000Z

numba/tests/test_array_reductions.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

11

2020-07-12T16:18:07.000Z

2022-02-05T16:48:35.000Z

from itertools import product, combinations_with_replacement import numpy as np from numba import jit, typeof from numba.core.compiler import compile_isolated from numba.tests.support import TestCase, MemoryLeakMixin, tag import unittest def array_all(arr): return arr.all() def array_all_global(arr): return np.all(arr) def array_any(arr): return arr.any() def array_any_global(arr): return np.any(arr) def array_cumprod(arr): return arr.cumprod() def array_cumprod_global(arr): return np.cumprod(arr) def array_nancumprod(arr): return np.nancumprod(arr) def array_cumsum(arr): return arr.cumsum() def array_cumsum_global(arr): return np.cumsum(arr) def array_nancumsum(arr): return np.nancumsum(arr) def array_sum(arr): return arr.sum() def array_sum_global(arr): return np.sum(arr) def array_prod(arr): return arr.prod() def array_prod_global(arr): return np.prod(arr) def array_mean(arr): return arr.mean() def array_mean_global(arr): return np.mean(arr) def array_var(arr): return arr.var() def array_var_global(arr): return np.var(arr) def array_std(arr): return arr.std() def array_std_global(arr): return np.std(arr) def array_min(arr): return arr.min() def array_min_global(arr): return np.min(arr) def array_max(arr): return arr.max() def array_max_global(arr): return np.max(arr) def array_argmin(arr): return arr.argmin() def array_argmin_global(arr): return np.argmin(arr) def array_argmax(arr): return arr.argmax() def array_argmax_global(arr): return np.argmax(arr) def array_median_global(arr): return np.median(arr) def array_nanmin(arr): return np.nanmin(arr) def array_nanmax(arr): return np.nanmax(arr) def array_nanmean(arr): return np.nanmean(arr) def array_nansum(arr): return np.nansum(arr) def array_nanprod(arr): return np.nanprod(arr) def array_nanstd(arr): return np.nanstd(arr) def array_nanvar(arr): return np.nanvar(arr) def array_nanmedian_global(arr): return np.nanmedian(arr) def array_percentile_global(arr, q): return np.percentile(arr, q) def array_nanpercentile_global(arr, q): return np.nanpercentile(arr, q) def array_ptp_global(a): return np.ptp(a) def array_quantile_global(arr, q): return np.quantile(arr, q) def array_nanquantile_global(arr, q): return np.nanquantile(arr, q) def base_test_arrays(dtype): if dtype == np.bool_: def factory(n): assert n % 2 == 0 return np.bool_([0, 1] * (n // 2)) else: def factory(n): return np.arange(n, dtype=dtype) + 1 a1 = factory(10) a2 = factory(10).reshape(2, 5) # The prod() of this array fits in a 32-bit int a3 = (factory(12))[::-1].reshape((2, 3, 2), order='A') assert not (a3.flags.c_contiguous or a3.flags.f_contiguous) return [a1, a2, a3] def full_test_arrays(dtype): array_list = base_test_arrays(dtype) # Add floats with some mantissa if dtype == np.float32: array_list += [a / 10 for a in array_list] # add imaginary part if dtype == np.complex64: acc = [] for a in array_list: tmp = a / 10 + 1j * a / 11 tmp[::2] = np.conj(tmp[::2]) acc.append(tmp) array_list.extend(acc) for a in array_list: assert a.dtype == np.dtype(dtype) return array_list def run_comparative(compare_func, test_array): arrty = typeof(test_array) cres = compile_isolated(compare_func, [arrty]) numpy_result = compare_func(test_array) numba_result = cres.entry_point(test_array) return numpy_result, numba_result class TestArrayReductions(MemoryLeakMixin, TestCase): """ Test array reduction methods and functions such as .sum(), .max(), etc. """ def setUp(self): super(TestArrayReductions, self).setUp() np.random.seed(42) def check_reduction_basic(self, pyfunc, **kwargs): # Basic reduction checks on 1-d float64 arrays cfunc = jit(nopython=True)(pyfunc) def check(arr): self.assertPreciseEqual(pyfunc(arr), cfunc(arr), **kwargs) arr = np.float64([1.0, 2.0, 0.0, -0.0, 1.0, -1.5]) check(arr) arr = np.float64([-0.0, -1.5]) check(arr) arr = np.float64([-1.5, 2.5, 'inf']) check(arr) arr = np.float64([-1.5, 2.5, '-inf']) check(arr) arr = np.float64([-1.5, 2.5, 'inf', '-inf']) check(arr) arr = np.float64(['nan', -1.5, 2.5, 'nan', 3.0]) check(arr) arr = np.float64(['nan', -1.5, 2.5, 'nan', 'inf', '-inf', 3.0]) check(arr) arr = np.float64([5.0, 'nan', -1.5, 'nan']) check(arr) # Only NaNs arr = np.float64(['nan', 'nan']) check(arr) def test_all_basic(self, pyfunc=array_all): cfunc = jit(nopython=True)(pyfunc) def check(arr): self.assertPreciseEqual(pyfunc(arr), cfunc(arr)) arr = np.float64([1.0, 0.0, float('inf'), float('nan')]) check(arr) arr[1] = -0.0 check(arr) arr[1] = 1.5 check(arr) arr = arr.reshape((2, 2)) check(arr) check(arr[::-1]) def test_any_basic(self, pyfunc=array_any): cfunc = jit(nopython=True)(pyfunc) def check(arr): self.assertPreciseEqual(pyfunc(arr), cfunc(arr)) arr = np.float64([0.0, -0.0, 0.0, 0.0]) check(arr) arr[2] = float('nan') check(arr) arr[2] = float('inf') check(arr) arr[2] = 1.5 check(arr) arr = arr.reshape((2, 2)) check(arr) check(arr[::-1]) def test_sum_basic(self): self.check_reduction_basic(array_sum) def test_mean_basic(self): self.check_reduction_basic(array_mean) def test_var_basic(self): self.check_reduction_basic(array_var, prec='double') def test_std_basic(self): self.check_reduction_basic(array_std) def test_min_basic(self): self.check_reduction_basic(array_min) def test_max_basic(self): self.check_reduction_basic(array_max) def test_argmin_basic(self): self.check_reduction_basic(array_argmin) def test_argmax_basic(self): self.check_reduction_basic(array_argmax) def test_nanmin_basic(self): self.check_reduction_basic(array_nanmin) def test_nanmax_basic(self): self.check_reduction_basic(array_nanmax) def test_nanmean_basic(self): self.check_reduction_basic(array_nanmean) def test_nansum_basic(self): self.check_reduction_basic(array_nansum) def test_nanprod_basic(self): self.check_reduction_basic(array_nanprod) def test_nanstd_basic(self): self.check_reduction_basic(array_nanstd) def test_nanvar_basic(self): self.check_reduction_basic(array_nanvar, prec='double') def check_median_basic(self, pyfunc, array_variations): cfunc = jit(nopython=True)(pyfunc) def check(arr): expected = pyfunc(arr) got = cfunc(arr) self.assertPreciseEqual(got, expected) # Odd sizes def check_odd(a): check(a) a = a.reshape((9, 7)) check(a) check(a.T) for a in array_variations(np.arange(63) + 10.5): check_odd(a) # Even sizes def check_even(a): check(a) a = a.reshape((4, 16)) check(a) check(a.T) for a in array_variations(np.arange(64) + 10.5): check_even(a) @staticmethod def _array_variations(a): # Sorted, reversed, random, many duplicates, many NaNs, all NaNs yield a a = a[::-1].copy() yield a np.random.shuffle(a) yield a a[a % 4 >= 1] = 3.5 yield a a[a % 4 >= 2] = np.nan yield a a[:] = np.nan yield a def test_median_basic(self): pyfunc = array_median_global def variations(a): # Sorted, reversed, random, many duplicates yield a a = a[::-1].copy() yield a np.random.shuffle(a) yield a a[a % 4 >= 1] = 3.5 yield a self.check_median_basic(pyfunc, variations) def check_percentile_and_quantile(self, pyfunc, q_upper_bound): cfunc = jit(nopython=True)(pyfunc) def check(a, q, abs_tol=1e-12): expected = pyfunc(a, q) got = cfunc(a, q) self.assertPreciseEqual(got, expected, abs_tol=abs_tol) a = self.random.randn(27).reshape(3, 3, 3) q = np.linspace(0, q_upper_bound, 14)[::-1] check(a, q) check(a, 0) check(a, q_upper_bound / 2) check(a, q_upper_bound) not_finite = [np.nan, -np.inf, np.inf] a.flat[:10] = self.random.choice(not_finite, 10) self.random.shuffle(a) self.random.shuffle(q) check(a, q) a = a.flatten().tolist() q = q.flatten().tolist() check(a, q) check(tuple(a), tuple(q)) a = self.random.choice([1, 2, 3, 4], 10) q = np.linspace(0, q_upper_bound, 5) check(a, q) # tests inspired by # https://github.com/numpy/numpy/blob/345b2f6e/numpy/lib/tests/test_function_base.py x = np.arange(8) * 0.5 np.testing.assert_equal(cfunc(x, 0), 0.) np.testing.assert_equal(cfunc(x, q_upper_bound), 3.5) np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 1.75) x = np.arange(12).reshape(3, 4) q = np.array((0.25, 0.5, 1.0)) * q_upper_bound np.testing.assert_equal(cfunc(x, q), [2.75, 5.5, 11.0]) x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) q = np.array((0.25, 0.50)) * q_upper_bound np.testing.assert_equal(cfunc(x, q).shape, (2,)) q = np.array((0.25, 0.50, 0.75)) * q_upper_bound np.testing.assert_equal(cfunc(x, q).shape, (3,)) x = np.arange(12).reshape(3, 4) np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 5.5) self.assertTrue(np.isscalar(cfunc(x, q_upper_bound / 2))) np.testing.assert_equal(cfunc([1, 2, 3], 0), 1) a = np.array([2, 3, 4, 1]) cfunc(a, [q_upper_bound / 2]) np.testing.assert_equal(a, np.array([2, 3, 4, 1])) def check_percentile_edge_cases(self, pyfunc, q_upper_bound=100): cfunc = jit(nopython=True)(pyfunc) def check(a, q, abs_tol=1e-14): expected = pyfunc(a, q) got = cfunc(a, q) self.assertPreciseEqual(got, expected, abs_tol=abs_tol) def convert_to_float_and_check(a, q, abs_tol=1e-14): expected = pyfunc(a, q).astype(np.float64) got = cfunc(a, q) self.assertPreciseEqual(got, expected, abs_tol=abs_tol) def _array_combinations(elements): for i in range(1, 10): for comb in combinations_with_replacement(elements, i): yield np.array(comb) # high number of combinations, many including non-finite values q = (0, 0.1 * q_upper_bound, 0.2 * q_upper_bound, q_upper_bound) element_pool = (1, -1, np.nan, np.inf, -np.inf) for a in _array_combinations(element_pool): check(a, q) # edge cases - numpy exhibits behavioural differences across # platforms, see: https://github.com/numpy/numpy/issues/13272 if q_upper_bound == 1: _check = convert_to_float_and_check else: _check = check a = np.array(5) q = np.array(1) _check(a, q) a = True q = False _check(a, q) a = np.array([False, True, True]) q = a _check(a, q) a = 5 q = q_upper_bound / 2 _check(a, q) def check_percentile_exceptions(self, pyfunc): cfunc = jit(nopython=True)(pyfunc) def check_err(a, q): with self.assertRaises(ValueError) as raises: cfunc(a, q) self.assertEqual( "Percentiles must be in the range [0, 100]", str(raises.exception) ) # Exceptions leak references self.disable_leak_check() a = np.arange(5) check_err(a, -5) # q less than 0 check_err(a, (1, 10, 105)) # q contains value greater than 100 check_err(a, (1, 10, np.nan)) # q contains nan with self.assertTypingError() as e: a = np.arange(5) * 1j q = 0.1 cfunc(a, q) self.assertIn('Not supported for complex dtype', str(e.exception)) def check_quantile_exceptions(self, pyfunc): cfunc = jit(nopython=True)(pyfunc) def check_err(a, q): with self.assertRaises(ValueError) as raises: cfunc(a, q) self.assertEqual( "Quantiles must be in the range [0, 1]", str(raises.exception) ) # Exceptions leak references self.disable_leak_check() a = np.arange(5) check_err(a, -0.5) # q less than 0 check_err(a, (0.1, 0.10, 1.05)) # q contains value greater than 1 check_err(a, (0.1, 0.10, np.nan)) # q contains nan with self.assertTypingError() as e: a = np.arange(5) * 1j q = 0.1 cfunc(a, q) self.assertIn('Not supported for complex dtype', str(e.exception)) def test_percentile_basic(self): pyfunc = array_percentile_global self.check_percentile_and_quantile(pyfunc, q_upper_bound=100) self.check_percentile_edge_cases(pyfunc, q_upper_bound=100) self.check_percentile_exceptions(pyfunc) def test_nanpercentile_basic(self): pyfunc = array_nanpercentile_global self.check_percentile_and_quantile(pyfunc, q_upper_bound=100) self.check_percentile_edge_cases(pyfunc, q_upper_bound=100) self.check_percentile_exceptions(pyfunc) def test_quantile_basic(self): pyfunc = array_quantile_global self.check_percentile_and_quantile(pyfunc, q_upper_bound=1) self.check_percentile_edge_cases(pyfunc, q_upper_bound=1) self.check_quantile_exceptions(pyfunc) def test_nanquantile_basic(self): pyfunc = array_nanquantile_global self.check_percentile_and_quantile(pyfunc, q_upper_bound=1) self.check_percentile_edge_cases(pyfunc, q_upper_bound=1) self.check_quantile_exceptions(pyfunc) def test_nanmedian_basic(self): pyfunc = array_nanmedian_global self.check_median_basic(pyfunc, self._array_variations) def test_array_sum_global(self): arr = np.arange(10, dtype=np.int32) arrty = typeof(arr) self.assertEqual(arrty.ndim, 1) self.assertEqual(arrty.layout, 'C') cres = compile_isolated(array_sum_global, [arrty]) cfunc = cres.entry_point self.assertEqual(np.sum(arr), cfunc(arr)) def test_array_prod_int_1d(self): arr = np.arange(10, dtype=np.int32) + 1 arrty = typeof(arr) self.assertEqual(arrty.ndim, 1) self.assertEqual(arrty.layout, 'C') cres = compile_isolated(array_prod, [arrty]) cfunc = cres.entry_point self.assertEqual(arr.prod(), cfunc(arr)) def test_array_prod_float_1d(self): arr = np.arange(10, dtype=np.float32) + 1 / 10 arrty = typeof(arr) self.assertEqual(arrty.ndim, 1) self.assertEqual(arrty.layout, 'C') cres = compile_isolated(array_prod, [arrty]) cfunc = cres.entry_point np.testing.assert_allclose(arr.prod(), cfunc(arr)) def test_array_prod_global(self): arr = np.arange(10, dtype=np.int32) arrty = typeof(arr) self.assertEqual(arrty.ndim, 1) self.assertEqual(arrty.layout, 'C') cres = compile_isolated(array_prod_global, [arrty]) cfunc = cres.entry_point np.testing.assert_allclose(np.prod(arr), cfunc(arr)) def check_cumulative(self, pyfunc): arr = np.arange(2, 10, dtype=np.int16) expected, got = run_comparative(pyfunc, arr) self.assertPreciseEqual(got, expected) arr = np.linspace(2, 8, 6) expected, got = run_comparative(pyfunc, arr) self.assertPreciseEqual(got, expected) arr = arr.reshape((3, 2)) expected, got = run_comparative(pyfunc, arr) self.assertPreciseEqual(got, expected) def test_array_cumsum(self): self.check_cumulative(array_cumsum) def test_array_cumsum_global(self): self.check_cumulative(array_cumsum_global) def test_array_cumprod(self): self.check_cumulative(array_cumprod) def test_array_cumprod_global(self): self.check_cumulative(array_cumprod_global) def check_aggregation_magnitude(self, pyfunc, is_prod=False): """ Check that integer overflows are avoided (issue #931). """ # Overflows are avoided here (ints are cast either to intp # or float64). n_items = 2 if is_prod else 10 # avoid overflow on prod() arr = (np.arange(n_items) + 40000).astype('int16') npr, nbr = run_comparative(pyfunc, arr) self.assertPreciseEqual(npr, nbr) # Overflows are avoided for functions returning floats here. # Other functions may wrap around. arr = (np.arange(10) + 2**60).astype('int64') npr, nbr = run_comparative(pyfunc, arr) self.assertPreciseEqual(npr, nbr) arr = arr.astype('uint64') npr, nbr = run_comparative(pyfunc, arr) self.assertPreciseEqual(npr, nbr) def test_sum_magnitude(self): self.check_aggregation_magnitude(array_sum) self.check_aggregation_magnitude(array_sum_global) def test_cumsum_magnitude(self): self.check_aggregation_magnitude(array_cumsum) self.check_aggregation_magnitude(array_cumsum_global) def test_nancumsum_magnitude(self): self.check_aggregation_magnitude(array_nancumsum, is_prod=True) def test_prod_magnitude(self): self.check_aggregation_magnitude(array_prod, is_prod=True) self.check_aggregation_magnitude(array_prod_global, is_prod=True) def test_cumprod_magnitude(self): self.check_aggregation_magnitude(array_cumprod, is_prod=True) self.check_aggregation_magnitude(array_cumprod_global, is_prod=True) def test_nancumprod_magnitude(self): self.check_aggregation_magnitude(array_nancumprod, is_prod=True) def test_mean_magnitude(self): self.check_aggregation_magnitude(array_mean) self.check_aggregation_magnitude(array_mean_global) def test_var_magnitude(self): self.check_aggregation_magnitude(array_var) self.check_aggregation_magnitude(array_var_global) def test_std_magnitude(self): self.check_aggregation_magnitude(array_std) self.check_aggregation_magnitude(array_std_global) def _do_check_nptimedelta(self, pyfunc, arr): arrty = typeof(arr) cfunc = jit(nopython=True)(pyfunc) self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) # Even vs. odd size, for np.median self.assertPreciseEqual(cfunc(arr[:-1]), pyfunc(arr[:-1])) # Test with different orders, for np.median arr = arr[::-1].copy() # Keep 'C' layout self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) np.random.shuffle(arr) self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) # Test with a NaT arr[arr.size // 2] = 'NaT' self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) if 'median' not in pyfunc.__name__: # Test with (val, NaT)^N (and with the random NaT from above) # use a loop, there's some weird thing/bug with arr[1::2] = 'NaT' # Further Numba has bug(s) relating to NaN/NaT handling in anything # using a partition such as np.median for x in range(1, len(arr), 2): arr[x] = 'NaT' self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) # Test with all NaTs arr.fill(arrty.dtype('NaT')) self.assertPreciseEqual(cfunc(arr), pyfunc(arr)) def check_npdatetime(self, pyfunc): arr = np.arange(10).astype(dtype='M8[Y]') self._do_check_nptimedelta(pyfunc, arr) def check_nptimedelta(self, pyfunc): arr = np.arange(10).astype(dtype='m8[s]') self._do_check_nptimedelta(pyfunc, arr) def test_min_npdatetime(self): self.check_npdatetime(array_min) self.check_nptimedelta(array_min) def test_max_npdatetime(self): self.check_npdatetime(array_max) self.check_nptimedelta(array_max) def test_argmin_npdatetime(self): self.check_npdatetime(array_argmin) self.check_nptimedelta(array_argmin) def test_argmax_npdatetime(self): self.check_npdatetime(array_argmax) self.check_nptimedelta(array_argmax) def test_median_npdatetime(self): self.check_nptimedelta(array_median_global) def test_sum_npdatetime(self): self.check_nptimedelta(array_sum) def test_cumsum_npdatetime(self): self.check_nptimedelta(array_cumsum) def test_mean_npdatetime(self): self.check_nptimedelta(array_mean) def check_nan_cumulative(self, pyfunc): cfunc = jit(nopython=True)(pyfunc) def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) def _set_some_values_to_nan(a): p = a.size // 2 # set approx half elements to NaN np.put(a, np.random.choice(range(a.size), p, replace=False), np.nan) return a def a_variations(): yield np.linspace(-1, 3, 60).reshape(3, 4, 5) yield np.array([np.inf, 3, 4]) yield np.array([True, True, True, False]) yield np.arange(1, 10) yield np.asfortranarray(np.arange(1, 64) - 33.3) yield np.arange(1, 10, dtype=np.float32)[::-1] for a in a_variations(): check(a) # no nans check(_set_some_values_to_nan(a.astype(np.float64))) # about 50% nans # edge cases check(np.array([])) check(np.full(10, np.nan)) parts = np.array([np.nan, 2, np.nan, 4, 5, 6, 7, 8, 9]) a = parts + 1j * parts[::-1] a = a.reshape(3, 3) check(a) def test_nancumprod_basic(self): self.check_cumulative(array_nancumprod) self.check_nan_cumulative(array_nancumprod) def test_nancumsum_basic(self): self.check_cumulative(array_nancumsum) self.check_nan_cumulative(array_nancumsum) def test_ptp_basic(self): pyfunc = array_ptp_global cfunc = jit(nopython=True)(pyfunc) def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) def a_variations(): yield np.arange(10) yield np.array([-1.1, np.nan, 2.2]) yield np.array([-np.inf, 5]) yield (4, 2, 5) yield (1,) yield np.full(5, 5) yield [2.2, -2.3, 0.1] a = np.linspace(-10, 10, 16).reshape(4, 2, 2) yield a yield np.asfortranarray(a) yield a[::-1] np.random.RandomState(0).shuffle(a) yield a yield 6 yield 6.5 yield -np.inf yield 1 + 4j yield [2.2, np.nan] yield [2.2, np.inf] yield ((4.1, 2.0, -7.6), (4.3, 2.7, 5.2)) yield np.full(5, np.nan) yield 1 + np.nan * 1j yield np.nan + np.nan * 1j yield np.nan for a in a_variations(): check(a) def test_ptp_complex(self): pyfunc = array_ptp_global cfunc = jit(nopython=True)(pyfunc) def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) def make_array(real_nan=False, imag_nan=False): real = np.linspace(-4, 4, 25) if real_nan: real[4:9] = np.nan imag = np.linspace(-5, 5, 25) if imag_nan: imag[7:12] = np.nan return (real + 1j * imag).reshape(5, 5) for real_nan, imag_nan in product([True, False], repeat=2): comp = make_array(real_nan, imag_nan) check(comp) real = np.ones(8) imag = np.arange(-4, 4) comp = real + 1j * imag check(comp) comp = real - 1j * imag check(comp) comp = np.full((4, 4), fill_value=(1 - 1j)) check(comp) def test_ptp_exceptions(self): pyfunc = array_ptp_global cfunc = jit(nopython=True)(pyfunc) # Exceptions leak references self.disable_leak_check() with self.assertTypingError() as e: cfunc(np.array((True, True, False))) msg = "Boolean dtype is unsupported (as per NumPy)" self.assertIn(msg, str(e.exception)) with self.assertRaises(ValueError) as e: cfunc(np.array([])) msg = "zero-size array reduction not possible" self.assertIn(msg, str(e.exception)) def test_min_max_complex_basic(self): pyfuncs = array_min_global, array_max_global for pyfunc in pyfuncs: cfunc = jit(nopython=True)(pyfunc) def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) real = np.linspace(-10, 10, 40) real[:4] = real[-1] imag = real * 2 a = real - imag * 1j check(a) for _ in range(10): self.random.shuffle(real) self.random.shuffle(imag) dtype = self.random.choice([np.complex64, np.complex128]) a = real - imag * 1j a[:4] = a[-1] check(a.astype(dtype)) def test_nanmin_nanmax_complex_basic(self): pyfuncs = array_nanmin, array_nanmax for pyfunc in pyfuncs: cfunc = jit(nopython=True)(pyfunc) def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) real = np.linspace(-10, 10, 40) real[:4] = real[-1] real[5:9] = np.nan imag = real * 2 imag[7:12] = np.nan a = real - imag * 1j check(a) for _ in range(10): self.random.shuffle(real) self.random.shuffle(imag) a = real - imag * 1j a[:4] = a[-1] check(a) def test_nanmin_nanmax_non_array_inputs(self): pyfuncs = array_nanmin, array_nanmax def check(a): expected = pyfunc(a) got = cfunc(a) self.assertPreciseEqual(expected, got) def a_variations(): yield [1, 6, 4, 2] yield ((-10, 4, -12), (5, 200, -30)) yield np.array(3) yield (2,) yield 3.142 yield False yield (np.nan, 3.142, -5.2, 3.0) yield [np.inf, np.nan, -np.inf] yield [(np.nan, 1.1), (-4.4, 8.7)] for pyfunc in pyfuncs: cfunc = jit(nopython=True)(pyfunc) for a in a_variations(): check(a) @classmethod def install_generated_tests(cls): # These form a testing product where each of the combinations are tested # these function are tested in real and complex space reduction_funcs = [array_sum, array_sum_global, array_prod, array_prod_global, array_mean, array_mean_global, array_var, array_var_global, array_std, array_std_global, array_all, array_all_global, array_any, array_any_global, array_min, array_min_global, array_max, array_max_global, array_nanmax, array_nanmin, array_nansum, ] # these functions only work in real space as no complex comparison # operator is implemented reduction_funcs_rspace = [array_argmin, array_argmin_global, array_argmax, array_argmax_global] reduction_funcs += [array_nanmean, array_nanstd, array_nanvar] reduction_funcs += [array_nanprod] dtypes_to_test = [np.int32, np.float32, np.bool_, np.complex64] def install_tests(dtypes, funcs): # Install tests on class for dt in dtypes: test_arrays = full_test_arrays(dt) for red_func, test_array in product(funcs, test_arrays): # Create the name for the test function test_name = "test_{0}_{1}_{2}d" test_name = test_name.format(red_func.__name__, test_array.dtype.name, test_array.ndim) def new_test_function(self, redFunc=red_func, testArray=test_array, testName=test_name): ulps = 1 if 'prod' in red_func.__name__ and \ np.iscomplexobj(testArray): # prod family accumulate slightly more error on # some architectures (power, 32bit) for complex input ulps = 3 npr, nbr = run_comparative(redFunc, testArray) self.assertPreciseEqual(npr, nbr, msg=testName, prec="single", ulps=ulps) # Install it into the class setattr(cls, test_name, new_test_function) # install tests for reduction functions that only work in real space install_tests(dtypes_to_test[:-1], reduction_funcs_rspace) # install tests for reduction functions install_tests(dtypes_to_test, reduction_funcs) TestArrayReductions.install_generated_tests() class TestArrayReductionsExceptions(MemoryLeakMixin, TestCase): # int64, size 0 zero_size = np.arange(0) def check_exception(self, pyfunc, msg): cfunc = jit(nopython=True)(pyfunc) # make sure NumPy raises consistently/no behaviour change with self.assertRaises(BaseException): pyfunc(self.zero_size) # check numba impl raises expected with self.assertRaises(ValueError) as e: cfunc(self.zero_size) self.assertIn(msg, str(e.exception)) @classmethod def install(cls): fn_to_msg = dict() empty_seq = "attempt to get {0} of an empty sequence" op_no_ident = ("zero-size array to reduction operation " "{0}") for x in [array_argmax, array_argmax_global, array_argmin, array_argmin_global]: fn_to_msg[x] = empty_seq for x in [array_max, array_max, array_min, array_min]: fn_to_msg[x] = op_no_ident name_template = "test_zero_size_array_{0}" for fn, msg in fn_to_msg.items(): test_name = name_template.format(fn.__name__) lmsg = msg.format(fn.__name__) lmsg = lmsg.replace('array_','').replace('_global','') def test_fn(self, func=fn, message=lmsg): self.check_exception(func, message) setattr(cls, test_name, test_fn) TestArrayReductionsExceptions.install() if __name__ == '__main__': unittest.main()

31.199807

92

0.588497

1b2984c83bccefc7229589b51dabfb4e47b3e6d5

13,720

py

Python

pyro/util.py

ssameerr/pyro

c04fc931631ec9e8694def207b5ca0e432d5e501

[ "MIT" ]

null

pyro/util.py

ssameerr/pyro

c04fc931631ec9e8694def207b5ca0e432d5e501

[ "MIT" ]

null

pyro/util.py

ssameerr/pyro

c04fc931631ec9e8694def207b5ca0e432d5e501

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function import functools import re import warnings import graphviz import numpy as np import torch from torch.autograd import Variable from torch.nn import Parameter from pyro.poutine.poutine import _PYRO_STACK from pyro.poutine.util import site_is_subsample def parse_torch_version(): """ Parses `torch.__version__` into a semver-ish version tuple. This is needed to handle subpatch `_n` parts outside of the semver spec. :returns: a tuple `(major, minor, patch, extra_stuff)` """ match = re.match(r"(\d\.\d\.\d)(.*)", torch.__version__) major, minor, patch = map(int, match.group(1).split(".")) extra_stuff = match.group(2) return major, minor, patch, extra_stuff def detach_iterable(iterable): if isinstance(iterable, Variable): return iterable.detach() else: return [var.detach() for var in iterable] def _dict_to_tuple(d): """ Recursively converts a dictionary to a list of key-value tuples Only intended for use as a helper function inside memoize!! May break when keys cant be sorted, but that is not an expected use-case """ if isinstance(d, dict): return tuple([(k, _dict_to_tuple(d[k])) for k in sorted(d.keys())]) else: return d def get_tensor_data(t): if isinstance(t, Variable): return t.data return t def memoize(fn): """ https://stackoverflow.com/questions/1988804/what-is-memoization-and-how-can-i-use-it-in-python unbounded memoize alternate in py3: https://docs.python.org/3/library/functools.html lru_cache """ mem = {} def _fn(*args, **kwargs): kwargs_tuple = _dict_to_tuple(kwargs) if (args, kwargs_tuple) not in mem: mem[(args, kwargs_tuple)] = fn(*args, **kwargs) return mem[(args, kwargs_tuple)] return _fn def set_rng_seed(rng_seed): """ Sets seeds of torch, numpy, and torch.cuda (if available). :param int rng_seed: The seed value. """ torch.manual_seed(rng_seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(rng_seed) np.random.seed(rng_seed) def ones(*args, **kwargs): """ :param torch.Tensor type_as: optional argument for tensor type A convenience function for Parameter(torch.ones(...)) """ retype = kwargs.pop('type_as', None) p_tensor = torch.ones(*args, **kwargs) return Parameter(p_tensor if retype is None else p_tensor.type_as(retype)) def zeros(*args, **kwargs): """ :param torch.Tensor type_as: optional argument for tensor type A convenience function for Parameter(torch.zeros(...)) """ retype = kwargs.pop('type_as', None) p_tensor = torch.zeros(*args, **kwargs) return Parameter(p_tensor if retype is None else p_tensor.type_as(retype)) def ng_ones(*args, **kwargs): """ :param torch.Tensor type_as: optional argument for tensor type A convenience function for Variable(torch.ones(...), requires_grad=False) """ retype = kwargs.pop('type_as', None) p_tensor = torch.ones(*args, **kwargs) return Variable(p_tensor if retype is None else p_tensor.type_as(retype), requires_grad=False) def ng_zeros(*args, **kwargs): """ :param torch.Tensor type_as: optional argument for tensor type A convenience function for Variable(torch.ones(...), requires_grad=False) """ retype = kwargs.pop('type_as', None) p_tensor = torch.zeros(*args, **kwargs) return Variable(p_tensor if retype is None else p_tensor.type_as(retype), requires_grad=False) def log_sum_exp(vecs): n = len(vecs.size()) if n == 1: vecs = vecs.view(1, -1) _, idx = torch.max(vecs, 1) max_score = torch.index_select(vecs, 1, idx.view(-1)) ret = max_score + torch.log(torch.sum(torch.exp(vecs - max_score.expand_as(vecs)))) if n == 1: return ret.view(-1) return ret def zero_grads(tensors): """ Sets gradients of list of Variables to zero in place """ for p in tensors: if p.grad is not None: if p.grad.volatile: p.grad.data.zero_() else: data = p.grad.data p.grad = Variable(data.new().resize_as_(data).zero_()) def apply_stack(initial_msg): """ :param dict initial_msg: the starting version of the trace site :returns: an updated message that is the final version of the trace site Execute the poutine stack at a single site according to the following scheme: 1. Walk down the stack from top to bottom, collecting into the message all information necessary to execute the stack at that site 2. For each poutine in the stack from bottom to top: Execute the poutine with the message; If the message field "stop" is True, stop; Otherwise, continue 3. Return the updated message """ stack = _PYRO_STACK # TODO check at runtime if stack is valid # msg is used to pass information up and down the stack msg = initial_msg # first, gather all information necessary to apply the stack to this site for frame in reversed(stack): msg = frame._prepare_site(msg) # go until time to stop? for frame in stack: assert msg["type"] in ("sample", "param"), \ "{} is an invalid site type, how did that get there?".format(msg["type"]) msg["value"] = getattr(frame, "_pyro_{}".format(msg["type"]))(msg) if msg["stop"]: break return msg class NonlocalExit(Exception): """ Exception for exiting nonlocally from poutine execution. Used by poutine.EscapePoutine to return site information. """ def __init__(self, site, *args, **kwargs): """ :param site: message at a pyro site constructor. Just stores the input site. """ super(NonlocalExit, self).__init__(*args, **kwargs) self.site = site def enum_extend(trace, msg, num_samples=None): """ :param trace: a partial trace :param msg: the message at a pyro primitive site :param num_samples: maximum number of extended traces to return. :returns: a list of traces, copies of input trace with one extra site Utility function to copy and extend a trace with sites based on the input site whose values are enumerated from the support of the input site's distribution. Used for exact inference and integrating out discrete variables. """ if num_samples is None: num_samples = -1 # Batched .enumerate_support() assumes batched values are independent. batch_shape = msg["fn"].batch_shape(msg["value"], *msg["args"], **msg["kwargs"]) is_batched = any(size > 1 for size in batch_shape) inside_iarange = any(frame.vectorized for frame in msg["cond_indep_stack"]) if is_batched and not inside_iarange: raise ValueError( "Tried to enumerate a batched pyro.sample site '{}' outside of a pyro.iarange. " "To fix, either enclose in a pyro.iarange, or avoid batching.".format(msg["name"])) extended_traces = [] for i, s in enumerate(msg["fn"].enumerate_support(*msg["args"], **msg["kwargs"])): if i > num_samples and num_samples >= 0: break msg_copy = msg.copy() msg_copy.update(value=s) tr_cp = trace.copy() tr_cp.add_node(msg["name"], **msg_copy) extended_traces.append(tr_cp) return extended_traces def mc_extend(trace, msg, num_samples=None): """ :param trace: a partial trace :param msg: the message at a pyro primitive site :param num_samples: maximum number of extended traces to return. :returns: a list of traces, copies of input trace with one extra site Utility function to copy and extend a trace with sites based on the input site whose values are sampled from the input site's function. Used for Monte Carlo marginalization of individual sample sites. """ if num_samples is None: num_samples = 1 extended_traces = [] for i in range(num_samples): msg_copy = msg.copy() msg_copy["value"] = msg_copy["fn"](*msg_copy["args"], **msg_copy["kwargs"]) tr_cp = trace.copy() tr_cp.add_node(msg_copy["name"], **msg_copy) extended_traces.append(tr_cp) return extended_traces def discrete_escape(trace, msg): """ :param trace: a partial trace :param msg: the message at a pyro primitive site :returns: boolean decision value Utility function that checks if a sample site is discrete and not already in a trace. Used by EscapePoutine to decide whether to do a nonlocal exit at a site. Subroutine for integrating out discrete variables for variance reduction. """ return (msg["type"] == "sample") and \ (not msg["is_observed"]) and \ (msg["name"] not in trace) and \ (getattr(msg["fn"], "enumerable", False)) def all_escape(trace, msg): """ :param trace: a partial trace :param msg: the message at a pyro primitive site :returns: boolean decision value Utility function that checks if a site is not already in a trace. Used by EscapePoutine to decide whether to do a nonlocal exit at a site. Subroutine for approximately integrating out variables for variance reduction. """ return (msg["type"] == "sample") and \ (not msg["is_observed"]) and \ (msg["name"] not in trace) def save_visualization(trace, graph_output): """ :param pyro.poutine.Trace trace: a trace to be visualized :param graph_output: the graph will be saved to graph_output.pdf :type graph_output: str Take a trace generated by poutine.trace with `graph_type='dense'` and render the graph with the output saved to file. - non-reparameterized stochastic nodes are salmon - reparameterized stochastic nodes are half salmon, half grey - observation nodes are green Example: trace = pyro.poutine.trace(model, graph_type="dense").get_trace() save_visualization(trace, 'output') """ g = graphviz.Digraph() for label, node in trace.nodes.items(): if site_is_subsample(node): continue shape = 'ellipse' if label in trace.stochastic_nodes and label not in trace.reparameterized_nodes: fillcolor = 'salmon' elif label in trace.reparameterized_nodes: fillcolor = 'lightgrey;.5:salmon' elif label in trace.observation_nodes: fillcolor = 'darkolivegreen3' else: # only visualize RVs continue g.node(label, label=label, shape=shape, style='filled', fillcolor=fillcolor) for label1, label2 in trace.edges: if site_is_subsample(trace.nodes[label1]): continue if site_is_subsample(trace.nodes[label2]): continue g.edge(label1, label2) g.render(graph_output, view=False, cleanup=True) def check_model_guide_match(model_trace, guide_trace): """ :param pyro.poutine.Trace model_trace: Trace object of the model :param pyro.poutine.Trace guide_trace: Trace object of the guide :raises: RuntimeWarning, ValueError Checks that (1) there is a bijection between the samples in the guide and the samples in the model, (2) each `iarange` statement in the guide also appears in the model, (3) at each sample site that appears in both the model and guide, the model and guide agree on sample shape. """ # Check ordinary sample sites. model_vars = set(name for name, site in model_trace.nodes.items() if site["type"] == "sample" and not site["is_observed"] if type(site["fn"]).__name__ != "_Subsample") guide_vars = set(name for name, site in guide_trace.nodes.items() if site["type"] == "sample" if type(site["fn"]).__name__ != "_Subsample") if not (guide_vars <= model_vars): warnings.warn("Found vars in guide but not model: {}".format(guide_vars - model_vars)) if not (model_vars <= guide_vars): warnings.warn("Found vars in model but not guide: {}".format(model_vars - guide_vars)) # Check shapes agree. for name in model_vars & guide_vars: model_site = model_trace.nodes[name] guide_site = guide_trace.nodes[name] if hasattr(model_site["fn"], "shape") and hasattr(guide_site["fn"], "shape"): model_shape = model_site["fn"].shape(None, *model_site["args"], **model_site["kwargs"]) guide_shape = guide_site["fn"].shape(None, *guide_site["args"], **guide_site["kwargs"]) if model_shape != guide_shape: raise ValueError("Model and guide dims disagree at site '{}': {} vs {}".format( name, model_shape, guide_shape)) # Check subsample sites introduced by iarange. model_vars = set(name for name, site in model_trace.nodes.items() if site["type"] == "sample" and not site["is_observed"] if type(site["fn"]).__name__ == "_Subsample") guide_vars = set(name for name, site in guide_trace.nodes.items() if site["type"] == "sample" if type(site["fn"]).__name__ == "_Subsample") if not (guide_vars <= model_vars): warnings.warn("Found iarange statements in guide but not model: {}".format(guide_vars - model_vars)) def deep_getattr(obj, name): """ Python getattr() for arbitrarily deep attributes Throws an AttributeError if bad attribute """ return functools.reduce(getattr, name.split("."), obj)

34.734177

108

0.654373

5eddd41fc903f23c5ff489f5db219a6d12f5ee1f

64

py

Python

google_screener_data_extract/__init__.py

spidezad/google_screener_data_extract

8efe14e73918808182d8745ef38c38f1ac686f6e

[ "BSD-3-Clause" ]

28

2015-09-27T21:11:23.000Z

2021-05-17T06:33:20.000Z

google_screener_data_extract/__init__.py

spidezad/google_screener_data_extract

8efe14e73918808182d8745ef38c38f1ac686f6e

[ "BSD-3-Clause" ]

1

2015-10-18T23:11:03.000Z

2018-03-27T05:58:10.000Z

google_screener_data_extract/__init__.py

spidezad/google_screener_data_extract

8efe14e73918808182d8745ef38c38f1ac686f6e

[ "BSD-3-Clause" ]

24

2016-01-14T09:53:48.000Z

2018-05-17T02:00:56.000Z

from .google_screener_data_extract import GoogleStockDataExtract

64

0.9375

fe7c4626a7c0b1bb731c5b6a94b98e65b194c35e

4,755

py

Python

ps2/PeachPy/tor_tmsk_tmrc.py

SeiichiroMine/Tales-of-Rebirth

5cb00825dd19affed4062f1f849906b74bb7fcc0

[ "MIT" ]

2

2021-06-17T14:56:59.000Z

2021-11-04T02:50:34.000Z

ps2/PeachPy/tor_tmsk_tmrc.py

SeiichiroMine/Tales-of-Rebirth

5cb00825dd19affed4062f1f849906b74bb7fcc0

[ "MIT" ]

null

ps2/PeachPy/tor_tmsk_tmrc.py

SeiichiroMine/Tales-of-Rebirth

5cb00825dd19affed4062f1f849906b74bb7fcc0

[ "MIT" ]

3

2021-06-17T14:57:16.000Z

2021-11-29T19:32:40.000Z

import sys import os import json import struct import re import subprocess import shutil import string tmsk_pointer_begin = 0x410 #tmsk_isize = 0xAC00 tmrc_pointer_begin = 0x450 extension = 'tm2' #tmsk_num = data[0x404:2] #tmrc_num = data[0x406:2] #palette = data[:0x400] ##Header construction info TIM2_header_magic = b'TIM2\x04\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00' TIM2_header_tmskdata = b'\x40\xB0\x00\x00\x00\x04\x00\x00\x00\xAC\x00\x00\x30\x00\x00\x01\x00\x01\x03\x05\x00\x01\xAC\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' TIM2_palette_length = b'\x00\x04\x00\x00' TIM2_header_length = b'\x30\x00\x00\x01\x00\x01\x03\x05' blah_blah_blah = b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' ##and then, pointer_end of tmsk/tmrc pointers is tmsk_num(or tmrc_num)+(n*4) ##each pointer is 4 bytes long ##pointer is relative to the end of the palette data, so the first pointer will read 0x100, but it's actually pointing to 0x500 in the file (0x400+0x100)=0x500 ##tmsk all have same data length, image width, image height, could probably just write a single header for those... ##tmrc have variable image length, width, height. Image length isn't indicated in the header, but it is width*height ##final tim2 should be tim header magic + header data + image data + palette def mkdir(name): try: os.mkdir(name) except: pass def extract_tmsk(): mkdir('FILE/tmsk/tim2') for file in os.listdir('FILE/tmsk/'): if not file.endswith('tmsk'): continue f = open('FILE/tmsk/' + file, 'rb') data = f.read() palette = data[:0x400] tmsk_num = data [0x404:0x406] tmsk_num_int = int.from_bytes(tmsk_num, 'little') tmsk_pointer_end = tmsk_pointer_begin + (tmsk_num_int * 4) sexy_file = file.replace('.tmsk', '') f.seek(tmsk_pointer_begin, 0) pointers = [] while f.tell() < tmsk_pointer_end: p = struct.unpack('<L', f.read(4))[0] pointers.append(p) for i in range(len(pointers)): start = pointers[i] + 0x400 size = 0xAC00 f.seek(start, 0) tmsk_idata = f.read(size) o = open('FILE/tmsk/tim2/' + sexy_file +'_' + '%02d.%s' % (i, extension), 'wb') o.write(TIM2_header_magic + TIM2_header_tmskdata + tmsk_idata + palette) o.close() f.close() def extract_tmrc(): mkdir('FILE/tmsk/tim2') for file in os.listdir('FILE/tmsk/'): if not file.endswith('tmsk'): continue f = open('FILE/tmsk/' + file, 'rb') data = f.read() palette = data[:0x400] tmrc_num = data[0x406:0x408] tmrc_num_int = int.from_bytes(tmrc_num, 'little') tmrc_pointer_end = tmrc_pointer_begin + (tmrc_num_int * 4) sexy_file = file.replace('.tmsk', '') f.seek(tmrc_pointer_begin, 0) pointers = [] while f.tell() < tmrc_pointer_end: p = struct.unpack('<L', f.read(4))[0] pointers.append(p) for i in range(len(pointers)): w_start = pointers[i] + 0x400 + 8 h_start = pointers[i] + 0x400 + 10 h_end = pointers[i] + 0x400 + 12 tmrc_w = data[w_start:h_start] tmrc_h = data[h_start:h_end] tmrc_w_int = int.from_bytes(tmrc_w, 'little') tmrc_h_int = int.from_bytes(tmrc_h, 'little') i_start = pointers[i] + 0x400 + 128 isize = tmrc_w_int * tmrc_h_int f.seek(i_start) tmrc_idata = f.read(isize) TIM2_size = isize + 0x40 + 0x400 TIM2_size_bytes = TIM2_size.to_bytes(4, 'little') img_size_bytes = isize.to_bytes(4, 'little') o = open('FILE/tmsk/tim2/' + sexy_file +'_' + 'tmrc' + '_' + '%02d.%s' % (i, extension), 'wb') o.write(TIM2_header_magic + TIM2_size_bytes + TIM2_palette_length + img_size_bytes + TIM2_header_length + tmrc_w + tmrc_h + blah_blah_blah + tmrc_idata + palette) o.close() f.close() if __name__ == '__main__': if sys.argv[1] == 'extract' and sys.argv[2] == 'tmsk': extract_tmsk() elif sys.argv[1] == 'extract' and sys.argv[2] == 'tmrc': extract_tmrc() elif sys.argv[1] == 'help': print('Tales of Rebirth Skit Image to TIM2 Converter\n') print('By SymphoniaLauren\n') print('USAGE:\n') print('python tor_tmsk_tmrc.py extract [tmsk]/[tmrc]\n') print('TMSK is what I call the skit faces, TMRC are the little animated tiles\nfor the small parts like the eyes and mouth') else: sys.exit(1)

39.625

218

0.617035

16b7e8181d9a4136104c0a75fc0de84740ca772b

946

py

Python

banners/migrations/0001_initial.py

AlexGolovaschenko/OwenAgriculture

4d393da3736d0a71b1d25b720ed16af38013b682

[ "Apache-2.0" ]

null

banners/migrations/0001_initial.py

AlexGolovaschenko/OwenAgriculture

4d393da3736d0a71b1d25b720ed16af38013b682

[ "Apache-2.0" ]

7

2021-03-19T03:36:56.000Z

2022-01-13T02:44:37.000Z

banners/migrations/0001_initial.py

AlexGolovaschenko/OwenAgriculture

4d393da3736d0a71b1d25b720ed16af38013b682

[ "Apache-2.0" ]

null

# Generated by Django 3.0.6 on 2020-06-18 08:12 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Banner', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, verbose_name='Название')), ('banner_image', models.ImageField(upload_to='banners', verbose_name='Картинка баннера')), ('order', models.PositiveSmallIntegerField(default=0, verbose_name='Порядок отображения')), ('display', models.BooleanField(default=True, verbose_name='Показывать')), ], options={ 'verbose_name': 'Баннер', 'verbose_name_plural': 'Баннеры', }, ), ]

32.62069

114

0.584567

7552955cfda3953c5be741bc73e47097a19e94bf

12,400

py

Python

test/test_related_events.py

LaudateCorpus1/hyper-h2

7dfab8f8e0e8605c4a2a90706b217d0a0a0c45b7

[ "MIT" ]

2

2020-07-01T20:46:51.000Z

2021-04-28T21:28:48.000Z

test/test_related_events.py

LaudateCorpus1/hyper-h2

7dfab8f8e0e8605c4a2a90706b217d0a0a0c45b7

[ "MIT" ]

null

test/test_related_events.py

LaudateCorpus1/hyper-h2

7dfab8f8e0e8605c4a2a90706b217d0a0a0c45b7

[ "MIT" ]

3

2021-06-03T10:10:16.000Z

2022-03-17T19:57:00.000Z

# -*- coding: utf-8 -*- """ test_related_events.py ~~~~~~~~~~~~~~~~~~~~~~ Specific tests to validate the "related events" logic used by certain events inside hyper-h2. """ import h2.connection import h2.events class TestRelatedEvents(object): """ Related events correlate all those events that happen on a single frame. """ example_request_headers = [ (':authority', 'example.com'), (':path', '/'), (':scheme', 'https'), (':method', 'GET'), ] example_response_headers = [ (':status', '200'), ('server', 'fake-serv/0.1.0') ] informational_response_headers = [ (':status', '100'), ('server', 'fake-serv/0.1.0') ] example_trailers = [ ('another', 'field'), ] def test_request_received_related_all(self, frame_factory): """ RequestReceived has two possible related events: PriorityUpdated and StreamEnded, all fired when a single HEADERS frame is received. """ c = h2.connection.H2Connection(client_side=False) c.initiate_connection() c.receive_data(frame_factory.preamble()) input_frame = frame_factory.build_headers_frame( headers=self.example_request_headers, flags=['END_STREAM', 'PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 3 base_event = events[0] other_events = events[1:] assert base_event.stream_ended in other_events assert isinstance(base_event.stream_ended, h2.events.StreamEnded) assert base_event.priority_updated in other_events assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_request_received_related_priority(self, frame_factory): """ RequestReceived can be related to PriorityUpdated. """ c = h2.connection.H2Connection(client_side=False) c.initiate_connection() c.receive_data(frame_factory.preamble()) input_frame = frame_factory.build_headers_frame( headers=self.example_request_headers, flags=['PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] priority_updated_event = events[1] assert base_event.priority_updated is priority_updated_event assert base_event.stream_ended is None assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_request_received_related_stream_ended(self, frame_factory): """ RequestReceived can be related to StreamEnded. """ c = h2.connection.H2Connection(client_side=False) c.initiate_connection() c.receive_data(frame_factory.preamble()) input_frame = frame_factory.build_headers_frame( headers=self.example_request_headers, flags=['END_STREAM'], ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] stream_ended_event = events[1] assert base_event.stream_ended is stream_ended_event assert base_event.priority_updated is None assert isinstance(base_event.stream_ended, h2.events.StreamEnded) def test_response_received_related_nothing(self, frame_factory): """ ResponseReceived is ordinarily related to no events. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.example_response_headers, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 1 base_event = events[0] assert base_event.stream_ended is None assert base_event.priority_updated is None def test_response_received_related_all(self, frame_factory): """ ResponseReceived has two possible related events: PriorityUpdated and StreamEnded, all fired when a single HEADERS frame is received. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.example_response_headers, flags=['END_STREAM', 'PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 3 base_event = events[0] other_events = events[1:] assert base_event.stream_ended in other_events assert isinstance(base_event.stream_ended, h2.events.StreamEnded) assert base_event.priority_updated in other_events assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_response_received_related_priority(self, frame_factory): """ ResponseReceived can be related to PriorityUpdated. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.example_response_headers, flags=['PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] priority_updated_event = events[1] assert base_event.priority_updated is priority_updated_event assert base_event.stream_ended is None assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_response_received_related_stream_ended(self, frame_factory): """ ResponseReceived can be related to StreamEnded. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.example_response_headers, flags=['END_STREAM'], ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] stream_ended_event = events[1] assert base_event.stream_ended is stream_ended_event assert base_event.priority_updated is None assert isinstance(base_event.stream_ended, h2.events.StreamEnded) def test_trailers_received_related_all(self, frame_factory): """ TrailersReceived has two possible related events: PriorityUpdated and StreamEnded, all fired when a single HEADERS frame is received. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) f = frame_factory.build_headers_frame( headers=self.example_response_headers, ) c.receive_data(f.serialize()) input_frame = frame_factory.build_headers_frame( headers=self.example_trailers, flags=['END_STREAM', 'PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 3 base_event = events[0] other_events = events[1:] assert base_event.stream_ended in other_events assert isinstance(base_event.stream_ended, h2.events.StreamEnded) assert base_event.priority_updated in other_events assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_trailers_received_related_stream_ended(self, frame_factory): """ TrailersReceived can be related to StreamEnded by itself. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) f = frame_factory.build_headers_frame( headers=self.example_response_headers, ) c.receive_data(f.serialize()) input_frame = frame_factory.build_headers_frame( headers=self.example_trailers, flags=['END_STREAM'], ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] stream_ended_event = events[1] assert base_event.stream_ended is stream_ended_event assert base_event.priority_updated is None assert isinstance(base_event.stream_ended, h2.events.StreamEnded) def test_informational_response_related_nothing(self, frame_factory): """ InformationalResponseReceived in the standard case is related to nothing. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.informational_response_headers, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 1 base_event = events[0] assert base_event.priority_updated is None def test_informational_response_received_related_all(self, frame_factory): """ InformationalResponseReceived has one possible related event: PriorityUpdated, fired when a single HEADERS frame is received. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) input_frame = frame_factory.build_headers_frame( headers=self.informational_response_headers, flags=['PRIORITY'], stream_weight=15, depends_on=0, exclusive=False, ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] priority_updated_event = events[1] assert base_event.priority_updated is priority_updated_event assert isinstance( base_event.priority_updated, h2.events.PriorityUpdated ) def test_data_received_normally_relates_to_nothing(self, frame_factory): """ A plain DATA frame leads to DataReceieved with no related events. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) f = frame_factory.build_headers_frame( headers=self.example_response_headers, ) c.receive_data(f.serialize()) input_frame = frame_factory.build_data_frame( data=b'some data', ) events = c.receive_data(input_frame.serialize()) assert len(events) == 1 base_event = events[0] assert base_event.stream_ended is None def test_data_received_related_stream_ended(self, frame_factory): """ DataReceived can be related to StreamEnded by itself. """ c = h2.connection.H2Connection() c.initiate_connection() c.send_headers(stream_id=1, headers=self.example_request_headers) f = frame_factory.build_headers_frame( headers=self.example_response_headers, ) c.receive_data(f.serialize()) input_frame = frame_factory.build_data_frame( data=b'some data', flags=['END_STREAM'], ) events = c.receive_data(input_frame.serialize()) assert len(events) == 2 base_event = events[0] stream_ended_event = events[1] assert base_event.stream_ended is stream_ended_event assert isinstance(base_event.stream_ended, h2.events.StreamEnded)

33.695652

78

0.647823

ed69fb60ee18639c056e88d7ff043799e6bee82e

26,335

py

Python

AppServer/lib/django-1.4/tests/modeltests/invalid_models/invalid_models/models.py

loftwah/appscale

586fc1347ebc743d7a632de698f4dbfb09ae38d6

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

AppServer/lib/django-1.4/tests/modeltests/invalid_models/invalid_models/models.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

AppServer/lib/django-1.4/tests/modeltests/invalid_models/invalid_models/models.py

nlake44/appscale

6944af660ca4cb772c9b6c2332ab28e5ef4d849f

[ "Apache-2.0" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

#encoding=utf-8 """ 26. Invalid models This example exists purely to point out errors in models. """ from django.db import connection, models class FieldErrors(models.Model): charfield = models.CharField() charfield2 = models.CharField(max_length=-1) charfield3 = models.CharField(max_length="bad") decimalfield = models.DecimalField() decimalfield2 = models.DecimalField(max_digits=-1, decimal_places=-1) decimalfield3 = models.DecimalField(max_digits="bad", decimal_places="bad") decimalfield4 = models.DecimalField(max_digits=9, decimal_places=10) decimalfield5 = models.DecimalField(max_digits=10, decimal_places=10) filefield = models.FileField() choices = models.CharField(max_length=10, choices='bad') choices2 = models.CharField(max_length=10, choices=[(1,2,3),(1,2,3)]) index = models.CharField(max_length=10, db_index='bad') field_ = models.CharField(max_length=10) nullbool = models.BooleanField(null=True) class Target(models.Model): tgt_safe = models.CharField(max_length=10) clash1 = models.CharField(max_length=10) clash2 = models.CharField(max_length=10) clash1_set = models.CharField(max_length=10) class Clash1(models.Model): src_safe = models.CharField(max_length=10) foreign = models.ForeignKey(Target) m2m = models.ManyToManyField(Target) class Clash2(models.Model): src_safe = models.CharField(max_length=10) foreign_1 = models.ForeignKey(Target, related_name='id') foreign_2 = models.ForeignKey(Target, related_name='src_safe') m2m_1 = models.ManyToManyField(Target, related_name='id') m2m_2 = models.ManyToManyField(Target, related_name='src_safe') class Target2(models.Model): clash3 = models.CharField(max_length=10) foreign_tgt = models.ForeignKey(Target) clashforeign_set = models.ForeignKey(Target) m2m_tgt = models.ManyToManyField(Target) clashm2m_set = models.ManyToManyField(Target) class Clash3(models.Model): src_safe = models.CharField(max_length=10) foreign_1 = models.ForeignKey(Target2, related_name='foreign_tgt') foreign_2 = models.ForeignKey(Target2, related_name='m2m_tgt') m2m_1 = models.ManyToManyField(Target2, related_name='foreign_tgt') m2m_2 = models.ManyToManyField(Target2, related_name='m2m_tgt') class ClashForeign(models.Model): foreign = models.ForeignKey(Target2) class ClashM2M(models.Model): m2m = models.ManyToManyField(Target2) class SelfClashForeign(models.Model): src_safe = models.CharField(max_length=10) selfclashforeign = models.CharField(max_length=10) selfclashforeign_set = models.ForeignKey("SelfClashForeign") foreign_1 = models.ForeignKey("SelfClashForeign", related_name='id') foreign_2 = models.ForeignKey("SelfClashForeign", related_name='src_safe') class ValidM2M(models.Model): src_safe = models.CharField(max_length=10) validm2m = models.CharField(max_length=10) # M2M fields are symmetrical by default. Symmetrical M2M fields # on self don't require a related accessor, so many potential # clashes are avoided. validm2m_set = models.ManyToManyField("self") m2m_1 = models.ManyToManyField("self", related_name='id') m2m_2 = models.ManyToManyField("self", related_name='src_safe') m2m_3 = models.ManyToManyField('self') m2m_4 = models.ManyToManyField('self') class SelfClashM2M(models.Model): src_safe = models.CharField(max_length=10) selfclashm2m = models.CharField(max_length=10) # Non-symmetrical M2M fields _do_ have related accessors, so # there is potential for clashes. selfclashm2m_set = models.ManyToManyField("self", symmetrical=False) m2m_1 = models.ManyToManyField("self", related_name='id', symmetrical=False) m2m_2 = models.ManyToManyField("self", related_name='src_safe', symmetrical=False) m2m_3 = models.ManyToManyField('self', symmetrical=False) m2m_4 = models.ManyToManyField('self', symmetrical=False) class Model(models.Model): "But it's valid to call a model Model." year = models.PositiveIntegerField() #1960 make = models.CharField(max_length=10) #Aston Martin name = models.CharField(max_length=10) #DB 4 GT class Car(models.Model): colour = models.CharField(max_length=5) model = models.ForeignKey(Model) class MissingRelations(models.Model): rel1 = models.ForeignKey("Rel1") rel2 = models.ManyToManyField("Rel2") class MissingManualM2MModel(models.Model): name = models.CharField(max_length=5) missing_m2m = models.ManyToManyField(Model, through="MissingM2MModel") class Person(models.Model): name = models.CharField(max_length=5) class Group(models.Model): name = models.CharField(max_length=5) primary = models.ManyToManyField(Person, through="Membership", related_name="primary") secondary = models.ManyToManyField(Person, through="Membership", related_name="secondary") tertiary = models.ManyToManyField(Person, through="RelationshipDoubleFK", related_name="tertiary") class GroupTwo(models.Model): name = models.CharField(max_length=5) primary = models.ManyToManyField(Person, through="Membership") secondary = models.ManyToManyField(Group, through="MembershipMissingFK") class Membership(models.Model): person = models.ForeignKey(Person) group = models.ForeignKey(Group) not_default_or_null = models.CharField(max_length=5) class MembershipMissingFK(models.Model): person = models.ForeignKey(Person) class PersonSelfRefM2M(models.Model): name = models.CharField(max_length=5) friends = models.ManyToManyField('self', through="Relationship") too_many_friends = models.ManyToManyField('self', through="RelationshipTripleFK") class PersonSelfRefM2MExplicit(models.Model): name = models.CharField(max_length=5) friends = models.ManyToManyField('self', through="ExplicitRelationship", symmetrical=True) class Relationship(models.Model): first = models.ForeignKey(PersonSelfRefM2M, related_name="rel_from_set") second = models.ForeignKey(PersonSelfRefM2M, related_name="rel_to_set") date_added = models.DateTimeField() class ExplicitRelationship(models.Model): first = models.ForeignKey(PersonSelfRefM2MExplicit, related_name="rel_from_set") second = models.ForeignKey(PersonSelfRefM2MExplicit, related_name="rel_to_set") date_added = models.DateTimeField() class RelationshipTripleFK(models.Model): first = models.ForeignKey(PersonSelfRefM2M, related_name="rel_from_set_2") second = models.ForeignKey(PersonSelfRefM2M, related_name="rel_to_set_2") third = models.ForeignKey(PersonSelfRefM2M, related_name="too_many_by_far") date_added = models.DateTimeField() class RelationshipDoubleFK(models.Model): first = models.ForeignKey(Person, related_name="first_related_name") second = models.ForeignKey(Person, related_name="second_related_name") third = models.ForeignKey(Group, related_name="rel_to_set") date_added = models.DateTimeField() class AbstractModel(models.Model): name = models.CharField(max_length=10) class Meta: abstract = True class AbstractRelationModel(models.Model): fk1 = models.ForeignKey('AbstractModel') fk2 = models.ManyToManyField('AbstractModel') class UniqueM2M(models.Model): """ Model to test for unique ManyToManyFields, which are invalid. """ unique_people = models.ManyToManyField(Person, unique=True) class NonUniqueFKTarget1(models.Model): """ Model to test for non-unique FK target in yet-to-be-defined model: expect an error """ tgt = models.ForeignKey('FKTarget', to_field='bad') class UniqueFKTarget1(models.Model): """ Model to test for unique FK target in yet-to-be-defined model: expect no error """ tgt = models.ForeignKey('FKTarget', to_field='good') class FKTarget(models.Model): bad = models.IntegerField() good = models.IntegerField(unique=True) class NonUniqueFKTarget2(models.Model): """ Model to test for non-unique FK target in previously seen model: expect an error """ tgt = models.ForeignKey(FKTarget, to_field='bad') class UniqueFKTarget2(models.Model): """ Model to test for unique FK target in previously seen model: expect no error """ tgt = models.ForeignKey(FKTarget, to_field='good') class NonExistingOrderingWithSingleUnderscore(models.Model): class Meta: ordering = ("does_not_exist",) class InvalidSetNull(models.Model): fk = models.ForeignKey('self', on_delete=models.SET_NULL) class InvalidSetDefault(models.Model): fk = models.ForeignKey('self', on_delete=models.SET_DEFAULT) class UnicodeForeignKeys(models.Model): """Foreign keys which can translate to ascii should be OK, but fail if they're not.""" good = models.ForeignKey(u'FKTarget') also_good = models.ManyToManyField(u'FKTarget', related_name='unicode2') # In Python 3 this should become legal, but currently causes unicode errors # when adding the errors in core/management/validation.py #bad = models.ForeignKey(u'★') class PrimaryKeyNull(models.Model): my_pk_field = models.IntegerField(primary_key=True, null=True) class OrderByPKModel(models.Model): """ Model to test that ordering by pk passes validation. Refs #8291 """ name = models.CharField(max_length=100, blank=True) class Meta: ordering = ('pk',) model_errors = """invalid_models.fielderrors: "charfield": CharFields require a "max_length" attribute that is a positive integer. invalid_models.fielderrors: "charfield2": CharFields require a "max_length" attribute that is a positive integer. invalid_models.fielderrors: "charfield3": CharFields require a "max_length" attribute that is a positive integer. invalid_models.fielderrors: "decimalfield": DecimalFields require a "decimal_places" attribute that is a non-negative integer. invalid_models.fielderrors: "decimalfield": DecimalFields require a "max_digits" attribute that is a positive integer. invalid_models.fielderrors: "decimalfield2": DecimalFields require a "decimal_places" attribute that is a non-negative integer. invalid_models.fielderrors: "decimalfield2": DecimalFields require a "max_digits" attribute that is a positive integer. invalid_models.fielderrors: "decimalfield3": DecimalFields require a "decimal_places" attribute that is a non-negative integer. invalid_models.fielderrors: "decimalfield3": DecimalFields require a "max_digits" attribute that is a positive integer. invalid_models.fielderrors: "decimalfield4": DecimalFields require a "max_digits" attribute value that is greater than or equal to the value of the "decimal_places" attribute. invalid_models.fielderrors: "filefield": FileFields require an "upload_to" attribute. invalid_models.fielderrors: "choices": "choices" should be iterable (e.g., a tuple or list). invalid_models.fielderrors: "choices2": "choices" should be a sequence of two-tuples. invalid_models.fielderrors: "choices2": "choices" should be a sequence of two-tuples. invalid_models.fielderrors: "index": "db_index" should be either None, True or False. invalid_models.fielderrors: "field_": Field names cannot end with underscores, because this would lead to ambiguous queryset filters. invalid_models.fielderrors: "nullbool": BooleanFields do not accept null values. Use a NullBooleanField instead. invalid_models.clash1: Accessor for field 'foreign' clashes with field 'Target.clash1_set'. Add a related_name argument to the definition for 'foreign'. invalid_models.clash1: Accessor for field 'foreign' clashes with related m2m field 'Target.clash1_set'. Add a related_name argument to the definition for 'foreign'. invalid_models.clash1: Reverse query name for field 'foreign' clashes with field 'Target.clash1'. Add a related_name argument to the definition for 'foreign'. invalid_models.clash1: Accessor for m2m field 'm2m' clashes with field 'Target.clash1_set'. Add a related_name argument to the definition for 'm2m'. invalid_models.clash1: Accessor for m2m field 'm2m' clashes with related field 'Target.clash1_set'. Add a related_name argument to the definition for 'm2m'. invalid_models.clash1: Reverse query name for m2m field 'm2m' clashes with field 'Target.clash1'. Add a related_name argument to the definition for 'm2m'. invalid_models.clash2: Accessor for field 'foreign_1' clashes with field 'Target.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash2: Accessor for field 'foreign_1' clashes with related m2m field 'Target.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash2: Reverse query name for field 'foreign_1' clashes with field 'Target.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash2: Reverse query name for field 'foreign_1' clashes with related m2m field 'Target.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash2: Accessor for field 'foreign_2' clashes with related m2m field 'Target.src_safe'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash2: Reverse query name for field 'foreign_2' clashes with related m2m field 'Target.src_safe'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash2: Accessor for m2m field 'm2m_1' clashes with field 'Target.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash2: Accessor for m2m field 'm2m_1' clashes with related field 'Target.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash2: Reverse query name for m2m field 'm2m_1' clashes with field 'Target.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash2: Reverse query name for m2m field 'm2m_1' clashes with related field 'Target.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash2: Accessor for m2m field 'm2m_2' clashes with related field 'Target.src_safe'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clash2: Reverse query name for m2m field 'm2m_2' clashes with related field 'Target.src_safe'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clash3: Accessor for field 'foreign_1' clashes with field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash3: Accessor for field 'foreign_1' clashes with related m2m field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash3: Reverse query name for field 'foreign_1' clashes with field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash3: Reverse query name for field 'foreign_1' clashes with related m2m field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.clash3: Accessor for field 'foreign_2' clashes with m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash3: Accessor for field 'foreign_2' clashes with related m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash3: Reverse query name for field 'foreign_2' clashes with m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash3: Reverse query name for field 'foreign_2' clashes with related m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.clash3: Accessor for m2m field 'm2m_1' clashes with field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash3: Accessor for m2m field 'm2m_1' clashes with related field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash3: Reverse query name for m2m field 'm2m_1' clashes with field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash3: Reverse query name for m2m field 'm2m_1' clashes with related field 'Target2.foreign_tgt'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.clash3: Accessor for m2m field 'm2m_2' clashes with m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clash3: Accessor for m2m field 'm2m_2' clashes with related field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clash3: Reverse query name for m2m field 'm2m_2' clashes with m2m field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clash3: Reverse query name for m2m field 'm2m_2' clashes with related field 'Target2.m2m_tgt'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.clashforeign: Accessor for field 'foreign' clashes with field 'Target2.clashforeign_set'. Add a related_name argument to the definition for 'foreign'. invalid_models.clashm2m: Accessor for m2m field 'm2m' clashes with m2m field 'Target2.clashm2m_set'. Add a related_name argument to the definition for 'm2m'. invalid_models.target2: Accessor for field 'foreign_tgt' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'foreign_tgt'. invalid_models.target2: Accessor for field 'foreign_tgt' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'foreign_tgt'. invalid_models.target2: Accessor for field 'foreign_tgt' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'foreign_tgt'. invalid_models.target2: Accessor for field 'clashforeign_set' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'clashforeign_set'. invalid_models.target2: Accessor for field 'clashforeign_set' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'clashforeign_set'. invalid_models.target2: Accessor for field 'clashforeign_set' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'clashforeign_set'. invalid_models.target2: Accessor for m2m field 'm2m_tgt' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'm2m_tgt'. invalid_models.target2: Accessor for m2m field 'm2m_tgt' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'm2m_tgt'. invalid_models.target2: Accessor for m2m field 'm2m_tgt' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'm2m_tgt'. invalid_models.target2: Accessor for m2m field 'm2m_tgt' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'm2m_tgt'. invalid_models.target2: Accessor for m2m field 'm2m_tgt' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'm2m_tgt'. invalid_models.target2: Accessor for m2m field 'clashm2m_set' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'clashm2m_set'. invalid_models.target2: Accessor for m2m field 'clashm2m_set' clashes with related field 'Target.target2_set'. Add a related_name argument to the definition for 'clashm2m_set'. invalid_models.target2: Accessor for m2m field 'clashm2m_set' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'clashm2m_set'. invalid_models.target2: Accessor for m2m field 'clashm2m_set' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'clashm2m_set'. invalid_models.target2: Accessor for m2m field 'clashm2m_set' clashes with related m2m field 'Target.target2_set'. Add a related_name argument to the definition for 'clashm2m_set'. invalid_models.selfclashforeign: Accessor for field 'selfclashforeign_set' clashes with field 'SelfClashForeign.selfclashforeign_set'. Add a related_name argument to the definition for 'selfclashforeign_set'. invalid_models.selfclashforeign: Reverse query name for field 'selfclashforeign_set' clashes with field 'SelfClashForeign.selfclashforeign'. Add a related_name argument to the definition for 'selfclashforeign_set'. invalid_models.selfclashforeign: Accessor for field 'foreign_1' clashes with field 'SelfClashForeign.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.selfclashforeign: Reverse query name for field 'foreign_1' clashes with field 'SelfClashForeign.id'. Add a related_name argument to the definition for 'foreign_1'. invalid_models.selfclashforeign: Accessor for field 'foreign_2' clashes with field 'SelfClashForeign.src_safe'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.selfclashforeign: Reverse query name for field 'foreign_2' clashes with field 'SelfClashForeign.src_safe'. Add a related_name argument to the definition for 'foreign_2'. invalid_models.selfclashm2m: Accessor for m2m field 'selfclashm2m_set' clashes with m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'selfclashm2m_set'. invalid_models.selfclashm2m: Reverse query name for m2m field 'selfclashm2m_set' clashes with field 'SelfClashM2M.selfclashm2m'. Add a related_name argument to the definition for 'selfclashm2m_set'. invalid_models.selfclashm2m: Accessor for m2m field 'selfclashm2m_set' clashes with related m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'selfclashm2m_set'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_1' clashes with field 'SelfClashM2M.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_2' clashes with field 'SelfClashM2M.src_safe'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.selfclashm2m: Reverse query name for m2m field 'm2m_1' clashes with field 'SelfClashM2M.id'. Add a related_name argument to the definition for 'm2m_1'. invalid_models.selfclashm2m: Reverse query name for m2m field 'm2m_2' clashes with field 'SelfClashM2M.src_safe'. Add a related_name argument to the definition for 'm2m_2'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_3' clashes with m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_3'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_3' clashes with related m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_3'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_3' clashes with related m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_3'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_4' clashes with m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_4'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_4' clashes with related m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_4'. invalid_models.selfclashm2m: Accessor for m2m field 'm2m_4' clashes with related m2m field 'SelfClashM2M.selfclashm2m_set'. Add a related_name argument to the definition for 'm2m_4'. invalid_models.selfclashm2m: Reverse query name for m2m field 'm2m_3' clashes with field 'SelfClashM2M.selfclashm2m'. Add a related_name argument to the definition for 'm2m_3'. invalid_models.selfclashm2m: Reverse query name for m2m field 'm2m_4' clashes with field 'SelfClashM2M.selfclashm2m'. Add a related_name argument to the definition for 'm2m_4'. invalid_models.missingrelations: 'rel1' has a relation with model Rel1, which has either not been installed or is abstract. invalid_models.missingrelations: 'rel2' has an m2m relation with model Rel2, which has either not been installed or is abstract. invalid_models.grouptwo: 'primary' is a manually-defined m2m relation through model Membership, which does not have foreign keys to Person and GroupTwo invalid_models.grouptwo: 'secondary' is a manually-defined m2m relation through model MembershipMissingFK, which does not have foreign keys to Group and GroupTwo invalid_models.missingmanualm2mmodel: 'missing_m2m' specifies an m2m relation through model MissingM2MModel, which has not been installed invalid_models.group: The model Group has two manually-defined m2m relations through the model Membership, which is not permitted. Please consider using an extra field on your intermediary model instead. invalid_models.group: Intermediary model RelationshipDoubleFK has more than one foreign key to Person, which is ambiguous and is not permitted. invalid_models.personselfrefm2m: Many-to-many fields with intermediate tables cannot be symmetrical. invalid_models.personselfrefm2m: Intermediary model RelationshipTripleFK has more than two foreign keys to PersonSelfRefM2M, which is ambiguous and is not permitted. invalid_models.personselfrefm2mexplicit: Many-to-many fields with intermediate tables cannot be symmetrical. invalid_models.abstractrelationmodel: 'fk1' has a relation with model AbstractModel, which has either not been installed or is abstract. invalid_models.abstractrelationmodel: 'fk2' has an m2m relation with model AbstractModel, which has either not been installed or is abstract. invalid_models.uniquem2m: ManyToManyFields cannot be unique. Remove the unique argument on 'unique_people'. invalid_models.nonuniquefktarget1: Field 'bad' under model 'FKTarget' must have a unique=True constraint. invalid_models.nonuniquefktarget2: Field 'bad' under model 'FKTarget' must have a unique=True constraint. invalid_models.nonexistingorderingwithsingleunderscore: "ordering" refers to "does_not_exist", a field that doesn't exist. invalid_models.invalidsetnull: 'fk' specifies on_delete=SET_NULL, but cannot be null. invalid_models.invalidsetdefault: 'fk' specifies on_delete=SET_DEFAULT, but has no default value. """ if not connection.features.interprets_empty_strings_as_nulls: model_errors += """invalid_models.primarykeynull: "my_pk_field": Primary key fields cannot have null=True. """

73.356546

214

0.793924

ab09bf83d4fa5149f01f0a01c26fdbc04f8e91ff

6,963

py

Python

tests/unit_tests/test_report.py

ljhopkins2/sqlfmt

439811ada91e6a274b2b757c452f5140a05ecc06

[ "Apache-2.0" ]

36

2021-11-02T04:08:22.000Z

2022-03-30T14:47:49.000Z

tests/unit_tests/test_report.py

ljhopkins2/sqlfmt

439811ada91e6a274b2b757c452f5140a05ecc06

[ "Apache-2.0" ]

85

2021-11-01T19:22:59.000Z

2022-03-31T03:33:41.000Z

tests/unit_tests/test_report.py

ljhopkins2/sqlfmt

439811ada91e6a274b2b757c452f5140a05ecc06

[ "Apache-2.0" ]

1

2022-01-30T23:20:52.000Z

from pathlib import Path from typing import List import pytest from sqlfmt.api import SqlFormatResult from sqlfmt.mode import Mode from sqlfmt.report import Report @pytest.fixture def no_change_results() -> List[SqlFormatResult]: results = [ SqlFormatResult( source_path=Path("~/path/to/file.sql"), source_string="select * from my_table\n", formatted_string="select * from my_table\n", ), SqlFormatResult( source_path=Path("~/path/to/another_file.sql"), source_string="select * from my_table where true\n", formatted_string="select * from my_table where true\n", ), ] return results @pytest.fixture def changed_results() -> List[SqlFormatResult]: results = [ SqlFormatResult( source_path=Path("~/path/to/file.sql"), source_string="select * from my_table\n", formatted_string="select * from my_table\n", ), SqlFormatResult( source_path=Path("~/path/to/another_file.sql"), source_string="SELECT * from my_table where true", formatted_string="select * from my_table where true\n", ), SqlFormatResult( source_path=Path("~/path/to/yet_another_file.sql"), source_string="select a,\n b\n * from my_table where \n a = b\n", formatted_string="select a, b from my_table where a = b\n", ), ] return results def test_no_change_report( no_change_results: List[SqlFormatResult], default_mode: Mode ) -> None: report = Report(no_change_results, default_mode) assert report assert str(report) == "2 files left unchanged." def test_no_change_verbose_report( no_change_results: List[SqlFormatResult], verbose_mode: Mode ) -> None: report = Report(no_change_results, verbose_mode) assert report expected_report = ( "2 files left unchanged.\n" f"{Path('~/path/to/another_file.sql')} left unchanged.\n" f"{Path('~/path/to/file.sql')} left unchanged." ) assert str(report) == expected_report def test_changed_report_default_mode( changed_results: List[SqlFormatResult], default_mode: Mode ) -> None: report = Report(changed_results, default_mode) assert report expected_report = ( "\x1b[1m2 files formatted.\x1b[0m\n" "1 file left unchanged.\n" f"{Path('~/path/to/another_file.sql')} formatted.\n" f"{Path('~/path/to/yet_another_file.sql')} formatted." ) assert str(report) == expected_report def test_changed_report_verbose_mode( changed_results: List[SqlFormatResult], verbose_mode: Mode ) -> None: report = Report(changed_results, verbose_mode) assert report expected_report = ( "\x1b[1m2 files formatted.\x1b[0m\n" "1 file left unchanged.\n" f"{Path('~/path/to/another_file.sql')} formatted.\n" f"{Path('~/path/to/yet_another_file.sql')} formatted.\n" f"{Path('~/path/to/file.sql')} left unchanged." ) assert str(report) == expected_report def test_changed_report_check_mode( changed_results: List[SqlFormatResult], check_mode: Mode ) -> None: report = Report(changed_results, check_mode) assert report expected_report = ( "\x1b[1m2 files failed formatting check.\x1b[0m\n" "1 file passed formatting check.\n" f"{Path('~/path/to/another_file.sql')} failed formatting check.\n" f"{Path('~/path/to/yet_another_file.sql')} failed formatting check." ) assert str(report) == expected_report def test_changed_report_verbose_check_mode( changed_results: List[SqlFormatResult], verbose_check_mode: Mode ) -> None: report = Report(changed_results, verbose_check_mode) assert report expected_report = ( "\x1b[1m2 files failed formatting check.\x1b[0m\n" "1 file passed formatting check.\n" f"{Path('~/path/to/another_file.sql')} failed formatting check.\n" f"{Path('~/path/to/yet_another_file.sql')} failed formatting check.\n" f"{Path('~/path/to/file.sql')} passed formatting check." ) assert str(report) == expected_report def test_no_change_report_check_mode( no_change_results: List[SqlFormatResult], check_mode: Mode ) -> None: report = Report(no_change_results, check_mode) assert report assert str(report) == "2 files passed formatting check." def test_no_change_report_diff_mode( no_change_results: List[SqlFormatResult], diff_mode: Mode ) -> None: report = Report(no_change_results, diff_mode) assert report assert str(report) == "2 files passed formatting check." def test_changed_report_diff_mode( changed_results: List[SqlFormatResult], diff_mode: Mode ) -> None: report = Report(changed_results, diff_mode) expected_report = ( "\x1b[1m2 files failed formatting check.\x1b[0m\n" "1 file passed formatting check.\n" f"{Path('~/path/to/another_file.sql')} failed formatting check.\n" "\x1b[31m\x1b[22m--- source_query\n" "\x1b[0m\x1b[32m\x1b[22m+++ formatted_query\n" "\x1b[0m\x1b[36m\x1b[22m@@ -1 +1 @@\n" "\x1b[0m\x1b[31m\x1b[22m-SELECT * from my_table where true\n" "\x1b[0m\\ No newline at end of file\n" "\x1b[32m\x1b[22m+select * from my_table where true\n" "\x1b[0m\n" f"{Path('~/path/to/yet_another_file.sql')} failed formatting check.\n" "\x1b[31m\x1b[22m--- source_query\n" "\x1b[0m\x1b[32m\x1b[22m+++ formatted_query\n" "\x1b[0m\x1b[36m\x1b[22m@@ -1,4 +1 @@\n" "\x1b[0m\x1b[31m\x1b[22m-select a,\n" "\x1b[0m\x1b[31m\x1b[22m- b\n" "\x1b[0m\x1b[31m\x1b[22m- * from my_table where \n" "\x1b[0m\x1b[31m\x1b[22m- a = b\n" "\x1b[0m\x1b[32m\x1b[22m+select a, b from my_table where a = b\n" "\x1b[0m" ) assert report assert str(report) == expected_report def test_changed_report_no_color_diff_mode( changed_results: List[SqlFormatResult], no_color_diff_mode: Mode ) -> None: report = Report(changed_results, no_color_diff_mode) expected_report = ( "2 files failed formatting check.\n" "1 file passed formatting check.\n" f"{Path('~/path/to/another_file.sql')} failed formatting check.\n" "--- source_query\n" "+++ formatted_query\n" "@@ -1 +1 @@\n" "-SELECT * from my_table where true\n" "\\ No newline at end of file\n" "+select * from my_table where true\n" "\n" f"{Path('~/path/to/yet_another_file.sql')} failed formatting check.\n" "--- source_query\n" "+++ formatted_query\n" "@@ -1,4 +1 @@\n" "-select a,\n" "- b\n" "- * from my_table where \n" "- a = b\n" "+select a, b from my_table where a = b\n" "" ) assert report assert str(report) == expected_report

34.132353

78

0.637082

b87020f297335880d8429d3cb7a34e720f16a5e6

3,546

py

Python

neural_spline_flows/nde/transforms/splines/linear.py

VincentStimper/nsf

6bde505639ebcb67bffa227ea0021e3de235e03d

[ "MIT" ]

null

neural_spline_flows/nde/transforms/splines/linear.py

VincentStimper/nsf

6bde505639ebcb67bffa227ea0021e3de235e03d

[ "MIT" ]

null

neural_spline_flows/nde/transforms/splines/linear.py

VincentStimper/nsf

6bde505639ebcb67bffa227ea0021e3de235e03d

[ "MIT" ]

null

import math import torch from torch.nn import functional as F import numpy as np from neural_spline_flows import utils from neural_spline_flows.nde import transforms def unconstrained_linear_spline(inputs, unnormalized_pdf, inverse=False, tail_bound=1., tails='linear'): inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) outside_interval_mask = ~inside_interval_mask outputs = torch.zeros_like(inputs) logabsdet = torch.zeros_like(inputs) if tails == 'linear': outputs[outside_interval_mask] = inputs[outside_interval_mask] logabsdet[outside_interval_mask] = 0 else: raise RuntimeError('{} tails are not implemented.'.format(tails)) outputs[inside_interval_mask], logabsdet[inside_interval_mask] = linear_spline( inputs=inputs[inside_interval_mask], unnormalized_pdf=unnormalized_pdf[inside_interval_mask, :], inverse=inverse, left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound ) return outputs, logabsdet def linear_spline(inputs, unnormalized_pdf, inverse=False, left=0., right=1., bottom=0., top=1.): """ Reference: > Müller et al., Neural Importance Sampling, arXiv:1808.03856, 2018. """ if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right): raise transforms.InputOutsideDomain() elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top): raise transforms.InputOutsideDomain() if inverse: inputs = (inputs - bottom) / (top - bottom) else: inputs = (inputs - left) / (right - left) num_bins = unnormalized_pdf.size(-1) pdf = F.softmax(unnormalized_pdf, dim=-1) cdf = torch.cumsum(pdf, dim=-1) cdf[..., -1] = 1. cdf = F.pad(cdf, pad=(1, 0), mode='constant', value=0.0) if inverse: inv_bin_idx = utils.searchsorted(cdf, inputs) bin_boundaries = (torch.linspace(0, 1, num_bins+1) .view([1] * inputs.dim() + [-1]) .expand(*inputs.shape, -1)) slopes = ((cdf[..., 1:] - cdf[..., :-1]) / (bin_boundaries[..., 1:] - bin_boundaries[..., :-1])) offsets = cdf[..., 1:] - slopes * bin_boundaries[..., 1:] inv_bin_idx = inv_bin_idx.unsqueeze(-1) input_slopes = slopes.gather(-1, inv_bin_idx)[..., 0] input_offsets = offsets.gather(-1, inv_bin_idx)[..., 0] outputs = (inputs - input_offsets) / input_slopes outputs = torch.clamp(outputs, 0, 1) logabsdet = -torch.log(input_slopes) else: bin_pos = inputs * num_bins bin_idx = torch.floor(bin_pos).long() bin_idx[bin_idx >= num_bins] = num_bins - 1 alpha = bin_pos - bin_idx.float() input_pdfs = pdf.gather(-1, bin_idx[..., None])[..., 0] outputs = cdf.gather(-1, bin_idx[..., None])[..., 0] outputs += alpha * input_pdfs outputs = torch.clamp(outputs, 0, 1) bin_width = 1.0 / num_bins logabsdet = torch.log(input_pdfs) - np.log(bin_width) if inverse: outputs = outputs * (right - left) + left logabsdet = logabsdet - math.log(top - bottom) + math.log(right - left) else: outputs = outputs * (top - bottom) + bottom logabsdet = logabsdet + math.log(top - bottom) - math.log(right - left) return outputs, logabsdet

33.771429

83

0.601241

f48317cdbaefddc652c16b156c48efb6943f9586

28,694

py

Python

src/olympia/lib/crypto/tests/test_packaged.py

akanksha1612/addons-server

b125ad213a513bcbd97805105d862b400fbf9720

[ "BSD-3-Clause" ]

null

src/olympia/lib/crypto/tests/test_packaged.py

akanksha1612/addons-server

b125ad213a513bcbd97805105d862b400fbf9720

[ "BSD-3-Clause" ]

null

src/olympia/lib/crypto/tests/test_packaged.py

akanksha1612/addons-server

b125ad213a513bcbd97805105d862b400fbf9720

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import base64 import hashlib import os import shutil import tempfile import zipfile from django.conf import settings from django.core import mail from django.core.files.storage import default_storage as storage from django.test.utils import override_settings import mock import pytest import responses from signing_clients.apps import SignatureInfo from waffle.models import Flag from olympia import amo from olympia.addons.models import AddonUser from olympia.amo.tests import TestCase, create_flag from olympia.files.utils import extract_xpi from olympia.lib.crypto import packaged, tasks from olympia.versions.compare import version_int @override_settings( SIGNING_SERVER='http://signing.server', ENABLE_ADDON_SIGNING=True) class TestPackagedTrunion(TestCase): def setUp(self): super(TestPackagedTrunion, self).setUp() # Change addon file name self.addon = amo.tests.addon_factory() self.addon.update(guid='xxxxx') self.version = self.addon.current_version self.file_ = self.version.all_files[0] self.file_.update(filename='addon-a.xpi') # Add actual file to addons if not os.path.exists(os.path.dirname(self.file_.file_path)): os.makedirs(os.path.dirname(self.file_.file_path)) fp = zipfile.ZipFile(self.file_.file_path, 'w') fp.writestr('install.rdf', ( '<?xml version="1.0"?><RDF ' ' xmlns="http://www.w3.org/1999/02/22-rdf-syntax-ns#" ' ' xmlns:em="http://www.mozilla.org/2004/em-rdf#">' '<Description about="urn:mozilla:install-manifest">' ' <em:id>foo@jetpack</em:id>' ' <em:type>2</em:type>' ' <em:bootstrap>true</em:bootstrap>' ' <em:unpack>false</em:unpack>' ' <em:version>0.1</em:version>' ' <em:name>foo</em:name>' ' <em:description>foo bar</em:description>' ' <em:optionsType>2</em:optionsType>' ' <em:targetApplication></em:targetApplication>' '</Description>' '</RDF>')) fp.close() self._register_urls() def tearDown(self): if os.path.exists(self.file_.file_path): os.unlink(self.file_.file_path) super(TestPackagedTrunion, self).tearDown() def _register_urls(self): signature_path = os.path.join( settings.ROOT, 'src/olympia/lib/crypto/tests/', 'webextension_signed.rsa') with open(signature_path, 'rb') as fobj: signature = fobj.read() responses.add( responses.POST, 'http://signing.server/1.0/sign_addon', json={'mozilla.rsa': base64.b64encode(signature)}, status=200) def _sign_file(self, file_): packaged.sign_file(file_) def assert_not_signed(self): assert not self.file_.is_signed assert not self.file_.cert_serial_num assert not self.file_.hash assert not packaged.is_signed(self.file_.file_path) assert not responses.calls def assert_signed(self): assert self.file_.is_signed assert self.file_.cert_serial_num assert self.file_.hash assert packaged.is_signed(self.file_.file_path) assert len(responses.calls) == 1 @responses.activate def test_supports_firefox_old_not_default_to_compatible(self): max_appversion = self.version.apps.first().max # Old, and not default to compatible. max_appversion.update(version='4', version_int=version_int('4')) self.file_.update(binary_components=True, strict_compatibility=True) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() @responses.activate def test_supports_firefox_android_old_not_default_to_compatible(self): max_appversion = self.version.apps.first().max # Old, and not default to compatible. max_appversion.update(application=amo.ANDROID.id, version='4', version_int=version_int('4')) self.file_.update(binary_components=True, strict_compatibility=True) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() @responses.activate def test_supports_firefox_old_default_to_compatible(self): max_appversion = self.version.apps.first().max # Old, and default to compatible. max_appversion.update(version='4', version_int=version_int('4')) self.file_.update(binary_components=False, strict_compatibility=False) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() @responses.activate def test_supports_firefox_android_old_default_to_compatible(self): max_appversion = self.version.apps.first().max # Old, and default to compatible. max_appversion.update(application=amo.ANDROID.id, version='4', version_int=version_int('4')) self.file_.update(binary_components=False, strict_compatibility=False) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() @responses.activate def test_supports_firefox_recent_default_to_compatible(self): max_appversion = self.version.apps.first().max # Recent, default to compatible. max_appversion.update(version='37', version_int=version_int('37')) self.file_.update(binary_components=False, strict_compatibility=False) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() @responses.activate def test_supports_firefox_android_recent_not_default_to_compatible(self): max_appversion = self.version.apps.first().max # Recent, not default to compatible. max_appversion.update(application=amo.ANDROID.id, version='37', version_int=version_int('37')) self.file_.update(binary_components=True, strict_compatibility=True) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() def test_get_trunion_endpoint(self): assert self.addon.status == amo.STATUS_PUBLIC expected = 'http://signing.server/1.0/sign_addon' assert ( packaged.get_trunion_endpoint(settings.SIGNING_SERVER) == expected) def test_no_server_full(self): with self.settings(SIGNING_SERVER=''): self._sign_file(self.file_) self.assert_not_signed() @responses.activate def test_sign_file(self): self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() # Make sure there's two newlines at the end of the mozilla.sf file (see # bug 1158938). with zipfile.ZipFile(self.file_.file_path, mode='r') as zf: with zf.open('META-INF/mozilla.sf', 'r') as mozillasf: assert mozillasf.read().endswith('\n\n') @responses.activate def test_sign_file_non_ascii_filename(self): src = self.file_.file_path self.file_.update(filename=u'jétpack.xpi') shutil.move(src, self.file_.file_path) self.assert_not_signed() self._sign_file(self.file_) self.assert_signed() def test_no_sign_missing_file(self): os.unlink(self.file_.file_path) assert not self.file_.is_signed assert not self.file_.cert_serial_num assert not self.file_.hash self._sign_file(self.file_) assert not self.file_.is_signed assert not self.file_.cert_serial_num assert not self.file_.hash assert not packaged.is_signed(self.file_.file_path) def test_no_sign_hotfix_addons(self): """Don't sign hotfix addons.""" for hotfix_guid in settings.HOTFIX_ADDON_GUIDS: self.addon.update(guid=hotfix_guid) self._sign_file(self.file_) self.assert_not_signed() def test_no_sign_again_mozilla_signed_extensions(self): """Don't try to resign mozilla signed extensions.""" self.file_.update(is_mozilla_signed_extension=True) self._sign_file(self.file_) self.assert_not_signed() @responses.activate def test_is_signed(self): assert not packaged.is_signed(self.file_.file_path) self._sign_file(self.file_) assert packaged.is_signed(self.file_.file_path) @responses.activate def test_size_updated(self): unsigned_size = storage.size(self.file_.file_path) self._sign_file(self.file_) signed_size = storage.size(self.file_.file_path) assert self.file_.size == signed_size assert unsigned_size < signed_size @responses.activate def test_sign_file_multi_package(self): fpath = 'src/olympia/files/fixtures/files/multi-package.xpi' with amo.tests.copy_file(fpath, self.file_.file_path, overwrite=True): self.file_.update(is_multi_package=True) self.assert_not_signed() self._sign_file(self.file_) self.assert_not_signed() # The multi-package itself isn't signed. assert not packaged.is_signed(self.file_.file_path) # The internal extensions aren't either. folder = tempfile.mkdtemp(dir=settings.TMP_PATH) try: extract_xpi(self.file_.file_path, folder) # The extension isn't. assert not packaged.is_signed( os.path.join(folder, 'random_extension.xpi')) # And the theme isn't either. assert not packaged.is_signed( os.path.join(folder, 'random_theme.xpi')) finally: amo.utils.rm_local_tmp_dir(folder) @responses.activate def test_call_signing(self): packaged.call_signing(self.file_) call = responses.calls[0].request assert call.url == 'http://signing.server/1.0/sign_addon' assert 'name="addon_id"\r\n\r\nxxxxx' in call.body assert ( 'name="file"; filename="mozilla.sf"\r\n\r\n' 'Signature-Version: 1.0\n' 'MD5-Digest-Manifest: UrEJ9n5q8I9UW2KlFUJDkA==\n' 'SHA1-Digest-Manifest: lTdbRmVMF7o/C+BT9GnMQne2Ap4=') in call.body @responses.activate def test_call_signing_too_long_guid_bug_1203365(self): long_guid = 'x' * 65 hashed = hashlib.sha256(long_guid).hexdigest() self.addon.update(guid=long_guid) packaged.call_signing(self.file_) call = responses.calls[0].request assert call.url == 'http://signing.server/1.0/sign_addon' assert 'name="addon_id"\r\n\r\n{0}'.format(hashed) in call.body assert ( 'name="file"; filename="mozilla.sf"\r\n\r\n' 'Signature-Version: 1.0\n' 'MD5-Digest-Manifest: UrEJ9n5q8I9UW2KlFUJDkA==\n' 'SHA1-Digest-Manifest: lTdbRmVMF7o/C+BT9GnMQne2Ap4=') in call.body def test_get_id_short_guid(self): assert len(self.addon.guid) <= 64 assert len(packaged.get_id(self.addon)) <= 64 assert packaged.get_id(self.addon) == self.addon.guid def test_get_id_longest_allowed_guid_bug_1203365(self): long_guid = 'x' * 64 self.addon.update(guid=long_guid) assert packaged.get_id(self.addon) == self.addon.guid def test_get_id_long_guid_bug_1203365(self): long_guid = 'x' * 65 hashed = hashlib.sha256(long_guid).hexdigest() self.addon.update(guid=long_guid) assert len(self.addon.guid) > 64 assert len(packaged.get_id(self.addon)) <= 64 assert packaged.get_id(self.addon) == hashed @override_settings(ENABLE_ADDON_SIGNING=True) class TestPackagedAutograph(TestPackagedTrunion): def setUp(self): create_flag('activate-autograph-signing') super(TestPackagedAutograph, self).setUp() def tearDown(self): Flag.objects.filter(name='activate-autograph-signing').delete() super(TestPackagedAutograph, self).tearDown() def _register_urls(self): responses.add_passthru(settings.AUTOGRAPH_CONFIG['server_url']) def _get_signature_info(self): with zipfile.ZipFile(self.file_.file_path, mode='r') as zobj: with zobj.open('META-INF/mozilla.rsa', 'r') as fobj: pkcs7 = fobj.read() return SignatureInfo(pkcs7) def _sign_file(self, file_): packaged.sign_file(file_, use_autograph=True) def assert_not_signed(self): # Overwritten to not rely on `responses` but check the real deal assert not self.file_.is_signed assert not self.file_.cert_serial_num assert not self.file_.hash assert not packaged.is_signed(self.file_.file_path) def assert_signed(self): # Overwritten to not rely on `responses` but check the real deal assert self.file_.is_signed assert self.file_.cert_serial_num assert self.file_.hash assert packaged.is_signed(self.file_.file_path) def test_no_server_full(self): # Test not needed for autograph return def test_call_signing(self): self._sign_file(self.file_) signature_info = self._get_signature_info() subject_info = signature_info.signer_certificate['subject'] assert subject_info['common_name'] == 'xxxxx' def test_call_signing_too_long_guid_bug_1203365(self): long_guid = 'x' * 65 hashed = hashlib.sha256(long_guid).hexdigest() self.addon.update(guid=long_guid) self._sign_file(self.file_) signature_info = self._get_signature_info() subject_info = signature_info.signer_certificate['subject'] assert subject_info['common_name'] == hashed class TestTasks(TestCase): fixtures = ['base/users'] def setUp(self): super(TestTasks, self).setUp() self.addon = amo.tests.addon_factory(version_kw={'version': '1.3'}) self.version = self.addon.current_version # Make sure our file/version is at least compatible with FF # '37'. self.max_appversion = self.version.apps.first().max self.set_max_appversion('37') self.file_ = self.version.all_files[0] self.file_.update(filename='jetpack.xpi') def tearDown(self): if os.path.exists(self.get_backup_file_path()): os.unlink(self.get_backup_file_path()) super(TestTasks, self).tearDown() def get_backup_file_path(self): return u'{0}.backup_signature'.format(self.file_.file_path) def set_max_appversion(self, version): """Set self.max_appversion to the given version.""" self.max_appversion.update(version=version, version_int=version_int(version)) def assert_backup(self): """Make sure there's a backup file.""" assert os.path.exists(self.get_backup_file_path()) def assert_no_backup(self): """Make sure there's no backup file.""" assert not os.path.exists(self.get_backup_file_path()) @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_no_bump_unreviewed(self, mock_sign_file): """Don't bump nor sign unreviewed files.""" for status in (amo.UNREVIEWED_FILE_STATUSES + (amo.STATUS_BETA,)): self.file_.update(status=status) fpath = 'src/olympia/files/fixtures/files/jetpack.xpi' with amo.tests.copy_file(fpath, self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert not mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_bump_version_in_model(self, mock_sign_file): # We want to make sure each file has been signed. self.file2 = amo.tests.file_factory(version=self.version) self.file2.update(filename='jetpack-b.xpi') backup_file2_path = u'{0}.backup_signature'.format( self.file2.file_path) try: fpath = 'src/olympia/files/fixtures/files/jetpack.xpi' with amo.tests.copy_file(fpath, self.file_.file_path): with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file2.file_path): file_hash = self.file_.generate_hash() file2_hash = self.file2.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.call_count == 2 self.version.reload() assert self.version.version == '1.3.1-signed' assert self.version.version_int == version_int( '1.3.1-signed') assert file_hash != self.file_.generate_hash() assert file2_hash != self.file2.generate_hash() self.assert_backup() assert os.path.exists(backup_file2_path) finally: if os.path.exists(backup_file2_path): os.unlink(backup_file2_path) @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_full(self, mock_sign_file): """Use the signing server if files are approved.""" self.file_.update(status=amo.STATUS_PUBLIC) with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): tasks.sign_addons([self.addon.pk]) mock_sign_file.assert_called_with(self.file_, use_autograph=False) @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_supported_applications(self, mock_sign_file): """Make sure we sign for all supported applications.""" with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): for app in (amo.ANDROID.id, amo.FIREFOX.id): self.max_appversion.update(application=app) tasks.sign_addons([self.addon.pk]) mock_sign_file.assert_called_with( self.file_, use_autograph=False) mock_sign_file.reset_mock() def assert_not_signed(self, mock_sign_file, file_hash): assert not mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_dont_sign_dont_bump_other_applications(self, mock_sign_file): """Don't sign files which are for applications we don't sign for.""" path = 'src/olympia/files/fixtures/files/jetpack.xpi' with amo.tests.copy_file(path, self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') apps_without_signing = [app for app in amo.APPS_ALL.keys() if app not in packaged.SIGN_FOR_APPS] for app in apps_without_signing: self.max_appversion.update(application=app) tasks.sign_addons([self.addon.pk]) self.assert_not_signed(mock_sign_file, file_hash) @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_bump_non_ascii_filename(self, mock_sign_file): """Sign files which have non-ascii filenames.""" self.file_.update(filename=u'jétpack.xpi') with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3.1-signed' assert self.version.version_int == version_int('1.3.1-signed') assert file_hash != self.file_.generate_hash() self.assert_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_bump_non_ascii_version(self, mock_sign_file): """Sign versions which have non-ascii version numbers.""" self.version.update(version=u'é1.3') with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == u'é1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == u'é1.3.1-signed' assert self.version.version_int == version_int(u'é1.3.1-signed') assert file_hash != self.file_.generate_hash() self.assert_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_bump_old_versions_default_compat(self, mock_sign_file): """Sign files which are old, but default to compatible.""" with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') self.set_max_appversion('4') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3.1-signed' assert self.version.version_int == version_int('1.3.1-signed') assert file_hash != self.file_.generate_hash() self.assert_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_bump_new_versions_not_default_compat(self, mock_sign_file): """Sign files which are recent, event if not default to compatible.""" with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') self.file_.update(binary_components=True, strict_compatibility=True) tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3.1-signed' assert self.version.version_int == version_int('1.3.1-signed') assert file_hash != self.file_.generate_hash() self.assert_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_dont_resign_dont_bump_version_in_model(self, mock_sign_file): with amo.tests.copy_file( 'src/olympia/files/fixtures/files/new-addon-signature.xpi', self.file_.file_path): self.file_.update(is_signed=True) file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert not mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_dont_sign_dont_bump_version_bad_zipfile(self, mock_sign_file): with amo.tests.copy_file(__file__, self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert not mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_dont_sign_dont_bump_sign_error(self, mock_sign_file): mock_sign_file.side_effect = IOError() fpath = 'src/olympia/files/fixtures/files/jetpack.xpi' with amo.tests.copy_file(fpath, self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_dont_bump_not_signed(self, mock_sign_file): mock_sign_file.return_value = None # Pretend we didn't sign. fpath = 'src/olympia/files/fixtures/files/jetpack.xpi' with amo.tests.copy_file(fpath, self.file_.file_path): file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk]) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') assert file_hash == self.file_.generate_hash() self.assert_no_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_resign_bump_version_in_model_if_force(self, mock_sign_file): with amo.tests.copy_file( 'src/olympia/files/fixtures/files/new-addon-signature.xpi', self.file_.file_path): self.file_.update(is_signed=True) file_hash = self.file_.generate_hash() assert self.version.version == '1.3' assert self.version.version_int == version_int('1.3') tasks.sign_addons([self.addon.pk], force=True) assert mock_sign_file.called self.version.reload() assert self.version.version == '1.3.1-signed' assert self.version.version_int == version_int('1.3.1-signed') assert file_hash != self.file_.generate_hash() self.assert_backup() @mock.patch('olympia.lib.crypto.tasks.sign_file') def test_sign_mail(self, mock_sign_file): """Check that an email reason can be provided.""" self.file_.update(status=amo.STATUS_PUBLIC) AddonUser.objects.create(addon=self.addon, user_id=999) with amo.tests.copy_file( 'src/olympia/files/fixtures/files/jetpack.xpi', self.file_.file_path): tasks.sign_addons([self.addon.pk], reason='expiry') mock_sign_file.assert_called_with(self.file_, use_autograph=False) assert 'expiration' in mail.outbox[0].message().as_string() @pytest.mark.parametrize(('old', 'new'), [ ('1.1', '1.1.1-signed'), ('1.1.1-signed.1', '1.1.1-signed.1.1-signed'), ('1.1.1-signed', '1.1.1-signed-2'), ('1.1.1-signed-3', '1.1.1-signed-4'), ('1.1.1-signed.1-signed-16', '1.1.1-signed.1-signed-17') ]) def test_get_new_version_number(old, new): assert tasks.get_new_version_number(old) == new

41.889051

79

0.637903

f36dec897f5598b673a91a63b8334367fd242101

1,633

py

Python

sample-demo/venv/Lib/site-packages/PyQt6/lupdate/translations.py

rupc/bsp-protos

58833e7ab9ff53f3633708fb5f95edfdd152c5ea

[ "Apache-2.0" ]

null

sample-demo/venv/Lib/site-packages/PyQt6/lupdate/translations.py

rupc/bsp-protos

58833e7ab9ff53f3633708fb5f95edfdd152c5ea

[ "Apache-2.0" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

sample-demo/venv/Lib/site-packages/PyQt6/lupdate/translations.py

rupc/bsp-protos

58833e7ab9ff53f3633708fb5f95edfdd152c5ea

[ "Apache-2.0" ]

null

# Copyright (c) 2021 Riverbank Computing Limited <info@riverbankcomputing.com> # # This file is part of PyQt6. # # This file may be used under the terms of the GNU General Public License # version 3.0 as published by the Free Software Foundation and appearing in # the file LICENSE included in the packaging of this file. Please review the # following information to ensure the GNU General Public License version 3.0 # requirements will be met: http://www.gnu.org/copyleft/gpl.html. # # If you do not wish to use this file under the terms of the GPL version 3.0 # then you may purchase a commercial license. For more information contact # info@riverbankcomputing.com. # # This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE # WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. class Context: """ Encapsulate a message context. """ def __init__(self, name): """ Initialise the context. """ self.name = name self.messages = [] class EmbeddedComments: """ Encapsulate information for a translator embedded in comments. """ def __init__(self): """ Initialise the object. """ self.message_id = '' self.extra_comments = [] self.extras = [] class Message: """ Encapsulate a message. """ def __init__(self, filename, line_nr, source, comment, numerus): """ Initialise the message. """ self.filename = filename self.line_nr = line_nr self.source = source self.comment = comment self.numerus = numerus self.embedded_comments = EmbeddedComments()

31.403846

78

0.685242

67840a505e9240bb969e4c9e53e0341ec6912859

138,027

py

Python

tensorflow/python/keras/engine/training_v1.py

ProctorU/tensorflow

fd05051846fd9ceb090206600afd1a71ba852e20

[ "Apache-2.0" ]

1

2020-02-15T14:00:01.000Z

tensorflow/python/keras/engine/training_v1.py

alubanana/tensorflow

454f89ab3baacbac567d6bcceef4c743f23ce58b

[ "Apache-2.0" ]

null

tensorflow/python/keras/engine/training_v1.py

alubanana/tensorflow

454f89ab3baacbac567d6bcceef4c743f23ce58b

[ "Apache-2.0" ]

1

2020-02-14T10:12:19.000Z

# Copyright 2015 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. # ============================================================================== """V1 Training-related part of the Keras engine.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import numpy as np from tensorflow.python import tf2 from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.eager import context from tensorflow.python.eager import def_function from tensorflow.python.eager import monitoring from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import composite_tensor_utils from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.framework import type_spec from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import optimizers from tensorflow.python.keras.distribute import distributed_training_utils from tensorflow.python.keras.engine import network from tensorflow.python.keras.engine import training as training_lib from tensorflow.python.keras.engine import training_arrays from tensorflow.python.keras.engine import training_distributed from tensorflow.python.keras.engine import training_eager from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.engine import training_v2 from tensorflow.python.keras.engine import training_v2_utils from tensorflow.python.keras.mixed_precision.experimental import loss_scale_optimizer from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.keras.saving.saved_model import model_serialization from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils import losses_utils from tensorflow.python.keras.utils.mode_keys import ModeKeys from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.losses import util as tf_losses_utils from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.util import deprecation from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect from tensorflow.python.util.compat import collections_abc try: from scipy.sparse import issparse # pylint: disable=g-import-not-at-top except ImportError: issparse = None _keras_api_gauge = monitoring.BoolGauge('/tensorflow/api/keras/model_v1', 'keras model v1 usage', 'method') class Model(training_lib.Model): """`Model` groups layers into an object with training and inference features. There are two ways to instantiate a `Model`: 1 - With the "functional API", where you start from `Input`, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs: ```python import tensorflow as tf inputs = tf.keras.Input(shape=(3,)) x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs) outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) ``` 2 - By subclassing the `Model` class: in that case, you should define your layers in `__init__` and you should implement the model's forward pass in `call`. ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) def call(self, inputs): x = self.dense1(inputs) return self.dense2(x) model = MyModel() ``` If you subclass `Model`, you can optionally have a `training` argument (boolean) in `call`, which you can use to specify a different behavior in training and inference: ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, inputs, training=False): x = self.dense1(inputs) if training: x = self.dropout(x, training=training) return self.dense2(x) model = MyModel() ``` """ def __init__(self, *args, **kwargs): super(Model, self).__init__(*args, **kwargs) _keras_api_gauge.get_cell('model_v1').set(True) # initializing _distribution_strategy here since it is possible to call # predict on a model without compiling it. self._distribution_strategy = None self._compile_time_distribution_strategy = None if (ops.executing_eagerly_outside_functions() and distribution_strategy_context.has_strategy()): self._set_strategy( distribution_strategy_context.get_strategy()) # This flag is used to track if the user is using the deprecated path of # passing distribution strategy to compile rather than creating the model # under distribution strategy scope. self._compile_distribution = False self._run_eagerly = None self._experimental_run_tf_function = ( ops.executing_eagerly_outside_functions()) @trackable.no_automatic_dependency_tracking def _set_strategy(self, strategy): self._compile_time_distribution_strategy = strategy def get_weights(self): """Retrieves the weights of the model. Returns: A flat list of Numpy arrays. """ strategy = (self._distribution_strategy or self._compile_time_distribution_strategy) if strategy: with strategy.scope(): return network.Network.get_weights(self) return network.Network.get_weights(self) def load_weights(self, filepath, by_name=False, skip_mismatch=False): """Loads all layer weights, either from a TensorFlow or an HDF5 weight file. If `by_name` is False weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights. If `by_name` is True, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed. Only topological loading (`by_name=False`) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for user-defined classes inheriting from `tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the object-local names of attributes to which layers are assigned in the `Model`'s constructor. Arguments: filepath: String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to `save_weights`). by_name: Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when `by_name=True`). Returns: When loading a weight file in TensorFlow format, returns the same status object as `tf.train.Checkpoint.restore`. When graph building, restore ops are run automatically as soon as the network is built (on first call for user-defined classes inheriting from `Model`, immediately if it is already built). When loading weights in HDF5 format, returns `None`. Raises: ImportError: If h5py is not available and the weight file is in HDF5 format. ValueError: If `skip_mismatch` is set to `True` when `by_name` is `False`. """ if distributed_training_utils.is_tpu_strategy(self._distribution_strategy): if (self._distribution_strategy.extended.steps_per_run > 1 and (not network._is_hdf5_filepath(filepath))): # pylint: disable=protected-access raise ValueError('Load weights is not yet supported with TPUStrategy ' 'with steps_per_run greater than 1.') return super(Model, self).load_weights(filepath, by_name, skip_mismatch) @trackable.no_automatic_dependency_tracking def compile(self, optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, distribute=None, **kwargs): """Configures the model for training. Arguments: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`. loss: String (name of objective function), objective function or `tf.keras.losses.Loss` instance. See `tf.keras.losses`. An objective function is any callable with the signature `scalar_loss = fn(y_true, y_pred)`. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses. metrics: List of metrics to be evaluated by the model during training and testing. Typically you will use `metrics=['accuracy']`. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`. You can also pass a list (len = len(outputs)) of lists of metrics such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`. loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the *weighted sum* of all individual losses, weighted by the `loss_weights` coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a tensor, it is expected to map output names (strings) to scalar coefficients. sample_weight_mode: If you need to do timestep-wise sample weighting (2D weights), set this to `"temporal"`. `None` defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different `sample_weight_mode` on each output by passing a dictionary or a list of modes. weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing. target_tensors: By default, Keras will create placeholders for the model's target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the `target_tensors` argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors. distribute: NOT SUPPORTED IN TF 2.0, please create and compile the model under distribution strategy scope instead of passing it to compile. **kwargs: Any additional arguments. Raises: ValueError: In case of invalid arguments for `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ self._run_eagerly = kwargs.pop('run_eagerly', None) self._experimental_run_tf_function = kwargs.pop( 'experimental_run_tf_function', True) # Prepare Session arguments (legacy). kwargs.pop('cloning', None) # Legacy DistStrat argument, never used. allowed_kwargs = {'feed_dict', 'fetches', 'options', 'run_metadata'} unknown_kwargs = set(kwargs.keys()) - allowed_kwargs if unknown_kwargs: raise TypeError( 'Invalid keyword argument(s) in `compile`: %s' % (unknown_kwargs,)) self._function_kwargs = kwargs if self._function_kwargs: self._experimental_run_tf_function = False if self.run_eagerly: raise ValueError( 'Session keyword arguments are not supported ' 'when `run_eagerly=True`. You passed the following ' 'Session arguments: %s' % (self._function_kwargs,)) self._set_optimizer(optimizer) is_any_keras_optimizer_v1 = any( (isinstance(opt, optimizers.Optimizer) and not isinstance(opt, optimizers.TFOptimizer) ) for opt in nest.flatten(self.optimizer)) if is_any_keras_optimizer_v1 and ops.executing_eagerly_outside_functions(): raise ValueError('`tf.compat.v1.keras` Optimizer (', optimizer, ') is ' 'not supported when eager execution is enabled. Use a ' '`tf.keras` Optimizer instead, or disable eager ' 'execution.') if ((target_tensors is not None) or not ops.executing_eagerly_outside_functions()): # Fallback out of things that aren't supported with v2 loops self._experimental_run_tf_function = False if distribute is not None: if tf2.enabled() or self._experimental_run_tf_function: raise ValueError( 'Distribute argument in compile is not available in TF 2.0 please ' 'create the model under the distribution strategy scope.') logging.warning('Distribute argument in compile is deprecated please ' 'create the model under the distribution strategy scope.') self._distribution_strategy = distribute self._compile_distribution = True else: if distribution_strategy_context.has_strategy(): # When the user builds the model in the DS scope and cross replica # context we want distribution strategy to be set but when building the # replica copies of the models internally we should not be compiling # with distribution strategy and use the default compilation path. if distribution_strategy_context.in_cross_replica_context(): self._distribution_strategy = ( distribution_strategy_context.get_strategy()) if not self._experimental_run_tf_function: self._validate_compile_param_for_distribution_strategy(self.run_eagerly, sample_weight_mode, target_tensors, weighted_metrics) # We've disabled automatic dependency tracking for this method, but do want # to add a checkpoint dependency on the optimizer if it's trackable. if isinstance(self.optimizer, trackable.Trackable): self._track_trackable( self.optimizer, name='optimizer', overwrite=True) self.loss = loss or {} self.loss_weights = loss_weights self.sample_weight_mode = sample_weight_mode self._compile_metrics = metrics or [] self._compile_weighted_metrics = weighted_metrics if self.run_eagerly and target_tensors is not None: raise ValueError( 'target_tensors argument is not supported when ' 'running a model eagerly.') # _training_endpoints contains a list of _TrainingEndpoint object, which has # all the model output/target/loss and related metadata. self._training_endpoints = [] # Used to freeze the behavior of the Model once `compile` has been called. self._compiled_trainable_state = self._get_trainable_state() # Set tf.distribute.Strategy specific parameters. self._distributed_model_cache = {} self._distributed_function_cache = {} # Clear any `_eager_losses` that was added. self._clear_losses() if (not context.executing_eagerly() and self._distribution_strategy is not None): # Ensures a Session is created and configured correctly for Distribution # Strategy. K.configure_and_create_distributed_session(self._distribution_strategy) # Initialize model metric attributes. self._init_metric_attributes() if not self.built or not self.inputs or not self.outputs: # Model is not compilable because it does not know its number of inputs # and outputs, nor their shapes and names. We will compile after the first # time the model gets called on training data. return self._is_compiled = True _keras_api_gauge.get_cell('compile_v1').set(True) # Prepare list of loss functions, same size of model outputs. self.loss_functions = training_utils.prepare_loss_functions( self.loss, self.output_names) target_tensors = self._process_target_tensor_for_compile(target_tensors) for o, n, l, t in zip(self.outputs, self.output_names, self.loss_functions, target_tensors): endpoint = _TrainingEndpoint(o, n, l) endpoint.create_training_target(t, run_eagerly=self.run_eagerly) self._training_endpoints.append(endpoint) # Prepare list loss weights, same size of model outputs. training_utils.prepare_loss_weights(self._training_endpoints, loss_weights) # Initialization for Eager mode execution. if self.run_eagerly: self._compile_eagerly(metrics, weighted_metrics, sample_weight_mode) return with K.get_graph().as_default(): # Save all metric attributes per output of the model. self._cache_output_metric_attributes(metrics, weighted_metrics) # Set metric attributes on model. self._set_metric_attributes() # Invoke metric functions (unweighted) for all the outputs. self._handle_metrics( self.outputs, targets=self._targets, skip_target_masks=self._prepare_skip_target_masks(), masks=self._prepare_output_masks()) # Prepare sample weight modes. List with the same length as model outputs. training_utils.prepare_sample_weight_modes( self._training_endpoints, sample_weight_mode) # Creates the model loss and weighted metrics sub-graphs. self._compile_weights_loss_and_weighted_metrics() # Functions for train, test and predict will # be compiled lazily when required. # This saves time when the user is not using all functions. self.train_function = None self.test_function = None self.predict_function = None # Collected trainable weights, sorted in topological order. self._collected_trainable_weights = self.trainable_weights # Validate all variables were correctly created in distribution scope. if self._distribution_strategy and not self._compile_distribution: for v in self.variables: strategy = self._distribution_strategy if not strategy.extended.variable_created_in_scope(v): raise ValueError( 'Variable (%s) was not created in the distribution strategy ' 'scope of (%s). It is most likely due to not all layers or ' 'the model or optimizer being created outside the distribution ' 'strategy scope. Try to make sure your code looks similar ' 'to the following.\n' 'with strategy.scope():\n' ' model=_create_model()\n' ' model.compile(...)'% (v, strategy)) @trackable.no_automatic_dependency_tracking def _init_distributed_function_cache_if_not_compiled(self): if not hasattr(self, '_distributed_function_cache'): self._distributed_function_cache = {} @property def metrics(self): """Returns the model's metrics added using `compile`, `add_metric` APIs.""" metrics = [] if self._is_compiled: metrics += self._compile_metric_functions metrics.extend(self._metrics) metrics.extend(_get_metrics_from_layers(self._layers)) return metrics @property def metrics_names(self): """Returns the model's display labels for all outputs.""" # This property includes all output names including `loss` and per-output # losses for backward compatibility. metrics_names = ['loss'] if self._is_compiled: # Add output loss metric names to the metric names list. if len(self._training_endpoints) > 1: metrics_names.extend([ e.loss_name() for e in self._training_endpoints if not e.should_skip_target() ]) # Add all metric names. metrics_names += [m.name for m in self.metrics] return metrics_names @property def run_eagerly(self): """Settable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. Returns: Boolean, whether the model should run eagerly. """ if self._run_eagerly is True and not context.executing_eagerly(): raise ValueError('You can only set `run_eagerly=True` if eager execution ' 'is enabled.') if not self.dynamic: if self._run_eagerly is None: # Respect `tf.config.experimental_run_functions_eagerly` unless # `run_eagerly` was explicitly passed to `compile`. return def_function.RUN_FUNCTIONS_EAGERLY else: return self._run_eagerly else: if not context.executing_eagerly(): raise ValueError('Your model contains layers that can only be ' 'successfully run in eager execution (layers ' 'constructed with `dynamic=True`). ' 'You must enable eager execution with ' '`tf.enable_eager_execution()`.') if self._run_eagerly is False: # TODO(fchollet): consider using py_func to enable this. raise ValueError('Your model contains layers that can only be ' 'successfully run in eager execution (layers ' 'constructed with `dynamic=True`). ' 'You cannot set `run_eagerly=False`.') return context.executing_eagerly() @run_eagerly.setter def run_eagerly(self, value): self._run_eagerly = value def _select_training_loop(self, inputs): """Select training loop for fit/eval/predict based on the inputs.""" # TODO(kaftan) or TODO(scottzhu): This check should eventually be nicely # integrated into the data adapters in the v2 loop. We can't do this yet # because we currently have to fall back for unhandled data types. if isinstance(inputs, (iterator_ops.Iterator, iterator_ops.OwnedIterator)): raise ValueError('For performance reasons Keras `fit`, `evaluate` and' '`predict` accept tf.data `Datasets` as input but not ' 'iterators that have been manually generated from ' 'Datasets by users. Please directly pass in the ' 'original `Dataset` object instead of passing in ' '`iter(dataset)`.') # Experiment training loop with default DS path. if context.executing_eagerly() and self._experimental_run_tf_function: if self._in_multi_worker_mode(): return training_distributed.DistributionMultiWorkerTrainingLoop( training_v2.Loop()) else: return training_v2.Loop() # Case 1: distribution strategy. if self._distribution_strategy: if self._in_multi_worker_mode(): return training_distributed.DistributionMultiWorkerTrainingLoop( training_distributed.DistributionSingleWorkerTrainingLoop()) else: return training_distributed.DistributionSingleWorkerTrainingLoop() # Case 2: generator-like. Input is Python generator, or Sequence object, # or a non-distributed Dataset or iterator in eager execution. if data_utils.is_generator_or_sequence(inputs): return training_generator.GeneratorOrSequenceTrainingLoop() if training_utils.is_eager_dataset_or_iterator(inputs): return training_generator.EagerDatasetOrIteratorTrainingLoop() # Case 3: Symbolic tensors or Numpy array-like. # This includes Datasets and iterators in graph mode (since they # generate symbolic tensors). if self.run_eagerly: return training_generator.GeneratorLikeTrainingLoop() else: return training_arrays.ArrayLikeTrainingLoop() def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0., validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs): """Trains the model for a fixed number of epochs (iterations on a dataset). Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample weights)`. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator, or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from `x`). batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of symbolic tensors, datasets, generators, or `keras.utils.Sequence` instances (since they generate batches). epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided. Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached. verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See `tf.keras.callbacks`. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling. This argument is not supported when `x` is a dataset, generator or `keras.utils.Sequence` instance. validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. `validation_data` will override `validation_split`. `validation_data` could be: - tuple `(x_val, y_val)` of Numpy arrays or tensors - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays - dataset For the first two cases, `batch_size` must be provided. For the last case, `validation_steps` could be provided. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when `steps_per_epoch` is not `None`. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. sample_weight: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. This argument is not supported when `x` is a dataset, generator, or `keras.utils.Sequence` instance, instead provide the sample_weights as the third element of `x`. initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run). steps_per_epoch: Integer or `None`. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default `None` is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a `tf.data` dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. This argument is not supported with array inputs. validation_steps: Only relevant if `validation_data` is provided and is a `tf.data` dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted. In the case of a infinite dataset, it will run into a infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time. validation_freq: Only relevant if validation data is provided. Integer or `collections_abc.Container` instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. `validation_freq=2` runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the end of the 1st, 2nd, and 10th epochs. max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. **kwargs: Used for backwards compatibility. Returns: A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). Raises: RuntimeError: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects. """ _keras_api_gauge.get_cell('fit_v1').set(True) # Legacy support if 'nb_epoch' in kwargs: logging.warning( 'The `nb_epoch` argument in `fit` has been renamed `epochs`.') epochs = kwargs.pop('nb_epoch') if kwargs: raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) self._assert_compile_was_called() self._check_call_args('fit') func = self._select_training_loop(x) return func.fit( self, x=x, y=y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_split=validation_split, validation_data=validation_data, shuffle=shuffle, class_weight=class_weight, sample_weight=sample_weight, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, validation_freq=validation_freq, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) def evaluate(self, x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False): """Returns the loss value & metrics values for the model in test mode. Computation is done in batches. Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. - A generator or `keras.utils.Sequence` instance. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from the iterator/dataset). batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of symbolic tensors, dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar. sample_weight: Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. This argument is not supported when `x` is a dataset, instead pass sample weights as the third element of `x`. steps: Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: ValueError: in case of invalid arguments. """ _keras_api_gauge.get_cell('evaluate_v1').set(True) self._assert_compile_was_called() self._check_call_args('evaluate') func = self._select_training_loop(x) return func.evaluate( self, x=x, y=y, batch_size=batch_size, verbose=verbose, sample_weight=sample_weight, steps=steps, callbacks=callbacks, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) def predict(self, x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False): """Generates output predictions for the input samples. Computation is done in batches. Arguments: x: Input samples. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A `tf.data` dataset. - A generator or `keras.utils.Sequence` instance. batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of symbolic tensors, dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: Verbosity mode, 0 or 1. steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, `predict` will run until the input dataset is exhausted. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during prediction. See [callbacks](/api_docs/python/tf/keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. Returns: Numpy array(s) of predictions. Raises: ValueError: In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. """ _keras_api_gauge.get_cell('predict_v1').set(True) self._check_call_args('predict') func = self._select_training_loop(x) return func.predict( self, x=x, batch_size=batch_size, verbose=verbose, steps=steps, callbacks=callbacks, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) def reset_metrics(self): """Resets the state of metrics.""" metrics = self._get_training_eval_metrics() for m in metrics: m.reset_states() # Reset metrics on all the distributed (cloned) models. if self._distribution_strategy: distributed_training_utils._reset_metrics(self) # pylint: disable=protected-access def train_on_batch(self, x, y=None, sample_weight=None, class_weight=None, reset_metrics=True): """Runs a single gradient update on a single batch of data. Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, `y` should not be specified (since targets will be obtained from the iterator). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile(). This argument is not supported when `x` is a dataset. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. Returns: Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: ValueError: In case of invalid user-provided arguments. """ self._assert_compile_was_called() self._check_call_args('train_on_batch') if self._experimental_run_tf_function: outputs = training_v2_utils.train_on_batch( self, x, y=y, sample_weight=sample_weight, class_weight=class_weight, reset_metrics=reset_metrics, standalone=True) outputs = (outputs['total_loss'] + outputs['output_losses'] + outputs['metrics']) outputs = [ training_v2_utils._non_none_constant_value(v) for v in outputs] # pylint: disable=protected-access if len(outputs) == 1: outputs = outputs[0] return outputs # If at this point we are in the replica context, then it is okay to execute # the Eager code path. The expected way to get here is to call `fit` that # calls `train_on_batch` on each replica. if (self._distribution_strategy and distribution_strategy_context.in_cross_replica_context()): raise NotImplementedError('`train_on_batch` is not supported for models ' 'distributed with tf.distribute.Strategy.') # Validate and standardize user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight, extract_tensors_from_dataset=True) # If `self._distribution_strategy` is True, then we are in a replica context # at this point because of the check above. `train_on_batch` is being run # for each replica by `self._distribution_strategy` and the same code path # as Eager is expected to be taken. if self.run_eagerly or self._distribution_strategy: output_dict = training_eager.train_on_batch( self, x, y, sample_weights=sample_weights, output_loss_metrics=self._output_loss_metrics) outputs = (output_dict['total_loss'] + output_dict['output_losses'] + output_dict['metrics']) outputs = [ training_v2_utils._non_none_constant_value(v) for v in outputs] # pylint: disable=protected-access else: x = training_utils.ModelInputs(x).as_list() ins = x + list(y or []) + list(sample_weights or []) if not isinstance(K.symbolic_learning_phase(), int): ins += [True] # Add learning phase value. self._update_sample_weight_modes(sample_weights=sample_weights) self._make_train_function() outputs = self.train_function(ins) # pylint: disable=not-callable if reset_metrics: self.reset_metrics() if len(outputs) == 1: return outputs[0] return outputs def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True): """Test the model on a single batch of samples. Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset `y` should not be specified (since targets will be obtained from the iterator). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile(). This argument is not supported when `x` is a dataset. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: ValueError: In case of invalid user-provided arguments. """ self._assert_compile_was_called() self._check_call_args('test_on_batch') if self._experimental_run_tf_function: outputs = training_v2_utils.test_on_batch( self, x, y=y, sample_weight=sample_weight, reset_metrics=reset_metrics, standalone=True) outputs = (outputs['total_loss'] + outputs['output_losses'] + outputs['metrics']) outputs = [ training_v2_utils._non_none_constant_value(v) for v in outputs] # pylint: disable=protected-access if len(outputs) == 1: outputs = outputs[0] return outputs if (self._distribution_strategy and distribution_strategy_context.in_cross_replica_context()): raise NotImplementedError('`test_on_batch` is not supported for models ' 'distributed with tf.distribute.Strategy.') # Validate and standardize user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, extract_tensors_from_dataset=True) # If `self._distribution_strategy` is True, then we are in a replica context # at this point. if self.run_eagerly or self._distribution_strategy: output_dict = training_eager.test_on_batch( self, x, y, sample_weights=sample_weights, output_loss_metrics=self._output_loss_metrics) outputs = (output_dict['total_loss'] + output_dict['output_losses'] + output_dict['metrics']) outputs = [ training_v2_utils._non_none_constant_value(v) for v in outputs] # pylint: disable=protected-access else: x = training_utils.ModelInputs(x).as_list() inputs = x + list(y or []) + list(sample_weights or []) self._update_sample_weight_modes(sample_weights=sample_weights) self._make_test_function() outputs = self.test_function(inputs) # pylint: disable=not-callable if reset_metrics: self.reset_metrics() if len(outputs) == 1: return outputs[0] return outputs def predict_on_batch(self, x): """Returns predictions for a single batch of samples. Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A `tf.data` dataset. Returns: Numpy array(s) of predictions. Raises: ValueError: In case of mismatch between given number of inputs and expectations of the model. """ self._check_call_args('predict_on_batch') if self._experimental_run_tf_function: return training_v2_utils.predict_on_batch(self, x, standalone=True) if (self._distribution_strategy and distribution_strategy_context.in_cross_replica_context()): raise NotImplementedError( '`predict_on_batch` is not supported for models distributed with' ' tf.distribute.Strategy.') # Validate and standardize user data. inputs, _, _ = self._standardize_user_data( x, extract_tensors_from_dataset=True) # If `self._distribution_strategy` is True, then we are in a replica context # at this point. if self.run_eagerly or self._distribution_strategy: inputs = training_utils.cast_if_floating_dtype(inputs) if isinstance(inputs, collections_abc.Sequence): # Unwrap lists with only one input, as we do when training on batch if len(inputs) == 1: inputs = inputs[0] return self(inputs) # pylint: disable=not-callable self._make_predict_function() outputs = self.predict_function(inputs) if len(outputs) == 1: return outputs[0] return outputs @deprecation.deprecated( None, 'Please use Model.fit, which supports generators.') def fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0): """Fits the model on data yielded batch-by-batch by a Python generator. DEPRECATED: `Model.fit` now supports generators, so there is no longer any need to use this endpoint. """ return self.fit( generator, steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=validation_data, validation_steps=validation_steps, validation_freq=validation_freq, class_weight=class_weight, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, shuffle=shuffle, initial_epoch=initial_epoch) @deprecation.deprecated( None, 'Please use Model.evaluate, which supports generators.') def evaluate_generator(self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0): """Evaluates the model on a data generator. DEPRECATED: `Model.evaluate` now supports generators, so there is no longer any need to use this endpoint. """ self._check_call_args('evaluate_generator') return self.evaluate( generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose, callbacks=callbacks) @deprecation.deprecated( None, 'Please use Model.predict, which supports generators.') def predict_generator(self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0): """Generates predictions for the input samples from a data generator. DEPRECATED: `Model.predict` now supports generators, so there is no longer any need to use this endpoint. """ return self.predict( generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose, callbacks=callbacks) def _check_call_args(self, method_name): """Check that `call` has only one positional arg.""" # Always allow first arg, regardless of arg name. fullargspec = self._call_full_argspec if fullargspec.defaults: positional_args = fullargspec.args[:-len(fullargspec.defaults)] else: positional_args = fullargspec.args if 'training' in positional_args: positional_args.remove('training') # self and first arg can be positional. if len(positional_args) > 2: extra_args = positional_args[2:] raise ValueError( 'Models passed to `' + method_name + '` can only have `training` ' 'and the first argument in `call` as positional arguments, ' 'found: ' + str(extra_args) + '.') def _set_optimizer(self, optimizer): """Sets self.optimizer. Sets self.optimizer to `optimizer`, potentially wrapping it with a LossScaleOptimizer. Args: optimizer: The optimizer(s) to assign to self.optimizer. """ if isinstance(optimizer, (list, tuple)): self.optimizer = [optimizers.get(opt) for opt in optimizer] else: self.optimizer = optimizers.get(optimizer) if (self._dtype_policy.loss_scale is not None and not isinstance(self.optimizer, loss_scale_optimizer.LossScaleOptimizer)): if isinstance(self.optimizer, list): raise ValueError('When a dtype policy with a loss scale is used, you ' 'can only pass a single optimizer. Using policy %s ' 'and got optimizers: %s' % self._dtype_policy, self.optimizer) if not isinstance(self.optimizer, optimizer_v2.OptimizerV2): raise ValueError('"optimizer" must be an instance of ' 'tf.keras.optimizers.Optimizer when a dype policy ' 'with a loss scale used, but got: %s. Using policy: ' '%s' % (self.optimizer, self._dtype_policy)) self.optimizer = loss_scale_optimizer.LossScaleOptimizer( self.optimizer, self._dtype_policy.loss_scale) if (isinstance(self.optimizer, loss_scale_optimizer.LossScaleOptimizer) and self._dtype_policy.loss_scale and self.optimizer.loss_scale != self._dtype_policy.loss_scale): logging.warning('LossScale of LossScaleOptimizer passed to compile (%s) ' 'is not the same as the dtype policy\'s loss scale (%s). ' 'Because the dtype policy has a loss scale, you should ' 'pass an optimizer that is not wrapped with a ' 'LossScaleOptimizer,' % (self.optimizer.loss_scale, self._dtype_policy.loss_scale)) def _prepare_validation_data(self, validation_data, batch_size, validation_steps): """Unpack and check the validation data.""" val_x, val_y, val_sample_weights = training_utils.unpack_validation_data( validation_data) return self._standardize_user_data( val_x, val_y, sample_weight=val_sample_weights, batch_size=batch_size, steps=validation_steps, steps_name='validation_steps') def _validate_compile_param_for_distribution_strategy( self, run_eagerly, sample_weight_mode, target_tensors, weighted_metrics): # Validate that arguments passed by the user to `compile` are supported by # tf.distribute.Strategy. if self._distribution_strategy: if sample_weight_mode: raise NotImplementedError('sample_weight_mode is not supported with ' 'tf.distribute.Strategy.') if weighted_metrics: raise NotImplementedError('weighted_metrics is not supported with ' 'tf.distribute.Strategy.') if target_tensors: raise ValueError('target_tensors is not supported with ' 'tf.distribute.Strategy.') if run_eagerly: raise ValueError( 'We currently do not support enabling `run_eagerly` with ' 'distribution strategy.') if (distributed_training_utils.is_distributing_by_cloning(self) and (not self.built or not self.inputs or not self.outputs)): raise ValueError( 'We currently do not support distribution strategy with a ' '`Sequential` model that is created without `input_shape`/' '`input_dim` set in its first layer or a subclassed model.') def _process_target_tensor_for_compile(self, target_tensors): if self.run_eagerly: # target tensor is not supported with run_eagerly. Create a list with None # as placeholder for each output. return [None for _ in self.output_names] if target_tensors is not None and not (isinstance(target_tensors, list) and target_tensors == []): # pylint: disable=g-explicit-bool-comparison if isinstance(target_tensors, list): if len(target_tensors) != len(self.outputs): raise ValueError( 'When passing a list as `target_tensors`, ' 'it should have one entry per model output. ' 'The model has %s outputs, but you passed target_tensors=%s' % (len(self.outputs), target_tensors)) elif isinstance(target_tensors, dict): unexpected_target_tensor_names = set(target_tensors.keys()).difference( self.output_names) if unexpected_target_tensor_names: raise ValueError( 'Unknown entry in `target_tensors` dictionary: "{name}". ' 'Only expected the following keys: {keys}'.format( name=unexpected_target_tensor_names, keys=str(self.output_names))) tmp_target_tensors = [] for name in self.output_names: tmp_target_tensors.append(target_tensors.get(name, None)) target_tensors = tmp_target_tensors elif tensor_util.is_tensor(target_tensors): target_tensors = [target_tensors] else: raise TypeError('Expected `target_tensors` to be a list or tuple or ' 'dict or a single tensor, but got:', target_tensors) else: # In case target tensor is empty or None, create a list with Nones # that has same length as self.output_names. With that, the None check of # target tensor can be skipped downstream. target_tensors = [None for _ in self.output_names] return target_tensors def _compile_eagerly(self, metrics, weighted_metrics, sample_weight_mode): # Prepare sample weight modes. List with the same length as model outputs. training_utils.prepare_sample_weight_modes( self._training_endpoints, sample_weight_mode) # Prepare sample weights. self._prepare_sample_weights() # Save all metric attributes per output of the model. self._cache_output_metric_attributes(metrics, weighted_metrics) self.total_loss = None # Set metric attributes on model. self._set_metric_attributes() self._collected_trainable_weights = self.trainable_weights def _update_sample_weight_modes(self, sample_weights=None): """Updates sample weight modes based on training/eval inputs. Sample weight placeholders will be created for all or no outputs based on whether sample_weight is provided for any output. If model contains `_sample_weight_modes` we check if the input `sample_weights` corresponds to the sample weight modes. 1. Set sample weight mode to be 'temporal' for output i, if `compile` sample_weight_mode was set to `temporal` and sample weight inputs are given for one or more outputs. 2. Set sample weight mode to be 'samplewise' for output i, if `compile` sample_weight_mode was not set and sample weight inputs are given for one or more outputs. 3. Reset sample weight mode to None for output i if sample weight mode was set but there is no sample weight input. Args: sample_weights: List of sample weights of the same length as model outputs or None. """ if not self._is_compiled: return if sample_weights and any(s is not None for s in sample_weights): for endpoint in self._training_endpoints: endpoint.sample_weight_mode = ( endpoint.sample_weight_mode or 'samplewise') else: for endpoint in self._training_endpoints: endpoint.sample_weight_mode = None def _recompile_weights_loss_and_weighted_metrics(self): if not self._is_compiled: return False recompile = any([e.sample_weights_mismatch() for e in self._training_endpoints]) if recompile: self._compile_weights_loss_and_weighted_metrics() return recompile @trackable.no_automatic_dependency_tracking def _compile_weights_loss_and_weighted_metrics(self, sample_weights=None): """Compiles the model loss and weighted metric sub-graphs. This may be used to set graph tensors as sample weights (instead of creating placeholders). This functionality is necessary for `tf.keras.estimator.model_to_estimator`, which calls Keras models in a v1 graph, and creates iterator tensors for inputs, targets, and sample weights. Args: sample_weights: List of tensors to use as the sample weights. Must be the same length as the number of outputs. If left as `None`, placeholders are used instead. """ with K.get_graph().as_default(): if sample_weights is not None: self._update_sample_weight_modes(sample_weights) self._prepare_sample_weights(sample_weights) masks = self._prepare_output_masks() # Compute weighted metrics. self._handle_metrics( self.outputs, targets=self._targets, skip_target_masks=self._prepare_skip_target_masks(), sample_weights=self.sample_weights, masks=masks, return_weighted_metrics=True) # Compute total loss. # Used to keep track of the total loss value (stateless). # eg., total_loss = loss_weight_1 * output_1_loss_fn(...) + # loss_weight_2 * output_2_loss_fn(...) + # layer losses. self.total_loss = self._prepare_total_loss(masks) def _prepare_skip_target_masks(self): """Boolean mask for whether the target in the output list should be skipped. If the loss function corresponding to a model output is None, then this output will be skipped during total loss calculation and feed targets preparation. Returns: A boolean list for whether the corresponding target in the output list should be skipped during loss calculation. """ return [l is None for l in self.loss_functions] def _prepare_output_masks(self): """Returns masks corresponding to model outputs.""" return [getattr(x, '_keras_mask', None) for x in self.outputs] def _prepare_total_loss(self, masks): """Computes total loss from loss functions. Arguments: masks: List of mask values corresponding to each model output. Returns: A list of loss weights of python floats. Raises: TypeError: If model run_eagerly is True. """ if self.run_eagerly: raise TypeError('total loss can not be computed when compiled with ' 'run_eagerly = True.') total_loss = None with K.name_scope('loss'): for endpoint, mask in zip(self._training_endpoints, masks): if endpoint.should_skip_target(): continue y_true = endpoint.training_target.target y_pred = endpoint.output loss_fn = endpoint.loss_fn loss_weight = endpoint.loss_weight loss_name = endpoint.loss_name() sample_weight = endpoint.sample_weight with K.name_scope(loss_name): if mask is not None: mask = math_ops.cast(mask, y_pred.dtype) # Update weights with mask. if sample_weight is None: sample_weight = mask else: # Update dimensions of weights to match with mask if possible. mask, _, sample_weight = ( tf_losses_utils.squeeze_or_expand_dimensions( mask, sample_weight=sample_weight)) sample_weight *= mask if hasattr(loss_fn, 'reduction'): per_sample_losses = loss_fn.call(y_true, y_pred) weighted_losses = losses_utils.compute_weighted_loss( per_sample_losses, sample_weight=sample_weight, reduction=losses_utils.ReductionV2.NONE) loss_reduction = loss_fn.reduction # `AUTO` loss reduction defaults to `SUM_OVER_BATCH_SIZE` for all # compile use cases. if loss_reduction == losses_utils.ReductionV2.AUTO: loss_reduction = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE # Compute the stateless loss value. output_loss = losses_utils.reduce_weighted_loss( weighted_losses, reduction=loss_reduction) else: # Compute the stateless loss value for a custom loss class. # Here we assume that the class takes care of loss reduction # because if this class returns a vector value we cannot # differentiate between use case where a custom optimizer # expects a vector loss value vs unreduced per-sample loss value. output_loss = loss_fn(y_true, y_pred, sample_weight=sample_weight) loss_reduction = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE if len(self.outputs) > 1: # Keep track of stateful result tensor for the loss. endpoint.output_loss_metric(output_loss) # Scale output loss for distribution. For custom losses we assume # reduction was mean. if loss_reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE: output_loss = losses_utils.scale_loss_for_distribution(output_loss) if total_loss is None: total_loss = loss_weight * output_loss else: total_loss += loss_weight * output_loss if total_loss is None: if not self.losses: raise ValueError('The model cannot be compiled ' 'because it has no loss to optimize.') else: total_loss = 0. # Add regularization penalties and other layer-specific losses. custom_losses = self.get_losses_for(None) + self.get_losses_for( self.inputs) if custom_losses: total_loss += losses_utils.scale_loss_for_distribution( math_ops.add_n(custom_losses)) return total_loss def _get_callback_model(self): """Returns the Callback Model for this Model.""" if hasattr(self, '_replicated_model') and self._replicated_model: # When using training_distributed, we set the callback model # to an instance of the `DistributedModel` that we create in # the `compile` call. The `DistributedModel` is initialized # with the first replicated model. We need to set the callback # model to a DistributedModel to allow us to override saving # and loading weights when we checkpoint the model during training. return self._replicated_model if hasattr(self, 'callback_model') and self.callback_model: return self.callback_model return self @trackable.no_automatic_dependency_tracking def _make_callback_model(self, grouped_model): first_replicated_model = self._distribution_strategy.unwrap( grouped_model)[0] # We initialize the callback model with the first replicated model. self._replicated_model = DistributedCallbackModel(first_replicated_model) self._replicated_model.set_original_model(self) def _validate_or_infer_batch_size(self, batch_size, steps, x): """Validates that the `batch_size` provided is consistent with InputLayer. It's possible that the user specified a static batch size in their InputLayer. If so, this method checks the provided `batch_size` and `x` arguments are consistent with this static batch size. Also, if `batch_size` is `None`, this method will attempt to infer the batch size from the static batch size of the InputLayer. Lastly, ValueError will be raised if `x` is a tf.data.Dataset and `batch_size` is specified as we expect users to provide batched datasets. Arguments: batch_size: The batch_size provided as an argument to fit/evaluate/predict. steps: The steps provided as an argument to fit/evaluate/predict. x: The data passed as `x` to fit/evaluate/predict. Returns: The validated batch_size, auto-inferred from the first layer if not provided. """ if (isinstance(x, (dataset_ops.DatasetV1, dataset_ops.DatasetV2, data_utils.Sequence)) or tf_inspect.isgenerator(x)): if batch_size is not None: raise ValueError( 'The `batch_size` argument must not be specified for the given ' 'input type. Received input: {}, batch_size: {}'.format( x, batch_size)) return # Avoids the override in Sequential.layers which filters Input layers. # (Which are often the very layers that we're after.) layers = trackable_layer_utils.filter_empty_layer_containers(self._layers) first_layer = next(layers, None) if first_layer: # The per-replica static batch size. static_batch_size = training_utils.get_static_batch_size(first_layer) if static_batch_size is not None: # Determine number of times the user-supplied batch size will be split. if (self._distribution_strategy and distributed_training_utils.global_batch_size_supported( self._distribution_strategy)): num_splits_for_ds = self._distribution_strategy.num_replicas_in_sync else: num_splits_for_ds = 1 # Check `batch_size` argument is consistent with InputLayer. if batch_size is not None: if batch_size % num_splits_for_ds != 0: raise ValueError('The `batch_size` argument ({}) must be divisible ' 'the by number of replicas ({})'.format( batch_size, num_splits_for_ds)) per_replica_batch_size = batch_size // num_splits_for_ds if per_replica_batch_size != static_batch_size: raise ValueError('The `batch_size` argument value {} is ' 'incompatible with the specified batch size of ' 'your Input Layer: {}'.format( per_replica_batch_size, static_batch_size)) # Check Dataset/Iterator batch size is consistent with InputLayer. if isinstance(x, (dataset_ops.DatasetV2, iterator_ops.Iterator, iterator_ops.OwnedIterator)): ds_batch_size = tensor_shape.as_dimension( nest.flatten(dataset_ops.get_legacy_output_shapes(x))[0][0]).value if ds_batch_size is not None: if ds_batch_size % num_splits_for_ds != 0: raise ValueError( 'The batch output shape of your `Dataset` {} ' 'cannot be divisible by number of replicas {}'.format( ds_batch_size, num_splits_for_ds)) ds_per_replica_batch_size = ds_batch_size // num_splits_for_ds if ds_per_replica_batch_size != static_batch_size: raise ValueError('The batch output shape of your `Dataset` is ' '{}, which is incompatible with the specified ' 'batch size of your Input Layer: {}'.format( ds_per_replica_batch_size, static_batch_size)) # Set inferred batch size from the InputLayer. if steps is None: batch_size = static_batch_size * num_splits_for_ds if batch_size is None and steps is None: # Backwards compatibility batch_size = 32 return batch_size def _prepare_sample_weights(self, sample_weights=None): """Sets sample weight attribute on the model.""" # List with the same length as model outputs. if sample_weights is not None: if len(sample_weights) != len(self._training_endpoints): raise ValueError('Provided sample weights must have same length as the ' 'number of outputs. Expected: {}, got: {}.'.format( len(self._training_endpoints), len(sample_weights))) else: sample_weights = [None] * len(self._training_endpoints) for endpoint, weight in zip(self._training_endpoints, sample_weights): endpoint.populate_sample_weight(weight, endpoint.sample_weight_mode) def _cache_output_metric_attributes(self, metrics, weighted_metrics): """Caches metric name and function attributes for every model output.""" output_shapes = [] for output in self.outputs: if output is None or output.shape.rank is None: output_shapes.append(None) else: output_shapes.append(output.shape.as_list()) self._per_output_metrics = training_utils.collect_per_output_metric_info( metrics, self.output_names, output_shapes, self.loss_functions) self._per_output_weighted_metrics = ( training_utils.collect_per_output_metric_info( weighted_metrics, self.output_names, output_shapes, self.loss_functions, is_weighted=True)) def _add_unique_metric_name(self, metric_name, output_index): """Makes the metric name unique and adds it to the model's metric name list. If there are multiple outputs for which the metrics are calculated, the metric names have to be made unique by appending an integer. Arguments: metric_name: Metric name that corresponds to the metric specified by the user. For example: 'acc'. output_index: The index of the model output for which the metric name is being added. Returns: string, name of the model's unique metric name """ if len(self.output_names) > 1: metric_name = '%s_%s' % (self.output_names[output_index], metric_name) j = 1 base_metric_name = metric_name while metric_name in self.metrics_names: metric_name = '%s_%d' % (base_metric_name, j) j += 1 return metric_name def _init_metric_attributes(self): """Initialized model metric attributes.""" # List of stateful metric functions. Used for resetting metric state during # training/eval. self._compile_metric_functions = [] def _set_per_output_metric_attributes(self, metrics_dict, output_index): """Sets the metric attributes on the model for the given output. Arguments: metrics_dict: A dict with metric names as keys and metric fns as values. output_index: The index of the model output for which the metric attributes are added. Returns: Metrics dict updated with unique metric names as keys. """ updated_metrics_dict = collections.OrderedDict() for metric_name, metric_fn in metrics_dict.items(): metric_name = self._add_unique_metric_name(metric_name, output_index) # Update the name on the metric class to be the unique generated name. metric_fn._name = metric_name # pylint: disable=protected-access updated_metrics_dict[metric_name] = metric_fn # Keep track of metric name and function. self._compile_metric_functions.append(metric_fn) return updated_metrics_dict def _set_metric_attributes(self): """Sets the metric attributes on the model for all the model outputs.""" updated_per_output_metrics = [] updated_per_output_weighted_metrics = [] for i, endpoint in enumerate(self._training_endpoints): if endpoint.should_skip_target(): updated_per_output_metrics.append(self._per_output_metrics[i]) updated_per_output_weighted_metrics.append( self._per_output_weighted_metrics[i]) continue updated_per_output_metrics.append( self._set_per_output_metric_attributes(self._per_output_metrics[i], i)) updated_per_output_weighted_metrics.append( self._set_per_output_metric_attributes( self._per_output_weighted_metrics[i], i)) # Create a metric wrapper for each output loss. This computes mean of an # output loss across mini-batches (irrespective of how we reduce within a # batch). if len(self._training_endpoints) > 1: for endpoint in self._training_endpoints: if not endpoint.should_skip_target(): endpoint.output_loss_metric = metrics_module.Mean( name=endpoint.loss_name()) self._per_output_metrics = updated_per_output_metrics self._per_output_weighted_metrics = updated_per_output_weighted_metrics def _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights=None): """Calls metric functions for a single output. Arguments: metrics_dict: A dict with metric names as keys and metric fns as values. y_true: Target output. y_pred: Predicted output. mask: Computed mask value for the current output. weights: Weights to be applied on the current output. Returns: A list of metric result tensors. """ metric_results = [] for metric_name, metric_fn in metrics_dict.items(): with K.name_scope(metric_name): metric_result = training_utils.call_metric_function( metric_fn, y_true, y_pred, weights=weights, mask=mask) metric_results.append(metric_result) return metric_results def _handle_metrics(self, outputs, targets=None, skip_target_masks=None, sample_weights=None, masks=None, return_weighted_metrics=False, return_weighted_and_unweighted_metrics=False): """Handles calling metric functions. Arguments: outputs: List of outputs (predictions). targets: List of targets. skip_target_masks: Optional. List of boolean for whether the corresponding target should be ignored or not. sample_weights: Optional list of sample weight arrays. masks: List of computed output mask values. return_weighted_metrics: Flag that indicates whether weighted metrics should be computed instead of unweighted metrics. This flag is ignored when `return_weighted_and_unweighted_metrics` is enabled. return_weighted_and_unweighted_metrics: Flag that is used to indicate whether both weighted and unweighted metrics should be computed. When this is not enabled, we use `return_weighted_metrics` param to indicate whether weighted or unweighted metrics should be returned. Returns: A list of metric result tensors. """ # TODO(scottzhu): Update this to use the new training_endpoints. Currently # the eager and graph logic is bit different. skip_target_masks = skip_target_masks or [False] * len(outputs) metric_results = [] with K.name_scope('metrics'): # Invoke all metrics added using `compile`. for i in range(len(outputs)): if skip_target_masks[i]: continue output = outputs[i] if outputs else None target = targets[i] if targets else None output_mask = masks[i] if masks else None if (return_weighted_and_unweighted_metrics or not return_weighted_metrics): metric_results.extend( self._handle_per_output_metrics(self._per_output_metrics[i], target, output, output_mask)) if return_weighted_and_unweighted_metrics or return_weighted_metrics: metric_results.extend( self._handle_per_output_metrics( self._per_output_weighted_metrics[i], target, output, output_mask, weights=sample_weights[i] if sample_weights else None)) return metric_results def _check_trainable_weights_consistency(self): """Check trainable weights count consistency. This will raise a warning if `trainable_weights` and `_collected_trainable_weights` are inconsistent (i.e. have different number of parameters). Inconsistency will typically arise when one modifies `model.trainable` without calling `model.compile` again. """ if not hasattr(self, '_collected_trainable_weights'): return if len(self.trainable_weights) != len(self._collected_trainable_weights): logging.log_first_n( logging.WARN, 'Discrepancy between trainable weights and collected' ' trainable weights, did you set `model.trainable`' ' without calling `model.compile` after ?', 1) def _make_train_function(self): has_recompiled = self._recompile_weights_loss_and_weighted_metrics() self._check_trainable_weights_consistency() if isinstance(self.optimizer, list): raise ValueError('The `optimizer` in `compile` should be a single ' 'optimizer.') # If we have re-compiled the loss/weighted metric sub-graphs then create # train function even if one exists already. This is because # `_feed_sample_weights` list has been updated on re-compile. if getattr(self, 'train_function', None) is None or has_recompiled: # Restore the compiled trainable state. current_trainable_state = self._get_trainable_state() self._set_trainable_state(self._compiled_trainable_state) inputs = (self._feed_inputs + self._feed_targets + self._feed_sample_weights) if not isinstance(K.symbolic_learning_phase(), int): inputs += [K.symbolic_learning_phase()] with K.get_graph().as_default(): with K.name_scope('training'): # Training updates updates = self.optimizer.get_updates( params=self._collected_trainable_weights, loss=self.total_loss) # Unconditional updates updates += self.get_updates_for(None) # Conditional updates relevant to this model updates += self.get_updates_for(self.inputs) metrics = self._get_training_eval_metrics() metrics_tensors = [ m._call_result for m in metrics if hasattr(m, '_call_result') # pylint: disable=protected-access ] with K.name_scope('training'): # Gets loss and metrics. Updates weights at each call. fn = K.function( inputs, [self.total_loss] + metrics_tensors, updates=updates, name='train_function', **self._function_kwargs) setattr(self, 'train_function', fn) # Restore the current trainable state self._set_trainable_state(current_trainable_state) def _make_test_function(self): has_recompiled = self._recompile_weights_loss_and_weighted_metrics() # If we have re-compiled the loss/weighted metric sub-graphs then create # test function even if one exists already. This is because # `_feed_sample_weights` list has been updated on re-compile. if getattr(self, 'test_function', None) is None or has_recompiled: inputs = (self._feed_inputs + self._feed_targets + self._feed_sample_weights) with K.get_graph().as_default(): metrics = self._get_training_eval_metrics() metrics_tensors = [ m._call_result for m in metrics if hasattr(m, '_call_result') # pylint: disable=protected-access ] with K.name_scope('evaluation'): updates = self.state_updates # Return loss and metrics, no gradient updates. # Does update the network states. fn = K.function( inputs, [self.total_loss] + metrics_tensors, updates=updates, name='test_function', **self._function_kwargs) setattr(self, 'test_function', fn) def _make_predict_function(self): if not hasattr(self, 'predict_function'): self.predict_function = None if self.predict_function is None: inputs = self._feed_inputs # Gets network outputs. Does not update weights. # Does update the network states. kwargs = getattr(self, '_function_kwargs', {}) with K.name_scope(ModeKeys.PREDICT): self.predict_function = K.function( inputs, self.outputs, updates=self.state_updates, name='predict_function', **kwargs) def _make_execution_function(self, mode): if mode == ModeKeys.TRAIN: self._make_train_function() return self.train_function if mode == ModeKeys.TEST: self._make_test_function() return self.test_function if mode == ModeKeys.PREDICT: self._make_predict_function() return self.predict_function def _distribution_standardize_user_data(self, x, y=None, sample_weight=None, class_weight=None, batch_size=None, validation_split=0, shuffle=False, epochs=1, allow_partial_batch=False): """Runs validation checks on input and target data passed by the user. This is called when using tf.distribute.Strategy to train, evaluate or serve the model. Args: x: Input data. A numpy array or `tf.data` dataset. y: Target data. A numpy array or None if x is a `tf.data` dataset. sample_weight: An optional sample-weight array passed by the user to weight the importance of each sample in `x`. class_weight: An optional class-weight array by the user to weight the importance of samples in `x` based on the class they belong to, as conveyed by `y`. batch_size: Integer batch size. If provided, it is used to run additional validation checks on stateful models. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. shuffle: Boolean whether to shuffle the training data before each epoch. epochs: Integer epochs. If > 1, repeat the numpy training data epochs times when converting to training dataset. allow_partial_batch: Boolean whether to enforce that all batches have the same size. Returns: Dataset instance. Raises: ValueError: In case of invalid user-provided data. RuntimeError: If the model was never compiled. """ if class_weight: raise NotImplementedError('`class_weight` is currently not supported ' 'when using tf.distribute.Strategy.') if (sample_weight is not None and sample_weight.all() and distributed_training_utils.is_tpu_strategy( self._distribution_strategy)): raise NotImplementedError('`sample_weight` is currently not supported ' 'when using TPUStrategy.') # Validates `steps` and `shuffle` arguments right at the beginning # since we use it to construct the dataset object. # TODO(anjalisridhar): Remove this check once we refactor the # _standardize_user_data code path. This check is already present elsewhere # in the codebase. if isinstance(x, dataset_ops.DatasetV2): if shuffle: training_utils.verify_dataset_shuffled(x) strategy = self._distribution_strategy with strategy.scope(): # We should be sure to call get_session() inside the strategy.scope() # so the strategy can affect the session options. if ops.executing_eagerly_outside_functions(): session = None else: session = K.get_session() first_x_value = nest.flatten(x)[0] if isinstance(first_x_value, np.ndarray): x = training_utils.list_to_tuple(x) if y is not None: y = training_utils.list_to_tuple(y) if sample_weight is not None: sample_weight = training_utils.list_to_tuple(sample_weight) in_tuple = (x, y, sample_weight) else: in_tuple = (x, y) else: in_tuple = x ds = strategy.extended.experimental_make_numpy_dataset(in_tuple, session=session) if shuffle: # We want a buffer size that is larger than the batch size provided by # the user and provides sufficient randomness. Note that larger # numbers introduce more memory usage based on the size of each # sample. ds = ds.shuffle(max(1024, batch_size * 8)) if epochs > 1: ds = ds.repeat(epochs) # We need to use the drop_remainder argument to get a known static # input shape which is required for TPUs. drop_remainder = (not allow_partial_batch and strategy.extended.experimental_require_static_shapes) # TODO(b/131720208): We still drop remainder here if number of examples # is divisible by batch size, as sometimes dynamic padder will time out # with keras.metrics.CategoricalAccuracy() metric. if distributed_training_utils.is_tpu_strategy( strategy) and not drop_remainder: dataset_size = first_x_value.shape[0] if dataset_size % batch_size == 0: drop_remainder = True x = ds.batch(batch_size, drop_remainder=drop_remainder) else: assert isinstance(x, dataset_ops.DatasetV2) training_utils.validate_dataset_input(x, y, sample_weight, validation_split) return x def _standardize_user_data(self, x, y=None, sample_weight=None, class_weight=None, batch_size=None, check_steps=False, steps_name='steps', steps=None, validation_split=0, shuffle=False, extract_tensors_from_dataset=False): """Runs validation checks on input and target data passed by the user. Also standardizes the data to lists of arrays, in order. Also builds and compiles the model on the fly if it is a subclassed model that has never been called before (and thus has no inputs/outputs). This is a purely internal method, subject to refactoring at any time. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, `y` should not be specified (since targets will be obtained from the iterator). sample_weight: An optional sample-weight array passed by the user to weight the importance of each sample in `x`. class_weight: An optional class-weight array by the user to weight the importance of samples in `x` based on the class they belong to, as conveyed by `y`. If both `sample_weight` and `class_weight` are provided, the weights are multiplied. batch_size: Integer batch size. If provided, it is used to run additional validation checks on stateful models. check_steps: boolean, True if we want to check for validity of `steps` and False, otherwise. For example, when we are standardizing one batch of data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps` value is not required and we should not check for its validity in these cases. steps_name: The public API's parameter name for `steps`. steps: Integer or `None`. Total number of steps (batches of samples) to execute. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. shuffle: Boolean whether to shuffle the training data before each epoch. extract_tensors_from_dataset: Boolean. When `x` is a dataset instance, this indicates whether to extract actual tensors from the dataset or instead output the dataset instance itself. Set to True when calling from `train_on_batch`/etc. Returns: A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a dict or not), target arrays, sample-weight arrays. If the model's input and targets are symbolic, these lists are empty (since the model takes no user-provided data, instead the data comes from the symbolic inputs/targets). Raises: ValueError: In case of invalid user-provided data. RuntimeError: If the model was never compiled. """ if isinstance(x, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)): # Graph mode dataset. We'll pass the dataset as-is (unless # `extract_tensors_from_dataset` is True, in which case we extract # the tensors from the dataset and we output them. training_utils.validate_dataset_input(x, y, sample_weight, validation_split) if shuffle: training_utils.verify_dataset_shuffled(x) is_dataset = True if extract_tensors_from_dataset: # We do this for `train_on_batch`/etc. x, y, sample_weight = training_utils.extract_tensors_from_dataset(x) elif isinstance(x, iterator_ops.Iterator): # Graph mode iterator. We extract the symbolic tensors. training_utils.validate_dataset_input(x, y, sample_weight, validation_split) iterator = x x, y, sample_weight = training_utils.unpack_iterator_input(iterator) is_dataset = True else: is_dataset = False # Validates `steps` argument based on x's type. if check_steps: training_utils.check_steps_argument(x, steps, steps_name) # First, we build the model on the fly if necessary. if not self.inputs: all_inputs, y_input, dict_inputs = self._build_model_with_inputs(x, y) is_build_called = True else: all_inputs = [] # Whether this is a subclassed model that expects dictionary inputs # rather than list inputs (e.g. FeatureColumn-based models). dict_inputs = isinstance(self.inputs, dict) is_build_called = False y_input = y # Second, we compile the model on the fly if necessary, mostly for subclass # models. is_compile_called = False if not self._is_compiled and self.optimizer: self._compile_from_inputs(all_inputs, y_input, x, y) is_compile_called = True # In graph mode, if we had just set inputs and targets as symbolic tensors # by invoking build and compile on the model respectively, we do not have to # feed anything to the model. Model already has input and target data as # part of the graph. # Note: in this case, `any` and `all` are equivalent since we disallow # mixed symbolic/value inputs. # self.run_eagerly is not free to compute, so we want to reuse the value. run_eagerly = self.run_eagerly if (not run_eagerly and is_build_called and is_compile_called and not is_dataset and any(_is_symbolic_tensor(v) for v in all_inputs)): return [], [], None return self._standardize_tensors( x, y, sample_weight, run_eagerly=run_eagerly, dict_inputs=dict_inputs, is_dataset=is_dataset, class_weight=class_weight, batch_size=batch_size) def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight=None, batch_size=None): if run_eagerly: # In eager mode, do not do shape validation # since the network has no input nodes (placeholders) to be fed. feed_input_names = self.input_names feed_input_shapes = None elif not self._is_graph_network: # Case: symbolic-mode subclassed network. Do not do shape validation. feed_input_names = self._feed_input_names feed_input_shapes = None else: # Case: symbolic-mode graph network. # In this case, we run extensive shape validation checks. feed_input_names = self._feed_input_names feed_input_shapes = self._feed_input_shapes # Standardize the inputs. if not isinstance(x, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)): # TODO(fchollet): run static checks with dataset output shape(s). x = training_utils.standardize_input_data( x, feed_input_names, feed_input_shapes, check_batch_axis=False, # Don't enforce the batch size. exception_prefix='input') # Get typespecs for the input data and sanitize it if necessary. # TODO(momernick): This should be capable of doing full input validation # at all times - validate that this is so and refactor the standardization # code. if isinstance(x, dataset_ops.DatasetV2): x_shapes = dataset_ops.get_structure(x) if isinstance(x_shapes, tuple): # If the output of a Dataset is a tuple, we assume it's either of the # form (x_data, y_data) or (x_data, y_data, sample_weights). In either # case, we only care about x_data here. x_shapes = x_shapes[0] else: flat_inputs = nest.flatten(x, expand_composites=False) flat_expected_inputs = nest.flatten(self.inputs, expand_composites=False) converted_x = [] for (a, b) in zip(flat_inputs, flat_expected_inputs): converted_x.append(_convert_scipy_sparse_tensor(a, b)) x = nest.pack_sequence_as(x, converted_x, expand_composites=False) def _type_spec_from_value(value): """Grab type_spec without converting array-likes to tensors.""" if isinstance(value, composite_tensor.CompositeTensor): return value._type_spec # pylint: disable=protected-access # Get a TensorSpec for array-like data without # converting the data to a Tensor if hasattr(value, 'shape') and hasattr(value, 'dtype'): return tensor_spec.TensorSpec(value.shape, value.dtype) else: return type_spec.type_spec_from_value(value) x_shapes = nest.map_structure(_type_spec_from_value, x) flat_inputs = nest.flatten(x_shapes, expand_composites=False) flat_expected_inputs = nest.flatten(self.inputs, expand_composites=False) for (a, b) in zip(flat_inputs, flat_expected_inputs): nest.assert_same_structure(a, b, expand_composites=True) if y is not None: # Prepare self._sample_weight_modes. List with the same length as # model outputs. training_utils.prepare_sample_weight_modes(self._training_endpoints, self.sample_weight_mode) feed_output_names = self._feed_output_names feed_sample_weight_modes = self._sample_weight_modes if not self._is_graph_network: feed_output_shapes = None else: feed_output_shapes = self._feed_output_shapes # Standardize the outputs. y = training_utils.standardize_input_data( y, feed_output_names, # Don't enforce target shapes to match output shapes. # Precise checks will be run in `check_loss_and_target_compatibility`. shapes=None, check_batch_axis=False, # Don't enforce the batch size. exception_prefix='target') # Generate sample-wise weight values given the `sample_weight` and # `class_weight` arguments. sample_weights = training_utils.standardize_sample_weights( sample_weight, feed_output_names) class_weights = training_utils.standardize_class_weights( class_weight, feed_output_names) sample_weights = [ training_utils.standardize_weights(ref, sw, cw, mode) for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, feed_sample_weight_modes) ] # Check that all arrays have the same length. if not self._distribution_strategy: training_utils.check_array_lengths(x, y, sample_weights) if self._is_graph_network and not run_eagerly: # Additional checks to avoid users mistakenly using improper loss fns. training_utils.check_loss_and_target_compatibility( y, self._feed_loss_fns, feed_output_shapes) sample_weights, _, _ = training_utils.handle_partial_sample_weights( y, sample_weights, feed_sample_weight_modes, check_all_flat=True) else: y = [] sample_weights = None if self.stateful and batch_size and not is_dataset: # Check that for stateful networks, number of samples is a multiple # of the static batch size. if x[0].shape[0] % batch_size != 0: raise ValueError('In a stateful network, ' 'you should only pass inputs with ' 'a number of samples that can be ' 'divided by the batch size. Found: ' + str(x[0].shape[0]) + ' samples') # If dictionary inputs were provided, we return a dictionary as well. if dict_inputs and not isinstance(x, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)): x = dict(zip(feed_input_names, x)) return x, y, sample_weights def _build_model_with_inputs(self, inputs, targets): """Build the model (set model inputs/outputs), mainly for subclass model.""" processed_inputs = [] is_dict_inputs = False orig_inputs = inputs # We need to use `inputs` to set the model inputs. # If input data is a dataset iterator in graph mode or if it is an eager # iterator and only one batch of samples is required, we fetch the data # tensors from the iterator and then standardize them. if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)): inputs, targets, _ = training_utils.extract_tensors_from_dataset(inputs) # We type-check that `inputs` and `targets` are either single arrays # or lists of arrays, and extract a flat list of inputs from the passed # structure. training_utils.validate_input_types(inputs, orig_inputs) if isinstance(inputs, (list, tuple)): processed_inputs += list(inputs) elif isinstance(inputs, dict): is_dict_inputs = True keys = sorted(inputs.keys()) processed_inputs = [inputs[k] for k in keys] else: processed_inputs.append(inputs) # Now that we have a flat set of inputs, we make sure that none of them # are CompositeTensors or CompositeTensorValues of any type (or scipy # sparse arrays, which we treat as SparseTensor values). We cannot safely # infer input data from an arbitrary composite tensor, so we don't try - # users should explicitly add composite tensor inputs to their subclassed # models. for input_tensor in processed_inputs: if composite_tensor_utils.is_composite_or_composite_value(input_tensor): # TODO(b/132691975): Document subclass-model CT input handling. raise ValueError( 'All SparseTensor and RaggedTensor inputs must be explicitly ' 'declared using a keras.Input() with sparse=True or ragged=True. ' 'We found an undeclared input %s. For Sequential models, please ' 'add a keras.Input() as your first Layer. For subclassed models, ' 'please call self._set_inputs() on your input set, which you can ' 'create using keras.Input() for each input to your model.' % (input_tensor,)) # Build the model using the retrieved inputs (value or symbolic). # If values are generated from a dataset, then in symbolic-mode # placeholders will be created to match the value shapes. if isinstance(orig_inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2, iterator_ops.Iterator)): if not self.inputs: # For subclassed models, a robust input spec is not available so we # must cast to the model dtype. inputs = training_utils.cast_if_floating_dtype(inputs, self.dtype) def create_tensor_spec(t): return tensor_spec.TensorSpec(t.shape, t.dtype) cast_inputs = nest.map_structure(create_tensor_spec, inputs) elif training_utils.has_tensors(inputs): cast_inputs = training_utils.cast_if_floating_dtype(inputs) else: cast_inputs = inputs self._set_inputs(cast_inputs) return processed_inputs, targets, is_dict_inputs def _compile_from_inputs(self, all_inputs, target, orig_inputs, orig_target): if target is not None: # We need to use `y` to set the model targets. if training_utils.has_tensors(target): target = training_utils.cast_if_floating_dtype_and_mismatch( target, self.outputs) training_utils.validate_input_types(target, orig_target, allow_dict=False, field_name='target') if isinstance(target, (list, tuple)): all_inputs += list(target) else: all_inputs.append(target) # Type check that all inputs are *either* value *or* symbolic. # TODO(fchollet): this check could be removed in Eager mode? if any(tensor_util.is_tensor(v) for v in all_inputs): if not all(tensor_util.is_tensor(v) for v in all_inputs): raise ValueError('Do not pass inputs that mix Numpy arrays and ' 'TensorFlow tensors. ' 'You passed: x=' + str(orig_inputs) + '; y=' + str(orig_target)) is_dataset = isinstance(orig_inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2, iterator_ops.Iterator)) if is_dataset or context.executing_eagerly(): target_tensors = None else: # Handle target tensors if any passed. if target is not None: if not isinstance(target, (list, tuple)): target = [target] target_tensors = [v for v in target if _is_symbolic_tensor(v)] else: target_tensors = None self.compile( optimizer=self.optimizer, loss=self.loss, metrics=self._compile_metrics, weighted_metrics=self._compile_weighted_metrics, loss_weights=self.loss_weights, target_tensors=target_tensors, sample_weight_mode=self.sample_weight_mode, run_eagerly=self.run_eagerly, experimental_run_tf_function=self._experimental_run_tf_function) # TODO(omalleyt): Consider changing to a more descriptive function name. def _set_inputs(self, inputs, outputs=None, training=None): """Set model's input and output specs based on the input data received. This is to be used for Model subclasses, which do not know at instantiation time what their inputs look like. Args: inputs: Single array, or list of arrays. The arrays could be placeholders, Numpy arrays, data tensors, or TensorSpecs. - if placeholders: the model is built on top of these placeholders, and we expect Numpy data to be fed for them when calling `fit`/etc. - if Numpy data or TensorShapes: we create placeholders matching the TensorShapes or shapes of the Numpy arrays. We expect Numpy data to be fed for these placeholders when calling `fit`/etc. - if data tensors: the model is built on top of these tensors. We do not expect any Numpy data to be provided when calling `fit`/etc. outputs: None, a data tensor, or a list of tensors. If None, the outputs will be determined by invoking `self.call()`, otherwise the provided value will be used. training: Boolean or None. Only relevant in symbolic mode. Specifies whether to build the model's graph in inference mode (False), training mode (True), or using the Keras learning phase (None). Raises: ValueError: If dict inputs are passed to a Sequential Model where the first layer isn't FeatureLayer. """ inputs = self._set_input_attrs(inputs) if outputs is None: kwargs = {} if self._expects_training_arg: # In V2 mode, feeding `training=None` is not allowed because any value # explicitly passed by the user is respected, even `None`.` if training is None and not ops.executing_eagerly_outside_functions(): training = K.learning_phase() if training is not None: kwargs['training'] = training try: outputs = self(inputs, **kwargs) except NotImplementedError: # This Model or a submodel is dynamic and hasn't overridden # `compute_output_shape`. outputs = None self._set_output_attrs(outputs) @trackable.no_automatic_dependency_tracking def _set_input_attrs(self, inputs): """Sets attributes related to the inputs of the Model.""" if self.inputs: raise ValueError('Model inputs are already set.') if self.__class__.__name__ == 'Sequential' and not self.built: if tensor_util.is_tensor(inputs): input_shape = (None,) + tuple(inputs.shape.as_list()[1:]) elif isinstance(inputs, tensor_shape.TensorShape): input_shape = (None,) + tuple(inputs.as_list()[1:]) elif isinstance(inputs, dict): # We assert that the first layer is a FeatureLayer. if not training_utils.is_feature_layer(self.layers[0]): raise ValueError('Passing a dictionary input to a Sequential Model ' 'which doesn\'t have FeatureLayer as the first layer' ' is an error.') input_shape = (None,) else: input_shape = (None,) + tuple(inputs.shape[1:]) self._build_input_shape = input_shape # Cast inputs to the compute dtype. This is primarily used # when saving to determine the correct dtype in the input signature. inputs = self._maybe_cast_inputs(inputs) # On-the-fly setting of symbolic model inputs (either by using the tensor # provided, or by creating a placeholder if Numpy data was provided). model_inputs = training_utils.ModelInputs(inputs) inputs = model_inputs.get_symbolic_inputs() self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True) self.input_names = model_inputs.get_input_names() self._feed_inputs = [] self._feed_input_names = [] self._feed_input_shapes = [] for k, v in model_inputs.as_dict(): if K.is_placeholder(v): self._feed_input_names.append(k) self._feed_inputs.append(v) self._feed_input_shapes.append(K.int_shape(v)) return inputs @trackable.no_automatic_dependency_tracking def _set_output_attrs(self, outputs): """Sets attributes related to the outputs of the Model.""" # NOTE(taylorrobie): This convention cannot be changed without updating the # data adapter since it assumes nest.flatten ordering. outputs = nest.flatten(outputs) self.outputs = outputs self.output_names = training_utils.generic_output_names(outputs) # TODO(scottzhu): Should we cleanup the self._training_endpoints here? self.built = True @property def _targets(self): """The output target tensors for the model.""" return [ e.training_target.target for e in self._training_endpoints if e.has_training_target() ] @property def _feed_targets(self): return [ e.training_target.target for e in self._training_endpoints if e.has_feedable_training_target() ] @property def _feed_output_names(self): return [ e.output_name for e in self._training_endpoints if e.has_feedable_training_target() ] @property def _feed_output_shapes(self): return [ e.feed_output_shape for e in self._training_endpoints if e.has_feedable_training_target() ] @property def _feed_loss_fns(self): return [ e.loss_fn for e in self._training_endpoints if e.has_feedable_training_target() ] @property def _loss_weights_list(self): return [e.loss_weight for e in self._training_endpoints] @property def _output_loss_metrics(self): if hasattr(self, '_training_endpoints'): return [ e.output_loss_metric for e in self._training_endpoints if e.output_loss_metric is not None ] return None @property def sample_weights(self): return [e.sample_weight for e in self._training_endpoints] @property def _sample_weight_modes(self): return [e.sample_weight_mode for e in self._training_endpoints] @property def _feed_sample_weights(self): return [e.sample_weight for e in self._training_endpoints if e.sample_weight is not None] def _maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode): """Maybe load initial epoch from ckpt considering possible worker recovery. Refer to tensorflow/python/keras/distribute/multi_worker_training_state.py for more information. Arguments: initial_epoch: The original initial_epoch user passes in in `fit()`. mode: The mode for running `model.fit()`. Returns: If the training is recovering from previous failure under multi-worker training setting, return the epoch the training is supposed to continue at. Otherwise, return the `initial_epoch` the user passes in. """ if hasattr(self, '_training_state'): return self._training_state.maybe_load_initial_epoch_from_ckpt( initial_epoch, mode) return initial_epoch def _get_training_eval_metrics(self): """Returns all the metrics that are to be reported. This includes the output loss metrics, compile metrics/weighted metrics, add_metric metrics. """ metrics = [] metrics.extend(getattr(self, '_output_loss_metrics', None) or []) metrics.extend(getattr(self, 'metrics', None) or []) return metrics def _assert_compile_was_called(self): # Checks whether `compile` has been called. If it has been called, # then the optimizer is set. This is different from whether the # model is compiled # (i.e. whether the model is built and its inputs/outputs are set). if not self.optimizer: raise RuntimeError('You must compile your model before ' 'training/testing. ' 'Use `model.compile(optimizer, loss)`.') def _in_multi_worker_mode(self): """Method to infer if this `Model` is working in multi-worker settings. Multi-worker training refers to the setup where the training is distributed across multiple workers, as opposed to the case where only a local process performs the training. This function is used to infer for example whether or not a distribute coordinator should be run, and thus TensorFlow servers should be started for communication with other servers in the cluster, or whether or not saving/restoring checkpoints is relevant for preemption fault tolerance. Experimental. Signature and implementation are subject to change. Returns: Whether this model indicates it's working in multi-worker settings. """ strategy = self._get_distribution_strategy() return strategy and strategy.extended._in_multi_worker_mode() # pylint: disable=protected-access def _get_distribution_strategy(self): # If the model was compiled under the scope of a `tf.distribute.Strategy', # `self._distribution_strategy` would have been set and model should infer # that as the used strategy (even if it's out of strategy scope already). strategy = self._distribution_strategy # Otherwise, use the strategy whose scope this is in. if not strategy and distribution_strategy_context.has_strategy(): strategy = distribution_strategy_context.get_strategy() return strategy @property def _trackable_saved_model_saver(self): return model_serialization.ModelSavedModelSaver(self) class DistributedCallbackModel(Model): """Model that is used for callbacks with tf.distribute.Strategy.""" def __init__(self, model): super(DistributedCallbackModel, self).__init__() self.optimizer = model.optimizer def set_original_model(self, orig_model): self._original_model = orig_model def save_weights(self, filepath, overwrite=True, save_format=None): self._replicated_model.save_weights(filepath, overwrite=overwrite, save_format=save_format) def save(self, filepath, overwrite=True, include_optimizer=True): # save weights from the distributed model to the original model distributed_model_weights = self.get_weights() self._original_model.set_weights(distributed_model_weights) # TODO(anjalisridhar): Do we need to save the original model here? # Saving the first replicated model works as well. self._original_model.save(filepath, overwrite=True, include_optimizer=False) def load_weights(self, filepath, by_name=False): self._original_model.load_weights(filepath, by_name=False) # Copy the weights from the original model to each of the replicated models. orig_model_weights = self._original_model.get_weights() distributed_training_utils.set_weights( self._original_model._distribution_strategy, self, # pylint: disable=protected-access orig_model_weights) def __getattr__(self, item): # Whitelisted attributes of the model that can be accessed by the user # during a callback. if item not in ('_setattr_tracking', '_layers'): logging.warning('You are accessing attribute ' + item + ' of the ' 'DistributedCallbackModel that may not have been set ' 'correctly.') return super(DistributedCallbackModel, self).__getattr__(item) class _TrainingEndpoint(object): """A container for the training output/target and related entities. In the case of model with multiple outputs, there is a one-to-one mapping between model output (y_pred), model target (y_true), loss, metrics etc. By unifying these entities into one class, different entity can access information between each other, rather than currently access different list of attributes of the model. """ def __init__(self, output, output_name, loss_fn, loss_weight=None, training_target=None, output_loss_metric=None, sample_weight=None, sample_weight_mode=None): """Initialize the _TrainingEndpoint. Note that the output and output_name should be stable as long as the model structure doesn't change. The training_target suppose to be mutable since the information is provided via `compile()` Args: output: the output tensor of the model. output_name: the unique name of the output tensor. loss_fn: the loss function for the output tensor. loss_weight: float, the weights for the loss. training_target: the _TrainingTarget for the model. output_loss_metric: the metric object for the loss function. sample_weight: the weights for how a sample is weighted during metric and loss calculation. Could be None. sample_weight_mode: string, 'temporal', 'samplewise' or None. The mode for how the sample_weight is populated. """ self._output = output self._output_name = output_name self._loss_fn = loss_fn self._loss_weight = loss_weight self._training_target = training_target self._output_loss_metric = output_loss_metric self._sample_weight = sample_weight self._sample_weight_mode = sample_weight_mode @property def output(self): return self._output @property def output_name(self): return self._output_name @property def shape(self): return K.int_shape(self.output) @property def loss_fn(self): return self._loss_fn @property def loss_weight(self): return self._loss_weight @loss_weight.setter def loss_weight(self, value): self._loss_weight = value @property def training_target(self): return self._training_target @training_target.setter def training_target(self, value): self._training_target = value def create_training_target(self, target, run_eagerly=False): """Create training_target instance and update the self.training_target. Note that the input target should just be a tensor or None, and corresponding training target will be created based on the output and loss_fn. Args: target: the target tensor for the current output. Could be None. run_eagerly: boolean, whether the model is in run_eagerly mode. Raises: ValueError if the training_target field for the current instance has already been populated. """ if self.has_training_target(): raise ValueError('The training_target field for the _TrainingEndpoint ' 'instance has already been populated') if run_eagerly: # When run_eagerly, the target tensor is ignored, and the None placeholder # is created instead. self.training_target = _TrainingTarget( None, feedable=True, skip_target_weights=False) return if self.should_skip_target(): self.training_target = _TrainingTarget(None) else: if target is not None and not K.is_placeholder(target): feedable = False skip_target_weights = True else: feedable = True skip_target_weights = False if target is None: target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get( self.loss_fn, K.dtype(self.output)) target = K.placeholder( ndim=len(self.shape), name=self.output_name + '_target', sparse=K.is_sparse(self.output), dtype=target_dtype) self.training_target = _TrainingTarget( target, feedable=feedable, skip_target_weights=skip_target_weights) @property def output_loss_metric(self): return self._output_loss_metric @output_loss_metric.setter def output_loss_metric(self, value): self._output_loss_metric = value @property def sample_weight(self): return self._sample_weight @sample_weight.setter def sample_weight(self, value): self._sample_weight = value @property def sample_weight_mode(self): return self._sample_weight_mode @sample_weight_mode.setter def sample_weight_mode(self, value): self._sample_weight_mode = value def should_skip_target(self): return self._loss_fn is None def should_skip_target_weights(self): return (self.should_skip_target() or self.training_target is None or self.training_target.skip_target_weights) def has_training_target(self): return self.training_target is not None def has_feedable_training_target(self): return (not self.should_skip_target() and self.training_target is not None and self.training_target.feedable) def loss_name(self): if self._loss_fn is not None: return self._output_name + '_loss' return None @property def feed_output_shape(self): """The output shape for the feedable target.""" if not self.has_feedable_training_target(): return None if ((isinstance(self.loss_fn, losses.LossFunctionWrapper) and self.loss_fn.fn == losses.sparse_categorical_crossentropy)) or ( isinstance(self.loss_fn, losses.SparseCategoricalCrossentropy)): if K.image_data_format() == 'channels_first': return (self.shape[0], 1) + self.shape[2:] else: return self.shape[:-1] + (1,) elif (not isinstance(self.loss_fn, losses.Loss) or (isinstance(self.loss_fn, losses.LossFunctionWrapper) and (getattr(losses, self.loss_fn.fn.__name__, None) is None))): # If the given loss is not an instance of the `Loss` class (custom # class) or if the loss function that is wrapped is not in the # `losses` module, then it is a user-defined loss and we make no # assumptions about it. return None else: return self.shape def sample_weights_mismatch(self): """Check if the sample weight and the mode match or not.""" # If there is a mismatch between sample weight mode and the placeholders # created, then recompile the sub-graphs that depend on sample weights. return ( (self.sample_weight_mode is not None and self.sample_weight is None) or (self.sample_weight_mode is None and self.sample_weight is not None)) def populate_sample_weight(self, sample_weight, sample_weight_mode): """Populate the sample weight and based on the sample weight mode.""" if (sample_weight is None and (self.should_skip_target_weights() or sample_weight_mode is None or context.executing_eagerly())): self._sample_weight = None return assert sample_weight_mode in ['temporal', 'samplewise'] if sample_weight_mode == 'temporal': default_value = [[1.]] shape = [None, None] else: # sample_weight_mode == 'samplewise' default_value = [1.] shape = [None] if sample_weight is not None: if not sample_weight.shape.is_compatible_with(shape): raise ValueError('Received sample weight with shape {}. Expected shape ' '{}.'.format(sample_weight.shape, shape)) self._sample_weight = sample_weight else: self._sample_weight = array_ops.placeholder_with_default( constant_op.constant(default_value, dtype=K.floatx()), shape=shape, name=self.output_name + '_sample_weights') class _TrainingTarget(object): """Container for a target tensor (y_true) and its metadata (shape, loss...). Arguments: target: A target tensor for the model. It may be `None` if the output is excluded from loss computation. It is still kept as None since each output of the model should have a corresponding target. If the target is None, the rest of the attributes will be None as well. feedable: Boolean, whether the target is feedable (requires data to be passed in `fit` or `train_on_batch`), or not (model compiled with `target_tensors` argument). skip_target_weights: Boolean, whether the target should be skipped during weights calculation. """ def __init__(self, target, feedable=False, skip_target_weights=True): self._target = target self._feedable = feedable self._skip_target_weights = skip_target_weights @property def target(self): return self._target @property def feedable(self): return self._feedable @property def skip_target_weights(self): return self._skip_target_weights def _is_symbolic_tensor(x): return tensor_util.is_tensor(x) and not isinstance(x, ops.EagerTensor) def _convert_scipy_sparse_tensor(value, expected_input): """Handle scipy sparse tensor conversions. This method takes a value 'value' and returns the proper conversion. If value is a scipy sparse tensor and the expected input is a dense tensor, we densify 'value'. If value is a scipy sparse tensor and the expected input is a TF SparseTensor, we convert 'value' to a SparseTensor. If 'value' is not a scipy sparse tensor, or scipy is not imported, we pass it through unchanged. Arguments: value: An object that may be a scipy sparse tensor expected_input: The expected input placeholder. Returns: The possibly-converted 'value'. """ if issparse is not None and issparse(value): if ops.is_dense_tensor_like(expected_input): if ops.executing_eagerly_outside_functions(): # In TF2 we do not silently densify sparse matrices. raise ValueError('A SciPy sparse matrix was passed to a model ' 'that expects dense inputs. Please densify your ' 'inputs first, such as by calling `x.toarray().') return value.toarray() else: sparse_coo = value.tocoo() row, col = sparse_coo.row, sparse_coo.col data, shape = sparse_coo.data, sparse_coo.shape indices = np.concatenate((np.expand_dims(row, 1), np.expand_dims(col, 1)), 1) return sparse_tensor.SparseTensor(indices, data, shape) else: return value def _get_metrics_from_layers(layers): """Returns list of metrics from the given layers. This will not include the `compile` metrics of a model layer. Arguments: layers: List of layers. Returns: List of metrics. """ metrics = [] layers = trackable_layer_utils.filter_empty_layer_containers(layers) for layer in layers: if isinstance(layer, Model): # We cannot call 'metrics' on the model because we do not want to # include the metrics that were added in compile API of a nested model. metrics.extend(layer._metrics) # pylint: disable=protected-access metrics.extend(_get_metrics_from_layers(layer.layers)) else: metrics.extend(layer.metrics) return metrics

43.241541

111

0.667898

e7a4d1b45612a008c503a518e0d5976b688b53d0

940

py

Python

magic/magic/items.py

TxarlyToad/Magic-Cardmarket-Spider

c2f5c5eeefbea0a30855dc8396d9fdd85af07637

[ "MIT" ]

2

2021-09-11T23:30:57.000Z

2021-09-14T17:45:58.000Z

magic/magic/items.py

TxarlyToad/Magic-Cardmarket-Spider

c2f5c5eeefbea0a30855dc8396d9fdd85af07637

[ "MIT" ]

null

magic/magic/items.py

TxarlyToad/Magic-Cardmarket-Spider

c2f5c5eeefbea0a30855dc8396d9fdd85af07637

[ "MIT" ]

1

2022-03-09T16:56:28.000Z

# Define here the models for your scraped items # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html from scrapy import Item, Field class MagicCardMarketInformation(Item): url = Field(type=str) name = Field(type=str) set_number = Field(type=str) card_set = Field(type=str) #price minimun = Field(type=float) price_trend = Field(type=float) average_price_30_days = Field(type=float) average_price_7_days = Field(type=float) average_price_1_day = Field(type=float) class MagicCardMarketOffer(Item): country = Field(type=str) seller = Field(type=str) card_condition = Field(type=str) card_language = Field(type=str) professional_type = Field(type=str) is_foil = Field(type=bool) is_signed = Field(type=bool) is_playset = Field(type=bool) product_comments = Field(type=str) price = Field(type=float) item_count = Field(type=int)

28.484848

53

0.705319

aae80ffd40b36ec673a1221c0a5bf18116bc41b0

22,611

py

Python

lib/termineter/core.py

jayaram24/Termineter-Modified

2cab514ff1640809337c6fe17f24433bcdec2260

[ "MIT" ]

null

lib/termineter/core.py

jayaram24/Termineter-Modified

2cab514ff1640809337c6fe17f24433bcdec2260

[ "MIT" ]

null

lib/termineter/core.py

jayaram24/Termineter-Modified

2cab514ff1640809337c6fe17f24433bcdec2260

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # termineter/core.py # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * 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. # * Neither the name of the project 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 # OWNER 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. # from __future__ import unicode_literals import binascii import importlib import logging import logging.handlers import os import re import sys import c1218.connection import c1218.errors import termineter.module import termineter.errors import termineter.options import termineter.utilities import serial import serial.serialutil import tabulate import termcolor import pdb class Framework(object): """ This is the main instance of the framework. It contains and manages the serial connection as well as all of the loaded modules. """ def __init__(self, stdout=None): #initialize/constructor. When we create method in a class. They receive the instance as the first argument automatically. Call the instance self. #After that we can add other arguments.er self.__package__ = '.'.join(self.__module__.split('.')[:-1]) package_path = importlib.import_module(self.__package__).__path__[0] # that's some python black magic trickery for you if stdout is None: stdout = sys.stdout self.stdout = stdout self.logger = logging.getLogger('termineter.framework') #Calling the class Namespace from termineter/utilities.py. It doesn't really do anything, it's used for organizational purposes only. self.directories = termineter.utilities.Namespace() #os.path.expanduser: On Unix and Windows, return argument with an initial component of ~ or ~user replaced by that user's home directory. On Unix, an initial ~ is replaced by the #environment variable HOME if it is set; otherwise the current user's home directory is looked up in the password directory through the built-in module pwd. An initial ~user is looked up #directly in the password directory. On Windows, HOME and USERPROFILE will be used if set, otherwise a combination of HOMEPATH and HOMEDRIVE will be used. An initial ~user is handled by #stripping the last directory component from the created user path derived above. self.directories.user_data = os.path.abspath(os.path.join(os.path.expanduser('~'), '.termineter')) #os.path.join(path,*paths): Join one or more path components intelligently. The return value is the concatenation of path and any members of path and any members of *paths with exactly one directory separator #(os.sep) following each non-empty part except the last, meaning that the result will only end in a separator if the last part is empty. #If a component is an absolute path, all previous components are thrown away and joining continues from the absolute path component. self.directories.data_path = os.path.abspath(os.path.join(package_path, 'data')) if not os.path.isdir(self.directories.data_path): self.logger.critical('path to data not found') raise termineter.errors.FrameworkConfigurationError('path to data not found') if not os.path.isdir(self.directories.user_data): os.mkdir(self.directories.user_data) self.serial_connection = None self._serial_connected = False # setup logging stuff main_file_handler = logging.handlers.RotatingFileHandler(os.path.join(self.directories.user_data, self.__package__ + '.log'), maxBytes=262144, backupCount=5) main_file_handler.setLevel(logging.DEBUG) main_file_handler.setFormatter(logging.Formatter("%(asctime)s %(name)-50s %(levelname)-10s %(message)s")) logging.getLogger('').addHandler(main_file_handler) # setup and configure options # Whether or not these are 'required' is really enforced by the individual # modules get_missing_options method and by which options they require based # on their respective types. See framework/templates.py for more info. # call option function from termineter/options self.options = termineter.options.Options(self.directories) self.options.add_boolean('USE_COLOR', 'enable color on the console interface', default=False) self.options.add_string('SERIAL_CONNECTION', 'serial connection string') self.options.add_string('USERNAME', 'serial username', default='0000') #Original Code #self.options.add_string('USERNAME', 'serial username', default='1') #Modified Code #self.options.add_integer('USERNAME', 'serial username', default=80) #50 in HEX and 80 in decimal. Modified. #self.options.add_integer('USER_ID', 'serial user id', default=0) #Original Code self.options.add_integer('USER_ID', 'serial user id', default=5) # Modified self.options.add_string('PASSWORD', 'serial c12.18 password', default='00000000000000000000') self.options.add_boolean('PASSWORD_HEX', 'if the password is in hex', default=True) # Call AdvancedOption function from termineter/options but pass. # Keywords: AUTO_CONNECT, CACHE_TABLES and etc. self.advanced_options = termineter.options.AdvancedOptions(self.directories) self.advanced_options.add_boolean('AUTO_CONNECT', 'automatically handle connections for modules', default=True) self.advanced_options.add_boolean('CACHE_TABLES', 'cache certain read-only tables', default=True) self.advanced_options.set_callback('CACHE_TABLES', self._opt_callback_set_cache_tables) #self.advanced_options.add_integer('C1218_MAX_PACKETS', 'c12.18 maximum packets for reassembly', default=2) # Original Code self.advanced_options.add_integer('C1218_MAX_PACKETS', 'c12.18 maximum packets for reassembly', default=1) # try default=1, 2, 3 or 4. #self.advanced_options.add_integer('C1218_PACKET_SIZE', 'c12.18 maximum packet size', default=512) # Original self.advanced_options.add_integer('C1218_PACKET_SIZE', 'c12.18 maximum packet size', default=64) #64, 128, 256 and 512 are working and pass the errors #C12.18 | 4.7.1/Page 23 Basic data: Data rate The maximum transmitting speed shall be at least 9600.,n self.advanced_options.add_integer('SERIAL_BAUD_RATE', 'serial connection baud rate', default=9600) self.advanced_options.add_integer('SERIAL_BYTE_SIZE', 'serial connection byte size', default=serial.EIGHTBITS) self.advanced_options.add_integer('SERIAL_STOP_BITS', 'serial connection stop bits', default=serial.STOPBITS_ONE) self.advanced_options.add_string('TABLE_FORMAT', 'the format to print tables in', default='simple') self.advanced_options.set_callback('TABLE_FORMAT', self._opt_callback_set_table_format) if sys.platform.startswith('linux'): self.options.set_option_value('USE_COLOR', 'True') # start loading modules self.current_module = None self.modules = termineter.module.ManagerManager(self, [ os.path.abspath(os.path.join(__file__, '..', 'modules')), os.path.abspath(os.path.join(self.directories.user_data, 'modules')) ]) self.logger.info("successfully loaded {0:,} modules into the framework".format(len(self.modules))) return def __repr__(self): return '<' + self.__class__.__name__ + ' Loaded Modules: ' + str(len(self.modules)) + ', Serial Connected: ' + str(self.is_serial_connected()) + ' >' def _opt_callback_set_cache_tables(self, policy, _): if self.is_serial_connected(): self.serial_connection.set_table_cache_policy(policy) return True def _opt_callback_set_table_format(self, table_format, _): if table_format not in tabulate.tabulate_formats: self.print_error('TABLE_FORMAT must be one of: ' + ', '.join(tabulate.tabulate_formats)) return False return True def _run_optical(self, module): print("core.py-155*** Inside Run_Optical *** ") if not self._serial_connected: self.print_error('The serial interface has not been connected') return False try: self.serial_get() except Exception as error: self.print_exception(error) return False ConnectionState = termineter.module.ConnectionState if not self.advanced_options['AUTO_CONNECT']: return True if module.connection_state == ConnectionState.none: return True try: self.serial_connect() except Exception as error: self.print_exception(error) return self.print_good('*****core.py-174: Successfully connected and the device is responding') pdb.set_trace() if module.connection_state == ConnectionState.connected: return True if not self.serial_login(): self.logger.warning('meter login failed, some tables may not be accessible') if module.connection_state == ConnectionState.authenticated: return True self.logger.warning('unknown optical connection state: ' + module.connection_state.name) return True def reload_module(self, module_path=None): """ Reloads a module into the framework. If module_path is not specified, then the current_module variable is used. Returns True on success, False on error. @type module_path: String @param module_path: The name of the module to reload """ if module_path is None: if self.current_module is not None: module_path = self.current_module.name else: self.logger.warning('must specify module if not module is currently being used') return False if module_path not in self.module: self.logger.error('invalid module requested for reload') raise termineter.errors.FrameworkRuntimeError('invalid module requested for reload') self.logger.info('reloading module: ' + module_path) module_instance = self.import_module(module_path, reload_module=True) if not isinstance(module_instance, termineter.module.TermineterModule): self.logger.error('module: ' + module_path + ' is not derived from the TermineterModule class') raise termineter.errors.FrameworkRuntimeError('module: ' + module_path + ' is not derived from the TermineterModule class') if not hasattr(module_instance, 'run'): self.logger.error('module: ' + module_path + ' has no run() method') raise termineter.errors.FrameworkRuntimeError('module: ' + module_path + ' has no run() method') if not isinstance(module_instance.options, termineter.options.Options) or not isinstance(module_instance.advanced_options, termineter.options.Options): self.logger.error('module: ' + module_path + ' options and advanced_options must be termineter.options.Options instances') raise termineter.errors.FrameworkRuntimeError('options and advanced_options must be termineter.options.Options instances') module_instance.name = module_path.split('/')[-1] module_instance.path = module_path self.modules[module_path] = module_instance if self.current_module is not None: if self.current_module.path == module_instance.path: self.current_module = module_instance return True def run(self, module=None): #print("\n\n*******core.py: Beginning of self and module ********\n\n") if not isinstance(module, termineter.module.TermineterModule) and not isinstance(self.current_module, termineter.module.TermineterModule): raise termineter.errors.FrameworkRuntimeError('either the module or the current_module must be sent') if module is None: module = self.current_module #print("\n***** core.py.230: Run Func **** \n") if isinstance(module, termineter.module.TermineterModuleOptical) and not self._run_optical(module): return self.logger.info('running module: ' + module.path) #print("\n***** core.py.234 **** \n") try: result = module.run() #print("***********core.py/ result={}".format(result)) finally: if isinstance(module, termineter.module.TermineterModuleOptical) and self.serial_connection and self.advanced_options['AUTO_CONNECT']: self.serial_connection.stop() #print("\n***** core.py.240: End of Run - 240 **** \n") return result @property def use_colors(self): return self.options['USE_COLOR'] @use_colors.setter def use_colors(self, value): self.options.set_option_value('USE_COLOR', str(value)) def get_module_logger(self, name): """ This returns a logger for individual modules to allow them to be inherited from the framework and thus be named appropriately. @type name: String @param name: The name of the module requesting the logger """ return logging.getLogger('termineter.module.' + name) def import_module(self, module_path, reload_module=False): module = self.__package__ + '.modules.' + module_path.replace('/', '.') try: module = importlib.import_module(module) if reload_module: importlib.reload(module) module_instance = module.Module(self) except Exception: self.logger.error('failed to load module: ' + module_path, exc_info=True) raise termineter.errors.FrameworkRuntimeError('failed to load module: ' + module_path) return module_instance def print_exception(self, error): message = 'Caught ' + error.__class__.__name__ + ': ' + str(error) self.logger.error(message, exc_info=True) self.print_error(message) def print_error(self, message): prefix = '[-] ' if self.options['USE_COLOR']: prefix = termcolor.colored(prefix, 'red', attrs=('bold',)) self.stdout.write(prefix + (os.linesep + prefix).join(message.split(os.linesep)) + os.linesep) self.stdout.flush() def print_good(self, message): prefix = '[+] ' if self.options['USE_COLOR']: prefix = termcolor.colored(prefix, 'green', attrs=('bold',)) self.stdout.write(prefix + (os.linesep + prefix).join(message.split(os.linesep)) + os.linesep) self.stdout.flush() def print_hexdump(self, data): data_len = len(data) i = 0 while i < data_len: self.stdout.write("{0:04x} ".format(i)) for j in range(16): if i + j < data_len: self.stdout.write("{0:02x} ".format(data[i + j])) else: self.stdout.write(' ') if j % 16 == 7: self.stdout.write(' ') self.stdout.write(' ') r = '' for j in data[i:i + 16]: if 32 < j < 128: r += chr(j) else: r += '.' self.stdout.write(r + os.linesep) i += 16 self.stdout.flush() def print_line(self, message): self.stdout.write(message + os.linesep) self.stdout.flush() def print_status(self, message): prefix = '[*] ' if self.options['USE_COLOR']: prefix = termcolor.colored(prefix, 'blue', attrs=('bold',)) self.stdout.write(prefix + (os.linesep + prefix).join(message.split(os.linesep)) + os.linesep) self.stdout.flush() def print_table(self, table, headers=(), line_prefix=None, tablefmt=None): tablefmt = tablefmt or self.advanced_options['TABLE_FORMAT'] text = tabulate.tabulate(table, headers=headers, tablefmt=tablefmt) if line_prefix: text = '\n'.join(line_prefix + line for line in text.split('\n')) self.print_line(text) def print_warning(self, message): prefix = '[!] ' if self.options['USE_COLOR']: prefix = termcolor.colored(prefix, '', attrs=('bold',)) self.stdout.write(prefix + (os.linesep + prefix).join(message.split(os.linesep)) + os.linesep) self.stdout.flush() def is_serial_connected(self): """ Returns True if the serial interface is connected. """ #print("core.py - 338: Serial Connection: {}".format(self._serial_connected)) return self._serial_connected def serial_disconnect(self): """ Closes the serial connection to the meter and disconnects from the device. """ if self._serial_connected: try: self.serial_connection.close() except c1218.errors.C1218IOError as error: self.logger.error('caught C1218IOError: ' + str(error)) except serial.serialutil.SerialException as error: self.logger.error('caught SerialException: ' + str(error)) self._serial_connected = False self.logger.warning('the serial interface has been disconnected') return True def serial_get(self): """ Create the serial connection from the framework settings and return it, setting the framework instance in the process. """ #print("core.py - 372 - serial_get") frmwk_c1218_settings = { 'nbrpkts': self.advanced_options['C1218_MAX_PACKETS'], 'pktsize': self.advanced_options['C1218_PACKET_SIZE'] } frmwk_serial_settings = termineter.utilities.get_default_serial_settings() frmwk_serial_settings['baudrate'] = self.advanced_options['SERIAL_BAUD_RATE'] frmwk_serial_settings['bytesize'] = self.advanced_options['SERIAL_BYTE_SIZE'] frmwk_serial_settings['stopbits'] = self.advanced_options['SERIAL_STOP_BITS'] self.logger.info('opening serial device: ' + self.options['SERIAL_CONNECTION']) try: #pdb.set_trace() self.serial_connection = c1218.connection.Connection(self.options['SERIAL_CONNECTION'], c1218_settings=frmwk_c1218_settings, serial_settings=frmwk_serial_settings, enable_cache=self.advanced_options['CACHE_TABLES']) except Exception as error: self.logger.error('could not open the serial device') #raise error pass return self.serial_connection def serial_connect(self): """ Connect to the serial device. """ #print("core.py - 396 - serial_connect") self.serial_get() try: self.serial_connection.start() #print("Hi - you are in try") except c1218.errors.C1218IOError as error: #print("Hi - you got except") self.logger.error('serial connection has been opened but the meter is unresponsive') raise error self._serial_connected = True #print("Hi - you missed except") return True def serial_login(self): #print("\n\n****SERIAL LOGIN***\n\n\n") """ Attempt to log into the meter over the C12.18 protocol. Returns True on success, False on a failure. This can be called by modules in order to login with a username and password configured within the framework instance. """ #print("core.py - 399: Serial Connection: {}".format(self._serial_connected)) if not self._serial_connected: raise termineter.errors.FrameworkRuntimeError('the serial interface is disconnected') username = self.options['USERNAME'] user_id = self.options['USER_ID'] password = self.options['PASSWORD'] if self.options['PASSWORD_HEX']: hex_regex = re.compile('^([0-9a-fA-F]{2})+$') if hex_regex.match(password) is None: self.print_error('Invalid characters in password') raise termineter.errors.FrameworkConfigurationError('invalid characters in password') password = binascii.a2b_hex(password) ''' Original Code ''' #if len(username) > 10: #self.print_error('Username cannot be longer than 10 characters') #raise termineter.errors.FrameworkConfigurationError('username cannot be longer than 10 characters') if not (0 <= user_id <= 0xffff): self.print_error('User id must be between 0 and 0xffff') raise termineter.errors.FrameworkConfigurationError('user id must be between 0 and 0xffff') if len(password) > 20: self.print_error('Password cannot be longer than 20 characters') raise termineter.errors.FrameworkConfigurationError('password cannot be longer than 20 characters') if not self.serial_connection.login(username, user_id, password): return False return True def test_serial_connection(self): """ Connect to the serial device and then verifies that the meter is responding. Once the serial device is open, this function attempts to retrieve the contents of table #0 (GEN_CONFIG_TBL) to configure the endianess it will use. Returns True on success. """ #print("core.py- 448: Before Serial Connection") self.serial_connect() #pdb.set_trace() #print("\n\n\n****core.py- 449: Test Serial Connection ****\n\n\n") username = self.options['USERNAME'] user_id = self.options['USER_ID'] #print("core.py-454-Username: {} and User_id {}".format(username,user_id)) ''' Original Code if len(username) > 10: self.logger.error('username cannot be longer than 10 characters') raise termineter.errors.FrameworkConfigurationError('username cannot be longer than 10 characters') ''' if not (0 <= user_id <= 0xffff): self.logger.error('user id must be between 0 and 0xffff') raise termineter.errors.FrameworkConfigurationError('user id must be between 0 and 0xffff') try: #print("core.py-445-Username: {} and User_id {}".format(username,user_id)) #print("core.py - 446 - self.serial_connection.login(username, user_id): ".format(self.serial_connection.login(username, user_id))) if not self.serial_connection.login(username, user_id): self.logger.error('the meter has rejected the username and user id') raise termineter.errors.FrameworkConfigurationError('the meter has rejected the username and user id') except c1218.errors.C1218IOError as error: self.logger.error('serial connection has been opened but the meter is unresponsive') raise error try: #print("\n\n\n*****BEFORE serial_connection****") general_config_table = self.serial_connection.get_table_data(0) except c1218.errors.C1218ReadTableError as error: self.logger.error('serial connection as been opened but the general configuration table (table #0) could not be read') raise error if general_config_table[0] & 1: self.logger.info('setting the connection to use big-endian for C12.19 data') self.serial_connection.c1219_endian = '>' else: self.logger.info('setting the connection to use little-endian for C12.19 data') self.serial_connection.c1219_endian = '<' try: self.serial_connection.stop() except c1218.errors.C1218IOError as error: self.logger.error('serial connection has been opened but the meter is unresponsive') raise error self.logger.warning('the serial interface has been connected') #print("\n\n\n***the serial interface has been connected****\n\n\n") return True

44.863095

218

0.745699

25624f2c81c8cd43f0926551bcc2493a90f111d0

6,084

py

Python

kws_streaming/models/utils_test.py

ojInc/google-research

9929c88b664800a25b8716c22068dd77d80bd5ee

[ "Apache-2.0" ]

1

2020-10-25T04:07:57.000Z

kws_streaming/models/utils_test.py

ojInc/google-research

9929c88b664800a25b8716c22068dd77d80bd5ee

[ "Apache-2.0" ]

null

kws_streaming/models/utils_test.py

ojInc/google-research

9929c88b664800a25b8716c22068dd77d80bd5ee

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # 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. """Tests for kws_streaming.models.utils.""" from absl import flags from absl.testing import parameterized from kws_streaming.layers import modes from kws_streaming.layers.compat import tf from kws_streaming.layers.compat import tf1 from kws_streaming.models import model_params from kws_streaming.models import models from kws_streaming.models import utils from kws_streaming.train import model_flags tf1.disable_eager_execution() FLAGS = flags.FLAGS # two models are tested with all cobinations of speech frontend # and all models are tested with one frontend class UtilsTest(tf.test.TestCase, parameterized.TestCase): def _testTFLite(self, preprocess='raw', feature_type='mfcc_op', model_name='svdf'): params = model_params.HOTWORD_MODEL_PARAMS[model_name] params.clip_duration_ms = 100 # make it shorter for testing # set parameters to test params.preprocess = preprocess params.feature_type = feature_type params = model_flags.update_flags(params) # create model model = models.MODELS[params.model_name](params) # convert TF non streaming model to TFLite non streaming inference self.assertTrue( utils.model_to_tflite(self.sess, model, params, modes.Modes.NON_STREAM_INFERENCE)) def setUp(self): super(UtilsTest, self).setUp() tf1.reset_default_graph() config = tf1.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf1.Session(config=config) tf1.keras.backend.set_session(self.sess) @parameterized.named_parameters([ { 'testcase_name': 'raw with mfcc_tf', 'preprocess': 'raw', 'feature_type': 'mfcc_tf' }, { 'testcase_name': 'raw with mfcc_op', 'preprocess': 'raw', 'feature_type': 'mfcc_op' }, { 'testcase_name': 'mfcc', 'preprocess': 'mfcc', 'feature_type': 'mfcc_op' }, # feature_type will be ignored { 'testcase_name': 'micro', 'preprocess': 'micro', 'feature_type': 'mfcc_op' }, # feature_type will be ignored ]) def testPreprocessNonStreamInferenceTFandTFLite(self, preprocess, feature_type, model_name='svdf'): # Validate that model with different preprocessing # can be converted to non stream inference mode. self._testTFLite(preprocess, feature_type, model_name) @parameterized.named_parameters([ { 'testcase_name': 'raw with mfcc_tf', 'preprocess': 'raw', 'feature_type': 'mfcc_tf' }, { 'testcase_name': 'raw with mfcc_op', 'preprocess': 'raw', 'feature_type': 'mfcc_op' }, { 'testcase_name': 'mfcc', 'preprocess': 'mfcc', 'feature_type': 'mfcc_op' }, # feature_type will be ignored { 'testcase_name': 'micro', 'preprocess': 'micro', 'feature_type': 'mfcc_op' }, # feature_type will be ignored ]) def testPreprocessStreamInferenceModeTFandTFLite(self, preprocess, feature_type, model_name='gru'): # Validate that model with different preprocessing # can be converted to stream inference mode with TF and TFLite. params = model_params.HOTWORD_MODEL_PARAMS[model_name] # set parameters to test params.preprocess = preprocess params.feature_type = feature_type params = model_flags.update_flags(params) # create model model = models.MODELS[params.model_name](params) # convert TF non streaming model to TFLite streaming inference # with external states self.assertTrue(utils.model_to_tflite( self.sess, model, params, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE)) # convert TF non streaming model to TF streaming with external states self.assertTrue(utils.to_streaming_inference( model, params, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE)) # convert TF non streaming model to TF streaming with internal states self.assertTrue(utils.to_streaming_inference( model, params, modes.Modes.STREAM_INTERNAL_STATE_INFERENCE)) def test_model_to_saved(self, model_name='dnn'): """SavedModel supports both stateless and stateful graphs.""" params = model_params.HOTWORD_MODEL_PARAMS[model_name] params = model_flags.update_flags(params) # create model model = models.MODELS[params.model_name](params) utils.model_to_saved(model, params, FLAGS.test_tmpdir) def testNextPowerOfTwo(self): self.assertEqual(utils.next_power_of_two(11), 16) @parameterized.parameters('att_mh_rnn', 'att_rnn', 'dnn', 'ds_cnn', 'cnn', 'tc_resnet', 'crnn', 'gru', 'lstm', 'svdf', 'mobilenet', 'mobilenet_v2', 'xception', 'inception', 'inception_resnet', 'ds_tc_resnet') def testNonStreamInferenceTFandTFLite(self, model_name): # Validate that all models with selected preprocessing # can be converted to non stream inference mode. self._testTFLite(model_name=model_name) if __name__ == '__main__': tf.test.main()

36.214286

79

0.649244

9f08c6feaf839c3954a367eec3687cffcefb80f1

1,526

py

Python

test/functional/disablewallet.py

BitcoinBridgeOffical/Bitcoin-Bridge

d800625c9b4b6fe1ddc0f0615a854e43463b82ad

[ "MIT" ]

1

2018-01-13T18:02:47.000Z

test/functional/disablewallet.py

BitcoinBridgeOffical/Bitcoin-Bridge

d800625c9b4b6fe1ddc0f0615a854e43463b82ad

[ "MIT" ]

null

test/functional/disablewallet.py

BitcoinBridgeOffical/Bitcoin-Bridge

d800625c9b4b6fe1ddc0f0615a854e43463b82ad

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2015-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test a node with the -disablewallet option. - Test that validateaddress RPC works when running with -disablewallet - Test that it is not possible to mine to an invalid address. """ from test_framework.test_framework import BitcoinBridgeTestFramework from test_framework.util import * class DisableWalletTest (BitcoinBridgeTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [["-disablewallet"]] def run_test (self): # Make sure wallet is really disabled assert_raises_jsonrpc(-32601, 'Method not found', self.nodes[0].getwalletinfo) x = self.nodes[0].validateaddress('3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy') assert(x['isvalid'] == False) x = self.nodes[0].validateaddress('mneYUmWYsuk7kySiURxCi3AGxrAqZxLgPZ') assert(x['isvalid'] == True) # Checking mining to an address without a wallet. Generating to a valid address should succeed # but generating to an invalid address will fail. self.nodes[0].generatetoaddress(1, 'mneYUmWYsuk7kySiURxCi3AGxrAqZxLgPZ') assert_raises_jsonrpc(-5, "Invalid address", self.nodes[0].generatetoaddress, 1, '3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy') if __name__ == '__main__': DisableWalletTest ().main ()

43.6

126

0.731324

bce2c57196cb60c3d9b93df390eeb480d205fe0b

779

py

Python

webdriver/clickSpeedTest.py

DarkMaguz/CP-Python

aa1113d6c70c8f2c32fd29cf49bb39c41e819fae

[ "MIT" ]

1

2019-02-23T13:50:46.000Z

webdriver/clickSpeedTest.py

DarkMaguz/CP-Python

aa1113d6c70c8f2c32fd29cf49bb39c41e819fae

[ "MIT" ]

null

webdriver/clickSpeedTest.py

DarkMaguz/CP-Python

aa1113d6c70c8f2c32fd29cf49bb39c41e819fae

[ "MIT" ]

1

2019-03-08T14:40:47.000Z

from coockieClickerUtils import * # import os # import time # # from selenium import webdriver # from selenium.webdriver.common.by import By # # os.environ['PATH'] += os.pathsep + 'bin/' # driver = webdriver.Chrome() driver.get("https://clickspeedtest.com/5-seconds.html") driver.find_element(By.ID, 'ez-accept-all').click() time.sleep(1) driver.execute_script(''' document.getElementById('clicker').setAttribute('target', '_blank'); ''') print('Start clicking') id = driver.find_element(By.ID, 'clicker') while True: # click for ever try: id.click() except Exception as ex: # until it breaks print('Time is over') break time.sleep(1) # results are slow result = driver.find_element(By.CSS_SELECTOR, '.times') print(f'Result: {result.text}') driver.close()

22.911765

68

0.709884

33d20c1775dbde862204a516a8313cd41a24ccf9

5,605

py

Python

userbot/modules/chat_info.py

HitaloSama/PaperplaneMinimal

5cf45ca4ae90ad4a52ee6d6dc679053a69fbed32

[ "Naumen", "Condor-1.1", "MS-PL" ]

9

2020-06-11T18:47:48.000Z

2021-11-08T18:05:37.000Z

userbot/modules/chat_info.py

HitaloSama/PaperplaneMinimal

5cf45ca4ae90ad4a52ee6d6dc679053a69fbed32

[ "Naumen", "Condor-1.1", "MS-PL" ]

3

2020-08-28T18:37:46.000Z

2020-09-25T15:32:29.000Z

userbot/modules/chat_info.py

HitaloSama/PaperplaneMinimal

5cf45ca4ae90ad4a52ee6d6dc679053a69fbed32

[ "Naumen", "Condor-1.1", "MS-PL" ]

8

2020-06-14T02:08:41.000Z

2020-12-15T13:25:15.000Z

# Copyright (C) 2019 The Raphielscape Company LLC. # # Licensed under the Raphielscape Public License, Version 1.d (the "License"); # you may not use this file except in compliance with the License. # from typing import Union from kantex.md import (Bold, Link, SubSection, SubSubSection, KeyValueItem, Section, Code) from telethon.tl.functions.users import GetFullUserRequest from telethon.tl.types import Channel, User, ChatInviteExported from telethon.tl.types.messages import ChatFull from userbot import CMD_HELP from userbot.events import register from userbot.utils import (parse_arguments, list_admins, inline_mention, list_bots, get_chat_from_event) class FormattedBase: def __add__(self, other: Union[str, 'FormattedBase']) -> str: return str(self) + str(other) def __repr__(self) -> str: return f'{type(self).__name__}({self.text})' def __str__(self) -> str: return self.text class String(FormattedBase): def __init__(self, text: Union[str, int]) -> None: self.text = str(text) class TGDoc: def __init__(self, *args: Union[String, 'Section']) -> None: self.sections = args def __str__(self) -> str: return '\n\n'.join([str(section) for section in self.sections]) @register(outgoing=True, pattern=r"^\.c(?:hat)?(\s+[\S\s]+|$)") async def chat_info(e): params = e.pattern_match.group(1) or "" args, chat = parse_arguments( params, ['id', 'general', 'admins', 'bots', 'all']) args['chat'] = chat if isinstance(e.chat, User): from .user_info import fetch_info as fetch_user_info replied_user = await e.client(GetFullUserRequest(e.chat.id)) response = await fetch_user_info(replied_user, **args) else: full_chat: ChatFull = await get_chat_from_event(e, **args) await e.edit("**Fetching chat info...**") response = await fetch_info(e, full_chat, **args) await e.edit(str(response)) async def fetch_info(event, full_chat, **kwargs): chat = full_chat.chats[0] show_all = kwargs.get('all', False) id_only = kwargs.get('id', False) show_general = kwargs.get('general', True) show_admins = kwargs.get('admins', False) show_bots = kwargs.get('bots', False) is_private = False if isinstance(chat, Channel) and chat.username: name = chat.title if chat.title else chat.username title = Link(name, f"https://t.me/{chat.username}") elif chat.title: is_private = True title = Bold(chat.title) else: is_private = True title = Bold(f"Chat {chat.id}") if show_all: show_general = True show_admins = True show_bots = True elif id_only: return KeyValueItem(title, Code(str(chat.id))) admin_list = await list_admins(event) if show_general: exported_invite = full_chat.full_chat.exported_invite invite_link = exported_invite.link if isinstance( exported_invite, ChatInviteExported) else None admin_count = full_chat.full_chat.admins_count or len(admin_list) general = SubSection(Bold("general"), KeyValueItem("id", Code(str(chat.id))), KeyValueItem("title", Code(chat.title)), KeyValueItem("private", Code(str(is_private))), KeyValueItem("invite link", Link(invite_link.split('/')[-1], invite_link)) if invite_link else None, SubSubSection("participants", KeyValueItem("admins", Code(str(admin_count))), KeyValueItem("online", Code(str(full_chat.full_chat.online_count))), KeyValueItem("total", Code(str(full_chat.full_chat.participants_count))))) else: general = None if show_admins: admins = SubSection(Bold("admins")) for admin in admin_list: admins.items.append(String(inline_mention(admin))) if not admins: admins.items.append(String("No admins")) if show_bots: bots_list = await list_bots(event) bots = SubSection(Bold("bots")) for bot in bots_list: bots.items.append(String(inline_mention(bot))) if not bots: bots.items.append(String("No bots")) return TGDoc(Section(title, general if show_general else None, admins if show_admins else None, bots if show_bots else None)) CMD_HELP.update({"chat info": ['Chat Info', " - `chat [options]`: Returns stats for the current chat\n\n" "**Options:**\n\n" "`.id:` Return only the ID.\n" "`.general`: Show general information related to the chat.\n" "`.admins`: Show chat admins (does not mention them).\n" "`.all`: Show everything.\n\n" "**All commands can be used with** `.`"]})

36.875

108

0.551115

82c4ac6067a013e715ebe38452a97c9a40478b08

206

py

Python

dirutility.py

rlowrance/re-avm

d4cfa62e9f65d325e8ac98caa61d3fb666b8a6a2

[ "BSD-3-Clause" ]

25

2016-10-07T05:08:15.000Z

2022-03-22T01:36:51.000Z

dirutility.py

rlowrance/re-avm

d4cfa62e9f65d325e8ac98caa61d3fb666b8a6a2

[ "BSD-3-Clause" ]

1

2021-01-14T22:27:23.000Z

dirutility.py

rlowrance/re-avm

d4cfa62e9f65d325e8ac98caa61d3fb666b8a6a2

[ "BSD-3-Clause" ]

8

2016-08-12T07:26:29.000Z

2021-07-05T01:22:42.000Z

'''utilities for managing directories''' import os def assure_exists(dir_path): if not os.path.exists(dir_path): os.makedirs(dir_path) # make all intermediate directories return dir_path

22.888889

66

0.728155

2222a7cb37d4cf28316c4987311199244a6cd379

1,358

py

Python

fix_settings.py

prehensilecode/votca_helper

ebbe61aff6df1c5ca36a70ddc390bd150b57c639

[ "Unlicense" ]

null

fix_settings.py

prehensilecode/votca_helper

ebbe61aff6df1c5ca36a70ddc390bd150b57c639

[ "Unlicense" ]

null

fix_settings.py

prehensilecode/votca_helper

ebbe61aff6df1c5ca36a70ddc390bd150b57c639

[ "Unlicense" ]

null

#!/usr/bin/env python3 import sys import os from pathlib import Path import xml.dom.minidom ### README ### * Save this file as fix_settings.py in the same directory as your job script ### * Make it executable: chmod +x fix_settings.py def generate_hostfile(pe_hostfile): '''Convert Univa Grid Engine hostfile to Open MPI hostfile''' ompi_hostfile = Path('./hostfile.{}'.format(os.getenv('JOB_ID'))).resolve() with open(pe_hostfile, 'r') as f, open(ompi_hostfile, 'w') as g: for l in f: hostname, nslots = l.strip().split()[:2] g.write('{} slots={} max-slots={}\n'.format(hostname, nslots, nslots)) return ompi_hostfile def fix_settings_xml(ompi_hostfile): '''Fix VOTCA CSG settings.xml file''' settings = xml.dom.minidom.parse('settings.xml') ### read environment variable MPI_RUN for full path to mpirun command settings.getElementsByTagName('command')[0].childNodes[0].data = '{} -x LD_LIBRARY_PATH -x BASH_ENV --hostfile {} gmx_mpi mdrun'.format(os.getenv('MPI_RUN'), ompi_hostfile) ### XXX caution - this overwrites the settings.xml file with open('settings.xml', 'w') as f: f.write(settings.toxml()) if __name__ == '__main__': pe_hostfile = Path(os.getenv('PE_HOSTFILE')) ompi_hostfile = generate_hostfile(pe_hostfile) fix_settings_xml(ompi_hostfile)

34.820513

176

0.688513

84df66448edbd29765d091a8fc64fbab77432037

2,900

py

Python

stdplugins/_help.py

andromechanic/BotHub

18853e3a5f2a1ecdc93f9d6173411baf89dd8f00

[ "Apache-2.0" ]

25

2019-10-26T08:01:11.000Z

2022-02-21T08:18:00.000Z

stdplugins/_help.py

andromechanic/BotHub

18853e3a5f2a1ecdc93f9d6173411baf89dd8f00

[ "Apache-2.0" ]

2

2020-05-11T08:42:33.000Z

2020-05-21T02:30:09.000Z

stdplugins/_help.py

andromechanic/BotHub

18853e3a5f2a1ecdc93f9d6173411baf89dd8f00

[ "Apache-2.0" ]

291

2019-11-06T04:25:13.000Z

2021-10-03T15:56:23.000Z

"""COMMAND : .helpme, .dc, .exec ls stdplugins, .stdplugins, .syntax""" import sys from telethon import events, functions, __version__ from uniborg.util import admin_cmd @borg.on(admin_cmd(pattern="helpme ?(.*)", allow_sudo=False)) # pylint:disable=E0602 async def _(event): if event.fwd_from: return splugin_name = event.pattern_match.group(1) if splugin_name in borg._plugins: s_helpme_string = borg._plugins[splugin_name].__doc__ else: s_helpme_string = "****:" helpme_string = """@Bot_Hub_Official™️ ( **Custom Built By** @Three_Cube_TeKnoways_bot ) \n**Verified Account**: ✅\n**Official \n**NOTICE**: **COMMANDS** are CASE **sensitive**\n**DESCRIPTION**: https://telegra.ph/command-list-for-BotHub-Userbot-11-08\n """.format( sys.version, __version__ ) tgbotusername = Config.TG_BOT_USER_NAME_BF_HER # pylint:disable=E0602 if tgbotusername is not None: results = await borg.inline_query( # pylint:disable=E0602 tgbotusername, helpme_string + "\n\n" + s_helpme_string ) await results[0].click( event.chat_id, reply_to=event.reply_to_msg_id, hide_via=True ) await event.delete() else: await event.reply(helpme_string + "\n\n" + s_helpme_string) await event.delete() @borg.on(admin_cmd(pattern="dc")) # pylint:disable=E0602 async def _(event): if event.fwd_from: return result = await borg(functions.help.GetNearestDcRequest()) # pylint:disable=E0602 await event.edit(f"**Country** : `{result.country}`\n" f"**Nearest DC** : `{result.nearest_dc}`\n" f"**This DC** : `{result.this_dc}`") @borg.on(admin_cmd(pattern="config")) # pylint:disable=E0602 async def _(event): if event.fwd_from: return result = await borg(functions.help.GetConfigRequest()) # pylint:disable=E0602 result = result.stringify() logger.info(result) # pylint:disable=E0602 await event.edit("""Telethon UserBot powered by @Bot_Hub_Official""") @borg.on(admin_cmd(pattern="syntax ?(.*)" )) async def _(event): if event.fwd_from: return plugin_name = event.pattern_match.group(1) if plugin_name in borg._plugins: helpme_string = borg._plugins[plugin_name].__doc__ unload_string = f"Use `.unload {plugin_name}` to remove this plugin.\n © @Three_Cube_TeKnoways_Bot" if helpme_string: plugin_syntax = f"Syntax for plugin **{plugin_name}**:\n\n{helpme_string}\n{unload_string}" else: plugin_syntax = f"No DOCSTRING has been setup for {plugin_name} plugin." else: plugin_syntax = "Enter valid **Plugin** name.\nDo `.exec ls stdplugins` or `.helpme` or `.stdplugins` to get list of valid plugin names." await event.edit(plugin_syntax)

39.189189

257

0.64931

d2babe1e89a20554eac46fe4704f4b54f9ec3e14

677

py

Python

api/index.py

add830830/tg-serverless

6955387d8b8aece6c6e08766b11eeac6c5d7f03d

[ "MIT" ]

null

api/index.py

add830830/tg-serverless

6955387d8b8aece6c6e08766b11eeac6c5d7f03d

[ "MIT" ]

null

api/index.py

add830830/tg-serverless

6955387d8b8aece6c6e08766b11eeac6c5d7f03d

[ "MIT" ]

null

from jinja2 import Environment, FileSystemLoader from sanic import Sanic, response env = Environment(loader=FileSystemLoader("api/templates")) app = Sanic(__name__) @app.route("/") async def index(request): title = "tg-serverless" description = "A Telegram bot Python app use Vercel as Serverless Function!" color = "#2962ff" repo = "https://github.com/illvart/tg-serverless" template = env.get_template("app.html") content = template.render(title=title, description=description, color=color, repo=repo) return response.html(content, status=200) if __name__ == "__main__": app.run(debug=True, auto_reload=True, host="0.0.0.0", port=3000)

29.434783

91

0.720827

50b430bb45d4ace632e4d04ead7aa7002077f144

17,590

py

Python

qlib/config.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

[ "MIT" ]

null

qlib/config.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

[ "MIT" ]

null

qlib/config.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """ About the configs ================= The config will be based on _default_config. Two modes are supported - client - server """ from __future__ import annotations import os import re import copy import logging import platform import multiprocessing from pathlib import Path from typing import Callable, Optional, Union from typing import TYPE_CHECKING from qlib.constant import REG_CN, REG_US, REG_TW if TYPE_CHECKING: from qlib.utils.time import Freq class Config: def __init__(self, default_conf): self.__dict__["_default_config"] = copy.deepcopy(default_conf) # avoiding conflicts with __getattr__ self.reset() def __getitem__(self, key): return self.__dict__["_config"][key] def __getattr__(self, attr): if attr in self.__dict__["_config"]: return self.__dict__["_config"][attr] raise AttributeError(f"No such `{attr}` in self._config") def get(self, key, default=None): return self.__dict__["_config"].get(key, default) def __setitem__(self, key, value): self.__dict__["_config"][key] = value def __setattr__(self, attr, value): self.__dict__["_config"][attr] = value def __contains__(self, item): return item in self.__dict__["_config"] def __getstate__(self): return self.__dict__ def __setstate__(self, state): self.__dict__.update(state) def __str__(self): return str(self.__dict__["_config"]) def __repr__(self): return str(self.__dict__["_config"]) def reset(self): self.__dict__["_config"] = copy.deepcopy(self._default_config) def update(self, *args, **kwargs): self.__dict__["_config"].update(*args, **kwargs) def set_conf_from_C(self, config_c): self.update(**config_c.__dict__["_config"]) # pickle.dump protocol version: https://docs.python.org/3/library/pickle.html#data-stream-format PROTOCOL_VERSION = 4 NUM_USABLE_CPU = max(multiprocessing.cpu_count() - 2, 1) DISK_DATASET_CACHE = "DiskDatasetCache" SIMPLE_DATASET_CACHE = "SimpleDatasetCache" DISK_EXPRESSION_CACHE = "DiskExpressionCache" DEPENDENCY_REDIS_CACHE = (DISK_DATASET_CACHE, DISK_EXPRESSION_CACHE) _default_config = { # data provider config "calendar_provider": "LocalCalendarProvider", "instrument_provider": "LocalInstrumentProvider", "feature_provider": "LocalFeatureProvider", "expression_provider": "LocalExpressionProvider", "dataset_provider": "LocalDatasetProvider", "provider": "LocalProvider", # config it in qlib.init() # "provider_uri" str or dict: # # str # "~/.qlib/stock_data/cn_data" # # dict # {"day": "~/.qlib/stock_data/cn_data", "1min": "~/.qlib/stock_data/cn_data_1min"} # NOTE: provider_uri priority： # 1. backend_config: backend_obj["kwargs"]["provider_uri"] # 2. backend_config: backend_obj["kwargs"]["provider_uri_map"] # 3. qlib.init: provider_uri "provider_uri": "", # cache "expression_cache": None, "dataset_cache": None, "calendar_cache": None, # for simple dataset cache "local_cache_path": None, # kernels can be a fixed value or a callable function lie `def (freq: str) -> int` # If the kernels are arctic_kernels, `min(NUM_USABLE_CPU, 30)` may be a good value "kernels": NUM_USABLE_CPU, # pickle.dump protocol version "dump_protocol_version": PROTOCOL_VERSION, # How many tasks belong to one process. Recommend 1 for high-frequency data and None for daily data. "maxtasksperchild": None, # If joblib_backend is None, use loky "joblib_backend": "multiprocessing", "default_disk_cache": 1, # 0:skip/1:use "mem_cache_size_limit": 500, "mem_cache_limit_type": "length", # memory cache expire second, only in used 'DatasetURICache' and 'client D.calendar' # default 1 hour "mem_cache_expire": 60 * 60, # cache dir name "dataset_cache_dir_name": "dataset_cache", "features_cache_dir_name": "features_cache", # redis # in order to use cache "redis_host": "127.0.0.1", "redis_port": 6379, "redis_task_db": 1, # This value can be reset via qlib.init "logging_level": logging.INFO, # Global configuration of qlib log # logging_level can control the logging level more finely "logging_config": { "version": 1, "formatters": { "logger_format": { "format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s" } }, "filters": { "field_not_found": { "()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"], } }, "handlers": { "console": { "class": "logging.StreamHandler", "level": logging.DEBUG, "formatter": "logger_format", "filters": ["field_not_found"], } }, "loggers": {"qlib": {"level": logging.DEBUG, "handlers": ["console"]}}, }, # Default config for experiment manager "exp_manager": { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", "kwargs": { "uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment", }, }, # Default config for MongoDB "mongo": { "task_url": "mongodb://localhost:27017/", "task_db_name": "default_task_db", }, # Shift minute for highfreq minite data, used in backtest # if min_data_shift == 0, use default market time [9:30, 11:29, 1:00, 2:59] # if min_data_shift != 0, use shifted market time [9:30, 11:29, 1:00, 2:59] - shift*minute "min_data_shift": 0, } MODE_CONF = { "server": { # data provider config "calendar_provider": "LocalCalendarProvider", "instrument_provider": "LocalInstrumentProvider", "feature_provider": "LocalFeatureProvider", "expression_provider": "LocalExpressionProvider", "dataset_provider": "LocalDatasetProvider", "provider": "LocalProvider", # config it in qlib.init() "provider_uri": "", # redis "redis_host": "127.0.0.1", "redis_port": 6379, "redis_task_db": 1, "kernels": NUM_USABLE_CPU, # cache "expression_cache": DISK_EXPRESSION_CACHE, "dataset_cache": DISK_DATASET_CACHE, "local_cache_path": Path("~/.cache/qlib_simple_cache").expanduser().resolve(), "mount_path": None, }, "client": { # data provider config "calendar_provider": "LocalCalendarProvider", "instrument_provider": "LocalInstrumentProvider", "feature_provider": "LocalFeatureProvider", "expression_provider": "LocalExpressionProvider", "dataset_provider": "LocalDatasetProvider", "provider": "LocalProvider", # config it in user's own code "provider_uri": "~/.qlib/qlib_data/cn_data", # cache # Using parameter 'remote' to announce the client is using server_cache, and the writing access will be disabled. # Disable cache by default. Avoid introduce advanced features for beginners "expression_cache": None, "dataset_cache": None, # SimpleDatasetCache directory "local_cache_path": Path("~/.cache/qlib_simple_cache").expanduser().resolve(), "calendar_cache": None, # client config "kernels": NUM_USABLE_CPU, "mount_path": None, "auto_mount": False, # The nfs is already mounted on our server[auto_mount: False]. # The nfs should be auto-mounted by qlib on other # serversS(such as PAI) [auto_mount:True] "timeout": 100, "logging_level": logging.INFO, "region": REG_CN, # custom operator # each element of custom_ops should be Type[ExpressionOps] or dict # if element of custom_ops is Type[ExpressionOps], it represents the custom operator class # if element of custom_ops is dict, it represents the config of custom operator and should include `class` and `module_path` keys. "custom_ops": [], }, } HIGH_FREQ_CONFIG = { "provider_uri": "~/.qlib/qlib_data/cn_data_1min", "dataset_cache": None, "expression_cache": "DiskExpressionCache", "region": REG_CN, } _default_region_config = { REG_CN: { "trade_unit": 100, "limit_threshold": 0.095, "deal_price": "close", }, REG_US: { "trade_unit": 1, "limit_threshold": None, "deal_price": "close", }, REG_TW: { "trade_unit": 1000, "limit_threshold": 0.1, "deal_price": "close", }, } class QlibConfig(Config): # URI_TYPE LOCAL_URI = "local" NFS_URI = "nfs" DEFAULT_FREQ = "__DEFAULT_FREQ" def __init__(self, default_conf): super().__init__(default_conf) self._registered = False class DataPathManager: """ Motivation: - get the right path (e.g. data uri) for accessing data based on given information(e.g. provider_uri, mount_path and frequency) - some helper functions to process uri. """ def __init__(self, provider_uri: Union[str, Path, dict], mount_path: Union[str, Path, dict]): """ The relation of `provider_uri` and `mount_path` - `mount_path` is used only if provider_uri is an NFS path - otherwise, provider_uri will be used for accessing data """ self.provider_uri = provider_uri self.mount_path = mount_path @staticmethod def format_provider_uri(provider_uri: Union[str, dict, Path]) -> dict: if provider_uri is None: raise ValueError("provider_uri cannot be None") if isinstance(provider_uri, (str, dict, Path)): if not isinstance(provider_uri, dict): provider_uri = {QlibConfig.DEFAULT_FREQ: provider_uri} else: raise TypeError(f"provider_uri does not support {type(provider_uri)}") for freq, _uri in provider_uri.items(): if QlibConfig.DataPathManager.get_uri_type(_uri) == QlibConfig.LOCAL_URI: provider_uri[freq] = str(Path(_uri).expanduser().resolve()) return provider_uri @staticmethod def get_uri_type(uri: Union[str, Path]): uri = uri if isinstance(uri, str) else str(uri.expanduser().resolve()) is_win = re.match("^[a-zA-Z]:.*", uri) is not None # such as 'C:\\data', 'D:' # such as 'host:/data/' (User may define short hostname by themselves or use localhost) is_nfs_or_win = re.match("^[^/]+:.+", uri) is not None if is_nfs_or_win and not is_win: return QlibConfig.NFS_URI else: return QlibConfig.LOCAL_URI def get_data_uri(self, freq: Optional[Union[str, Freq]] = None) -> Path: """ please refer DataPathManager's __init__ and class doc """ if freq is not None: freq = str(freq) # converting Freq to string if freq is None or freq not in self.provider_uri: freq = QlibConfig.DEFAULT_FREQ _provider_uri = self.provider_uri[freq] if self.get_uri_type(_provider_uri) == QlibConfig.LOCAL_URI: return Path(_provider_uri) elif self.get_uri_type(_provider_uri) == QlibConfig.NFS_URI: if "win" in platform.system().lower(): # windows, mount_path is the drive _path = str(self.mount_path[freq]) return Path(f"{_path}:\\") if ":" not in _path else Path(_path) return Path(self.mount_path[freq]) else: raise NotImplementedError(f"This type of uri is not supported") def set_mode(self, mode): # raise KeyError self.update(MODE_CONF[mode]) # TODO: update region based on kwargs def set_region(self, region): # raise KeyError self.update(_default_region_config[region]) @staticmethod def is_depend_redis(cache_name: str): return cache_name in DEPENDENCY_REDIS_CACHE @property def dpm(self): return self.DataPathManager(self["provider_uri"], self["mount_path"]) def resolve_path(self): # resolve path _mount_path = self["mount_path"] _provider_uri = self.DataPathManager.format_provider_uri(self["provider_uri"]) if not isinstance(_mount_path, dict): _mount_path = {_freq: _mount_path for _freq in _provider_uri.keys()} # check provider_uri and mount_path _miss_freq = set(_provider_uri.keys()) - set(_mount_path.keys()) assert len(_miss_freq) == 0, f"mount_path is missing freq: {_miss_freq}" # resolve for _freq in _provider_uri.keys(): # mount_path _mount_path[_freq] = ( _mount_path[_freq] if _mount_path[_freq] is None else str(Path(_mount_path[_freq]).expanduser()) ) self["provider_uri"] = _provider_uri self["mount_path"] = _mount_path def set(self, default_conf: str = "client", **kwargs): """ configure qlib based on the input parameters The configuration will act like a dictionary. Normally, it literally is replaced the value according to the keys. However, sometimes it is hard for users to set the config when the configuration is nested and complicated So this API provides some special parameters for users to set the keys in a more convenient way. - region: REG_CN, REG_US - several region-related config will be changed Parameters ---------- default_conf : str the default config template chosen by user: "server", "client" """ from .utils import set_log_with_config, get_module_logger, can_use_cache # pylint: disable=C0415 self.reset() _logging_config = kwargs.get("logging_config", self.logging_config) # set global config if _logging_config: set_log_with_config(_logging_config) # FIXME: this logger ignored the level in config logger = get_module_logger("Initialization", level=logging.INFO) logger.info(f"default_conf: {default_conf}.") self.set_mode(default_conf) self.set_region(kwargs.get("region", self["region"] if "region" in self else REG_CN)) for k, v in kwargs.items(): if k not in self: logger.warning("Unrecognized config %s" % k) self[k] = v self.resolve_path() if not (self["expression_cache"] is None and self["dataset_cache"] is None): # check redis if not can_use_cache(): log_str = "" # check expression cache if self.is_depend_redis(self["expression_cache"]): log_str += self["expression_cache"] self["expression_cache"] = None # check dataset cache if self.is_depend_redis(self["dataset_cache"]): log_str += f" and {self['dataset_cache']}" if log_str else self["dataset_cache"] self["dataset_cache"] = None if log_str: logger.warning( f"redis connection failed(host={self['redis_host']} port={self['redis_port']}), " f"{log_str} will not be used!" ) def register(self): from .utils import init_instance_by_config # pylint: disable=C0415 from .data.ops import register_all_ops # pylint: disable=C0415 from .data.data import register_all_wrappers # pylint: disable=C0415 from .workflow import R, QlibRecorder # pylint: disable=C0415 from .workflow.utils import experiment_exit_handler # pylint: disable=C0415 register_all_ops(self) register_all_wrappers(self) # set up QlibRecorder exp_manager = init_instance_by_config(self["exp_manager"]) qr = QlibRecorder(exp_manager) R.register(qr) # clean up experiment when python program ends experiment_exit_handler() # Supporting user reset qlib version (useful when user want to connect to qlib server with old version) self.reset_qlib_version() self._registered = True def reset_qlib_version(self): import qlib # pylint: disable=C0415 reset_version = self.get("qlib_reset_version", None) if reset_version is not None: qlib.__version__ = reset_version else: qlib.__version__ = getattr(qlib, "__version__bak") # Due to a bug? that converting __version__ to _QlibConfig__version__bak # Using __version__bak instead of __version__ def get_kernels(self, freq: str): """get number of processors given frequency""" if isinstance(self["kernels"], Callable): return self["kernels"](freq) return self["kernels"] @property def registered(self): return self._registered # global config C = QlibConfig(_default_config)

36.418219

138

0.620523

dcbed37e387d963bef6365f7bf8074e902cdf13c

20,600

py

Python

chemprop/web/app/views.py

anonymous20201002/chemprop

3e36f6a3bb36194366feadb31be94dfc7e98fd91

[ "MIT" ]

1

2022-02-12T06:39:32.000Z

chemprop/web/app/views.py

anonymous20201002/chemprop

3e36f6a3bb36194366feadb31be94dfc7e98fd91

[ "MIT" ]

null

chemprop/web/app/views.py

anonymous20201002/chemprop

3e36f6a3bb36194366feadb31be94dfc7e98fd91

[ "MIT" ]

null

"""Defines a number of routes/views for the flask app.""" from functools import wraps import io import os import sys import shutil from tempfile import TemporaryDirectory, NamedTemporaryFile import time from typing import Callable, List, Tuple import multiprocessing as mp import zipfile from flask import json, jsonify, redirect, render_template, request, send_file, send_from_directory, url_for import numpy as np from rdkit import Chem from werkzeug.utils import secure_filename from chemprop.web.app import app, db sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))) from chemprop.args import PredictArgs, TrainArgs from chemprop.constants import MODEL_FILE_NAME, TRAIN_LOGGER_NAME from chemprop.data import get_data, get_header, get_smiles, validate_data from chemprop.train import make_predictions, run_training from chemprop.utils import create_logger, load_task_names, load_args TRAINING = 0 PROGRESS = mp.Value('d', 0.0) def check_not_demo(func: Callable) -> Callable: """ View wrapper, which will redirect request to site homepage if app is run in DEMO mode. :param func: A view which performs sensitive behavior. :return: A view with behavior adjusted based on DEMO flag. """ @wraps(func) def decorated_function(*args, **kwargs): if app.config['DEMO']: return redirect(url_for('home')) return func(*args, **kwargs) return decorated_function def progress_bar(args: TrainArgs, progress: mp.Value): """ Updates a progress bar displayed during training. :param args: Arguments. :param progress: The current progress. """ # no code to handle crashes in model training yet, though current_epoch = -1 while current_epoch < args.epochs - 1: if os.path.exists(os.path.join(args.save_dir, 'verbose.log')): with open(os.path.join(args.save_dir, 'verbose.log'), 'r') as f: content = f.read() if 'Epoch ' + str(current_epoch + 1) in content: current_epoch += 1 progress.value = (current_epoch + 1) * 100 / args.epochs else: pass time.sleep(0) def find_unused_path(path: str) -> str: """ Given an initial path, finds an unused path by appending different numbers to the filename. :param path: An initial path. :return: An unused path. """ if not os.path.exists(path): return path base_name, ext = os.path.splitext(path) i = 2 while os.path.exists(path): path = base_name + str(i) + ext i += 1 return path def name_already_exists_message(thing_being_named: str, original_name: str, new_name: str) -> str: """ Creates a message about a path already existing and therefore being renamed. :param thing_being_named: The thing being renamed (ex. Data, Checkpoint). :param original_name: The original name of the object. :param new_name: The new name of the object. :return: A string with a message about the changed name. """ return f'{thing_being_named} "{original_name} already exists. ' \ f'Saving to "{new_name}".' def get_upload_warnings_errors(upload_item: str) -> Tuple[List[str], List[str]]: """ Gets any upload warnings passed along in the request. :param upload_item: The thing being uploaded (ex. Data, Checkpoint). :return: A tuple with a list of warning messages and a list of error messages. """ warnings_raw = request.args.get(f'{upload_item}_upload_warnings') errors_raw = request.args.get(f'{upload_item}_upload_errors') warnings = json.loads(warnings_raw) if warnings_raw is not None else None errors = json.loads(errors_raw) if errors_raw is not None else None return warnings, errors def format_float(value: float, precision: int = 4) -> str: """ Formats a float value to a specific precision. :param value: The float value to format. :param precision: The number of decimal places to use. :return: A string containing the formatted float. """ return f'{value:.{precision}f}' def format_float_list(array: List[float], precision: int = 4) -> List[str]: """ Formats a list of float values to a specific precision. :param array: A list of float values to format. :param precision: The number of decimal places to use. :return: A list of strings containing the formatted floats. """ return [format_float(f, precision) for f in array] @app.route('/receiver', methods=['POST']) @check_not_demo def receiver(): """Receiver monitoring the progress of training.""" return jsonify(progress=PROGRESS.value, training=TRAINING) @app.route('/') def home(): """Renders the home page.""" return render_template('home.html', users=db.get_all_users()) @app.route('/create_user', methods=['GET', 'POST']) @check_not_demo def create_user(): """ If a POST request is made, creates a new user. Renders the create_user page. """ if request.method == 'GET': return render_template('create_user.html', users=db.get_all_users()) new_name = request.form['newUserName'] if new_name != None: db.insert_user(new_name) return redirect(url_for('create_user')) def render_train(**kwargs): """Renders the train page with specified kwargs.""" data_upload_warnings, data_upload_errors = get_upload_warnings_errors('data') return render_template('train.html', datasets=db.get_datasets(request.cookies.get('currentUser')), cuda=app.config['CUDA'], gpus=app.config['GPUS'], data_upload_warnings=data_upload_warnings, data_upload_errors=data_upload_errors, users=db.get_all_users(), **kwargs) @app.route('/train', methods=['GET', 'POST']) @check_not_demo def train(): """Renders the train page and performs training if request method is POST.""" global PROGRESS, TRAINING warnings, errors = [], [] if request.method == 'GET': return render_train() # Get arguments data_name, epochs, ensemble_size, checkpoint_name = \ request.form['dataName'], int(request.form['epochs']), \ int(request.form['ensembleSize']), request.form['checkpointName'] gpu = request.form.get('gpu') data_path = os.path.join(app.config['DATA_FOLDER'], f'{data_name}.csv') dataset_type = request.form.get('datasetType', 'regression') # Create and modify args args = TrainArgs().parse_args([ '--data_path', data_path, '--dataset_type', dataset_type, '--epochs', str(epochs), '--ensemble_size', str(ensemble_size) ]) # Check if regression/classification selection matches data data = get_data(path=data_path) targets = data.targets() unique_targets = {target for row in targets for target in row if target is not None} if dataset_type == 'classification' and len(unique_targets - {0, 1}) > 0: errors.append('Selected classification dataset but not all labels are 0 or 1. Select regression instead.') return render_train(warnings=warnings, errors=errors) if dataset_type == 'regression' and unique_targets <= {0, 1}: errors.append('Selected regression dataset but all labels are 0 or 1. Select classification instead.') return render_train(warnings=warnings, errors=errors) if gpu is not None: if gpu == 'None': args.cuda = False else: args.gpu = int(gpu) current_user = request.cookies.get('currentUser') if not current_user: # Use DEFAULT as current user if the client's cookie is not set. current_user = app.config['DEFAULT_USER_ID'] ckpt_id, ckpt_name = db.insert_ckpt(checkpoint_name, current_user, args.dataset_type, args.epochs, args.ensemble_size, len(targets)) with TemporaryDirectory() as temp_dir: args.save_dir = temp_dir process = mp.Process(target=progress_bar, args=(args, PROGRESS)) process.start() TRAINING = 1 # Run training logger = create_logger(name=TRAIN_LOGGER_NAME, save_dir=args.save_dir, quiet=args.quiet) task_scores = run_training(args, logger) process.join() # Reset globals TRAINING = 0 PROGRESS = mp.Value('d', 0.0) # Check if name overlap if checkpoint_name != ckpt_name: warnings.append(name_already_exists_message('Checkpoint', checkpoint_name, ckpt_name)) # Move models for root, _, files in os.walk(args.save_dir): for fname in files: if fname.endswith('.pt'): model_id = db.insert_model(ckpt_id) save_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model_id}.pt') shutil.move(os.path.join(args.save_dir, root, fname), save_path) return render_train(trained=True, metric=args.metric, num_tasks=len(args.task_names), task_names=args.task_names, task_scores=format_float_list(task_scores), mean_score=format_float(np.mean(task_scores)), warnings=warnings, errors=errors) def render_predict(**kwargs): """Renders the predict page with specified kwargs""" checkpoint_upload_warnings, checkpoint_upload_errors = get_upload_warnings_errors('checkpoint') return render_template('predict.html', checkpoints=db.get_ckpts(request.cookies.get('currentUser')), cuda=app.config['CUDA'], gpus=app.config['GPUS'], checkpoint_upload_warnings=checkpoint_upload_warnings, checkpoint_upload_errors=checkpoint_upload_errors, users=db.get_all_users(), **kwargs) @app.route('/predict', methods=['GET', 'POST']) def predict(): """Renders the predict page and makes predictions if the method is POST.""" if request.method == 'GET': return render_predict() # Get arguments ckpt_id = request.form['checkpointName'] if request.form['textSmiles'] != '': smiles = request.form['textSmiles'].split() elif request.form['drawSmiles'] != '': smiles = [request.form['drawSmiles']] else: # Upload data file with SMILES data = request.files['data'] data_name = secure_filename(data.filename) data_path = os.path.join(app.config['TEMP_FOLDER'], data_name) data.save(data_path) # Check if header is smiles possible_smiles = get_header(data_path)[0] smiles = [possible_smiles] if Chem.MolFromSmiles(possible_smiles) is not None else [] # Get remaining smiles smiles.extend(get_smiles(data_path)) models = db.get_models(ckpt_id) model_paths = [os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model["id"]}.pt') for model in models] task_names = load_task_names(model_paths[0]) num_tasks = len(task_names) gpu = request.form.get('gpu') train_args = load_args(model_paths[0]) # Build arguments arguments = [ '--test_path', 'None', '--preds_path', os.path.join(app.config['TEMP_FOLDER'], app.config['PREDICTIONS_FILENAME']), '--checkpoint_paths', *model_paths ] if gpu is not None: if gpu == 'None': arguments.append('--no_cuda') else: arguments += ['--gpu', gpu] # Handle additional features if train_args.features_path is not None: # TODO: make it possible to specify the features generator if trained using features_path arguments += [ '--features_generator', 'rdkit_2d_normalized', '--no_features_scaling' ] elif train_args.features_generator is not None: arguments += ['--features_generator', *train_args.features_generator] if not train_args.features_scaling: arguments.append('--no_features_scaling') # Parse arguments args = PredictArgs().parse_args(arguments) # Run predictions preds = make_predictions(args=args, smiles=smiles) if all(p is None for p in preds): return render_predict(errors=['All SMILES are invalid']) # Replace invalid smiles with message invalid_smiles_warning = 'Invalid SMILES String' preds = [pred if pred is not None else [invalid_smiles_warning] * num_tasks for pred in preds] return render_predict(predicted=True, smiles=smiles, num_smiles=min(10, len(smiles)), show_more=max(0, len(smiles)-10), task_names=task_names, num_tasks=len(task_names), preds=preds, warnings=["List contains invalid SMILES strings"] if None in preds else None, errors=["No SMILES strings given"] if len(preds) == 0 else None) @app.route('/download_predictions') def download_predictions(): """Downloads predictions as a .csv file.""" return send_from_directory(app.config['TEMP_FOLDER'], app.config['PREDICTIONS_FILENAME'], as_attachment=True, cache_timeout=-1) @app.route('/data') @check_not_demo def data(): """Renders the data page.""" data_upload_warnings, data_upload_errors = get_upload_warnings_errors('data') return render_template('data.html', datasets=db.get_datasets(request.cookies.get('currentUser')), data_upload_warnings=data_upload_warnings, data_upload_errors=data_upload_errors, users=db.get_all_users()) @app.route('/data/upload/<string:return_page>', methods=['POST']) @check_not_demo def upload_data(return_page: str): """ Uploads a data .csv file. :param return_page: The name of the page to render to after uploading the dataset. """ warnings, errors = [], [] current_user = request.cookies.get('currentUser') if not current_user: # Use DEFAULT as current user if the client's cookie is not set. current_user = app.config['DEFAULT_USER_ID'] dataset = request.files['dataset'] with NamedTemporaryFile() as temp_file: dataset.save(temp_file.name) dataset_errors = validate_data(temp_file.name) if len(dataset_errors) > 0: errors.extend(dataset_errors) else: dataset_name = request.form['datasetName'] # dataset_class = load_args(ckpt).dataset_type # TODO: SWITCH TO ACTUALLY FINDING THE CLASS dataset_id, new_dataset_name = db.insert_dataset(dataset_name, current_user, 'UNKNOWN') dataset_path = os.path.join(app.config['DATA_FOLDER'], f'{dataset_id}.csv') if dataset_name != new_dataset_name: warnings.append(name_already_exists_message('Data', dataset_name, new_dataset_name)) shutil.copy(temp_file.name, dataset_path) warnings, errors = json.dumps(warnings), json.dumps(errors) return redirect(url_for(return_page, data_upload_warnings=warnings, data_upload_errors=errors)) @app.route('/data/download/<int:dataset>') @check_not_demo def download_data(dataset: int): """ Downloads a dataset as a .csv file. :param dataset: The id of the dataset to download. """ return send_from_directory(app.config['DATA_FOLDER'], f'{dataset}.csv', as_attachment=True, cache_timeout=-1) @app.route('/data/delete/<int:dataset>') @check_not_demo def delete_data(dataset: int): """ Deletes a dataset. :param dataset: The id of the dataset to delete. """ db.delete_dataset(dataset) os.remove(os.path.join(app.config['DATA_FOLDER'], f'{dataset}.csv')) return redirect(url_for('data')) @app.route('/checkpoints') @check_not_demo def checkpoints(): """Renders the checkpoints page.""" checkpoint_upload_warnings, checkpoint_upload_errors = get_upload_warnings_errors('checkpoint') return render_template('checkpoints.html', checkpoints=db.get_ckpts(request.cookies.get('currentUser')), checkpoint_upload_warnings=checkpoint_upload_warnings, checkpoint_upload_errors=checkpoint_upload_errors, users=db.get_all_users()) @app.route('/checkpoints/upload/<string:return_page>', methods=['POST']) @check_not_demo def upload_checkpoint(return_page: str): """ Uploads a checkpoint .pt file. :param return_page: The name of the page to render after uploading the checkpoint file. """ warnings, errors = [], [] current_user = request.cookies.get('currentUser') if not current_user: # Use DEFAULT as current user if the client's cookie is not set. current_user = app.config['DEFAULT_USER_ID'] ckpt = request.files['checkpoint'] ckpt_name = request.form['checkpointName'] ckpt_ext = os.path.splitext(ckpt.filename)[1] # Collect paths to all uploaded checkpoints (and unzip if necessary) temp_dir = TemporaryDirectory() ckpt_paths = [] if ckpt_ext.endswith('.pt'): ckpt_path = os.path.join(temp_dir.name, MODEL_FILE_NAME) ckpt.save(ckpt_path) ckpt_paths = [ckpt_path] elif ckpt_ext.endswith('.zip'): ckpt_dir = os.path.join(temp_dir.name, 'models') zip_path = os.path.join(temp_dir.name, 'models.zip') ckpt.save(zip_path) with zipfile.ZipFile(zip_path, mode='r') as z: z.extractall(ckpt_dir) for root, _, fnames in os.walk(ckpt_dir): ckpt_paths += [os.path.join(root, fname) for fname in fnames if fname.endswith('.pt')] else: errors.append(f'Uploaded checkpoint(s) file must be either .pt or .zip but got {ckpt_ext}') # Insert checkpoints into database if len(ckpt_paths) > 0: ckpt_args = load_args(ckpt_paths[0]) ckpt_id, new_ckpt_name = db.insert_ckpt(ckpt_name, current_user, ckpt_args.dataset_type, ckpt_args.epochs, len(ckpt_paths), ckpt_args.train_data_size) for ckpt_path in ckpt_paths: model_id = db.insert_model(ckpt_id) model_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model_id}.pt') if ckpt_name != new_ckpt_name: warnings.append(name_already_exists_message('Checkpoint', ckpt_name, new_ckpt_name)) shutil.copy(ckpt_path, model_path) temp_dir.cleanup() warnings, errors = json.dumps(warnings), json.dumps(errors) return redirect(url_for(return_page, checkpoint_upload_warnings=warnings, checkpoint_upload_errors=errors)) @app.route('/checkpoints/download/<int:checkpoint>') @check_not_demo def download_checkpoint(checkpoint: int): """ Downloads a zip of model .pt files. :param checkpoint: The name of the checkpoint to download. """ ckpt = db.query_db(f'SELECT * FROM ckpt WHERE id = {checkpoint}', one = True) models = db.get_models(checkpoint) model_data = io.BytesIO() with zipfile.ZipFile(model_data, mode='w') as z: for model in models: model_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model["id"]}.pt') z.write(model_path, os.path.basename(model_path)) model_data.seek(0) return send_file( model_data, mimetype='application/zip', as_attachment=True, attachment_filename=f'{ckpt["ckpt_name"]}.zip', cache_timeout=-1 ) @app.route('/checkpoints/delete/<int:checkpoint>') @check_not_demo def delete_checkpoint(checkpoint: int): """ Deletes a checkpoint file. :param checkpoint: The id of the checkpoint to delete. """ db.delete_ckpt(checkpoint) return redirect(url_for('checkpoints'))

34.915254

131

0.637816

72da179ad533a686b2d00e372789b251044cfc82

26,773

py

Python

src/utils/inference_utils.py

hynekdav/semi-supervised-VOS

6b29baef2e4fd018502fb434e978e8e924fb84b1

[ "MIT" ]

null

src/utils/inference_utils.py

hynekdav/semi-supervised-VOS

6b29baef2e4fd018502fb434e978e8e924fb84b1

[ "MIT" ]

2

2022-01-13T03:45:31.000Z

2022-03-12T00:57:40.000Z

src/utils/inference_utils.py

hynekdav/semi-supervised-VOS

6b29baef2e4fd018502fb434e978e8e924fb84b1

[ "MIT" ]

null

# -*- encoding: utf-8 -*- # ! python3 from __future__ import annotations from __future__ import generator_stop import numpy as np import torch import torch.nn.functional as F from tqdm import tqdm from src.config import Config from src.model.predict import prepare_first_frame, predict from src.utils.transforms import hflip from src.utils.utils import save_predictions, index_to_onehot REDUCTIONS = {'maximum': lambda x, y: torch.maximum(x, y), 'minimum': lambda x, y: torch.minimum(x, y), 'mean': lambda x, y: (x + y) / 2.0} def inference_single(model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, disable): global pred_visualize, palette, feats_history, label_history, weight_dense, weight_sparse, d frame_idx = 0 for input, (current_video,) in tqdm(inference_loader, total=total_len, disable=disable): if current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) frame_idx = 0 if frame_idx == 0: input = input.to(Config.DEVICE) with torch.cuda.amp.autocast(): feats_history = model(input) first_annotation = annotation_dir / current_video / '00000.png' label_history, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='single', probability_propagation=probability_propagation) frame_idx += 1 last_video = current_video continue (batch_size, num_channels, H, W) = input.shape input = input.to(Config.DEVICE) with torch.cuda.amp.autocast(): features = model(input) (_, feature_dim, H_d, W_d) = features.shape prediction = predict(feats_history, features[0], label_history, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label = prediction.unsqueeze(1) else: new_label = index_to_onehot(torch.argmax(prediction, 0), d).unsqueeze(1) label_history = torch.cat((label_history, new_label), 1) feats_history = torch.cat((feats_history, features), 0) prediction = torch.nn.functional.interpolate(prediction.view(1, d, H_d, W_d), size=(H, W), mode='nearest') prediction = torch.argmax(prediction, 1).cpu() # (1, H, W) last_video = current_video frame_idx += 1 if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) # save last video's prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) def inference_hor_flip(model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, reduction_str, disable): global pred_visualize, palette, feats_history_l, label_history_l, weight_dense, weight_sparse, feats_history_r, label_history_r, d frame_idx = 0 for input, (current_video,) in tqdm(inference_loader, total=total_len, disable=disable): if current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) frame_idx = 0 if frame_idx == 0: input_l = input[0].to(Config.DEVICE) input_r = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): feats_history_l = model(input_l) feats_history_r = model(input_r) first_annotation = annotation_dir / current_video / '00000.png' label_history_l, label_history_r, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='hor-flip', probability_propagation=probability_propagation) frame_idx += 1 last_video = current_video continue (batch_size, num_channels, H, W) = input[0].shape input_l = input[0].to(Config.DEVICE) input_r = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): features_l = model(input_l) features_r = model(input_r) (_, feature_dim, H_d, W_d) = features_l.shape prediction_l = predict(feats_history_l, features_l[0], label_history_l, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_l = prediction_l.unsqueeze(1) else: new_label_l = index_to_onehot(torch.argmax(prediction_l, 0), d).unsqueeze(1) label_history_l = torch.cat((label_history_l, new_label_l), 1) feats_history_l = torch.cat((feats_history_l, features_l), 0) prediction_l = torch.nn.functional.interpolate(prediction_l.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_l = torch.argmax(prediction_l, 1).squeeze() # (1, H, W) prediction_r = predict(feats_history_r, features_r[0], label_history_r, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_r = prediction_r.unsqueeze(1) else: new_label_r = index_to_onehot(torch.argmax(prediction_r, 0), d).unsqueeze(1) label_history_r = torch.cat((label_history_r, new_label_r), 1) feats_history_r = torch.cat((feats_history_r, features_r), 0) # 1. upsample, 2. argmax prediction_r = F.interpolate(prediction_r.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_r = torch.argmax(prediction_r, 1).squeeze() # (1, H, W) prediction_r = torch.fliplr(prediction_r).cpu() prediction_l = prediction_l.cpu() last_video = current_video frame_idx += 1 if probability_propagation: reduction = REDUCTIONS.get(reduction_str) prediction = reduction(prediction_l, prediction_r).cpu().half() prediction = torch.argmax(prediction, 1).cpu() # (1, H, W) else: prediction = torch.maximum(prediction_l, prediction_r).unsqueeze(0).cpu().half() if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) # save last video's prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) def inference_ver_flip(model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, reduction_str, disable): global pred_visualize, palette, feats_history_l, label_history_l, weight_dense, weight_sparse, feats_history_r, label_history_r, d frame_idx = 0 for input, (current_video,) in tqdm(inference_loader, total=total_len, disable=disable): if current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) frame_idx = 0 if frame_idx == 0: input_l = input[0].to(Config.DEVICE) input_r = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): feats_history_l = model(input_l) feats_history_r = model(input_r) first_annotation = annotation_dir / current_video / '00000.png' label_history_l, label_history_r, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='ver-flip', probability_propagation=probability_propagation) frame_idx += 1 last_video = current_video continue (batch_size, num_channels, H, W) = input[0].shape input_l = input[0].to(Config.DEVICE) input_r = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): features_l = model(input_l) features_r = model(input_r) (_, feature_dim, H_d, W_d) = features_l.shape prediction_l = predict(feats_history_l, features_l[0], label_history_l, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_l = prediction_l.unsqueeze(1) else: new_label_l = index_to_onehot(torch.argmax(prediction_l, 0), d).unsqueeze(1) label_history_l = torch.cat((label_history_l, new_label_l), 1) feats_history_l = torch.cat((feats_history_l, features_l), 0) prediction_l = torch.nn.functional.interpolate(prediction_l.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_l = torch.argmax(prediction_l, 1).squeeze() # (1, H, W) prediction_r = predict(feats_history_r, features_r[0], label_history_r, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_r = prediction_r.unsqueeze(1) else: new_label_r = index_to_onehot(torch.argmax(prediction_r, 0), d).unsqueeze(1) label_history_r = torch.cat((label_history_r, new_label_r), 1) feats_history_r = torch.cat((feats_history_r, features_r), 0) # 1. upsample, 2. argmax prediction_r = F.interpolate(prediction_r.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_r = torch.argmax(prediction_r, 1).squeeze() # (1, H, W) prediction_r = torch.fliplr(prediction_r).cpu() prediction_l = prediction_l.cpu() last_video = current_video frame_idx += 1 if probability_propagation: reduction = REDUCTIONS.get(reduction_str) prediction = reduction(prediction_l, prediction_r).cpu().half() prediction = torch.argmax(prediction, 1).cpu() # (1, H, W) else: prediction = torch.maximum(prediction_l, prediction_r).unsqueeze(0).cpu().half() if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) # save last video's prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) def inference_2_scale(model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, scale, reduction_str, flip_pred, disable): global pred_visualize, palette, feats_history_o, label_history_o, weight_dense_o, weight_sparse_o, feats_history_u, label_history_u, weight_dense_u, weight_sparse_u, d frame_idx = 0 for input, (current_video,) in tqdm(inference_loader, total=total_len, disable=disable): if current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) frame_idx = 0 if frame_idx == 0: input_o = input[0].to(Config.DEVICE) input_u = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): feats_history_o = model(input_o) feats_history_u = model(input_u) first_annotation = annotation_dir / current_video / '00000.png' label_history, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='2-scale', probability_propagation=probability_propagation, scale=scale) frame_idx += 1 last_video = current_video label_history_o, label_history_u = label_history weight_dense_o, weight_dense_u = weight_dense weight_sparse_o, weight_sparse_u = weight_sparse continue (_, _, H, W) = input[0].shape input_o = input[0].to(Config.DEVICE) input_u = input[1].to(Config.DEVICE) with torch.cuda.amp.autocast(): features_o = model(input_o) features_u = model(input_u) (_, feature_dim, H_d, W_d) = features_o.shape prediction_o = predict(feats_history_o, features_o[0], label_history_o, weight_dense_o, weight_sparse_o, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_o = prediction_o.unsqueeze(1) else: new_label_o = index_to_onehot(torch.argmax(prediction_o, 0), d).unsqueeze(1) label_history_o = torch.cat((label_history_o, new_label_o), 1) feats_history_o = torch.cat((feats_history_o, features_o), 0) prediction_o = torch.nn.functional.interpolate(prediction_o.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_o = torch.argmax(prediction_o, 1).cpu() # (1, H, W) (_, feature_dim, H_d, W_d) = features_u.shape prediction_u = predict(feats_history_u, features_u[0], label_history_u, weight_dense_u, weight_sparse_u, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_u = prediction_u.unsqueeze(1) else: new_label_u = index_to_onehot(torch.argmax(prediction_u, 0), d).unsqueeze(1) label_history_u = torch.cat((label_history_u, new_label_u), 1) feats_history_u = torch.cat((feats_history_u, features_u), 0) prediction_u = torch.nn.functional.interpolate(prediction_u.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_u = torch.argmax(prediction_u, 1).cpu() # (1, H, W) if flip_pred: prediction_u = hflip(prediction_u) if probability_propagation: reduction = REDUCTIONS.get(reduction_str) prediction = reduction(prediction_o, prediction_u).cpu().half() prediction = torch.argmax(prediction, 1).cpu() # (1, H, W) else: prediction = torch.maximum(prediction_o, prediction_u).cpu().half() last_video = current_video frame_idx += 1 if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) # save last video's prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) def inference_multimodel(model, additional_model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, reduction_str, disable): global pred_visualize, label_history_a, feats_history_a, weight_sparse, weight_dense, label_history_o, feats_history_o, d, palette frame_idx = 0 for input, (current_video,) in tqdm(inference_loader, total=total_len, disable=disable): if current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) frame_idx = 0 if frame_idx == 0: input = input.to(Config.DEVICE) with torch.cuda.amp.autocast(): feats_history_o = model(input) feats_history_a = additional_model(input) first_annotation = annotation_dir / current_video / '00000.png' label_history, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='multimodel', probability_propagation=probability_propagation) frame_idx += 1 last_video = current_video label_history_o = label_history label_history_a = label_history continue (_, _, H, W) = input.shape input = input.to(Config.DEVICE) with torch.cuda.amp.autocast(): features_o = model(input) features_a = additional_model(input) (_, feature_dim, H_d, W_d) = features_o.shape prediction_o = predict(feats_history_o, features_o[0], label_history_o, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_o = prediction_o.unsqueeze(1) else: new_label_o = index_to_onehot(torch.argmax(prediction_o, 0), d).unsqueeze(1) label_history_o = torch.cat((label_history_o, new_label_o), 1) feats_history_o = torch.cat((feats_history_o, features_o), 0) prediction_o = torch.nn.functional.interpolate(prediction_o.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_o = torch.argmax(prediction_o, 1).cpu() # (1, H, W) (_, feature_dim, H_d, W_d) = features_a.shape prediction_a = predict(feats_history_a, features_a[0], label_history_a, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label_a = prediction_a.unsqueeze(1) else: new_label_a = index_to_onehot(torch.argmax(prediction_a, 0), d).unsqueeze(1) label_history_a = torch.cat((label_history_a, new_label_a), 1) feats_history_a = torch.cat((feats_history_a, features_a), 0) prediction_a = torch.nn.functional.interpolate(prediction_a.view(1, d, H_d, W_d), size=(H, W), mode='nearest') if not probability_propagation: prediction_a = torch.argmax(prediction_a, 1).cpu() # (1, H, W) if probability_propagation: reduction = REDUCTIONS.get(reduction_str) prediction = reduction(prediction_o, prediction_a).cpu().half() prediction = torch.argmax(prediction, 1).cpu() # (1, H, W) else: prediction = torch.maximum(prediction_o, prediction_a).cpu().half() last_video = current_video frame_idx += 1 if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) # save last video's prediction pred_visualize = pred_visualize.cpu().numpy() save_predictions(pred_visualize, palette, save, last_video) def inference_3_scale(model, inference_loader, total_len, annotation_dir, last_video, save, sigma_1, sigma_2, frame_range, ref_num, temperature, probability_propagation, scale, disable): global pred_visualize, palette, feats_history, label_history, weight_dense, weight_sparse, d, current_video scales = [0.9, 1.0, scale] predictions = {} palettes = [] for scale in scales: frame_idx = 0 for i, (input, (current_video,)) in tqdm(enumerate(inference_loader), total=total_len, disable=disable): (_, _, H, W) = input.shape H_d = int(np.ceil(H * scale)) W_d = int(np.ceil(W * scale)) input = torch.nn.functional.interpolate(input, size=(H_d, W_d), mode='nearest').to(Config.DEVICE) if i != 0 and current_video != last_video: # save prediction pred_visualize = pred_visualize.cpu().numpy() if last_video not in predictions: predictions[last_video] = [] predictions[last_video].append(pred_visualize) frame_idx = 0 if frame_idx == 0: with torch.cuda.amp.autocast(): feats_history = model(input) first_annotation = annotation_dir / current_video / '00000.png' label_history, d, palette, weight_dense, weight_sparse = prepare_first_frame( current_video, save, first_annotation, sigma_1, sigma_2, inference_strategy='3-scale', probability_propagation=probability_propagation, scale=scale) frame_idx += 1 last_video = current_video palettes.append(palette) continue with torch.cuda.amp.autocast(): features = model(input) (_, feature_dim, H_d, W_d) = features.shape prediction = predict(feats_history, features[0], label_history, weight_dense, weight_sparse, frame_idx, frame_range, ref_num, temperature, probability_propagation) # Store all frames' features if probability_propagation: new_label = prediction.unsqueeze(1) else: new_label = index_to_onehot(torch.argmax(prediction, 0), d).unsqueeze(1) label_history = torch.cat((label_history, new_label), 1) feats_history = torch.cat((feats_history, features), 0) prediction = torch.nn.functional.interpolate(prediction.view(1, d, H_d, W_d), size=(480, 910), mode='nearest') prediction = torch.argmax(prediction, 1).cpu().type(torch.int8) # (1, H, W) last_video = current_video frame_idx += 1 if frame_idx == 2: pred_visualize = prediction else: pred_visualize = torch.cat((pred_visualize, prediction), 0) pred_visualize = pred_visualize.cpu().numpy() if current_video not in predictions: predictions[current_video] = [] predictions[current_video].append(pred_visualize) pred_visualize = None for (video_name, frames), palette in tqdm(zip(predictions.items(), palettes), desc='Saving', total=len(predictions)): prediction = np.maximum(np.maximum(frames[0], frames[1]), frames[2]) save_predictions(prediction, palette, save, video_name)

44.921141

171

0.563852

bf8626c52f23cfd6c0ccee0c6b673808dea7b45c

10,926

py

Python

sympy/printing/repr.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

[ "BSD-3-Clause" ]

2

2021-02-16T14:20:37.000Z

2021-02-16T16:37:47.000Z

sympy/printing/repr.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

[ "BSD-3-Clause" ]

null

sympy/printing/repr.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

[ "BSD-3-Clause" ]

1

2020-03-06T15:18:46.000Z

""" A Printer for generating executable code. The most important function here is srepr that returns a string so that the relation eval(srepr(expr))=expr holds in an appropriate environment. """ from __future__ import print_function, division from typing import Any, Dict from sympy.core.function import AppliedUndef from mpmath.libmp import repr_dps, to_str as mlib_to_str from .printer import Printer class ReprPrinter(Printer): printmethod = "_sympyrepr" _default_settings = { "order": None, "perm_cyclic" : True, } # type: Dict[str, Any] def reprify(self, args, sep): """ Prints each item in `args` and joins them with `sep`. """ return sep.join([self.doprint(item) for item in args]) def emptyPrinter(self, expr): """ The fallback printer. """ if isinstance(expr, str): return expr elif hasattr(expr, "__srepr__"): return expr.__srepr__() elif hasattr(expr, "args") and hasattr(expr.args, "__iter__"): l = [] for o in expr.args: l.append(self._print(o)) return expr.__class__.__name__ + '(%s)' % ', '.join(l) elif hasattr(expr, "__module__") and hasattr(expr, "__name__"): return "<'%s.%s'>" % (expr.__module__, expr.__name__) else: return str(expr) def _print_Add(self, expr, order=None): args = self._as_ordered_terms(expr, order=order) nargs = len(args) args = map(self._print, args) clsname = type(expr).__name__ if nargs > 255: # Issue #10259, Python < 3.7 return clsname + "(*[%s])" % ", ".join(args) return clsname + "(%s)" % ", ".join(args) def _print_Cycle(self, expr): return expr.__repr__() def _print_Permutation(self, expr): from sympy.combinatorics.permutations import Permutation, Cycle from sympy.utilities.exceptions import SymPyDeprecationWarning perm_cyclic = Permutation.print_cyclic if perm_cyclic is not None: SymPyDeprecationWarning( feature="Permutation.print_cyclic = {}".format(perm_cyclic), useinstead="init_printing(perm_cyclic={})" .format(perm_cyclic), issue=15201, deprecated_since_version="1.6").warn() else: perm_cyclic = self._settings.get("perm_cyclic", True) if perm_cyclic: if not expr.size: return 'Permutation()' # before taking Cycle notation, see if the last element is # a singleton and move it to the head of the string s = Cycle(expr)(expr.size - 1).__repr__()[len('Cycle'):] last = s.rfind('(') if not last == 0 and ',' not in s[last:]: s = s[last:] + s[:last] return 'Permutation%s' %s else: s = expr.support() if not s: if expr.size < 5: return 'Permutation(%s)' % str(expr.array_form) return 'Permutation([], size=%s)' % expr.size trim = str(expr.array_form[:s[-1] + 1]) + ', size=%s' % expr.size use = full = str(expr.array_form) if len(trim) < len(full): use = trim return 'Permutation(%s)' % use def _print_Function(self, expr): r = self._print(expr.func) r += '(%s)' % ', '.join([self._print(a) for a in expr.args]) return r def _print_FunctionClass(self, expr): if issubclass(expr, AppliedUndef): return 'Function(%r)' % (expr.__name__) else: return expr.__name__ def _print_Half(self, expr): return 'Rational(1, 2)' def _print_RationalConstant(self, expr): return str(expr) def _print_AtomicExpr(self, expr): return str(expr) def _print_NumberSymbol(self, expr): return str(expr) def _print_Integer(self, expr): return 'Integer(%i)' % expr.p def _print_Integers(self, expr): return 'Integers' def _print_Naturals(self, expr): return 'Naturals' def _print_Naturals0(self, expr): return 'Naturals0' def _print_Reals(self, expr): return 'Reals' def _print_EmptySet(self, expr): return 'EmptySet' def _print_EmptySequence(self, expr): return 'EmptySequence' def _print_list(self, expr): return "[%s]" % self.reprify(expr, ", ") def _print_MatrixBase(self, expr): # special case for some empty matrices if (expr.rows == 0) ^ (expr.cols == 0): return '%s(%s, %s, %s)' % (expr.__class__.__name__, self._print(expr.rows), self._print(expr.cols), self._print([])) l = [] for i in range(expr.rows): l.append([]) for j in range(expr.cols): l[-1].append(expr[i, j]) return '%s(%s)' % (expr.__class__.__name__, self._print(l)) def _print_MutableSparseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_SparseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_ImmutableSparseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_Matrix(self, expr): return self._print_MatrixBase(expr) def _print_DenseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_MutableDenseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_ImmutableMatrix(self, expr): return self._print_MatrixBase(expr) def _print_ImmutableDenseMatrix(self, expr): return self._print_MatrixBase(expr) def _print_BooleanTrue(self, expr): return "true" def _print_BooleanFalse(self, expr): return "false" def _print_NaN(self, expr): return "nan" def _print_Mul(self, expr, order=None): terms = expr.args if self.order != 'old': args = expr._new_rawargs(*terms).as_ordered_factors() else: args = terms nargs = len(args) args = map(self._print, args) clsname = type(expr).__name__ if nargs > 255: # Issue #10259, Python < 3.7 return clsname + "(*[%s])" % ", ".join(args) return clsname + "(%s)" % ", ".join(args) def _print_Rational(self, expr): return 'Rational(%s, %s)' % (self._print(expr.p), self._print(expr.q)) def _print_PythonRational(self, expr): return "%s(%d, %d)" % (expr.__class__.__name__, expr.p, expr.q) def _print_Fraction(self, expr): return 'Fraction(%s, %s)' % (self._print(expr.numerator), self._print(expr.denominator)) def _print_Float(self, expr): r = mlib_to_str(expr._mpf_, repr_dps(expr._prec)) return "%s('%s', precision=%i)" % (expr.__class__.__name__, r, expr._prec) def _print_Sum2(self, expr): return "Sum2(%s, (%s, %s, %s))" % (self._print(expr.f), self._print(expr.i), self._print(expr.a), self._print(expr.b)) def _print_Symbol(self, expr): d = expr._assumptions.generator # print the dummy_index like it was an assumption if expr.is_Dummy: d['dummy_index'] = expr.dummy_index if d == {}: return "%s(%s)" % (expr.__class__.__name__, self._print(expr.name)) else: attr = ['%s=%s' % (k, v) for k, v in d.items()] return "%s(%s, %s)" % (expr.__class__.__name__, self._print(expr.name), ', '.join(attr)) def _print_Predicate(self, expr): return "%s(%s)" % (expr.__class__.__name__, self._print(expr.name)) def _print_AppliedPredicate(self, expr): return "%s(%s, %s)" % (expr.__class__.__name__, expr.func, expr.arg) def _print_str(self, expr): return repr(expr) def _print_tuple(self, expr): if len(expr) == 1: return "(%s,)" % self._print(expr[0]) else: return "(%s)" % self.reprify(expr, ", ") def _print_WildFunction(self, expr): return "%s('%s')" % (expr.__class__.__name__, expr.name) def _print_AlgebraicNumber(self, expr): return "%s(%s, %s)" % (expr.__class__.__name__, self._print(expr.root), self._print(expr.coeffs())) def _print_PolyRing(self, ring): return "%s(%s, %s, %s)" % (ring.__class__.__name__, self._print(ring.symbols), self._print(ring.domain), self._print(ring.order)) def _print_FracField(self, field): return "%s(%s, %s, %s)" % (field.__class__.__name__, self._print(field.symbols), self._print(field.domain), self._print(field.order)) def _print_PolyElement(self, poly): terms = list(poly.terms()) terms.sort(key=poly.ring.order, reverse=True) return "%s(%s, %s)" % (poly.__class__.__name__, self._print(poly.ring), self._print(terms)) def _print_FracElement(self, frac): numer_terms = list(frac.numer.terms()) numer_terms.sort(key=frac.field.order, reverse=True) denom_terms = list(frac.denom.terms()) denom_terms.sort(key=frac.field.order, reverse=True) numer = self._print(numer_terms) denom = self._print(denom_terms) return "%s(%s, %s, %s)" % (frac.__class__.__name__, self._print(frac.field), numer, denom) def _print_FractionField(self, domain): cls = domain.__class__.__name__ field = self._print(domain.field) return "%s(%s)" % (cls, field) def _print_PolynomialRingBase(self, ring): cls = ring.__class__.__name__ dom = self._print(ring.domain) gens = ', '.join(map(self._print, ring.gens)) order = str(ring.order) if order != ring.default_order: orderstr = ", order=" + order else: orderstr = "" return "%s(%s, %s%s)" % (cls, dom, gens, orderstr) def _print_DMP(self, p): cls = p.__class__.__name__ rep = self._print(p.rep) dom = self._print(p.dom) if p.ring is not None: ringstr = ", ring=" + self._print(p.ring) else: ringstr = "" return "%s(%s, %s%s)" % (cls, rep, dom, ringstr) def _print_MonogenicFiniteExtension(self, ext): # The expanded tree shown by srepr(ext.modulus) # is not practical. return "FiniteExtension(%s)" % str(ext.modulus) def _print_ExtensionElement(self, f): rep = self._print(f.rep) ext = self._print(f.ext) return "ExtElem(%s, %s)" % (rep, ext) def srepr(expr, **settings): """return expr in repr form""" return ReprPrinter(settings).doprint(expr)

34.037383

99

0.576698

5ec5fc17bca9444bce675ea5f951bd248c8ef42d

542

py

Python

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/small-poetry-31544

960a0945af1b45d421be56a7164ab7f42b69ffdc

[ "FTL", "AML", "RSA-MD" ]

null

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/small-poetry-31544

960a0945af1b45d421be56a7164ab7f42b69ffdc

[ "FTL", "AML", "RSA-MD" ]

5

2021-10-19T08:15:10.000Z

2021-10-19T08:15:13.000Z

backend/home/migrations/0001_load_initial_data.py

crowdbotics-apps/small-poetry-31544

960a0945af1b45d421be56a7164ab7f42b69ffdc

[ "FTL", "AML", "RSA-MD" ]

null

from django.db import migrations def create_site(apps, schema_editor): Site = apps.get_model("sites", "Site") custom_domain = "small-poetry-31544.botics.co" site_params = { "name": "Small Poetry", } if custom_domain: site_params["domain"] = custom_domain Site.objects.update_or_create(defaults=site_params, id=1) class Migration(migrations.Migration): dependencies = [ ("sites", "0002_alter_domain_unique"), ] operations = [ migrations.RunPython(create_site), ]

20.846154

61

0.656827

365cc8757a4d8e529c2e3d79cc61141faf808d77

594

py

Python

schevo/store/tests/test_store_utils.py

Schevo/schevo

d57a41f8b7b514ed48dc0164dcd3412a89e9873b

[ "MIT" ]

1

2020-09-05T00:47:50.000Z

schevo/store/tests/test_store_utils.py

Schevo/schevo

d57a41f8b7b514ed48dc0164dcd3412a89e9873b

[ "MIT" ]

null

schevo/store/tests/test_store_utils.py

Schevo/schevo

d57a41f8b7b514ed48dc0164dcd3412a89e9873b

[ "MIT" ]

null

""" $URL: svn+ssh://svn/repos/trunk/durus/test/utest_utils.py $ $Id: utest_utils.py 27079 2005-07-25 20:54:05Z dbinger $ """ from schevo.store.utils import format_oid, u64, p64, u32, p32 class Test(object): def test_check_format_oid(self): assert format_oid('A'*8) == '4702111234474983745' def test_check_p64_u64(self): for x in range(3): assert len(p64(x)) == 8 assert u64(p64(x)) == x def test_check_p32_u32(self): for x in range(3): assert len(p32(x)) == 4 assert x == u32(p32(x))

27

62

0.579125

e6ed4d1c80720881b56b0151d2de44ff3a486aa4

254,550

py

Python

bigquery/tests/unit/test_client.py

codyoss/google-cloud-python

505d55357fbdffc5d55005c58712932c758737bd

[ "Apache-2.0" ]

null

bigquery/tests/unit/test_client.py

codyoss/google-cloud-python

505d55357fbdffc5d55005c58712932c758737bd

[ "Apache-2.0" ]

null

bigquery/tests/unit/test_client.py

codyoss/google-cloud-python

505d55357fbdffc5d55005c58712932c758737bd

[ "Apache-2.0" ]

null

# Copyright 2015 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. import copy import collections import datetime import decimal import email import gzip import io import json import operator import unittest import warnings import mock import requests import six from six.moves import http_client import pytest import pytz try: import pandas except (ImportError, AttributeError): # pragma: NO COVER pandas = None try: import pyarrow except (ImportError, AttributeError): # pragma: NO COVER pyarrow = None import google.api_core.exceptions from google.api_core.gapic_v1 import client_info import google.cloud._helpers from google.cloud import bigquery_v2 from google.cloud.bigquery.dataset import DatasetReference from tests.unit.helpers import make_connection def _make_credentials(): import google.auth.credentials return mock.Mock(spec=google.auth.credentials.Credentials) def _make_list_partitons_meta_info(project, dataset_id, table_id, num_rows=0): return { "tableReference": { "projectId": project, "datasetId": dataset_id, "tableId": "{}$__PARTITIONS_SUMMARY__".format(table_id), }, "schema": { "fields": [ {"name": "project_id", "type": "STRING", "mode": "NULLABLE"}, {"name": "dataset_id", "type": "STRING", "mode": "NULLABLE"}, {"name": "table_id", "type": "STRING", "mode": "NULLABLE"}, {"name": "partition_id", "type": "STRING", "mode": "NULLABLE"}, ] }, "etag": "ETAG", "numRows": num_rows, } class TestClient(unittest.TestCase): PROJECT = "PROJECT" DS_ID = "DATASET_ID" TABLE_ID = "TABLE_ID" MODEL_ID = "MODEL_ID" TABLE_REF = DatasetReference(PROJECT, DS_ID).table(TABLE_ID) KMS_KEY_NAME = "projects/1/locations/us/keyRings/1/cryptoKeys/1" LOCATION = "us-central" @staticmethod def _get_target_class(): from google.cloud.bigquery.client import Client return Client def _make_one(self, *args, **kw): return self._get_target_class()(*args, **kw) def _make_table_resource(self): return { "id": "%s:%s:%s" % (self.PROJECT, self.DS_ID, self.TABLE_ID), "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, } def test_ctor_defaults(self): from google.cloud.bigquery._http import Connection creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) self.assertIsInstance(client._connection, Connection) self.assertIs(client._connection.credentials, creds) self.assertIs(client._connection.http, http) self.assertIsNone(client.location) self.assertEqual( client._connection.API_BASE_URL, Connection.DEFAULT_API_ENDPOINT ) def test_ctor_w_empty_client_options(self): from google.api_core.client_options import ClientOptions creds = _make_credentials() http = object() client_options = ClientOptions() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, client_options=client_options, ) self.assertEqual( client._connection.API_BASE_URL, client._connection.DEFAULT_API_ENDPOINT ) def test_ctor_w_client_options_dict(self): creds = _make_credentials() http = object() client_options = {"api_endpoint": "https://www.foo-googleapis.com"} client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, client_options=client_options, ) self.assertEqual( client._connection.API_BASE_URL, "https://www.foo-googleapis.com" ) def test_ctor_w_client_options_object(self): from google.api_core.client_options import ClientOptions creds = _make_credentials() http = object() client_options = ClientOptions(api_endpoint="https://www.foo-googleapis.com") client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, client_options=client_options, ) self.assertEqual( client._connection.API_BASE_URL, "https://www.foo-googleapis.com" ) def test_ctor_w_location(self): from google.cloud.bigquery._http import Connection creds = _make_credentials() http = object() location = "us-central" client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=location ) self.assertIsInstance(client._connection, Connection) self.assertIs(client._connection.credentials, creds) self.assertIs(client._connection.http, http) self.assertEqual(client.location, location) def test_ctor_w_query_job_config(self): from google.cloud.bigquery._http import Connection from google.cloud.bigquery import QueryJobConfig creds = _make_credentials() http = object() location = "us-central" job_config = QueryJobConfig() job_config.dry_run = True client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=location, default_query_job_config=job_config, ) self.assertIsInstance(client._connection, Connection) self.assertIs(client._connection.credentials, creds) self.assertIs(client._connection.http, http) self.assertEqual(client.location, location) self.assertIsInstance(client._default_query_job_config, QueryJobConfig) self.assertTrue(client._default_query_job_config.dry_run) def test__get_query_results_miss_w_explicit_project_and_timeout(self): from google.cloud.exceptions import NotFound creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection() with self.assertRaises(NotFound): client._get_query_results( "nothere", None, project="other-project", location=self.LOCATION, timeout_ms=500, ) conn.api_request.assert_called_once_with( method="GET", path="/projects/other-project/queries/nothere", query_params={"maxResults": 0, "timeoutMs": 500, "location": self.LOCATION}, ) def test__get_query_results_miss_w_client_location(self): from google.cloud.exceptions import NotFound creds = _make_credentials() client = self._make_one(self.PROJECT, creds, location=self.LOCATION) conn = client._connection = make_connection() with self.assertRaises(NotFound): client._get_query_results("nothere", None) conn.api_request.assert_called_once_with( method="GET", path="/projects/PROJECT/queries/nothere", query_params={"maxResults": 0, "location": self.LOCATION}, ) def test__get_query_results_hit(self): job_id = "query_job" data = { "kind": "bigquery#getQueryResultsResponse", "etag": "some-tag", "schema": { "fields": [ {"name": "title", "type": "STRING", "mode": "NULLABLE"}, {"name": "unique_words", "type": "INTEGER", "mode": "NULLABLE"}, ] }, "jobReference": {"projectId": self.PROJECT, "jobId": job_id}, "totalRows": "10", "totalBytesProcessed": "2464625", "jobComplete": True, "cacheHit": False, } creds = _make_credentials() client = self._make_one(self.PROJECT, creds) client._connection = make_connection(data) query_results = client._get_query_results(job_id, None) self.assertEqual(query_results.total_rows, 10) self.assertTrue(query_results.complete) def test_get_service_account_email(self): path = "/projects/%s/serviceAccount" % (self.PROJECT,) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) email = "bq-123@bigquery-encryption.iam.gserviceaccount.com" resource = {"kind": "bigquery#getServiceAccountResponse", "email": email} conn = client._connection = make_connection(resource) service_account_email = client.get_service_account_email() conn.api_request.assert_called_once_with(method="GET", path=path) self.assertEqual(service_account_email, email) def test_get_service_account_email_w_alternate_project(self): project = "my-alternate-project" path = "/projects/%s/serviceAccount" % (project,) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) email = "bq-123@bigquery-encryption.iam.gserviceaccount.com" resource = {"kind": "bigquery#getServiceAccountResponse", "email": email} conn = client._connection = make_connection(resource) service_account_email = client.get_service_account_email(project=project) conn.api_request.assert_called_once_with(method="GET", path=path) self.assertEqual(service_account_email, email) def test_list_projects_defaults(self): from google.cloud.bigquery.client import Project PROJECT_1 = "PROJECT_ONE" PROJECT_2 = "PROJECT_TWO" TOKEN = "TOKEN" DATA = { "nextPageToken": TOKEN, "projects": [ { "kind": "bigquery#project", "id": PROJECT_1, "numericId": 1, "projectReference": {"projectId": PROJECT_1}, "friendlyName": "One", }, { "kind": "bigquery#project", "id": PROJECT_2, "numericId": 2, "projectReference": {"projectId": PROJECT_2}, "friendlyName": "Two", }, ], } creds = _make_credentials() client = self._make_one(PROJECT_1, creds) conn = client._connection = make_connection(DATA) iterator = client.list_projects() page = six.next(iterator.pages) projects = list(page) token = iterator.next_page_token self.assertEqual(len(projects), len(DATA["projects"])) for found, expected in zip(projects, DATA["projects"]): self.assertIsInstance(found, Project) self.assertEqual(found.project_id, expected["id"]) self.assertEqual(found.numeric_id, expected["numericId"]) self.assertEqual(found.friendly_name, expected["friendlyName"]) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/projects", query_params={} ) def test_list_projects_explicit_response_missing_projects_key(self): TOKEN = "TOKEN" DATA = {} creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_projects(max_results=3, page_token=TOKEN) page = six.next(iterator.pages) projects = list(page) token = iterator.next_page_token self.assertEqual(len(projects), 0) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path="/projects", query_params={"maxResults": 3, "pageToken": TOKEN}, ) def test_list_datasets_defaults(self): from google.cloud.bigquery.dataset import DatasetListItem DATASET_1 = "dataset_one" DATASET_2 = "dataset_two" PATH = "projects/%s/datasets" % self.PROJECT TOKEN = "TOKEN" DATA = { "nextPageToken": TOKEN, "datasets": [ { "kind": "bigquery#dataset", "id": "%s:%s" % (self.PROJECT, DATASET_1), "datasetReference": { "datasetId": DATASET_1, "projectId": self.PROJECT, }, "friendlyName": None, }, { "kind": "bigquery#dataset", "id": "%s:%s" % (self.PROJECT, DATASET_2), "datasetReference": { "datasetId": DATASET_2, "projectId": self.PROJECT, }, "friendlyName": "Two", }, ], } creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_datasets() page = six.next(iterator.pages) datasets = list(page) token = iterator.next_page_token self.assertEqual(len(datasets), len(DATA["datasets"])) for found, expected in zip(datasets, DATA["datasets"]): self.assertIsInstance(found, DatasetListItem) self.assertEqual(found.full_dataset_id, expected["id"]) self.assertEqual(found.friendly_name, expected["friendlyName"]) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={} ) def test_list_datasets_w_project(self): creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection({}) list(client.list_datasets(project="other-project")) conn.api_request.assert_called_once_with( method="GET", path="/projects/other-project/datasets", query_params={} ) def test_list_datasets_explicit_response_missing_datasets_key(self): PATH = "projects/%s/datasets" % self.PROJECT TOKEN = "TOKEN" FILTER = "FILTER" DATA = {} creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_datasets( include_all=True, filter=FILTER, max_results=3, page_token=TOKEN ) page = six.next(iterator.pages) datasets = list(page) token = iterator.next_page_token self.assertEqual(len(datasets), 0) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={ "all": True, "filter": FILTER, "maxResults": 3, "pageToken": TOKEN, }, ) def test_dataset_with_specified_project(self): from google.cloud.bigquery.dataset import DatasetReference creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) dataset = client.dataset(self.DS_ID, self.PROJECT) self.assertIsInstance(dataset, DatasetReference) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) def test_dataset_with_default_project(self): from google.cloud.bigquery.dataset import DatasetReference creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) dataset = client.dataset(self.DS_ID) self.assertIsInstance(dataset, DatasetReference) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) def test_get_dataset(self): from google.cloud.exceptions import ServerError path = "projects/%s/datasets/%s" % (self.PROJECT, self.DS_ID) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) resource = { "id": "%s:%s" % (self.PROJECT, self.DS_ID), "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, } conn = client._connection = make_connection(resource) dataset_ref = client.dataset(self.DS_ID) dataset = client.get_dataset(dataset_ref) conn.api_request.assert_called_once_with(method="GET", path="/%s" % path) self.assertEqual(dataset.dataset_id, self.DS_ID) # Test retry. # Not a cloud API exception (missing 'errors' field). client._connection = make_connection(Exception(""), resource) with self.assertRaises(Exception): client.get_dataset(dataset_ref) # Zero-length errors field. client._connection = make_connection(ServerError(""), resource) with self.assertRaises(ServerError): client.get_dataset(dataset_ref) # Non-retryable reason. client._connection = make_connection( ServerError("", errors=[{"reason": "serious"}]), resource ) with self.assertRaises(ServerError): client.get_dataset(dataset_ref) # Retryable reason, but retry is disabled. client._connection = make_connection( ServerError("", errors=[{"reason": "backendError"}]), resource ) with self.assertRaises(ServerError): client.get_dataset(dataset_ref, retry=None) # Retryable reason, default retry: success. client._connection = make_connection( ServerError("", errors=[{"reason": "backendError"}]), resource ) dataset = client.get_dataset( # Test with a string for dataset ID. dataset_ref.dataset_id ) self.assertEqual(dataset.dataset_id, self.DS_ID) def test_create_dataset_minimal(self): from google.cloud.bigquery.dataset import Dataset PATH = "projects/%s/datasets" % self.PROJECT RESOURCE = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(RESOURCE) ds_ref = client.dataset(self.DS_ID) before = Dataset(ds_ref) after = client.create_dataset(before) self.assertEqual(after.dataset_id, self.DS_ID) self.assertEqual(after.project, self.PROJECT) self.assertEqual(after.etag, RESOURCE["etag"]) self.assertEqual(after.full_dataset_id, RESOURCE["id"]) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, }, ) def test_create_dataset_w_attrs(self): from google.cloud.bigquery.dataset import Dataset, AccessEntry PATH = "projects/%s/datasets" % self.PROJECT DESCRIPTION = "DESC" FRIENDLY_NAME = "FN" LOCATION = "US" USER_EMAIL = "phred@example.com" LABELS = {"color": "red"} VIEW = { "projectId": "my-proj", "datasetId": "starry-skies", "tableId": "northern-hemisphere", } RESOURCE = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "description": DESCRIPTION, "friendlyName": FRIENDLY_NAME, "location": LOCATION, "defaultTableExpirationMs": "3600", "labels": LABELS, "access": [{"role": "OWNER", "userByEmail": USER_EMAIL}, {"view": VIEW}], } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(RESOURCE) entries = [ AccessEntry("OWNER", "userByEmail", USER_EMAIL), AccessEntry(None, "view", VIEW), ] ds_ref = client.dataset(self.DS_ID) before = Dataset(ds_ref) before.access_entries = entries before.description = DESCRIPTION before.friendly_name = FRIENDLY_NAME before.default_table_expiration_ms = 3600 before.location = LOCATION before.labels = LABELS after = client.create_dataset(before) self.assertEqual(after.dataset_id, self.DS_ID) self.assertEqual(after.project, self.PROJECT) self.assertEqual(after.etag, RESOURCE["etag"]) self.assertEqual(after.full_dataset_id, RESOURCE["id"]) self.assertEqual(after.description, DESCRIPTION) self.assertEqual(after.friendly_name, FRIENDLY_NAME) self.assertEqual(after.location, LOCATION) self.assertEqual(after.default_table_expiration_ms, 3600) self.assertEqual(after.labels, LABELS) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "description": DESCRIPTION, "friendlyName": FRIENDLY_NAME, "location": LOCATION, "defaultTableExpirationMs": "3600", "access": [ {"role": "OWNER", "userByEmail": USER_EMAIL}, {"view": VIEW}, ], "labels": LABELS, }, ) def test_create_dataset_w_custom_property(self): # The library should handle sending properties to the API that are not # yet part of the library from google.cloud.bigquery.dataset import Dataset path = "/projects/%s/datasets" % self.PROJECT resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "newAlphaProperty": "unreleased property", } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) ds_ref = client.dataset(self.DS_ID) before = Dataset(ds_ref) before._properties["newAlphaProperty"] = "unreleased property" after = client.create_dataset(before) self.assertEqual(after.dataset_id, self.DS_ID) self.assertEqual(after.project, self.PROJECT) self.assertEqual(after._properties["newAlphaProperty"], "unreleased property") conn.api_request.assert_called_once_with( method="POST", path=path, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "newAlphaProperty": "unreleased property", "labels": {}, }, ) def test_create_dataset_w_client_location_wo_dataset_location(self): from google.cloud.bigquery.dataset import Dataset PATH = "projects/%s/datasets" % self.PROJECT RESOURCE = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "location": self.LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection(RESOURCE) ds_ref = client.dataset(self.DS_ID) before = Dataset(ds_ref) after = client.create_dataset(before) self.assertEqual(after.dataset_id, self.DS_ID) self.assertEqual(after.project, self.PROJECT) self.assertEqual(after.etag, RESOURCE["etag"]) self.assertEqual(after.full_dataset_id, RESOURCE["id"]) self.assertEqual(after.location, self.LOCATION) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": self.LOCATION, }, ) def test_create_dataset_w_client_location_w_dataset_location(self): from google.cloud.bigquery.dataset import Dataset PATH = "projects/%s/datasets" % self.PROJECT OTHER_LOCATION = "EU" RESOURCE = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "location": OTHER_LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection(RESOURCE) ds_ref = client.dataset(self.DS_ID) before = Dataset(ds_ref) before.location = OTHER_LOCATION after = client.create_dataset(before) self.assertEqual(after.dataset_id, self.DS_ID) self.assertEqual(after.project, self.PROJECT) self.assertEqual(after.etag, RESOURCE["etag"]) self.assertEqual(after.full_dataset_id, RESOURCE["id"]) self.assertEqual(after.location, OTHER_LOCATION) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": OTHER_LOCATION, }, ) def test_create_dataset_w_reference(self): path = "/projects/%s/datasets" % self.PROJECT resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "location": self.LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection(resource) dataset = client.create_dataset(client.dataset(self.DS_ID)) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) self.assertEqual(dataset.etag, resource["etag"]) self.assertEqual(dataset.full_dataset_id, resource["id"]) self.assertEqual(dataset.location, self.LOCATION) conn.api_request.assert_called_once_with( method="POST", path=path, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": self.LOCATION, }, ) def test_create_dataset_w_fully_qualified_string(self): path = "/projects/%s/datasets" % self.PROJECT resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "location": self.LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection(resource) dataset = client.create_dataset("{}.{}".format(self.PROJECT, self.DS_ID)) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) self.assertEqual(dataset.etag, resource["etag"]) self.assertEqual(dataset.full_dataset_id, resource["id"]) self.assertEqual(dataset.location, self.LOCATION) conn.api_request.assert_called_once_with( method="POST", path=path, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": self.LOCATION, }, ) def test_create_dataset_w_string(self): path = "/projects/%s/datasets" % self.PROJECT resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "%s:%s" % (self.PROJECT, self.DS_ID), "location": self.LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection(resource) dataset = client.create_dataset(self.DS_ID) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) self.assertEqual(dataset.etag, resource["etag"]) self.assertEqual(dataset.full_dataset_id, resource["id"]) self.assertEqual(dataset.location, self.LOCATION) conn.api_request.assert_called_once_with( method="POST", path=path, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": self.LOCATION, }, ) def test_create_dataset_alreadyexists_w_exists_ok_false(self): creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) client._connection = make_connection( google.api_core.exceptions.AlreadyExists("dataset already exists") ) with pytest.raises(google.api_core.exceptions.AlreadyExists): client.create_dataset(self.DS_ID) def test_create_dataset_alreadyexists_w_exists_ok_true(self): post_path = "/projects/{}/datasets".format(self.PROJECT) get_path = "/projects/{}/datasets/{}".format(self.PROJECT, self.DS_ID) resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "id": "{}:{}".format(self.PROJECT, self.DS_ID), "location": self.LOCATION, } creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection( google.api_core.exceptions.AlreadyExists("dataset already exists"), resource ) dataset = client.create_dataset(self.DS_ID, exists_ok=True) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) self.assertEqual(dataset.etag, resource["etag"]) self.assertEqual(dataset.full_dataset_id, resource["id"]) self.assertEqual(dataset.location, self.LOCATION) conn.api_request.assert_has_calls( [ mock.call( method="POST", path=post_path, data={ "datasetReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, }, "labels": {}, "location": self.LOCATION, }, ), mock.call(method="GET", path=get_path), ] ) def test_create_routine_w_minimal_resource(self): from google.cloud.bigquery.routine import Routine from google.cloud.bigquery.routine import RoutineReference creds = _make_credentials() resource = { "routineReference": { "projectId": "test-routine-project", "datasetId": "test_routines", "routineId": "minimal_routine", } } client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) full_routine_id = "test-routine-project.test_routines.minimal_routine" routine = Routine(full_routine_id) actual_routine = client.create_routine(routine) conn.api_request.assert_called_once_with( method="POST", path="/projects/test-routine-project/datasets/test_routines/routines", data=resource, ) self.assertEqual( actual_routine.reference, RoutineReference.from_string(full_routine_id) ) def test_create_routine_w_conflict(self): from google.cloud.bigquery.routine import Routine creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection( google.api_core.exceptions.AlreadyExists("routine already exists") ) full_routine_id = "test-routine-project.test_routines.minimal_routine" routine = Routine(full_routine_id) with pytest.raises(google.api_core.exceptions.AlreadyExists): client.create_routine(routine) resource = { "routineReference": { "projectId": "test-routine-project", "datasetId": "test_routines", "routineId": "minimal_routine", } } conn.api_request.assert_called_once_with( method="POST", path="/projects/test-routine-project/datasets/test_routines/routines", data=resource, ) def test_create_routine_w_conflict_exists_ok(self): from google.cloud.bigquery.routine import Routine creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = { "routineReference": { "projectId": "test-routine-project", "datasetId": "test_routines", "routineId": "minimal_routine", } } conn = client._connection = make_connection( google.api_core.exceptions.AlreadyExists("routine already exists"), resource ) full_routine_id = "test-routine-project.test_routines.minimal_routine" routine = Routine(full_routine_id) actual_routine = client.create_routine(routine, exists_ok=True) self.assertEqual(actual_routine.project, "test-routine-project") self.assertEqual(actual_routine.dataset_id, "test_routines") self.assertEqual(actual_routine.routine_id, "minimal_routine") conn.api_request.assert_has_calls( [ mock.call( method="POST", path="/projects/test-routine-project/datasets/test_routines/routines", data=resource, ), mock.call( method="GET", path="/projects/test-routine-project/datasets/test_routines/routines/minimal_routine", ), ] ) def test_create_table_w_day_partition(self): from google.cloud.bigquery.table import Table from google.cloud.bigquery.table import TimePartitioning path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table.time_partitioning = TimePartitioning() got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "timePartitioning": {"type": "DAY"}, "labels": {}, }, ) self.assertEqual(table.time_partitioning.type_, "DAY") self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_custom_property(self): # The library should handle sending properties to the API that are not # yet part of the library from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() resource["newAlphaProperty"] = "unreleased property" conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table._properties["newAlphaProperty"] = "unreleased property" got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "newAlphaProperty": "unreleased property", "labels": {}, }, ) self.assertEqual(got._properties["newAlphaProperty"], "unreleased property") self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_encryption_configuration(self): from google.cloud.bigquery.encryption_configuration import ( EncryptionConfiguration, ) from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table.encryption_configuration = EncryptionConfiguration( kms_key_name=self.KMS_KEY_NAME ) got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, "encryptionConfiguration": {"kmsKeyName": self.KMS_KEY_NAME}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_day_partition_and_expire(self): from google.cloud.bigquery.table import Table from google.cloud.bigquery.table import TimePartitioning path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table.time_partitioning = TimePartitioning(expiration_ms=100) got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "timePartitioning": {"type": "DAY", "expirationMs": "100"}, "labels": {}, }, ) self.assertEqual(table.time_partitioning.type_, "DAY") self.assertEqual(table.time_partitioning.expiration_ms, 100) self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_schema_and_query(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) query = "SELECT * from %s:%s" % (self.DS_ID, self.TABLE_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() resource.update( { "schema": { "fields": [ { "name": "full_name", "type": "STRING", "mode": "REQUIRED", "description": None, }, { "name": "age", "type": "INTEGER", "mode": "REQUIRED", "description": None, }, ] }, "view": {"query": query}, } ) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), ] conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF, schema=schema) table.view_query = query got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "schema": { "fields": [ { "name": "full_name", "type": "STRING", "mode": "REQUIRED", "description": None, }, { "name": "age", "type": "INTEGER", "mode": "REQUIRED", "description": None, }, ] }, "view": {"query": query, "useLegacySql": False}, "labels": {}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) self.assertEqual(got.project, self.PROJECT) self.assertEqual(got.dataset_id, self.DS_ID) self.assertEqual(got.schema, schema) self.assertEqual(got.view_query, query) def test_create_table_w_external(self): from google.cloud.bigquery.external_config import ExternalConfig from google.cloud.bigquery.job import SourceFormat from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() resource.update( { "externalDataConfiguration": { "sourceFormat": SourceFormat.CSV, "autodetect": True, } } ) conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) ec = ExternalConfig("CSV") ec.autodetect = True table.external_data_configuration = ec got = client.create_table(table) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "externalDataConfiguration": { "sourceFormat": SourceFormat.CSV, "autodetect": True, }, "labels": {}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) self.assertEqual(got.project, self.PROJECT) self.assertEqual(got.dataset_id, self.DS_ID) self.assertEqual( got.external_data_configuration.source_format, SourceFormat.CSV ) self.assertEqual(got.external_data_configuration.autodetect, True) def test_create_table_w_reference(self): path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) got = client.create_table(self.TABLE_REF) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_fully_qualified_string(self): path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) got = client.create_table( "{}.{}.{}".format(self.PROJECT, self.DS_ID, self.TABLE_ID) ) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_w_string(self): path = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) resource = self._make_table_resource() conn = client._connection = make_connection(resource) got = client.create_table("{}.{}".format(self.DS_ID, self.TABLE_ID)) conn.api_request.assert_called_once_with( method="POST", path="/%s" % path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, }, ) self.assertEqual(got.table_id, self.TABLE_ID) def test_create_table_alreadyexists_w_exists_ok_false(self): post_path = "/projects/{}/datasets/{}/tables".format(self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection( google.api_core.exceptions.AlreadyExists("table already exists") ) with pytest.raises(google.api_core.exceptions.AlreadyExists): client.create_table("{}.{}".format(self.DS_ID, self.TABLE_ID)) conn.api_request.assert_called_once_with( method="POST", path=post_path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, }, ) def test_create_table_alreadyexists_w_exists_ok_true(self): post_path = "/projects/{}/datasets/{}/tables".format(self.PROJECT, self.DS_ID) get_path = "/projects/{}/datasets/{}/tables/{}".format( self.PROJECT, self.DS_ID, self.TABLE_ID ) resource = self._make_table_resource() creds = _make_credentials() client = self._make_one( project=self.PROJECT, credentials=creds, location=self.LOCATION ) conn = client._connection = make_connection( google.api_core.exceptions.AlreadyExists("table already exists"), resource ) got = client.create_table( "{}.{}".format(self.DS_ID, self.TABLE_ID), exists_ok=True ) self.assertEqual(got.project, self.PROJECT) self.assertEqual(got.dataset_id, self.DS_ID) self.assertEqual(got.table_id, self.TABLE_ID) conn.api_request.assert_has_calls( [ mock.call( method="POST", path=post_path, data={ "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, "labels": {}, }, ), mock.call(method="GET", path=get_path), ] ) def test_get_model(self): path = "projects/%s/datasets/%s/models/%s" % ( self.PROJECT, self.DS_ID, self.MODEL_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) resource = { "modelReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "modelId": self.MODEL_ID, } } conn = client._connection = make_connection(resource) model_ref = client.dataset(self.DS_ID).model(self.MODEL_ID) got = client.get_model(model_ref) conn.api_request.assert_called_once_with(method="GET", path="/%s" % path) self.assertEqual(got.model_id, self.MODEL_ID) def test_get_model_w_string(self): path = "projects/%s/datasets/%s/models/%s" % ( self.PROJECT, self.DS_ID, self.MODEL_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) resource = { "modelReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "modelId": self.MODEL_ID, } } conn = client._connection = make_connection(resource) model_id = "{}.{}.{}".format(self.PROJECT, self.DS_ID, self.MODEL_ID) got = client.get_model(model_id) conn.api_request.assert_called_once_with(method="GET", path="/%s" % path) self.assertEqual(got.model_id, self.MODEL_ID) def test_get_routine(self): from google.cloud.bigquery.routine import Routine from google.cloud.bigquery.routine import RoutineReference full_routine_id = "test-routine-project.test_routines.minimal_routine" routines = [ full_routine_id, Routine(full_routine_id), RoutineReference.from_string(full_routine_id), ] for routine in routines: creds = _make_credentials() resource = { "etag": "im-an-etag", "routineReference": { "projectId": "test-routine-project", "datasetId": "test_routines", "routineId": "minimal_routine", }, "routineType": "SCALAR_FUNCTION", } client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) actual_routine = client.get_routine(routine) conn.api_request.assert_called_once_with( method="GET", path="/projects/test-routine-project/datasets/test_routines/routines/minimal_routine", ) self.assertEqual( actual_routine.reference, RoutineReference.from_string(full_routine_id), msg="routine={}".format(repr(routine)), ) self.assertEqual( actual_routine.etag, "im-an-etag", msg="routine={}".format(repr(routine)), ) self.assertEqual( actual_routine.type_, "SCALAR_FUNCTION", msg="routine={}".format(repr(routine)), ) def test_get_table(self): path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) resource = self._make_table_resource() conn = client._connection = make_connection(resource) table = client.get_table(self.TABLE_REF) conn.api_request.assert_called_once_with(method="GET", path="/%s" % path) self.assertEqual(table.table_id, self.TABLE_ID) def test_get_table_sets_user_agent(self): creds = _make_credentials() http = mock.create_autospec(requests.Session) mock_response = http.request( url=mock.ANY, method=mock.ANY, headers=mock.ANY, data=mock.ANY ) http.reset_mock() mock_response.status_code = 200 mock_response.json.return_value = self._make_table_resource() user_agent_override = client_info.ClientInfo(user_agent="my-application/1.2.3") client = self._make_one( project=self.PROJECT, credentials=creds, client_info=user_agent_override, _http=http, ) client.get_table(self.TABLE_REF) expected_user_agent = user_agent_override.to_user_agent() http.request.assert_called_once_with( url=mock.ANY, method="GET", headers={ "X-Goog-API-Client": expected_user_agent, "Accept-Encoding": "gzip", "User-Agent": expected_user_agent, }, data=mock.ANY, ) self.assertIn("my-application/1.2.3", expected_user_agent) def test_update_dataset_w_invalid_field(self): from google.cloud.bigquery.dataset import Dataset creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(ValueError): client.update_dataset(Dataset(client.dataset(self.DS_ID)), ["foo"]) def test_update_dataset(self): from google.cloud.bigquery.dataset import Dataset, AccessEntry PATH = "projects/%s/datasets/%s" % (self.PROJECT, self.DS_ID) DESCRIPTION = "DESCRIPTION" FRIENDLY_NAME = "TITLE" LOCATION = "loc" LABELS = {"priority": "high"} ACCESS = [{"role": "OWNER", "userByEmail": "phred@example.com"}] EXP = 17 RESOURCE = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "etag": "etag", "description": DESCRIPTION, "friendlyName": FRIENDLY_NAME, "location": LOCATION, "defaultTableExpirationMs": EXP, "labels": LABELS, "access": ACCESS, } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(RESOURCE, RESOURCE) ds = Dataset(client.dataset(self.DS_ID)) ds.description = DESCRIPTION ds.friendly_name = FRIENDLY_NAME ds.location = LOCATION ds.default_table_expiration_ms = EXP ds.labels = LABELS ds.access_entries = [AccessEntry("OWNER", "userByEmail", "phred@example.com")] ds2 = client.update_dataset( ds, ["description", "friendly_name", "location", "labels", "access_entries"] ) conn.api_request.assert_called_once_with( method="PATCH", data={ "description": DESCRIPTION, "friendlyName": FRIENDLY_NAME, "location": LOCATION, "labels": LABELS, "access": ACCESS, }, path="/" + PATH, headers=None, ) self.assertEqual(ds2.description, ds.description) self.assertEqual(ds2.friendly_name, ds.friendly_name) self.assertEqual(ds2.location, ds.location) self.assertEqual(ds2.labels, ds.labels) self.assertEqual(ds2.access_entries, ds.access_entries) # ETag becomes If-Match header. ds._properties["etag"] = "etag" client.update_dataset(ds, []) req = conn.api_request.call_args self.assertEqual(req[1]["headers"]["If-Match"], "etag") def test_update_dataset_w_custom_property(self): # The library should handle sending properties to the API that are not # yet part of the library from google.cloud.bigquery.dataset import Dataset path = "/projects/%s/datasets/%s" % (self.PROJECT, self.DS_ID) resource = { "datasetReference": {"projectId": self.PROJECT, "datasetId": self.DS_ID}, "newAlphaProperty": "unreleased property", } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) dataset = Dataset(client.dataset(self.DS_ID)) dataset._properties["newAlphaProperty"] = "unreleased property" dataset = client.update_dataset(dataset, ["newAlphaProperty"]) conn.api_request.assert_called_once_with( method="PATCH", data={"newAlphaProperty": "unreleased property"}, path=path, headers=None, ) self.assertEqual(dataset.dataset_id, self.DS_ID) self.assertEqual(dataset.project, self.PROJECT) self.assertEqual(dataset._properties["newAlphaProperty"], "unreleased property") def test_update_model(self): from google.cloud.bigquery.model import Model path = "projects/%s/datasets/%s/models/%s" % ( self.PROJECT, self.DS_ID, self.MODEL_ID, ) description = "description" title = "title" expires = datetime.datetime( 2012, 12, 21, 16, 0, 0, tzinfo=google.cloud._helpers.UTC ) resource = { "modelReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "modelId": self.MODEL_ID, }, "description": description, "etag": "etag", "expirationTime": str(google.cloud._helpers._millis(expires)), "friendlyName": title, "labels": {"x": "y"}, } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource, resource) model_id = "{}.{}.{}".format(self.PROJECT, self.DS_ID, self.MODEL_ID) model = Model(model_id) model.description = description model.friendly_name = title model.expires = expires model.labels = {"x": "y"} updated_model = client.update_model( model, ["description", "friendly_name", "labels", "expires"] ) sent = { "description": description, "expirationTime": str(google.cloud._helpers._millis(expires)), "friendlyName": title, "labels": {"x": "y"}, } conn.api_request.assert_called_once_with( method="PATCH", data=sent, path="/" + path, headers=None ) self.assertEqual(updated_model.model_id, model.model_id) self.assertEqual(updated_model.description, model.description) self.assertEqual(updated_model.friendly_name, model.friendly_name) self.assertEqual(updated_model.labels, model.labels) self.assertEqual(updated_model.expires, model.expires) # ETag becomes If-Match header. model._proto.etag = "etag" client.update_model(model, []) req = conn.api_request.call_args self.assertEqual(req[1]["headers"]["If-Match"], "etag") def test_update_routine(self): from google.cloud.bigquery.routine import Routine from google.cloud.bigquery.routine import RoutineArgument full_routine_id = "routines-project.test_routines.updated_routine" resource = { "routineReference": { "projectId": "routines-project", "datasetId": "test_routines", "routineId": "updated_routine", }, "routineType": "SCALAR_FUNCTION", "language": "SQL", "definitionBody": "x * 3", "arguments": [{"name": "x", "dataType": {"typeKind": "INT64"}}], "returnType": None, "someNewField": "someValue", } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource, resource) routine = Routine(full_routine_id) routine.arguments = [ RoutineArgument( name="x", data_type=bigquery_v2.types.StandardSqlDataType( type_kind=bigquery_v2.enums.StandardSqlDataType.TypeKind.INT64 ), ) ] routine.body = "x * 3" routine.language = "SQL" routine.type_ = "SCALAR_FUNCTION" routine._properties["someNewField"] = "someValue" actual_routine = client.update_routine( routine, ["arguments", "language", "body", "type_", "return_type", "someNewField"], ) # TODO: routineReference isn't needed when the Routines API supports # partial updates. sent = resource conn.api_request.assert_called_once_with( method="PUT", data=sent, path="/projects/routines-project/datasets/test_routines/routines/updated_routine", headers=None, ) self.assertEqual(actual_routine.arguments, routine.arguments) self.assertEqual(actual_routine.body, routine.body) self.assertEqual(actual_routine.language, routine.language) self.assertEqual(actual_routine.type_, routine.type_) # ETag becomes If-Match header. routine._properties["etag"] = "im-an-etag" client.update_routine(routine, []) req = conn.api_request.call_args self.assertEqual(req[1]["headers"]["If-Match"], "im-an-etag") def test_update_table(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) description = "description" title = "title" resource = self._make_table_resource() resource.update( { "schema": { "fields": [ { "name": "full_name", "type": "STRING", "mode": "REQUIRED", "description": None, }, { "name": "age", "type": "INTEGER", "mode": "REQUIRED", "description": None, }, ] }, "etag": "etag", "description": description, "friendlyName": title, "labels": {"x": "y"}, } ) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), ] creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource, resource) table = Table(self.TABLE_REF, schema=schema) table.description = description table.friendly_name = title table.labels = {"x": "y"} updated_table = client.update_table( table, ["schema", "description", "friendly_name", "labels"] ) sent = { "schema": { "fields": [ { "name": "full_name", "type": "STRING", "mode": "REQUIRED", "description": None, }, { "name": "age", "type": "INTEGER", "mode": "REQUIRED", "description": None, }, ] }, "description": description, "friendlyName": title, "labels": {"x": "y"}, } conn.api_request.assert_called_once_with( method="PATCH", data=sent, path="/" + path, headers=None ) self.assertEqual(updated_table.description, table.description) self.assertEqual(updated_table.friendly_name, table.friendly_name) self.assertEqual(updated_table.schema, table.schema) self.assertEqual(updated_table.labels, table.labels) # ETag becomes If-Match header. table._properties["etag"] = "etag" client.update_table(table, []) req = conn.api_request.call_args self.assertEqual(req[1]["headers"]["If-Match"], "etag") def test_update_table_w_custom_property(self): from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) resource = self._make_table_resource() resource["newAlphaProperty"] = "unreleased property" creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table._properties["newAlphaProperty"] = "unreleased property" updated_table = client.update_table(table, ["newAlphaProperty"]) conn.api_request.assert_called_once_with( method="PATCH", path="/%s" % path, data={"newAlphaProperty": "unreleased property"}, headers=None, ) self.assertEqual( updated_table._properties["newAlphaProperty"], "unreleased property" ) def test_update_table_only_use_legacy_sql(self): from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) resource = self._make_table_resource() resource["view"] = {"useLegacySql": True} creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF) table.view_use_legacy_sql = True updated_table = client.update_table(table, ["view_use_legacy_sql"]) conn.api_request.assert_called_once_with( method="PATCH", path="/%s" % path, data={"view": {"useLegacySql": True}}, headers=None, ) self.assertEqual(updated_table.view_use_legacy_sql, table.view_use_legacy_sql) def test_update_table_w_query(self): import datetime from google.cloud._helpers import UTC from google.cloud._helpers import _millis from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) query = "select fullname, age from person_ages" location = "EU" exp_time = datetime.datetime(2015, 8, 1, 23, 59, 59, tzinfo=UTC) schema_resource = { "fields": [ { "name": "full_name", "type": "STRING", "mode": "REQUIRED", "description": None, }, { "name": "age", "type": "INTEGER", "mode": "REQUIRED", "description": None, }, ] } schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), ] resource = self._make_table_resource() resource.update( { "schema": schema_resource, "view": {"query": query, "useLegacySql": True}, "location": location, "expirationTime": _millis(exp_time), } ) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource) table = Table(self.TABLE_REF, schema=schema) table.expires = exp_time table.view_query = query table.view_use_legacy_sql = True updated_properties = ["schema", "view_query", "expires", "view_use_legacy_sql"] updated_table = client.update_table(table, updated_properties) self.assertEqual(updated_table.schema, table.schema) self.assertEqual(updated_table.view_query, table.view_query) self.assertEqual(updated_table.expires, table.expires) self.assertEqual(updated_table.view_use_legacy_sql, table.view_use_legacy_sql) self.assertEqual(updated_table.location, location) conn.api_request.assert_called_once_with( method="PATCH", path="/%s" % path, data={ "view": {"query": query, "useLegacySql": True}, "expirationTime": str(_millis(exp_time)), "schema": schema_resource, }, headers=None, ) def test_update_table_w_schema_None(self): # Simulate deleting schema: not sure if back-end will actually # allow this operation, but the spec says it is optional. path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) resource1 = self._make_table_resource() resource1.update( { "schema": { "fields": [ {"name": "full_name", "type": "STRING", "mode": "REQUIRED"}, {"name": "age", "type": "INTEGER", "mode": "REQUIRED"}, ] } } ) resource2 = self._make_table_resource() creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource1, resource2) table = client.get_table( # Test with string for table ID "{}.{}.{}".format( self.TABLE_REF.project, self.TABLE_REF.dataset_id, self.TABLE_REF.table_id, ) ) table.schema = None updated_table = client.update_table(table, ["schema"]) self.assertEqual(len(conn.api_request.call_args_list), 2) req = conn.api_request.call_args_list[1] self.assertEqual(req[1]["method"], "PATCH") sent = {"schema": None} self.assertEqual(req[1]["data"], sent) self.assertEqual(req[1]["path"], "/%s" % path) self.assertEqual(len(updated_table.schema), 0) def test_update_table_delete_property(self): from google.cloud.bigquery.table import Table description = "description" title = "title" path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) resource1 = self._make_table_resource() resource1.update({"description": description, "friendlyName": title}) resource2 = self._make_table_resource() resource2["description"] = None creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(resource1, resource2) table = Table(self.TABLE_REF) table.description = description table.friendly_name = title table2 = client.update_table(table, ["description", "friendly_name"]) self.assertEqual(table2.description, table.description) table2.description = None table3 = client.update_table(table2, ["description"]) self.assertEqual(len(conn.api_request.call_args_list), 2) req = conn.api_request.call_args_list[1] self.assertEqual(req[1]["method"], "PATCH") self.assertEqual(req[1]["path"], "/%s" % path) sent = {"description": None} self.assertEqual(req[1]["data"], sent) self.assertIsNone(table3.description) def test_list_tables_empty(self): path = "/projects/{}/datasets/{}/tables".format(self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection({}) dataset = client.dataset(self.DS_ID) iterator = client.list_tables(dataset) self.assertIs(iterator.dataset, dataset) page = six.next(iterator.pages) tables = list(page) token = iterator.next_page_token self.assertEqual(tables, []) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path=path, query_params={} ) def test_list_models_empty(self): path = "/projects/{}/datasets/{}/models".format(self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection({}) dataset_id = "{}.{}".format(self.PROJECT, self.DS_ID) iterator = client.list_models(dataset_id) page = six.next(iterator.pages) models = list(page) token = iterator.next_page_token self.assertEqual(models, []) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path=path, query_params={} ) def test_list_models_defaults(self): from google.cloud.bigquery.model import Model MODEL_1 = "model_one" MODEL_2 = "model_two" PATH = "projects/%s/datasets/%s/models" % (self.PROJECT, self.DS_ID) TOKEN = "TOKEN" DATA = { "nextPageToken": TOKEN, "models": [ { "modelReference": { "modelId": MODEL_1, "datasetId": self.DS_ID, "projectId": self.PROJECT, } }, { "modelReference": { "modelId": MODEL_2, "datasetId": self.DS_ID, "projectId": self.PROJECT, } }, ], } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(DATA) dataset = client.dataset(self.DS_ID) iterator = client.list_models(dataset) self.assertIs(iterator.dataset, dataset) page = six.next(iterator.pages) models = list(page) token = iterator.next_page_token self.assertEqual(len(models), len(DATA["models"])) for found, expected in zip(models, DATA["models"]): self.assertIsInstance(found, Model) self.assertEqual(found.model_id, expected["modelReference"]["modelId"]) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={} ) def test_list_models_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.list_models(client.dataset(self.DS_ID).model("foo")) def test_list_routines_empty(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection({}) iterator = client.list_routines("test-routines.test_routines") page = six.next(iterator.pages) routines = list(page) token = iterator.next_page_token self.assertEqual(routines, []) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path="/projects/test-routines/datasets/test_routines/routines", query_params={}, ) def test_list_routines_defaults(self): from google.cloud.bigquery.routine import Routine project_id = "test-routines" dataset_id = "test_routines" path = "/projects/test-routines/datasets/test_routines/routines" routine_1 = "routine_one" routine_2 = "routine_two" token = "TOKEN" resource = { "nextPageToken": token, "routines": [ { "routineReference": { "routineId": routine_1, "datasetId": dataset_id, "projectId": project_id, } }, { "routineReference": { "routineId": routine_2, "datasetId": dataset_id, "projectId": project_id, } }, ], } creds = _make_credentials() client = self._make_one(project=project_id, credentials=creds) conn = client._connection = make_connection(resource) dataset = client.dataset(dataset_id) iterator = client.list_routines(dataset) self.assertIs(iterator.dataset, dataset) page = six.next(iterator.pages) routines = list(page) actual_token = iterator.next_page_token self.assertEqual(len(routines), len(resource["routines"])) for found, expected in zip(routines, resource["routines"]): self.assertIsInstance(found, Routine) self.assertEqual( found.routine_id, expected["routineReference"]["routineId"] ) self.assertEqual(actual_token, token) conn.api_request.assert_called_once_with( method="GET", path=path, query_params={} ) def test_list_routines_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.list_routines(client.dataset(self.DS_ID).table("foo")) def test_list_tables_defaults(self): from google.cloud.bigquery.table import TableListItem TABLE_1 = "table_one" TABLE_2 = "table_two" PATH = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) TOKEN = "TOKEN" DATA = { "nextPageToken": TOKEN, "tables": [ { "kind": "bigquery#table", "id": "%s:%s.%s" % (self.PROJECT, self.DS_ID, TABLE_1), "tableReference": { "tableId": TABLE_1, "datasetId": self.DS_ID, "projectId": self.PROJECT, }, "type": "TABLE", }, { "kind": "bigquery#table", "id": "%s:%s.%s" % (self.PROJECT, self.DS_ID, TABLE_2), "tableReference": { "tableId": TABLE_2, "datasetId": self.DS_ID, "projectId": self.PROJECT, }, "type": "TABLE", }, ], } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(DATA) dataset = client.dataset(self.DS_ID) iterator = client.list_tables(dataset) self.assertIs(iterator.dataset, dataset) page = six.next(iterator.pages) tables = list(page) token = iterator.next_page_token self.assertEqual(len(tables), len(DATA["tables"])) for found, expected in zip(tables, DATA["tables"]): self.assertIsInstance(found, TableListItem) self.assertEqual(found.full_table_id, expected["id"]) self.assertEqual(found.table_type, expected["type"]) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={} ) def test_list_tables_explicit(self): from google.cloud.bigquery.table import TableListItem TABLE_1 = "table_one" TABLE_2 = "table_two" PATH = "projects/%s/datasets/%s/tables" % (self.PROJECT, self.DS_ID) TOKEN = "TOKEN" DATA = { "tables": [ { "kind": "bigquery#dataset", "id": "%s:%s.%s" % (self.PROJECT, self.DS_ID, TABLE_1), "tableReference": { "tableId": TABLE_1, "datasetId": self.DS_ID, "projectId": self.PROJECT, }, "type": "TABLE", }, { "kind": "bigquery#dataset", "id": "%s:%s.%s" % (self.PROJECT, self.DS_ID, TABLE_2), "tableReference": { "tableId": TABLE_2, "datasetId": self.DS_ID, "projectId": self.PROJECT, }, "type": "TABLE", }, ] } creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(DATA) dataset = client.dataset(self.DS_ID) iterator = client.list_tables( # Test with string for dataset ID. self.DS_ID, max_results=3, page_token=TOKEN, ) self.assertEqual(iterator.dataset, dataset) page = six.next(iterator.pages) tables = list(page) token = iterator.next_page_token self.assertEqual(len(tables), len(DATA["tables"])) for found, expected in zip(tables, DATA["tables"]): self.assertIsInstance(found, TableListItem) self.assertEqual(found.full_table_id, expected["id"]) self.assertEqual(found.table_type, expected["type"]) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={"maxResults": 3, "pageToken": TOKEN}, ) def test_list_tables_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.list_tables(client.dataset(self.DS_ID).table("foo")) def test_delete_dataset(self): from google.cloud.bigquery.dataset import Dataset from google.cloud.bigquery.dataset import DatasetReference ds_ref = DatasetReference(self.PROJECT, self.DS_ID) datasets = (ds_ref, Dataset(ds_ref), "{}.{}".format(self.PROJECT, self.DS_ID)) PATH = "projects/%s/datasets/%s" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection(*([{}] * len(datasets))) for arg in datasets: client.delete_dataset(arg) conn.api_request.assert_called_with( method="DELETE", path="/%s" % PATH, query_params={} ) def test_delete_dataset_delete_contents(self): from google.cloud.bigquery.dataset import Dataset PATH = "projects/%s/datasets/%s" % (self.PROJECT, self.DS_ID) creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) conn = client._connection = make_connection({}, {}) ds_ref = client.dataset(self.DS_ID) for arg in (ds_ref, Dataset(ds_ref)): client.delete_dataset(arg, delete_contents=True) conn.api_request.assert_called_with( method="DELETE", path="/%s" % PATH, query_params={"deleteContents": "true"}, ) def test_delete_dataset_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.delete_dataset(client.dataset(self.DS_ID).table("foo")) def test_delete_dataset_w_not_found_ok_false(self): path = "/projects/{}/datasets/{}".format(self.PROJECT, self.DS_ID) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("dataset not found") ) with self.assertRaises(google.api_core.exceptions.NotFound): client.delete_dataset(self.DS_ID) conn.api_request.assert_called_with(method="DELETE", path=path, query_params={}) def test_delete_dataset_w_not_found_ok_true(self): path = "/projects/{}/datasets/{}".format(self.PROJECT, self.DS_ID) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("dataset not found") ) client.delete_dataset(self.DS_ID, not_found_ok=True) conn.api_request.assert_called_with(method="DELETE", path=path, query_params={}) def test_delete_model(self): from google.cloud.bigquery.model import Model path = "projects/%s/datasets/%s/models/%s" % ( self.PROJECT, self.DS_ID, self.MODEL_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) model_id = "{}.{}.{}".format(self.PROJECT, self.DS_ID, self.MODEL_ID) models = ( model_id, client.dataset(self.DS_ID).model(self.MODEL_ID), Model(model_id), ) conn = client._connection = make_connection(*([{}] * len(models))) for arg in models: client.delete_model(arg) conn.api_request.assert_called_with(method="DELETE", path="/%s" % path) def test_delete_model_w_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.delete_model(client.dataset(self.DS_ID)) def test_delete_model_w_not_found_ok_false(self): path = "/projects/{}/datasets/{}/models/{}".format( self.PROJECT, self.DS_ID, self.MODEL_ID ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("model not found") ) with self.assertRaises(google.api_core.exceptions.NotFound): client.delete_model("{}.{}".format(self.DS_ID, self.MODEL_ID)) conn.api_request.assert_called_with(method="DELETE", path=path) def test_delete_model_w_not_found_ok_true(self): path = "/projects/{}/datasets/{}/models/{}".format( self.PROJECT, self.DS_ID, self.MODEL_ID ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("model not found") ) client.delete_model( "{}.{}".format(self.DS_ID, self.MODEL_ID), not_found_ok=True ) conn.api_request.assert_called_with(method="DELETE", path=path) def test_delete_routine(self): from google.cloud.bigquery.routine import Routine from google.cloud.bigquery.routine import RoutineReference full_routine_id = "test-routine-project.test_routines.minimal_routine" routines = [ full_routine_id, Routine(full_routine_id), RoutineReference.from_string(full_routine_id), ] creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(*([{}] * len(routines))) for routine in routines: client.delete_routine(routine) conn.api_request.assert_called_with( method="DELETE", path="/projects/test-routine-project/datasets/test_routines/routines/minimal_routine", ) def test_delete_routine_w_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.delete_routine(client.dataset(self.DS_ID)) def test_delete_routine_w_not_found_ok_false(self): creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("routine not found") ) with self.assertRaises(google.api_core.exceptions.NotFound): client.delete_routine("routines-project.test_routines.test_routine") conn.api_request.assert_called_with( method="DELETE", path="/projects/routines-project/datasets/test_routines/routines/test_routine", ) def test_delete_routine_w_not_found_ok_true(self): creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("routine not found") ) client.delete_routine( "routines-project.test_routines.test_routine", not_found_ok=True ) conn.api_request.assert_called_with( method="DELETE", path="/projects/routines-project/datasets/test_routines/routines/test_routine", ) def test_delete_table(self): from google.cloud.bigquery.table import Table tables = ( self.TABLE_REF, Table(self.TABLE_REF), "{}.{}.{}".format( self.TABLE_REF.project, self.TABLE_REF.dataset_id, self.TABLE_REF.table_id, ), ) path = "projects/%s/datasets/%s/tables/%s" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(*([{}] * len(tables))) for arg in tables: client.delete_table(arg) conn.api_request.assert_called_with(method="DELETE", path="/%s" % path) def test_delete_table_w_wrong_type(self): creds = _make_credentials() client = self._make_one(project=self.PROJECT, credentials=creds) with self.assertRaises(TypeError): client.delete_table(client.dataset(self.DS_ID)) def test_delete_table_w_not_found_ok_false(self): path = "/projects/{}/datasets/{}/tables/{}".format( self.PROJECT, self.DS_ID, self.TABLE_ID ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("table not found") ) with self.assertRaises(google.api_core.exceptions.NotFound): client.delete_table("{}.{}".format(self.DS_ID, self.TABLE_ID)) conn.api_request.assert_called_with(method="DELETE", path=path) def test_delete_table_w_not_found_ok_true(self): path = "/projects/{}/datasets/{}/tables/{}".format( self.PROJECT, self.DS_ID, self.TABLE_ID ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection( google.api_core.exceptions.NotFound("table not found") ) client.delete_table( "{}.{}".format(self.DS_ID, self.TABLE_ID), not_found_ok=True ) conn.api_request.assert_called_with(method="DELETE", path=path) def test_job_from_resource_unknown_type(self): from google.cloud.bigquery.job import UnknownJob creds = _make_credentials() client = self._make_one(self.PROJECT, creds) got = client.job_from_resource({}) # Can parse redacted job. self.assertIsInstance(got, UnknownJob) self.assertEqual(got.project, self.PROJECT) def test_get_job_miss_w_explict_project(self): from google.cloud.exceptions import NotFound OTHER_PROJECT = "OTHER_PROJECT" JOB_ID = "NONESUCH" creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection() with self.assertRaises(NotFound): client.get_job(JOB_ID, project=OTHER_PROJECT, location=self.LOCATION) conn.api_request.assert_called_once_with( method="GET", path="/projects/OTHER_PROJECT/jobs/NONESUCH", query_params={"projection": "full", "location": self.LOCATION}, ) def test_get_job_miss_w_client_location(self): from google.cloud.exceptions import NotFound OTHER_PROJECT = "OTHER_PROJECT" JOB_ID = "NONESUCH" creds = _make_credentials() client = self._make_one(self.PROJECT, creds, location=self.LOCATION) conn = client._connection = make_connection() with self.assertRaises(NotFound): client.get_job(JOB_ID, project=OTHER_PROJECT) conn.api_request.assert_called_once_with( method="GET", path="/projects/OTHER_PROJECT/jobs/NONESUCH", query_params={"projection": "full", "location": self.LOCATION}, ) def test_get_job_hit(self): from google.cloud.bigquery.job import CreateDisposition from google.cloud.bigquery.job import QueryJob from google.cloud.bigquery.job import WriteDisposition JOB_ID = "query_job" QUERY_DESTINATION_TABLE = "query_destination_table" QUERY = "SELECT * from test_dataset:test_table" ASYNC_QUERY_DATA = { "id": "{}:{}".format(self.PROJECT, JOB_ID), "jobReference": {"projectId": self.PROJECT, "jobId": "query_job"}, "state": "DONE", "configuration": { "query": { "query": QUERY, "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": QUERY_DESTINATION_TABLE, }, "createDisposition": CreateDisposition.CREATE_IF_NEEDED, "writeDisposition": WriteDisposition.WRITE_TRUNCATE, } }, } creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(ASYNC_QUERY_DATA) job = client.get_job(JOB_ID) self.assertIsInstance(job, QueryJob) self.assertEqual(job.job_id, JOB_ID) self.assertEqual(job.create_disposition, CreateDisposition.CREATE_IF_NEEDED) self.assertEqual(job.write_disposition, WriteDisposition.WRITE_TRUNCATE) conn.api_request.assert_called_once_with( method="GET", path="/projects/PROJECT/jobs/query_job", query_params={"projection": "full"}, ) def test_cancel_job_miss_w_explict_project(self): from google.cloud.exceptions import NotFound OTHER_PROJECT = "OTHER_PROJECT" JOB_ID = "NONESUCH" creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection() with self.assertRaises(NotFound): client.cancel_job(JOB_ID, project=OTHER_PROJECT, location=self.LOCATION) conn.api_request.assert_called_once_with( method="POST", path="/projects/OTHER_PROJECT/jobs/NONESUCH/cancel", query_params={"projection": "full", "location": self.LOCATION}, ) def test_cancel_job_miss_w_client_location(self): from google.cloud.exceptions import NotFound OTHER_PROJECT = "OTHER_PROJECT" JOB_ID = "NONESUCH" creds = _make_credentials() client = self._make_one(self.PROJECT, creds, location=self.LOCATION) conn = client._connection = make_connection() with self.assertRaises(NotFound): client.cancel_job(JOB_ID, project=OTHER_PROJECT) conn.api_request.assert_called_once_with( method="POST", path="/projects/OTHER_PROJECT/jobs/NONESUCH/cancel", query_params={"projection": "full", "location": self.LOCATION}, ) def test_cancel_job_hit(self): from google.cloud.bigquery.job import QueryJob JOB_ID = "query_job" QUERY = "SELECT * from test_dataset:test_table" QUERY_JOB_RESOURCE = { "id": "{}:{}".format(self.PROJECT, JOB_ID), "jobReference": {"projectId": self.PROJECT, "jobId": "query_job"}, "state": "RUNNING", "configuration": {"query": {"query": QUERY}}, } RESOURCE = {"job": QUERY_JOB_RESOURCE} creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(RESOURCE) job = client.cancel_job(JOB_ID) self.assertIsInstance(job, QueryJob) self.assertEqual(job.job_id, JOB_ID) self.assertEqual(job.query, QUERY) conn.api_request.assert_called_once_with( method="POST", path="/projects/PROJECT/jobs/query_job/cancel", query_params={"projection": "full"}, ) def test_list_jobs_defaults(self): from google.cloud.bigquery.job import CopyJob from google.cloud.bigquery.job import CreateDisposition from google.cloud.bigquery.job import ExtractJob from google.cloud.bigquery.job import LoadJob from google.cloud.bigquery.job import QueryJob from google.cloud.bigquery.job import WriteDisposition SOURCE_TABLE = "source_table" DESTINATION_TABLE = "destination_table" QUERY_DESTINATION_TABLE = "query_destination_table" SOURCE_URI = "gs://test_bucket/src_object*" DESTINATION_URI = "gs://test_bucket/dst_object*" JOB_TYPES = { "load_job": LoadJob, "copy_job": CopyJob, "extract_job": ExtractJob, "query_job": QueryJob, } PATH = "projects/%s/jobs" % self.PROJECT TOKEN = "TOKEN" QUERY = "SELECT * from test_dataset:test_table" ASYNC_QUERY_DATA = { "id": "%s:%s" % (self.PROJECT, "query_job"), "jobReference": {"projectId": self.PROJECT, "jobId": "query_job"}, "state": "DONE", "configuration": { "query": { "query": QUERY, "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": QUERY_DESTINATION_TABLE, }, "createDisposition": CreateDisposition.CREATE_IF_NEEDED, "writeDisposition": WriteDisposition.WRITE_TRUNCATE, } }, } EXTRACT_DATA = { "id": "%s:%s" % (self.PROJECT, "extract_job"), "jobReference": {"projectId": self.PROJECT, "jobId": "extract_job"}, "state": "DONE", "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE_TABLE, }, "destinationUris": [DESTINATION_URI], } }, } COPY_DATA = { "id": "%s:%s" % (self.PROJECT, "copy_job"), "jobReference": {"projectId": self.PROJECT, "jobId": "copy_job"}, "state": "DONE", "configuration": { "copy": { "sourceTables": [ { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE_TABLE, } ], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": DESTINATION_TABLE, }, } }, } LOAD_DATA = { "id": "%s:%s" % (self.PROJECT, "load_job"), "jobReference": {"projectId": self.PROJECT, "jobId": "load_job"}, "state": "DONE", "configuration": { "load": { "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE_TABLE, }, "sourceUris": [SOURCE_URI], } }, } DATA = { "nextPageToken": TOKEN, "jobs": [ASYNC_QUERY_DATA, EXTRACT_DATA, COPY_DATA, LOAD_DATA], } creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_jobs() page = six.next(iterator.pages) jobs = list(page) token = iterator.next_page_token self.assertEqual(len(jobs), len(DATA["jobs"])) for found, expected in zip(jobs, DATA["jobs"]): name = expected["jobReference"]["jobId"] self.assertIsInstance(found, JOB_TYPES[name]) self.assertEqual(found.job_id, name) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={"projection": "full"} ) def test_list_jobs_load_job_wo_sourceUris(self): from google.cloud.bigquery.job import LoadJob SOURCE_TABLE = "source_table" JOB_TYPES = {"load_job": LoadJob} PATH = "projects/%s/jobs" % self.PROJECT TOKEN = "TOKEN" LOAD_DATA = { "id": "%s:%s" % (self.PROJECT, "load_job"), "jobReference": {"projectId": self.PROJECT, "jobId": "load_job"}, "state": "DONE", "configuration": { "load": { "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE_TABLE, } } }, } DATA = {"nextPageToken": TOKEN, "jobs": [LOAD_DATA]} creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_jobs() page = six.next(iterator.pages) jobs = list(page) token = iterator.next_page_token self.assertEqual(len(jobs), len(DATA["jobs"])) for found, expected in zip(jobs, DATA["jobs"]): name = expected["jobReference"]["jobId"] self.assertIsInstance(found, JOB_TYPES[name]) self.assertEqual(found.job_id, name) self.assertEqual(token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={"projection": "full"} ) def test_list_jobs_explicit_missing(self): PATH = "projects/%s/jobs" % self.PROJECT DATA = {} TOKEN = "TOKEN" creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection(DATA) iterator = client.list_jobs( max_results=1000, page_token=TOKEN, all_users=True, state_filter="done" ) page = six.next(iterator.pages) jobs = list(page) token = iterator.next_page_token self.assertEqual(len(jobs), 0) self.assertIsNone(token) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={ "projection": "full", "maxResults": 1000, "pageToken": TOKEN, "allUsers": True, "stateFilter": "done", }, ) def test_list_jobs_w_project(self): creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection({}) list(client.list_jobs(project="other-project")) conn.api_request.assert_called_once_with( method="GET", path="/projects/other-project/jobs", query_params={"projection": "full"}, ) def test_list_jobs_w_time_filter(self): creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection({}) # One millisecond after the unix epoch. start_time = datetime.datetime(1970, 1, 1, 0, 0, 0, 1000) # One millisecond after the the 2038 31-bit signed int rollover end_time = datetime.datetime(2038, 1, 19, 3, 14, 7, 1000) end_time_millis = (((2 ** 31) - 1) * 1000) + 1 list(client.list_jobs(min_creation_time=start_time, max_creation_time=end_time)) conn.api_request.assert_called_once_with( method="GET", path="/projects/%s/jobs" % self.PROJECT, query_params={ "projection": "full", "minCreationTime": "1", "maxCreationTime": str(end_time_millis), }, ) def test_list_jobs_w_parent_job_filter(self): from google.cloud.bigquery import job creds = _make_credentials() client = self._make_one(self.PROJECT, creds) conn = client._connection = make_connection({}, {}) parent_job_args = ["parent-job-123", job._AsyncJob("parent-job-123", client)] for parent_job in parent_job_args: list(client.list_jobs(parent_job=parent_job)) conn.api_request.assert_called_once_with( method="GET", path="/projects/%s/jobs" % self.PROJECT, query_params={"projection": "full", "parentJobId": "parent-job-123"}, ) conn.api_request.reset_mock() def test_load_table_from_uri(self): from google.cloud.bigquery.job import LoadJob, LoadJobConfig JOB = "job_name" DESTINATION = "destination_table" SOURCE_URI = "http://example.com/source.csv" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "load": { "sourceUris": [SOURCE_URI], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": DESTINATION, }, } }, } creds = _make_credentials() http = object() job_config = LoadJobConfig() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) destination = client.dataset(self.DS_ID).table(DESTINATION) job = client.load_table_from_uri( SOURCE_URI, destination, job_id=JOB, job_config=job_config ) # Check that load_table_from_uri actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/%s/jobs" % self.PROJECT, data=RESOURCE ) self.assertIsInstance(job, LoadJob) self.assertIsInstance(job._configuration, LoadJobConfig) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(list(job.source_uris), [SOURCE_URI]) self.assertIs(job.destination, destination) conn = client._connection = make_connection(RESOURCE) job = client.load_table_from_uri([SOURCE_URI], destination, job_id=JOB) self.assertIsInstance(job, LoadJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(list(job.source_uris), [SOURCE_URI]) self.assertIs(job.destination, destination) def test_load_table_from_uri_w_explicit_project(self): job_id = "this-is-a-job-id" destination_id = "destination_table" source_uri = "gs://example/source.csv" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "load": { "sourceUris": [source_uri], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": destination_id, }, } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(resource) destination = client.dataset(self.DS_ID).table(destination_id) client.load_table_from_uri( source_uri, destination, job_id=job_id, project="other-project", location=self.LOCATION, ) # Check that load_table_from_uri actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_load_table_from_uri_w_client_location(self): job_id = "this-is-a-job-id" destination_id = "destination_table" source_uri = "gs://example/source.csv" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "load": { "sourceUris": [source_uri], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": destination_id, }, } }, } creds = _make_credentials() http = object() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=self.LOCATION ) conn = client._connection = make_connection(resource) client.load_table_from_uri( source_uri, # Test with string for table ID. "{}.{}".format(self.DS_ID, destination_id), job_id=job_id, project="other-project", ) # Check that load_table_from_uri actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_load_table_from_uri_w_invalid_job_config(self): from google.cloud.bigquery import job JOB = "job_name" DESTINATION = "destination_table" SOURCE_URI = "http://example.com/source.csv" creds = _make_credentials() http = object() job_config = job.CopyJobConfig() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) destination = client.dataset(self.DS_ID).table(DESTINATION) with self.assertRaises(TypeError) as exc: client.load_table_from_uri( SOURCE_URI, destination, job_id=JOB, job_config=job_config ) self.assertIn("Expected an instance of LoadJobConfig", exc.exception.args[0]) @staticmethod def _mock_requests_response(status_code, headers, content=b""): return mock.Mock( content=content, headers=headers, status_code=status_code, spec=["content", "headers", "status_code"], ) def _mock_transport(self, status_code, headers, content=b""): fake_transport = mock.Mock(spec=["request"]) fake_response = self._mock_requests_response( status_code, headers, content=content ) fake_transport.request.return_value = fake_response return fake_transport def _initiate_resumable_upload_helper(self, num_retries=None): from google.resumable_media.requests import ResumableUpload from google.cloud.bigquery.client import _DEFAULT_CHUNKSIZE from google.cloud.bigquery.client import _GENERIC_CONTENT_TYPE from google.cloud.bigquery.client import _get_upload_headers from google.cloud.bigquery.job import LoadJob from google.cloud.bigquery.job import LoadJobConfig from google.cloud.bigquery.job import SourceFormat # Create mocks to be checked for doing transport. resumable_url = "http://test.invalid?upload_id=hey-you" response_headers = {"location": resumable_url} fake_transport = self._mock_transport(http_client.OK, response_headers) client = self._make_one(project=self.PROJECT, _http=fake_transport) conn = client._connection = make_connection() # Create some mock arguments and call the method under test. data = b"goodbye gudbi gootbee" stream = io.BytesIO(data) config = LoadJobConfig() config.source_format = SourceFormat.CSV job = LoadJob(None, None, self.TABLE_REF, client, job_config=config) metadata = job.to_api_repr() upload, transport = client._initiate_resumable_upload( stream, metadata, num_retries ) # Check the returned values. self.assertIsInstance(upload, ResumableUpload) upload_url = ( "https://bigquery.googleapis.com/upload/bigquery/v2/projects/" + self.PROJECT + "/jobs?uploadType=resumable" ) self.assertEqual(upload.upload_url, upload_url) expected_headers = _get_upload_headers(conn.user_agent) self.assertEqual(upload._headers, expected_headers) self.assertFalse(upload.finished) self.assertEqual(upload._chunk_size, _DEFAULT_CHUNKSIZE) self.assertIs(upload._stream, stream) self.assertIsNone(upload._total_bytes) self.assertEqual(upload._content_type, _GENERIC_CONTENT_TYPE) self.assertEqual(upload.resumable_url, resumable_url) retry_strategy = upload._retry_strategy self.assertEqual(retry_strategy.max_sleep, 64.0) if num_retries is None: self.assertEqual(retry_strategy.max_cumulative_retry, 600.0) self.assertIsNone(retry_strategy.max_retries) else: self.assertIsNone(retry_strategy.max_cumulative_retry) self.assertEqual(retry_strategy.max_retries, num_retries) self.assertIs(transport, fake_transport) # Make sure we never read from the stream. self.assertEqual(stream.tell(), 0) # Check the mocks. request_headers = expected_headers.copy() request_headers["x-upload-content-type"] = _GENERIC_CONTENT_TYPE fake_transport.request.assert_called_once_with( "POST", upload_url, data=json.dumps(metadata).encode("utf-8"), headers=request_headers, timeout=mock.ANY, ) def test__initiate_resumable_upload(self): self._initiate_resumable_upload_helper() def test__initiate_resumable_upload_with_retry(self): self._initiate_resumable_upload_helper(num_retries=11) def _do_multipart_upload_success_helper(self, get_boundary, num_retries=None): from google.cloud.bigquery.client import _get_upload_headers from google.cloud.bigquery.job import LoadJob from google.cloud.bigquery.job import LoadJobConfig from google.cloud.bigquery.job import SourceFormat fake_transport = self._mock_transport(http_client.OK, {}) client = self._make_one(project=self.PROJECT, _http=fake_transport) conn = client._connection = make_connection() # Create some mock arguments. data = b"Bzzzz-zap \x00\x01\xf4" stream = io.BytesIO(data) config = LoadJobConfig() config.source_format = SourceFormat.CSV job = LoadJob(None, None, self.TABLE_REF, client, job_config=config) metadata = job.to_api_repr() size = len(data) response = client._do_multipart_upload(stream, metadata, size, num_retries) # Check the mocks and the returned value. self.assertIs(response, fake_transport.request.return_value) self.assertEqual(stream.tell(), size) get_boundary.assert_called_once_with() upload_url = ( "https://bigquery.googleapis.com/upload/bigquery/v2/projects/" + self.PROJECT + "/jobs?uploadType=multipart" ) payload = ( b"--==0==\r\n" + b"content-type: application/json; charset=UTF-8\r\n\r\n" + json.dumps(metadata).encode("utf-8") + b"\r\n" + b"--==0==\r\n" + b"content-type: */*\r\n\r\n" + data + b"\r\n" + b"--==0==--" ) headers = _get_upload_headers(conn.user_agent) headers["content-type"] = b'multipart/related; boundary="==0=="' fake_transport.request.assert_called_once_with( "POST", upload_url, data=payload, headers=headers, timeout=mock.ANY ) @mock.patch(u"google.resumable_media._upload.get_boundary", return_value=b"==0==") def test__do_multipart_upload(self, get_boundary): self._do_multipart_upload_success_helper(get_boundary) @mock.patch(u"google.resumable_media._upload.get_boundary", return_value=b"==0==") def test__do_multipart_upload_with_retry(self, get_boundary): self._do_multipart_upload_success_helper(get_boundary, num_retries=8) def test_copy_table(self): from google.cloud.bigquery.job import CopyJob JOB = "job_name" SOURCE = "source_table" DESTINATION = "destination_table" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "copy": { "sourceTables": [ { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE, } ], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": DESTINATION, }, } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) destination = dataset.table(DESTINATION) job = client.copy_table(source, destination, job_id=JOB) # Check that copy_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/%s/jobs" % self.PROJECT, data=RESOURCE ) self.assertIsInstance(job, CopyJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(list(job.sources), [source]) self.assertIs(job.destination, destination) conn = client._connection = make_connection(RESOURCE) source2 = dataset.table(SOURCE + "2") job = client.copy_table([source, source2], destination, job_id=JOB) self.assertIsInstance(job, CopyJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(list(job.sources), [source, source2]) self.assertIs(job.destination, destination) def test_copy_table_w_explicit_project(self): job_id = "this-is-a-job-id" source_id = "source_table" destination_id = "destination_table" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "copy": { "sourceTables": [ { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": source_id, } ], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": destination_id, }, } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(resource) dataset = client.dataset(self.DS_ID) source = dataset.table(source_id) destination = dataset.table(destination_id) client.copy_table( source, destination, job_id=job_id, project="other-project", location=self.LOCATION, ) # Check that copy_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_copy_table_w_client_location(self): job_id = "this-is-a-job-id" source_id = "source_table" destination_id = "destination_table" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "copy": { "sourceTables": [ { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": source_id, } ], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": destination_id, }, } }, } creds = _make_credentials() http = object() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=self.LOCATION ) conn = client._connection = make_connection(resource) client.copy_table( # Test with string for table IDs. "{}.{}".format(self.DS_ID, source_id), "{}.{}".format(self.DS_ID, destination_id), job_id=job_id, project="other-project", ) # Check that copy_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_copy_table_w_source_strings(self): creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) client._connection = make_connection({}) sources = [ "dataset_wo_proj.some_table", "other_project.other_dataset.other_table", client.dataset("dataset_from_ref").table("table_from_ref"), ] destination = "some_project.some_dataset.destination_table" job = client.copy_table(sources, destination) expected_sources = [ client.dataset("dataset_wo_proj").table("some_table"), client.dataset("other_dataset", project="other_project").table( "other_table" ), client.dataset("dataset_from_ref").table("table_from_ref"), ] self.assertEqual(list(job.sources), expected_sources) expected_destination = client.dataset( "some_dataset", project="some_project" ).table("destination_table") self.assertEqual(job.destination, expected_destination) def test_copy_table_w_invalid_job_config(self): from google.cloud.bigquery import job JOB = "job_name" SOURCE = "source_table" DESTINATION = "destination_table" creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) job_config = job.ExtractJobConfig() dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) destination = dataset.table(DESTINATION) with self.assertRaises(TypeError) as exc: client.copy_table(source, destination, job_id=JOB, job_config=job_config) self.assertIn("Expected an instance of CopyJobConfig", exc.exception.args[0]) def test_copy_table_w_valid_job_config(self): from google.cloud.bigquery.job import CopyJobConfig JOB = "job_name" SOURCE = "source_table" DESTINATION = "destination_table" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "copy": { "sourceTables": [ { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE, } ], "destinationTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": DESTINATION, }, } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) job_config = CopyJobConfig() conn = client._connection = make_connection(RESOURCE) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) destination = dataset.table(DESTINATION) job = client.copy_table(source, destination, job_id=JOB, job_config=job_config) # Check that copy_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/%s/jobs" % self.PROJECT, data=RESOURCE ) self.assertIsInstance(job._configuration, CopyJobConfig) def test_extract_table(self): from google.cloud.bigquery.job import ExtractJob JOB = "job_id" SOURCE = "source_table" DESTINATION = "gs://bucket_name/object_name" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE, }, "destinationUris": [DESTINATION], } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) job = client.extract_table(source, DESTINATION, job_id=JOB) # Check that extract_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/PROJECT/jobs", data=RESOURCE ) # Check the job resource. self.assertIsInstance(job, ExtractJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(job.source, source) self.assertEqual(list(job.destination_uris), [DESTINATION]) def test_extract_table_w_invalid_job_config(self): from google.cloud.bigquery import job JOB = "job_id" SOURCE = "source_table" DESTINATION = "gs://bucket_name/object_name" creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) job_config = job.LoadJobConfig() with self.assertRaises(TypeError) as exc: client.extract_table(source, DESTINATION, job_id=JOB, job_config=job_config) self.assertIn("Expected an instance of ExtractJobConfig", exc.exception.args[0]) def test_extract_table_w_explicit_project(self): job_id = "job_id" source_id = "source_table" destination = "gs://bucket_name/object_name" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": source_id, }, "destinationUris": [destination], } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(resource) dataset = client.dataset(self.DS_ID) source = dataset.table(source_id) client.extract_table( source, destination, job_id=job_id, project="other-project", location=self.LOCATION, ) # Check that extract_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_extract_table_w_client_location(self): job_id = "job_id" source_id = "source_table" destination = "gs://bucket_name/object_name" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": source_id, }, "destinationUris": [destination], } }, } creds = _make_credentials() http = object() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=self.LOCATION ) conn = client._connection = make_connection(resource) client.extract_table( # Test with string for table ID. "{}.{}".format(self.DS_ID, source_id), destination, job_id=job_id, project="other-project", ) # Check that extract_table actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_extract_table_generated_job_id(self): from google.cloud.bigquery.job import ExtractJob from google.cloud.bigquery.job import ExtractJobConfig from google.cloud.bigquery.job import DestinationFormat JOB = "job_id" SOURCE = "source_table" DESTINATION = "gs://bucket_name/object_name" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE, }, "destinationUris": [DESTINATION], "destinationFormat": "NEWLINE_DELIMITED_JSON", } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) job_config = ExtractJobConfig() job_config.destination_format = DestinationFormat.NEWLINE_DELIMITED_JSON job = client.extract_table(source, DESTINATION, job_config=job_config) # Check that extract_table actually starts the job. conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/projects/PROJECT/jobs") self.assertIsInstance(req["data"]["jobReference"]["jobId"], six.string_types) # Check the job resource. self.assertIsInstance(job, ExtractJob) self.assertIs(job._client, client) self.assertEqual(job.source, source) self.assertEqual(list(job.destination_uris), [DESTINATION]) def test_extract_table_w_destination_uris(self): from google.cloud.bigquery.job import ExtractJob JOB = "job_id" SOURCE = "source_table" DESTINATION1 = "gs://bucket_name/object_one" DESTINATION2 = "gs://bucket_name/object_two" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "extract": { "sourceTable": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": SOURCE, }, "destinationUris": [DESTINATION1, DESTINATION2], } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) dataset = client.dataset(self.DS_ID) source = dataset.table(SOURCE) job = client.extract_table(source, [DESTINATION1, DESTINATION2], job_id=JOB) # Check that extract_table actually starts the job. conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/projects/PROJECT/jobs") # Check the job resource. self.assertIsInstance(job, ExtractJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(job.source, source) self.assertEqual(list(job.destination_uris), [DESTINATION1, DESTINATION2]) def test_query_defaults(self): from google.cloud.bigquery.job import QueryJob QUERY = "select count(*) from persons" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": "some-random-id"}, "configuration": {"query": {"query": QUERY, "useLegacySql": False}}, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) job = client.query(QUERY) self.assertIsInstance(job, QueryJob) self.assertIsInstance(job.job_id, six.string_types) self.assertIs(job._client, client) self.assertEqual(job.query, QUERY) self.assertEqual(job.udf_resources, []) self.assertEqual(job.query_parameters, []) # Check that query actually starts the job. conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/projects/PROJECT/jobs") sent = req["data"] self.assertIsInstance(sent["jobReference"]["jobId"], six.string_types) sent_config = sent["configuration"]["query"] self.assertEqual(sent_config["query"], QUERY) self.assertFalse(sent_config["useLegacySql"]) def test_query_w_explicit_project(self): job_id = "some-job-id" query = "select count(*) from persons" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": {"query": {"query": query, "useLegacySql": False}}, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(resource) client.query( query, job_id=job_id, project="other-project", location=self.LOCATION ) # Check that query actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_query_w_explicit_job_config(self): job_id = "some-job-id" query = "select count(*) from persons" resource = { "jobReference": { "jobId": job_id, "projectId": self.PROJECT, "location": self.LOCATION, }, "configuration": { "query": { "query": query, "defaultDataset": { "projectId": self.PROJECT, "datasetId": "some-dataset", }, "useLegacySql": False, "useQueryCache": True, "maximumBytesBilled": "2000", } }, } creds = _make_credentials() http = object() from google.cloud.bigquery import QueryJobConfig, DatasetReference default_job_config = QueryJobConfig() default_job_config.default_dataset = DatasetReference( self.PROJECT, "some-dataset" ) default_job_config.maximum_bytes_billed = 1000 client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, default_query_job_config=default_job_config, ) conn = client._connection = make_connection(resource) job_config = QueryJobConfig() job_config.use_query_cache = True job_config.maximum_bytes_billed = 2000 client.query( query, job_id=job_id, location=self.LOCATION, job_config=job_config ) # Check that query actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/PROJECT/jobs", data=resource ) def test_query_w_invalid_job_config(self): from google.cloud.bigquery import QueryJobConfig, DatasetReference from google.cloud.bigquery import job job_id = "some-job-id" query = "select count(*) from persons" creds = _make_credentials() http = object() default_job_config = QueryJobConfig() default_job_config.default_dataset = DatasetReference( self.PROJECT, "some-dataset" ) default_job_config.maximum_bytes_billed = 1000 client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, default_query_job_config=default_job_config, ) job_config = job.LoadJobConfig() with self.assertRaises(TypeError) as exc: client.query( query, job_id=job_id, location=self.LOCATION, job_config=job_config ) self.assertIn("Expected an instance of QueryJobConfig", exc.exception.args[0]) def test_query_w_explicit_job_config_override(self): job_id = "some-job-id" query = "select count(*) from persons" resource = { "jobReference": { "jobId": job_id, "projectId": self.PROJECT, "location": self.LOCATION, }, "configuration": { "query": { "query": query, "defaultDataset": None, "useLegacySql": False, "useQueryCache": True, "maximumBytesBilled": "2000", } }, } creds = _make_credentials() http = object() from google.cloud.bigquery import QueryJobConfig, DatasetReference default_job_config = QueryJobConfig() default_job_config.default_dataset = DatasetReference( self.PROJECT, "some-dataset" ) default_job_config.maximum_bytes_billed = 1000 client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, default_query_job_config=default_job_config, ) conn = client._connection = make_connection(resource) job_config = QueryJobConfig() job_config.use_query_cache = True job_config.maximum_bytes_billed = 2000 job_config.default_dataset = None client.query( query, job_id=job_id, location=self.LOCATION, job_config=job_config ) # Check that query actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/PROJECT/jobs", data=resource ) def test_query_w_client_default_config_no_incoming(self): job_id = "some-job-id" query = "select count(*) from persons" resource = { "jobReference": { "jobId": job_id, "projectId": self.PROJECT, "location": self.LOCATION, }, "configuration": { "query": { "query": query, "useLegacySql": False, "maximumBytesBilled": "1000", } }, } creds = _make_credentials() http = object() from google.cloud.bigquery import QueryJobConfig default_job_config = QueryJobConfig() default_job_config.maximum_bytes_billed = 1000 client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, default_query_job_config=default_job_config, ) conn = client._connection = make_connection(resource) client.query(query, job_id=job_id, location=self.LOCATION) # Check that query actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/PROJECT/jobs", data=resource ) def test_query_w_invalid_default_job_config(self): job_id = "some-job-id" query = "select count(*) from persons" creds = _make_credentials() http = object() default_job_config = object() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, default_query_job_config=default_job_config, ) with self.assertRaises(TypeError) as exc: client.query(query, job_id=job_id, location=self.LOCATION) self.assertIn("Expected an instance of QueryJobConfig", exc.exception.args[0]) def test_query_w_client_location(self): job_id = "some-job-id" query = "select count(*) from persons" resource = { "jobReference": { "projectId": "other-project", "location": self.LOCATION, "jobId": job_id, }, "configuration": {"query": {"query": query, "useLegacySql": False}}, } creds = _make_credentials() http = object() client = self._make_one( project=self.PROJECT, credentials=creds, _http=http, location=self.LOCATION ) conn = client._connection = make_connection(resource) client.query(query, job_id=job_id, project="other-project") # Check that query actually starts the job. conn.api_request.assert_called_once_with( method="POST", path="/projects/other-project/jobs", data=resource ) def test_query_detect_location(self): query = "select count(*) from persons" resource_location = "EU" resource = { "jobReference": { "projectId": self.PROJECT, # Location not set in request, but present in the response. "location": resource_location, "jobId": "some-random-id", }, "configuration": {"query": {"query": query, "useLegacySql": False}}, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(resource) job = client.query(query) self.assertEqual(job.location, resource_location) # Check that request did not contain a location. conn.api_request.assert_called_once() _, req = conn.api_request.call_args sent = req["data"] self.assertIsNone(sent["jobReference"].get("location")) def test_query_w_udf_resources(self): from google.cloud.bigquery.job import QueryJob from google.cloud.bigquery.job import QueryJobConfig from google.cloud.bigquery.query import UDFResource RESOURCE_URI = "gs://some-bucket/js/lib.js" JOB = "job_name" QUERY = "select count(*) from persons" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "query": { "query": QUERY, "useLegacySql": True, "userDefinedFunctionResources": [{"resourceUri": RESOURCE_URI}], } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) udf_resources = [UDFResource("resourceUri", RESOURCE_URI)] config = QueryJobConfig() config.udf_resources = udf_resources config.use_legacy_sql = True job = client.query(QUERY, job_config=config, job_id=JOB) self.assertIsInstance(job, QueryJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(job.query, QUERY) self.assertEqual(job.udf_resources, udf_resources) self.assertEqual(job.query_parameters, []) # Check that query actually starts the job. conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/projects/PROJECT/jobs") sent = req["data"] self.assertIsInstance(sent["jobReference"]["jobId"], six.string_types) sent_config = sent["configuration"]["query"] self.assertEqual(sent_config["query"], QUERY) self.assertTrue(sent_config["useLegacySql"]) self.assertEqual( sent_config["userDefinedFunctionResources"][0], {"resourceUri": RESOURCE_URI}, ) def test_query_w_query_parameters(self): from google.cloud.bigquery.job import QueryJob from google.cloud.bigquery.job import QueryJobConfig from google.cloud.bigquery.query import ScalarQueryParameter JOB = "job_name" QUERY = "select count(*) from persons" RESOURCE = { "jobReference": {"projectId": self.PROJECT, "jobId": JOB}, "configuration": { "query": { "query": QUERY, "useLegacySql": False, "queryParameters": [ { "name": "foo", "parameterType": {"type": "INT64"}, "parameterValue": {"value": "123"}, } ], } }, } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESOURCE) query_parameters = [ScalarQueryParameter("foo", "INT64", 123)] config = QueryJobConfig() config.query_parameters = query_parameters job = client.query(QUERY, job_config=config, job_id=JOB) self.assertIsInstance(job, QueryJob) self.assertIs(job._client, client) self.assertEqual(job.job_id, JOB) self.assertEqual(job.query, QUERY) self.assertEqual(job.udf_resources, []) self.assertEqual(job.query_parameters, query_parameters) # Check that query actually starts the job. conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/projects/PROJECT/jobs") sent = req["data"] self.assertEqual(sent["jobReference"]["jobId"], JOB) sent_config = sent["configuration"]["query"] self.assertEqual(sent_config["query"], QUERY) self.assertFalse(sent_config["useLegacySql"]) self.assertEqual( sent_config["queryParameters"][0], { "name": "foo", "parameterType": {"type": "INT64"}, "parameterValue": {"value": "123"}, }, ) def test_insert_rows_wo_schema(self): from google.cloud.bigquery.table import Table creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) table = Table(self.TABLE_REF) ROWS = [ ("Phred Phlyntstone", 32), ("Bharney Rhubble", 33), ("Wylma Phlyntstone", 29), ("Bhettye Rhubble", 27), ] with self.assertRaises(ValueError) as exc: client.insert_rows(table, ROWS) self.assertIn("Could not determine schema for table", exc.exception.args[0]) def test_insert_rows_w_schema(self): import datetime from google.cloud._helpers import UTC from google.cloud._helpers import _datetime_to_rfc3339 from google.cloud._helpers import _microseconds_from_datetime from google.cloud.bigquery.schema import SchemaField WHEN_TS = 1437767599.006 WHEN = datetime.datetime.utcfromtimestamp(WHEN_TS).replace(tzinfo=UTC) PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), SchemaField("joined", "TIMESTAMP", mode="NULLABLE"), ] ROWS = [ ("Phred Phlyntstone", 32, _datetime_to_rfc3339(WHEN)), ("Bharney Rhubble", 33, WHEN + datetime.timedelta(seconds=1)), ("Wylma Phlyntstone", 29, WHEN + datetime.timedelta(seconds=2)), ("Bhettye Rhubble", 27, None), ] def _row_data(row): joined = row[2] if isinstance(row[2], datetime.datetime): joined = _microseconds_from_datetime(joined) * 1e-6 return {"full_name": row[0], "age": str(row[1]), "joined": joined} SENT = { "rows": [ {"json": _row_data(row), "insertId": str(i)} for i, row in enumerate(ROWS) ] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): # Test with using string IDs for the table. errors = client.insert_rows( "{}.{}".format(self.DS_ID, self.TABLE_ID), ROWS, selected_fields=schema ) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once() _, req = conn.api_request.call_args self.assertEqual(req["method"], "POST") self.assertEqual(req["path"], "/%s" % PATH) self.assertEqual(req["data"], SENT) def test_insert_rows_w_list_of_dictionaries(self): import datetime from google.cloud._helpers import UTC from google.cloud._helpers import _datetime_to_rfc3339 from google.cloud._helpers import _microseconds_from_datetime from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table WHEN_TS = 1437767599.006 WHEN = datetime.datetime.utcfromtimestamp(WHEN_TS).replace(tzinfo=UTC) PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), SchemaField("joined", "TIMESTAMP", mode="NULLABLE"), ] table = Table(self.TABLE_REF, schema=schema) ROWS = [ { "full_name": "Phred Phlyntstone", "age": 32, "joined": _datetime_to_rfc3339(WHEN), }, { "full_name": "Bharney Rhubble", "age": 33, "joined": WHEN + datetime.timedelta(seconds=1), }, { "full_name": "Wylma Phlyntstone", "age": 29, "joined": WHEN + datetime.timedelta(seconds=2), }, {"full_name": "Bhettye Rhubble", "age": 27, "joined": None}, ] def _row_data(row): joined = row["joined"] if isinstance(joined, datetime.datetime): row["joined"] = _microseconds_from_datetime(joined) * 1e-6 row["age"] = str(row["age"]) return row SENT = { "rows": [ {"json": _row_data(row), "insertId": str(i)} for i, row in enumerate(ROWS) ] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): errors = client.insert_rows(table, ROWS) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_w_list_of_Rows(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table from google.cloud.bigquery.table import Row PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), ] table = Table(self.TABLE_REF, schema=schema) f2i = {"full_name": 0, "age": 1} ROWS = [ Row(("Phred Phlyntstone", 32), f2i), Row(("Bharney Rhubble", 33), f2i), Row(("Wylma Phlyntstone", 29), f2i), Row(("Bhettye Rhubble", 27), f2i), ] def _row_data(row): return {"full_name": row[0], "age": str(row[1])} SENT = { "rows": [ {"json": _row_data(row), "insertId": str(i)} for i, row in enumerate(ROWS) ] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): errors = client.insert_rows(table, ROWS) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_w_skip_invalid_and_ignore_unknown(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) RESPONSE = { "insertErrors": [ { "index": 1, "errors": [ { "reason": "REASON", "location": "LOCATION", "debugInfo": "INFO", "message": "MESSAGE", } ], } ] } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(RESPONSE) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), SchemaField("voter", "BOOLEAN", mode="NULLABLE"), ] table = Table(self.TABLE_REF, schema=schema) ROWS = [ ("Phred Phlyntstone", 32, True), ("Bharney Rhubble", 33, False), ("Wylma Phlyntstone", 29, True), ("Bhettye Rhubble", 27, True), ] def _row_data(row): return { "full_name": row[0], "age": str(row[1]), "voter": row[2] and "true" or "false", } SENT = { "skipInvalidRows": True, "ignoreUnknownValues": True, "templateSuffix": "20160303", "rows": [ {"insertId": index, "json": _row_data(row)} for index, row in enumerate(ROWS) ], } errors = client.insert_rows( table, ROWS, row_ids=[index for index, _ in enumerate(ROWS)], skip_invalid_rows=True, ignore_unknown_values=True, template_suffix="20160303", ) self.assertEqual(len(errors), 1) self.assertEqual(errors[0]["index"], 1) self.assertEqual(len(errors[0]["errors"]), 1) self.assertEqual( errors[0]["errors"][0], RESPONSE["insertErrors"][0]["errors"][0] ) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_w_repeated_fields(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) color = SchemaField("color", "STRING", mode="REPEATED") items = SchemaField("items", "INTEGER", mode="REPEATED") score = SchemaField("score", "INTEGER") times = SchemaField("times", "TIMESTAMP", mode="REPEATED") distances = SchemaField("distances", "FLOAT", mode="REPEATED") structs = SchemaField( "structs", "RECORD", mode="REPEATED", fields=[score, times, distances] ) table = Table(self.TABLE_REF, schema=[color, items, structs]) ROWS = [ ( ["red", "green"], [1, 2], [ ( 12, [ datetime.datetime(2018, 12, 1, 12, 0, 0, tzinfo=pytz.utc), datetime.datetime(2018, 12, 1, 13, 0, 0, tzinfo=pytz.utc), ], [1.25, 2.5], ), { "score": 13, "times": [ datetime.datetime(2018, 12, 2, 12, 0, 0, tzinfo=pytz.utc), datetime.datetime(2018, 12, 2, 13, 0, 0, tzinfo=pytz.utc), ], "distances": [-1.25, -2.5], }, ], ), {"color": None, "items": [], "structs": [(None, [], [3.5])]}, ] SENT = { "rows": [ { "json": { "color": ["red", "green"], "items": ["1", "2"], "structs": [ { "score": "12", "times": [ 1543665600.0, # 2018-12-01 12:00 UTC 1543669200.0, # 2018-12-01 13:00 UTC ], "distances": [1.25, 2.5], }, { "score": "13", "times": [ 1543752000.0, # 2018-12-02 12:00 UTC 1543755600.0, # 2018-12-02 13:00 UTC ], "distances": [-1.25, -2.5], }, ], }, "insertId": "0", }, { "json": { "color": None, "items": [], "structs": [{"score": None, "times": [], "distances": [3.5]}], }, "insertId": "1", }, ] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): errors = client.insert_rows(table, ROWS) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_w_record_schema(self): from google.cloud.bigquery.schema import SchemaField PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) full_name = SchemaField("full_name", "STRING", mode="REQUIRED") area_code = SchemaField("area_code", "STRING", "REQUIRED") local_number = SchemaField("local_number", "STRING", "REQUIRED") rank = SchemaField("rank", "INTEGER", "REQUIRED") phone = SchemaField( "phone", "RECORD", mode="NULLABLE", fields=[area_code, local_number, rank] ) ROWS = [ ( "Phred Phlyntstone", {"area_code": "800", "local_number": "555-1212", "rank": 1}, ), ("Bharney Rhubble", ("877", "768-5309", 2)), ("Wylma Phlyntstone", None), ] SENT = { "rows": [ { "json": { "full_name": "Phred Phlyntstone", "phone": { "area_code": "800", "local_number": "555-1212", "rank": "1", }, }, "insertId": "0", }, { "json": { "full_name": "Bharney Rhubble", "phone": { "area_code": "877", "local_number": "768-5309", "rank": "2", }, }, "insertId": "1", }, { "json": {"full_name": "Wylma Phlyntstone", "phone": None}, "insertId": "2", }, ] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): errors = client.insert_rows( self.TABLE_REF, ROWS, selected_fields=[full_name, phone] ) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_w_explicit_none_insert_ids(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table PATH = "projects/{}/datasets/{}/tables/{}/insertAll".format( self.PROJECT, self.DS_ID, self.TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), ] table = Table(self.TABLE_REF, schema=schema) ROWS = [ {"full_name": "Phred Phlyntstone", "age": 32}, {"full_name": "Bharney Rhubble", "age": 33}, ] def _row_data(row): row["age"] = str(row["age"]) return row SENT = {"rows": [{"json": _row_data(row), "insertId": None} for row in ROWS]} errors = client.insert_rows(table, ROWS, row_ids=[None] * len(ROWS)) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/{}".format(PATH), data=SENT ) def test_insert_rows_errors(self): from google.cloud.bigquery.table import Table ROWS = [ ("Phred Phlyntstone", 32, True), ("Bharney Rhubble", 33, False), ("Wylma Phlyntstone", 29, True), ("Bhettye Rhubble", 27, True), ] creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) # table ref with no selected fields with self.assertRaises(ValueError): client.insert_rows(self.TABLE_REF, ROWS) # table with no schema with self.assertRaises(ValueError): client.insert_rows(Table(self.TABLE_REF), ROWS) # neither Table nor tableReference with self.assertRaises(TypeError): client.insert_rows(1, ROWS) def test_insert_rows_w_numeric(self): from google.cloud.bigquery import table from google.cloud.bigquery.schema import SchemaField project = "PROJECT" ds_id = "DS_ID" table_id = "TABLE_ID" creds = _make_credentials() http = object() client = self._make_one(project=project, credentials=creds, _http=http) conn = client._connection = make_connection({}) table_ref = DatasetReference(project, ds_id).table(table_id) schema = [SchemaField("account", "STRING"), SchemaField("balance", "NUMERIC")] insert_table = table.Table(table_ref, schema=schema) rows = [ ("Savings", decimal.Decimal("23.47")), ("Checking", decimal.Decimal("1.98")), ("Mortgage", decimal.Decimal("-12345678909.87654321")), ] with mock.patch("uuid.uuid4", side_effect=map(str, range(len(rows)))): errors = client.insert_rows(insert_table, rows) self.assertEqual(len(errors), 0) rows_json = [ {"account": "Savings", "balance": "23.47"}, {"account": "Checking", "balance": "1.98"}, {"account": "Mortgage", "balance": "-12345678909.87654321"}, ] sent = { "rows": [ {"json": row, "insertId": str(i)} for i, row in enumerate(rows_json) ] } conn.api_request.assert_called_once_with( method="POST", path="/projects/{}/datasets/{}/tables/{}/insertAll".format( project, ds_id, table_id ), data=sent, ) @unittest.skipIf(pandas is None, "Requires `pandas`") def test_insert_rows_from_dataframe(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table API_PATH = "/projects/{}/datasets/{}/tables/{}/insertAll".format( self.PROJECT, self.DS_ID, self.TABLE_REF.table_id ) dataframe = pandas.DataFrame( [ {"name": u"Little One", "age": 10, "adult": False}, {"name": u"Young Gun", "age": 20, "adult": True}, {"name": u"Dad", "age": 30, "adult": True}, {"name": u"Stranger", "age": 40, "adult": True}, ] ) # create client creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}, {}) # create table schema = [ SchemaField("name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), SchemaField("adult", "BOOLEAN", mode="REQUIRED"), ] table = Table(self.TABLE_REF, schema=schema) with mock.patch("uuid.uuid4", side_effect=map(str, range(len(dataframe)))): error_info = client.insert_rows_from_dataframe( table, dataframe, chunk_size=3 ) self.assertEqual(len(error_info), 2) for chunk_errors in error_info: assert chunk_errors == [] EXPECTED_SENT_DATA = [ { "rows": [ { "insertId": "0", "json": {"name": "Little One", "age": "10", "adult": "false"}, }, { "insertId": "1", "json": {"name": "Young Gun", "age": "20", "adult": "true"}, }, { "insertId": "2", "json": {"name": "Dad", "age": "30", "adult": "true"}, }, ] }, { "rows": [ { "insertId": "3", "json": {"name": "Stranger", "age": "40", "adult": "true"}, } ] }, ] actual_calls = conn.api_request.call_args_list for call, expected_data in six.moves.zip_longest( actual_calls, EXPECTED_SENT_DATA ): expected_call = mock.call(method="POST", path=API_PATH, data=expected_data) assert call == expected_call @unittest.skipIf(pandas is None, "Requires `pandas`") def test_insert_rows_from_dataframe_many_columns(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table API_PATH = "/projects/{}/datasets/{}/tables/{}/insertAll".format( self.PROJECT, self.DS_ID, self.TABLE_REF.table_id ) N_COLUMNS = 256 # should be >= 256 dataframe = pandas.DataFrame( [{"foo_{}".format(i): "bar_{}".format(i) for i in range(N_COLUMNS)}] ) # create client creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}, {}) # create table schema = [SchemaField("foo_{}".format(i), "STRING") for i in range(N_COLUMNS)] table = Table(self.TABLE_REF, schema=schema) with mock.patch("uuid.uuid4", side_effect=map(str, range(len(dataframe)))): error_info = client.insert_rows_from_dataframe( table, dataframe, chunk_size=3 ) assert len(error_info) == 1 assert error_info[0] == [] EXPECTED_SENT_DATA = { "rows": [ { "insertId": "0", "json": { "foo_{}".format(i): "bar_{}".format(i) for i in range(N_COLUMNS) }, } ] } expected_call = mock.call(method="POST", path=API_PATH, data=EXPECTED_SENT_DATA) actual_calls = conn.api_request.call_args_list assert len(actual_calls) == 1 assert actual_calls[0] == expected_call @unittest.skipIf(pandas is None, "Requires `pandas`") def test_insert_rows_from_dataframe_w_explicit_none_insert_ids(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table API_PATH = "/projects/{}/datasets/{}/tables/{}/insertAll".format( self.PROJECT, self.DS_ID, self.TABLE_REF.table_id ) dataframe = pandas.DataFrame( [ {"name": u"Little One", "adult": False}, {"name": u"Young Gun", "adult": True}, ] ) # create client creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}, {}) # create table schema = [ SchemaField("name", "STRING", mode="REQUIRED"), SchemaField("adult", "BOOLEAN", mode="REQUIRED"), ] table = Table(self.TABLE_REF, schema=schema) error_info = client.insert_rows_from_dataframe( table, dataframe, row_ids=[None] * len(dataframe) ) self.assertEqual(len(error_info), 1) assert error_info[0] == [] # no chunk errors EXPECTED_SENT_DATA = { "rows": [ {"insertId": None, "json": {"name": "Little One", "adult": "false"}}, {"insertId": None, "json": {"name": "Young Gun", "adult": "true"}}, ] } actual_calls = conn.api_request.call_args_list assert len(actual_calls) == 1 assert actual_calls[0] == mock.call( method="POST", path=API_PATH, data=EXPECTED_SENT_DATA ) def test_insert_rows_json(self): from google.cloud.bigquery.dataset import DatasetReference from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table PROJECT = "PROJECT" DS_ID = "DS_ID" TABLE_ID = "TABLE_ID" PATH = "projects/%s/datasets/%s/tables/%s/insertAll" % ( PROJECT, DS_ID, TABLE_ID, ) creds = _make_credentials() http = object() client = self._make_one(project=PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection({}) table_ref = DatasetReference(PROJECT, DS_ID).table(TABLE_ID) schema = [ SchemaField("full_name", "STRING", mode="REQUIRED"), SchemaField("age", "INTEGER", mode="REQUIRED"), SchemaField("joined", "TIMESTAMP", mode="NULLABLE"), ] table = Table(table_ref, schema=schema) ROWS = [ { "full_name": "Phred Phlyntstone", "age": "32", "joined": "2015-07-24T19:53:19.006000Z", }, {"full_name": "Bharney Rhubble", "age": "33", "joined": 1437767600.006}, {"full_name": "Wylma Phlyntstone", "age": "29", "joined": 1437767601.006}, {"full_name": "Bhettye Rhubble", "age": "27", "joined": None}, ] SENT = { "rows": [{"json": row, "insertId": str(i)} for i, row in enumerate(ROWS)] } with mock.patch("uuid.uuid4", side_effect=map(str, range(len(ROWS)))): errors = client.insert_rows_json(table, ROWS) self.assertEqual(len(errors), 0) conn.api_request.assert_called_once_with( method="POST", path="/%s" % PATH, data=SENT ) def test_insert_rows_json_with_string_id(self): rows = [{"col1": "val1"}] creds = _make_credentials() http = object() client = self._make_one( project="default-project", credentials=creds, _http=http ) conn = client._connection = make_connection({}) with mock.patch("uuid.uuid4", side_effect=map(str, range(len(rows)))): errors = client.insert_rows_json("proj.dset.tbl", rows) self.assertEqual(len(errors), 0) expected = { "rows": [{"json": row, "insertId": str(i)} for i, row in enumerate(rows)] } conn.api_request.assert_called_once_with( method="POST", path="/projects/proj/datasets/dset/tables/tbl/insertAll", data=expected, ) def test_insert_rows_json_w_explicit_none_insert_ids(self): rows = [{"col1": "val1"}, {"col2": "val2"}] creds = _make_credentials() http = object() client = self._make_one( project="default-project", credentials=creds, _http=http ) conn = client._connection = make_connection({}) errors = client.insert_rows_json( "proj.dset.tbl", rows, row_ids=[None] * len(rows), ) self.assertEqual(len(errors), 0) expected = {"rows": [{"json": row, "insertId": None} for row in rows]} conn.api_request.assert_called_once_with( method="POST", path="/projects/proj/datasets/dset/tables/tbl/insertAll", data=expected, ) def test_list_partitions(self): from google.cloud.bigquery.table import Table rows = 3 meta_info = _make_list_partitons_meta_info( self.PROJECT, self.DS_ID, self.TABLE_ID, rows ) data = { "totalRows": str(rows), "rows": [ {"f": [{"v": "20180101"}]}, {"f": [{"v": "20180102"}]}, {"f": [{"v": "20180103"}]}, ], } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) client._connection = make_connection(meta_info, data) table = Table(self.TABLE_REF) partition_list = client.list_partitions(table) self.assertEqual(len(partition_list), rows) self.assertIn("20180102", partition_list) def test_list_partitions_with_string_id(self): meta_info = _make_list_partitons_meta_info( self.PROJECT, self.DS_ID, self.TABLE_ID, 0 ) creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) client._connection = make_connection(meta_info, {}) partition_list = client.list_partitions( "{}.{}".format(self.DS_ID, self.TABLE_ID) ) self.assertEqual(len(partition_list), 0) def test_list_rows(self): import datetime from google.cloud._helpers import UTC from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table from google.cloud.bigquery.table import Row PATH = "projects/%s/datasets/%s/tables/%s/data" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) WHEN_TS = 1437767599.006 WHEN = datetime.datetime.utcfromtimestamp(WHEN_TS).replace(tzinfo=UTC) WHEN_1 = WHEN + datetime.timedelta(seconds=1) WHEN_2 = WHEN + datetime.timedelta(seconds=2) ROWS = 1234 TOKEN = "TOKEN" def _bigquery_timestamp_float_repr(ts_float): # Preserve microsecond precision for E+09 timestamps return "%0.15E" % (ts_float,) DATA = { "totalRows": str(ROWS), "pageToken": TOKEN, "rows": [ { "f": [ {"v": "Phred Phlyntstone"}, {"v": "32"}, {"v": _bigquery_timestamp_float_repr(WHEN_TS)}, ] }, { "f": [ {"v": "Bharney Rhubble"}, {"v": "33"}, {"v": _bigquery_timestamp_float_repr(WHEN_TS + 1)}, ] }, { "f": [ {"v": "Wylma Phlyntstone"}, {"v": "29"}, {"v": _bigquery_timestamp_float_repr(WHEN_TS + 2)}, ] }, {"f": [{"v": "Bhettye Rhubble"}, {"v": None}, {"v": None}]}, ], } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(DATA, DATA) full_name = SchemaField("full_name", "STRING", mode="REQUIRED") age = SchemaField("age", "INTEGER", mode="NULLABLE") joined = SchemaField("joined", "TIMESTAMP", mode="NULLABLE") table = Table(self.TABLE_REF, schema=[full_name, age, joined]) iterator = client.list_rows(table) page = six.next(iterator.pages) rows = list(page) total_rows = iterator.total_rows page_token = iterator.next_page_token f2i = {"full_name": 0, "age": 1, "joined": 2} self.assertEqual(len(rows), 4) self.assertEqual(rows[0], Row(("Phred Phlyntstone", 32, WHEN), f2i)) self.assertEqual(rows[1], Row(("Bharney Rhubble", 33, WHEN_1), f2i)) self.assertEqual(rows[2], Row(("Wylma Phlyntstone", 29, WHEN_2), f2i)) self.assertEqual(rows[3], Row(("Bhettye Rhubble", None, None), f2i)) self.assertEqual(total_rows, ROWS) self.assertEqual(page_token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={} ) def test_list_rows_empty_table(self): response = {"totalRows": "0", "rows": []} creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) client._connection = make_connection(response, response) # Table that has no schema because it's an empty table. rows = client.list_rows( # Test with using a string for the table ID. "{}.{}.{}".format( self.TABLE_REF.project, self.TABLE_REF.dataset_id, self.TABLE_REF.table_id, ), selected_fields=[], ) # When a table reference / string and selected_fields is provided, # total_rows can't be populated until iteration starts. self.assertIsNone(rows.total_rows) self.assertEqual(tuple(rows), ()) self.assertEqual(rows.total_rows, 0) def test_list_rows_query_params(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) table = Table( self.TABLE_REF, schema=[SchemaField("age", "INTEGER", mode="NULLABLE")] ) tests = [ ({}, {}), ({"start_index": 1}, {"startIndex": 1}), ({"max_results": 2}, {"maxResults": 2}), ({"start_index": 1, "max_results": 2}, {"startIndex": 1, "maxResults": 2}), ] conn = client._connection = make_connection(*len(tests) * [{}]) for i, test in enumerate(tests): iterator = client.list_rows(table, **test[0]) six.next(iterator.pages) req = conn.api_request.call_args_list[i] self.assertEqual(req[1]["query_params"], test[1], "for kwargs %s" % test[0]) def test_list_rows_repeated_fields(self): from google.cloud.bigquery.schema import SchemaField PATH = "projects/%s/datasets/%s/tables/%s/data" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) ROWS = 1234 TOKEN = "TOKEN" DATA = { "totalRows": ROWS, "pageToken": TOKEN, "rows": [ { "f": [ {"v": [{"v": "red"}, {"v": "green"}]}, { "v": [ { "v": { "f": [ {"v": [{"v": "1"}, {"v": "2"}]}, {"v": [{"v": "3.1415"}, {"v": "1.414"}]}, ] } } ] }, ] } ], } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(DATA) color = SchemaField("color", "STRING", mode="REPEATED") index = SchemaField("index", "INTEGER", "REPEATED") score = SchemaField("score", "FLOAT", "REPEATED") struct = SchemaField("struct", "RECORD", mode="REPEATED", fields=[index, score]) iterator = client.list_rows(self.TABLE_REF, selected_fields=[color, struct]) page = six.next(iterator.pages) rows = list(page) total_rows = iterator.total_rows page_token = iterator.next_page_token self.assertEqual(len(rows), 1) self.assertEqual(rows[0][0], ["red", "green"]) self.assertEqual(rows[0][1], [{"index": [1, 2], "score": [3.1415, 1.414]}]) self.assertEqual(total_rows, ROWS) self.assertEqual(page_token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={"selectedFields": "color,struct"}, ) def test_list_rows_w_record_schema(self): from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.table import Table PATH = "projects/%s/datasets/%s/tables/%s/data" % ( self.PROJECT, self.DS_ID, self.TABLE_ID, ) ROWS = 1234 TOKEN = "TOKEN" DATA = { "totalRows": ROWS, "pageToken": TOKEN, "rows": [ { "f": [ {"v": "Phred Phlyntstone"}, {"v": {"f": [{"v": "800"}, {"v": "555-1212"}, {"v": 1}]}}, ] }, { "f": [ {"v": "Bharney Rhubble"}, {"v": {"f": [{"v": "877"}, {"v": "768-5309"}, {"v": 2}]}}, ] }, {"f": [{"v": "Wylma Phlyntstone"}, {"v": None}]}, ], } creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(DATA) full_name = SchemaField("full_name", "STRING", mode="REQUIRED") area_code = SchemaField("area_code", "STRING", "REQUIRED") local_number = SchemaField("local_number", "STRING", "REQUIRED") rank = SchemaField("rank", "INTEGER", "REQUIRED") phone = SchemaField( "phone", "RECORD", mode="NULLABLE", fields=[area_code, local_number, rank] ) table = Table(self.TABLE_REF, schema=[full_name, phone]) iterator = client.list_rows(table) page = six.next(iterator.pages) rows = list(page) total_rows = iterator.total_rows page_token = iterator.next_page_token self.assertEqual(len(rows), 3) self.assertEqual(rows[0][0], "Phred Phlyntstone") self.assertEqual( rows[0][1], {"area_code": "800", "local_number": "555-1212", "rank": 1} ) self.assertEqual(rows[1][0], "Bharney Rhubble") self.assertEqual( rows[1][1], {"area_code": "877", "local_number": "768-5309", "rank": 2} ) self.assertEqual(rows[2][0], "Wylma Phlyntstone") self.assertIsNone(rows[2][1]) self.assertEqual(total_rows, ROWS) self.assertEqual(page_token, TOKEN) conn.api_request.assert_called_once_with( method="GET", path="/%s" % PATH, query_params={} ) def test_list_rows_with_missing_schema(self): from google.cloud.bigquery.table import Table, TableListItem table_path = "/projects/{}/datasets/{}/tables/{}".format( self.PROJECT, self.DS_ID, self.TABLE_ID ) tabledata_path = "{}/data".format(table_path) table_list_item_data = { "id": "%s:%s:%s" % (self.PROJECT, self.DS_ID, self.TABLE_ID), "tableReference": { "projectId": self.PROJECT, "datasetId": self.DS_ID, "tableId": self.TABLE_ID, }, } table_data = copy.deepcopy(table_list_item_data) # Intentionally make wrong, since total_rows can update during iteration. table_data["numRows"] = 2 table_data["schema"] = { "fields": [ {"name": "name", "type": "STRING"}, {"name": "age", "type": "INTEGER"}, ] } rows_data = { "totalRows": 3, "pageToken": None, "rows": [ {"f": [{"v": "Phred Phlyntstone"}, {"v": "32"}]}, {"f": [{"v": "Bharney Rhubble"}, {"v": "31"}]}, {"f": [{"v": "Wylma Phlyntstone"}, {"v": None}]}, ], } creds = _make_credentials() http = object() schemaless_tables = ( "{}.{}".format(self.DS_ID, self.TABLE_ID), self.TABLE_REF, Table(self.TABLE_REF), TableListItem(table_list_item_data), ) for table in schemaless_tables: client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) conn = client._connection = make_connection(table_data, rows_data) row_iter = client.list_rows(table) conn.api_request.assert_called_once_with(method="GET", path=table_path) conn.api_request.reset_mock() self.assertEqual(row_iter.total_rows, 2, msg=repr(table)) rows = list(row_iter) conn.api_request.assert_called_once_with( method="GET", path=tabledata_path, query_params={} ) self.assertEqual(row_iter.total_rows, 3, msg=repr(table)) self.assertEqual(rows[0].name, "Phred Phlyntstone", msg=repr(table)) self.assertEqual(rows[1].age, 31, msg=repr(table)) self.assertIsNone(rows[2].age, msg=repr(table)) def test_list_rows_error(self): creds = _make_credentials() http = object() client = self._make_one(project=self.PROJECT, credentials=creds, _http=http) # neither Table nor tableReference with self.assertRaises(TypeError): client.list_rows(1) class Test_make_job_id(unittest.TestCase): def _call_fut(self, job_id, prefix=None): from google.cloud.bigquery.client import _make_job_id return _make_job_id(job_id, prefix=prefix) def test__make_job_id_wo_suffix(self): job_id = self._call_fut("job_id") self.assertEqual(job_id, "job_id") def test__make_job_id_w_suffix(self): with mock.patch("uuid.uuid4", side_effect=["212345"]): job_id = self._call_fut(None, prefix="job_id") self.assertEqual(job_id, "job_id212345") def test__make_random_job_id(self): with mock.patch("uuid.uuid4", side_effect=["212345"]): job_id = self._call_fut(None) self.assertEqual(job_id, "212345") def test__make_job_id_w_job_id_overrides_prefix(self): job_id = self._call_fut("job_id", prefix="unused_prefix") self.assertEqual(job_id, "job_id") class TestClientUpload(object): # NOTE: This is a "partner" to `TestClient` meant to test some of the # "load_table_from_file" portions of `Client`. It also uses # `pytest`-style tests rather than `unittest`-style. from google.cloud.bigquery.job import SourceFormat TABLE_REF = DatasetReference("project_id", "test_dataset").table("test_table") LOCATION = "us-central" @staticmethod def _make_client(transport=None, location=None): from google.cloud.bigquery import _http from google.cloud.bigquery import client cl = client.Client( project="project_id", credentials=_make_credentials(), _http=transport, location=location, ) cl._connection = mock.create_autospec(_http.Connection, instance=True) return cl @staticmethod def _make_response(status_code, content="", headers={}): """Make a mock HTTP response.""" import requests response = requests.Response() response.request = requests.Request("POST", "http://example.com").prepare() response._content = content.encode("utf-8") response.headers.update(headers) response.status_code = status_code return response @classmethod def _make_do_upload_patch(cls, client, method, resource={}, side_effect=None): """Patches the low-level upload helpers.""" if side_effect is None: side_effect = [ cls._make_response( http_client.OK, json.dumps(resource), {"Content-Type": "application/json"}, ) ] return mock.patch.object(client, method, side_effect=side_effect, autospec=True) EXPECTED_CONFIGURATION = { "jobReference": {"projectId": "project_id", "jobId": "job_id"}, "configuration": { "load": { "sourceFormat": SourceFormat.CSV, "destinationTable": { "projectId": "project_id", "datasetId": "test_dataset", "tableId": "test_table", }, } }, } @staticmethod def _make_file_obj(): return io.BytesIO(b"hello, is it me you're looking for?") def _make_gzip_file_obj(self, writable): if writable: return gzip.GzipFile(mode="w", fileobj=io.BytesIO()) else: return gzip.GzipFile(mode="r", fileobj=self._make_file_obj()) @staticmethod def _make_config(): from google.cloud.bigquery.job import LoadJobConfig from google.cloud.bigquery.job import SourceFormat config = LoadJobConfig() config.source_format = SourceFormat.CSV return config # High-level tests def test_load_table_from_file_resumable(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client() file_obj = self._make_file_obj() do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, self.TABLE_REF, job_id="job_id", job_config=self._make_config(), ) do_upload.assert_called_once_with( file_obj, self.EXPECTED_CONFIGURATION, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_w_explicit_project(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client() file_obj = self._make_file_obj() do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, self.TABLE_REF, job_id="job_id", project="other-project", location=self.LOCATION, job_config=self._make_config(), ) expected_resource = copy.deepcopy(self.EXPECTED_CONFIGURATION) expected_resource["jobReference"]["location"] = self.LOCATION expected_resource["jobReference"]["projectId"] = "other-project" do_upload.assert_called_once_with( file_obj, expected_resource, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_w_client_location(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client(location=self.LOCATION) file_obj = self._make_file_obj() do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, # Test with string for table ID. "{}.{}.{}".format( self.TABLE_REF.project, self.TABLE_REF.dataset_id, self.TABLE_REF.table_id, ), job_id="job_id", project="other-project", job_config=self._make_config(), ) expected_resource = copy.deepcopy(self.EXPECTED_CONFIGURATION) expected_resource["jobReference"]["location"] = self.LOCATION expected_resource["jobReference"]["projectId"] = "other-project" do_upload.assert_called_once_with( file_obj, expected_resource, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_resumable_metadata(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery.job import CreateDisposition from google.cloud.bigquery.job import WriteDisposition client = self._make_client() file_obj = self._make_file_obj() config = self._make_config() config.allow_jagged_rows = False config.allow_quoted_newlines = False config.create_disposition = CreateDisposition.CREATE_IF_NEEDED config.encoding = "utf8" config.field_delimiter = "," config.ignore_unknown_values = False config.max_bad_records = 0 config.quote_character = '"' config.skip_leading_rows = 1 config.write_disposition = WriteDisposition.WRITE_APPEND config.null_marker = r"\N" expected_config = { "jobReference": {"projectId": "project_id", "jobId": "job_id"}, "configuration": { "load": { "destinationTable": { "projectId": self.TABLE_REF.project, "datasetId": self.TABLE_REF.dataset_id, "tableId": self.TABLE_REF.table_id, }, "sourceFormat": config.source_format, "allowJaggedRows": config.allow_jagged_rows, "allowQuotedNewlines": config.allow_quoted_newlines, "createDisposition": config.create_disposition, "encoding": config.encoding, "fieldDelimiter": config.field_delimiter, "ignoreUnknownValues": config.ignore_unknown_values, "maxBadRecords": config.max_bad_records, "quote": config.quote_character, "skipLeadingRows": str(config.skip_leading_rows), "writeDisposition": config.write_disposition, "nullMarker": config.null_marker, } }, } do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", expected_config ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, self.TABLE_REF, job_id="job_id", job_config=config ) do_upload.assert_called_once_with( file_obj, expected_config, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_multipart(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client() file_obj = self._make_file_obj() file_obj_size = 10 config = self._make_config() do_upload_patch = self._make_do_upload_patch( client, "_do_multipart_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, self.TABLE_REF, job_id="job_id", job_config=config, size=file_obj_size, ) do_upload.assert_called_once_with( file_obj, self.EXPECTED_CONFIGURATION, file_obj_size, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_with_retries(self): client = self._make_client() file_obj = self._make_file_obj() num_retries = 20 do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( file_obj, self.TABLE_REF, num_retries=num_retries, job_id="job_id", job_config=self._make_config(), ) do_upload.assert_called_once_with( file_obj, self.EXPECTED_CONFIGURATION, num_retries ) def test_load_table_from_file_with_rewind(self): client = self._make_client() file_obj = self._make_file_obj() file_obj.seek(2) with self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ): client.load_table_from_file(file_obj, self.TABLE_REF, rewind=True) assert file_obj.tell() == 0 def test_load_table_from_file_with_readable_gzip(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client() gzip_file = self._make_gzip_file_obj(writable=False) do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", self.EXPECTED_CONFIGURATION ) with do_upload_patch as do_upload: client.load_table_from_file( gzip_file, self.TABLE_REF, job_id="job_id", job_config=self._make_config(), ) do_upload.assert_called_once_with( gzip_file, self.EXPECTED_CONFIGURATION, _DEFAULT_NUM_RETRIES ) def test_load_table_from_file_with_writable_gzip(self): client = self._make_client() gzip_file = self._make_gzip_file_obj(writable=True) with pytest.raises(ValueError): client.load_table_from_file( gzip_file, self.TABLE_REF, job_id="job_id", job_config=self._make_config(), ) def test_load_table_from_file_failure(self): from google.resumable_media import InvalidResponse from google.cloud import exceptions client = self._make_client() file_obj = self._make_file_obj() response = self._make_response( content="Someone is already in this spot.", status_code=http_client.CONFLICT ) do_upload_patch = self._make_do_upload_patch( client, "_do_resumable_upload", side_effect=InvalidResponse(response) ) with do_upload_patch, pytest.raises(exceptions.Conflict) as exc_info: client.load_table_from_file(file_obj, self.TABLE_REF, rewind=True) assert response.text in exc_info.value.message assert exc_info.value.errors == [] def test_load_table_from_file_bad_mode(self): client = self._make_client() file_obj = mock.Mock(spec=["mode"]) file_obj.mode = "x" with pytest.raises(ValueError): client.load_table_from_file(file_obj, self.TABLE_REF) def test_load_table_from_file_w_invalid_job_config(self): from google.cloud.bigquery import job client = self._make_client() gzip_file = self._make_gzip_file_obj(writable=True) config = job.QueryJobConfig() with pytest.raises(TypeError) as exc: client.load_table_from_file( gzip_file, self.TABLE_REF, job_id="job_id", job_config=config ) err_msg = str(exc.value) assert "Expected an instance of LoadJobConfig" in err_msg @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}] dataframe = pandas.DataFrame(records) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, return_value=mock.Mock( schema=[SchemaField("id", "INTEGER"), SchemaField("age", "INTEGER")] ), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file, get_table_patch: client.load_table_from_dataframe(dataframe, self.TABLE_REF) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=None, project=None, job_config=mock.ANY, ) sent_file = load_table_from_file.mock_calls[0][1][1] assert sent_file.closed sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_client_location(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client(location=self.LOCATION) records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}] dataframe = pandas.DataFrame(records) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, return_value=mock.Mock( schema=[SchemaField("id", "INTEGER"), SchemaField("age", "INTEGER")] ), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file, get_table_patch: client.load_table_from_dataframe(dataframe, self.TABLE_REF) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_file = load_table_from_file.mock_calls[0][1][1] assert sent_file.closed sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_custom_job_config(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}] dataframe = pandas.DataFrame(records) job_config = job.LoadJobConfig( write_disposition=job.WriteDisposition.WRITE_TRUNCATE ) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, return_value=mock.Mock( schema=[SchemaField("id", "INTEGER"), SchemaField("age", "INTEGER")] ), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file, get_table_patch as get_table: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) # no need to fetch and inspect table schema for WRITE_TRUNCATE jobs assert not get_table.called load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET assert sent_config.write_disposition == job.WriteDisposition.WRITE_TRUNCATE @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_automatic_schema(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() df_data = collections.OrderedDict( [ ("int_col", [1, 2, 3]), ("float_col", [1.0, 2.0, 3.0]), ("bool_col", [True, False, True]), ( "dt_col", pandas.Series( [ datetime.datetime(2010, 1, 2, 3, 44, 50), datetime.datetime(2011, 2, 3, 14, 50, 59), datetime.datetime(2012, 3, 14, 15, 16), ], dtype="datetime64[ns]", ), ), ( "ts_col", pandas.Series( [ datetime.datetime(2010, 1, 2, 3, 44, 50), datetime.datetime(2011, 2, 3, 14, 50, 59), datetime.datetime(2012, 3, 14, 15, 16), ], dtype="datetime64[ns]", ).dt.tz_localize(pytz.utc), ), ] ) dataframe = pandas.DataFrame(df_data, columns=df_data.keys()) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, side_effect=google.api_core.exceptions.NotFound("Table not found"), ) with load_patch as load_table_from_file, get_table_patch: client.load_table_from_dataframe( dataframe, self.TABLE_REF, location=self.LOCATION ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET assert tuple(sent_config.schema) == ( SchemaField("int_col", "INTEGER"), SchemaField("float_col", "FLOAT"), SchemaField("bool_col", "BOOLEAN"), SchemaField("dt_col", "DATETIME"), SchemaField("ts_col", "TIMESTAMP"), ) @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_index_and_auto_schema(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() df_data = collections.OrderedDict( [("int_col", [10, 20, 30]), ("float_col", [1.0, 2.0, 3.0])] ) dataframe = pandas.DataFrame( df_data, index=pandas.Index(name="unique_name", data=["one", "two", "three"]), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, return_value=mock.Mock( schema=[ SchemaField("int_col", "INTEGER"), SchemaField("float_col", "FLOAT"), SchemaField("unique_name", "STRING"), ] ), ) with load_patch as load_table_from_file, get_table_patch: client.load_table_from_dataframe( dataframe, self.TABLE_REF, location=self.LOCATION ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET sent_schema = sorted(sent_config.schema, key=operator.attrgetter("name")) expected_sent_schema = [ SchemaField("float_col", "FLOAT"), SchemaField("int_col", "INTEGER"), SchemaField("unique_name", "STRING"), ] assert sent_schema == expected_sent_schema @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_unknown_table(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES client = self._make_client() records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}] dataframe = pandas.DataFrame(records) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, side_effect=google.api_core.exceptions.NotFound("Table not found"), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file, get_table_patch: # there should be no error client.load_table_from_dataframe(dataframe, self.TABLE_REF) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=None, project=None, job_config=mock.ANY, ) @unittest.skipIf(pandas is None, "Requires `pandas`") def test_load_table_from_dataframe_no_schema_warning_wo_pyarrow(self): client = self._make_client() # Pick at least one column type that translates to Pandas dtype # "object". A string column matches that. records = [{"name": "Monty", "age": 100}, {"name": "Python", "age": 60}] dataframe = pandas.DataFrame(records) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, side_effect=google.api_core.exceptions.NotFound("Table not found"), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) pyarrow_patch = mock.patch("google.cloud.bigquery.client.pyarrow", None) pyarrow_patch_helpers = mock.patch( "google.cloud.bigquery._pandas_helpers.pyarrow", None ) catch_warnings = warnings.catch_warnings(record=True) with get_table_patch, load_patch, pyarrow_patch, pyarrow_patch_helpers, catch_warnings as warned: client.load_table_from_dataframe( dataframe, self.TABLE_REF, location=self.LOCATION ) matches = [ warning for warning in warned if warning.category in (DeprecationWarning, PendingDeprecationWarning) and "could not be detected" in str(warning) and "please provide a schema" in str(warning) ] assert matches, "A missing schema deprecation warning was not raised." @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_struct_fields_error(self): from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() records = [{"float_column": 3.14, "struct_column": [{"foo": 1}, {"bar": -1}]}] dataframe = pandas.DataFrame(data=records) schema = [ SchemaField("float_column", "FLOAT"), SchemaField( "agg_col", "RECORD", fields=[SchemaField("foo", "INTEGER"), SchemaField("bar", "INTEGER")], ), ] job_config = job.LoadJobConfig(schema=schema) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with pytest.raises(ValueError) as exc_info, load_patch: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) err_msg = str(exc_info.value) assert "struct" in err_msg assert "not support" in err_msg @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_partial_schema(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() df_data = collections.OrderedDict( [ ("int_col", [1, 2, 3]), ("int_as_float_col", [1.0, float("nan"), 3.0]), ("float_col", [1.0, 2.0, 3.0]), ("bool_col", [True, False, True]), ( "dt_col", pandas.Series( [ datetime.datetime(2010, 1, 2, 3, 44, 50), datetime.datetime(2011, 2, 3, 14, 50, 59), datetime.datetime(2012, 3, 14, 15, 16), ], dtype="datetime64[ns]", ), ), ( "ts_col", pandas.Series( [ datetime.datetime(2010, 1, 2, 3, 44, 50), datetime.datetime(2011, 2, 3, 14, 50, 59), datetime.datetime(2012, 3, 14, 15, 16), ], dtype="datetime64[ns]", ).dt.tz_localize(pytz.utc), ), ("string_col", [u"abc", None, u"def"]), ("bytes_col", [b"abc", b"def", None]), ] ) dataframe = pandas.DataFrame(df_data, columns=df_data.keys()) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) schema = ( SchemaField("int_as_float_col", "INTEGER"), SchemaField("string_col", "STRING"), SchemaField("bytes_col", "BYTES"), ) job_config = job.LoadJobConfig(schema=schema) with load_patch as load_table_from_file: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET assert tuple(sent_config.schema) == ( SchemaField("int_col", "INTEGER"), SchemaField("int_as_float_col", "INTEGER"), SchemaField("float_col", "FLOAT"), SchemaField("bool_col", "BOOLEAN"), SchemaField("dt_col", "DATETIME"), SchemaField("ts_col", "TIMESTAMP"), SchemaField("string_col", "STRING"), SchemaField("bytes_col", "BYTES"), ) @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_partial_schema_extra_types(self): from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() df_data = collections.OrderedDict( [ ("int_col", [1, 2, 3]), ("int_as_float_col", [1.0, float("nan"), 3.0]), ("string_col", [u"abc", None, u"def"]), ] ) dataframe = pandas.DataFrame(df_data, columns=df_data.keys()) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) schema = ( SchemaField("int_as_float_col", "INTEGER"), SchemaField("string_col", "STRING"), SchemaField("unknown_col", "BYTES"), ) job_config = job.LoadJobConfig(schema=schema) with load_patch as load_table_from_file, pytest.raises( ValueError ) as exc_context: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) load_table_from_file.assert_not_called() message = str(exc_context.value) assert "bq_schema contains fields not present in dataframe" in message assert "unknown_col" in message @unittest.skipIf(pandas is None, "Requires `pandas`") def test_load_table_from_dataframe_w_partial_schema_missing_types(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() df_data = collections.OrderedDict( [ ("string_col", [u"abc", u"def", u"ghi"]), ("unknown_col", [b"jkl", None, b"mno"]), ] ) dataframe = pandas.DataFrame(df_data, columns=df_data.keys()) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) pyarrow_patch = mock.patch( "google.cloud.bigquery._pandas_helpers.pyarrow", None ) schema = (SchemaField("string_col", "STRING"),) job_config = job.LoadJobConfig(schema=schema) with pyarrow_patch, load_patch as load_table_from_file, warnings.catch_warnings( record=True ) as warned: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) assert warned # there should be at least one warning unknown_col_warnings = [ warning for warning in warned if "unknown_col" in str(warning) ] assert unknown_col_warnings assert unknown_col_warnings[0].category == UserWarning sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET assert sent_config.schema is None @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_schema_wo_pyarrow(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() records = [{"name": u"Monty", "age": 100}, {"name": u"Python", "age": 60}] dataframe = pandas.DataFrame(records, columns=["name", "age"]) schema = (SchemaField("name", "STRING"), SchemaField("age", "INTEGER")) job_config = job.LoadJobConfig(schema=schema) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) pyarrow_patch = mock.patch("google.cloud.bigquery.client.pyarrow", None) with load_patch as load_table_from_file, pyarrow_patch, warnings.catch_warnings( record=True ) as warned: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) assert warned # there should be at least one warning for warning in warned: assert "pyarrow" in str(warning) assert warning.category in (DeprecationWarning, PendingDeprecationWarning) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.PARQUET assert tuple(sent_config.schema) == schema @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_schema_arrow_custom_compression(self): from google.cloud.bigquery import job from google.cloud.bigquery.schema import SchemaField client = self._make_client() records = [{"name": u"Monty", "age": 100}, {"name": u"Python", "age": 60}] dataframe = pandas.DataFrame(records) schema = (SchemaField("name", "STRING"), SchemaField("age", "INTEGER")) job_config = job.LoadJobConfig(schema=schema) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) to_parquet_patch = mock.patch( "google.cloud.bigquery.client._pandas_helpers.dataframe_to_parquet", autospec=True, ) with load_patch, to_parquet_patch as fake_to_parquet: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION, parquet_compression="LZ4", ) call_args = fake_to_parquet.call_args assert call_args is not None assert call_args.kwargs.get("parquet_compression") == "LZ4" @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_wo_pyarrow_custom_compression(self): client = self._make_client() records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}] dataframe = pandas.DataFrame(records) get_table_patch = mock.patch( "google.cloud.bigquery.client.Client.get_table", autospec=True, side_effect=google.api_core.exceptions.NotFound("Table not found"), ) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) pyarrow_patch = mock.patch("google.cloud.bigquery.client.pyarrow", None) to_parquet_patch = mock.patch.object( dataframe, "to_parquet", wraps=dataframe.to_parquet ) with load_patch, get_table_patch, pyarrow_patch, to_parquet_patch as to_parquet_spy: client.load_table_from_dataframe( dataframe, self.TABLE_REF, location=self.LOCATION, parquet_compression="gzip", ) call_args = to_parquet_spy.call_args assert call_args is not None assert call_args.kwargs.get("compression") == "gzip" @unittest.skipIf(pandas is None, "Requires `pandas`") @unittest.skipIf(pyarrow is None, "Requires `pyarrow`") def test_load_table_from_dataframe_w_nulls(self): """Test that a DataFrame with null columns can be uploaded if a BigQuery schema is specified. See: https://github.com/googleapis/google-cloud-python/issues/7370 """ from google.cloud.bigquery.schema import SchemaField from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job client = self._make_client() records = [{"name": None, "age": None}, {"name": None, "age": None}] dataframe = pandas.DataFrame(records, columns=["name", "age"]) schema = [SchemaField("name", "STRING"), SchemaField("age", "INTEGER")] job_config = job.LoadJobConfig(schema=schema) load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, rewind=True, job_id=mock.ANY, job_id_prefix=None, location=self.LOCATION, project=None, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.schema == schema assert sent_config.source_format == job.SourceFormat.PARQUET @unittest.skipIf(pandas is None, "Requires `pandas`") def test_load_table_from_dataframe_w_invaild_job_config(self): from google.cloud.bigquery import job client = self._make_client() records = [{"float_column": 3.14, "struct_column": [{"foo": 1}, {"bar": -1}]}] dataframe = pandas.DataFrame(data=records) job_config = job.CopyJobConfig() with pytest.raises(TypeError) as exc: client.load_table_from_dataframe( dataframe, self.TABLE_REF, job_config=job_config, location=self.LOCATION ) err_msg = str(exc.value) assert "Expected an instance of LoadJobConfig" in err_msg def test_load_table_from_json_basic_use(self): from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery import job client = self._make_client() json_rows = [ {"name": "One", "age": 11, "birthday": "2008-09-10", "adult": False}, {"name": "Two", "age": 22, "birthday": "1997-08-09", "adult": True}, ] load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file: client.load_table_from_json(json_rows, self.TABLE_REF) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, job_id=mock.ANY, job_id_prefix=None, location=client.location, project=client.project, job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert sent_config.source_format == job.SourceFormat.NEWLINE_DELIMITED_JSON assert sent_config.schema is None assert sent_config.autodetect def test_load_table_from_json_non_default_args(self): from google.cloud.bigquery import job from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES from google.cloud.bigquery.schema import SchemaField client = self._make_client() json_rows = [ {"name": "One", "age": 11, "birthday": "2008-09-10", "adult": False}, {"name": "Two", "age": 22, "birthday": "1997-08-09", "adult": True}, ] schema = [ SchemaField("name", "STRING"), SchemaField("age", "INTEGER"), SchemaField("adult", "BOOLEAN"), ] job_config = job.LoadJobConfig(schema=schema) job_config._properties["load"]["unknown_field"] = "foobar" load_patch = mock.patch( "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True ) with load_patch as load_table_from_file: client.load_table_from_json( json_rows, self.TABLE_REF, job_config=job_config, project="project-x", location="EU", ) load_table_from_file.assert_called_once_with( client, mock.ANY, self.TABLE_REF, num_retries=_DEFAULT_NUM_RETRIES, job_id=mock.ANY, job_id_prefix=None, location="EU", project="project-x", job_config=mock.ANY, ) sent_config = load_table_from_file.mock_calls[0][2]["job_config"] assert job_config.source_format is None # the original was not modified assert sent_config.source_format == job.SourceFormat.NEWLINE_DELIMITED_JSON assert sent_config.schema == schema assert not sent_config.autodetect # all properties should have been cloned and sent to the backend assert sent_config._properties.get("load", {}).get("unknown_field") == "foobar" def test_load_table_from_json_w_invalid_job_config(self): from google.cloud.bigquery import job client = self._make_client() json_rows = [ {"name": "One", "age": 11, "birthday": "2008-09-10", "adult": False}, {"name": "Two", "age": 22, "birthday": "1997-08-09", "adult": True}, ] job_config = job.CopyJobConfig() with pytest.raises(TypeError) as exc: client.load_table_from_json( json_rows, self.TABLE_REF, job_config=job_config, project="project-x", location="EU", ) err_msg = str(exc.value) assert "Expected an instance of LoadJobConfig" in err_msg # Low-level tests @classmethod def _make_resumable_upload_responses(cls, size): """Make a series of responses for a successful resumable upload.""" from google import resumable_media resumable_url = "http://test.invalid?upload_id=and-then-there-was-1" initial_response = cls._make_response( http_client.OK, "", {"location": resumable_url} ) data_response = cls._make_response( resumable_media.PERMANENT_REDIRECT, "", {"range": "bytes=0-{:d}".format(size - 1)}, ) final_response = cls._make_response( http_client.OK, json.dumps({"size": size}), {"Content-Type": "application/json"}, ) return [initial_response, data_response, final_response] @staticmethod def _make_transport(responses=None): import google.auth.transport.requests transport = mock.create_autospec( google.auth.transport.requests.AuthorizedSession, instance=True ) transport.request.side_effect = responses return transport def test__do_resumable_upload(self): file_obj = self._make_file_obj() file_obj_len = len(file_obj.getvalue()) transport = self._make_transport( self._make_resumable_upload_responses(file_obj_len) ) client = self._make_client(transport) result = client._do_resumable_upload( file_obj, self.EXPECTED_CONFIGURATION, None ) content = result.content.decode("utf-8") assert json.loads(content) == {"size": file_obj_len} # Verify that configuration data was passed in with the initial # request. transport.request.assert_any_call( "POST", mock.ANY, data=json.dumps(self.EXPECTED_CONFIGURATION).encode("utf-8"), headers=mock.ANY, timeout=mock.ANY, ) def test__do_multipart_upload(self): transport = self._make_transport([self._make_response(http_client.OK)]) client = self._make_client(transport) file_obj = self._make_file_obj() file_obj_len = len(file_obj.getvalue()) client._do_multipart_upload( file_obj, self.EXPECTED_CONFIGURATION, file_obj_len, None ) # Verify that configuration data was passed in with the initial # request. request_args = transport.request.mock_calls[0][2] request_data = request_args["data"].decode("utf-8") request_headers = request_args["headers"] request_content = email.message_from_string( "Content-Type: {}\r\n{}".format( request_headers["content-type"].decode("utf-8"), request_data ) ) # There should be two payloads: the configuration and the binary daya. configuration_data = request_content.get_payload(0).get_payload() binary_data = request_content.get_payload(1).get_payload() assert json.loads(configuration_data) == self.EXPECTED_CONFIGURATION assert binary_data.encode("utf-8") == file_obj.getvalue() def test__do_multipart_upload_wrong_size(self): client = self._make_client() file_obj = self._make_file_obj() file_obj_len = len(file_obj.getvalue()) with pytest.raises(ValueError): client._do_multipart_upload(file_obj, {}, file_obj_len + 1, None) def test_schema_from_json_with_file_path(self): from google.cloud.bigquery.schema import SchemaField file_content = """[ { "description": "quarter", "mode": "REQUIRED", "name": "qtr", "type": "STRING" }, { "description": "sales representative", "mode": "NULLABLE", "name": "rep", "type": "STRING" }, { "description": "total sales", "mode": "NULLABLE", "name": "sales", "type": "FLOAT" } ]""" expected = [ SchemaField("qtr", "STRING", "REQUIRED", "quarter"), SchemaField("rep", "STRING", "NULLABLE", "sales representative"), SchemaField("sales", "FLOAT", "NULLABLE", "total sales"), ] client = self._make_client() mock_file_path = "/mocked/file.json" if six.PY2: open_patch = mock.patch( "__builtin__.open", mock.mock_open(read_data=file_content) ) else: open_patch = mock.patch( "builtins.open", new=mock.mock_open(read_data=file_content) ) with open_patch as _mock_file: actual = client.schema_from_json(mock_file_path) _mock_file.assert_called_once_with(mock_file_path) # This assert is to make sure __exit__ is called in the context # manager that opens the file in the function _mock_file().__exit__.assert_called_once() assert expected == actual def test_schema_from_json_with_file_object(self): from google.cloud.bigquery.schema import SchemaField file_content = """[ { "description": "quarter", "mode": "REQUIRED", "name": "qtr", "type": "STRING" }, { "description": "sales representative", "mode": "NULLABLE", "name": "rep", "type": "STRING" }, { "description": "total sales", "mode": "NULLABLE", "name": "sales", "type": "FLOAT" } ]""" expected = [ SchemaField("qtr", "STRING", "REQUIRED", "quarter"), SchemaField("rep", "STRING", "NULLABLE", "sales representative"), SchemaField("sales", "FLOAT", "NULLABLE", "total sales"), ] client = self._make_client() if six.PY2: fake_file = io.BytesIO(file_content) else: fake_file = io.StringIO(file_content) actual = client.schema_from_json(fake_file) assert expected == actual def test_schema_to_json_with_file_path(self): from google.cloud.bigquery.schema import SchemaField file_content = [ { "description": "quarter", "mode": "REQUIRED", "name": "qtr", "type": "STRING", }, { "description": "sales representative", "mode": "NULLABLE", "name": "rep", "type": "STRING", }, { "description": "total sales", "mode": "NULLABLE", "name": "sales", "type": "FLOAT", }, ] schema_list = [ SchemaField("qtr", "STRING", "REQUIRED", "quarter"), SchemaField("rep", "STRING", "NULLABLE", "sales representative"), SchemaField("sales", "FLOAT", "NULLABLE", "total sales"), ] client = self._make_client() mock_file_path = "/mocked/file.json" if six.PY2: open_patch = mock.patch("__builtin__.open", mock.mock_open()) else: open_patch = mock.patch("builtins.open", mock.mock_open()) with open_patch as mock_file, mock.patch("json.dump") as mock_dump: client.schema_to_json(schema_list, mock_file_path) mock_file.assert_called_once_with(mock_file_path, mode="w") # This assert is to make sure __exit__ is called in the context # manager that opens the file in the function mock_file().__exit__.assert_called_once() mock_dump.assert_called_with( file_content, mock_file.return_value, indent=2, sort_keys=True ) def test_schema_to_json_with_file_object(self): from google.cloud.bigquery.schema import SchemaField file_content = [ { "description": "quarter", "mode": "REQUIRED", "name": "qtr", "type": "STRING", }, { "description": "sales representative", "mode": "NULLABLE", "name": "rep", "type": "STRING", }, { "description": "total sales", "mode": "NULLABLE", "name": "sales", "type": "FLOAT", }, ] schema_list = [ SchemaField("qtr", "STRING", "REQUIRED", "quarter"), SchemaField("rep", "STRING", "NULLABLE", "sales representative"), SchemaField("sales", "FLOAT", "NULLABLE", "total sales"), ] if six.PY2: fake_file = io.BytesIO() else: fake_file = io.StringIO() client = self._make_client() client.schema_to_json(schema_list, fake_file) assert file_content == json.loads(fake_file.getvalue())

37.450346

106

0.567826

63b02d551772ab48b977037382797f342280817e

25,310

py

Python

selfdrive/car/honda/interface.py

jzluo/openpilot

99301a5d71a930e6645a4362896cb3a59d15d2b3

[ "MIT" ]

null

selfdrive/car/honda/interface.py

jzluo/openpilot

99301a5d71a930e6645a4362896cb3a59d15d2b3

[ "MIT" ]

null

selfdrive/car/honda/interface.py

jzluo/openpilot

99301a5d71a930e6645a4362896cb3a59d15d2b3

[ "MIT" ]

null

#!/usr/bin/env python3 import numpy as np from cereal import car from common.numpy_fast import clip, interp from common.realtime import DT_CTRL from selfdrive.swaglog import cloudlog from selfdrive.config import Conversions as CV from selfdrive.controls.lib.events import ET from selfdrive.car.honda.values import CruiseButtons, CAR, HONDA_BOSCH from selfdrive.car import STD_CARGO_KG, CivicParams, scale_rot_inertia, scale_tire_stiffness, gen_empty_fingerprint from selfdrive.controls.lib.longitudinal_planner import _A_CRUISE_MAX_V_FOLLOWING from selfdrive.car.interfaces import CarInterfaceBase A_ACC_MAX = max(_A_CRUISE_MAX_V_FOLLOWING) ButtonType = car.CarState.ButtonEvent.Type EventName = car.CarEvent.EventName def compute_gb_honda(accel, speed): creep_brake = 0.0 creep_speed = 2.3 creep_brake_value = 0.15 if speed < creep_speed: creep_brake = (creep_speed - speed) / creep_speed * creep_brake_value return float(accel) / 4.8 - creep_brake def get_compute_gb_acura(): # generate a function that takes in [desired_accel, current_speed] -> [-1.0, 1.0] # where -1.0 is max brake and 1.0 is max gas # see debug/dump_accel_from_fiber.py to see how those parameters were generated w0 = np.array([[ 1.22056961, -0.39625418, 0.67952657], [ 1.03691769, 0.78210306, -0.41343188]]) b0 = np.array([ 0.01536703, -0.14335321, -0.26932889]) w2 = np.array([[-0.59124422, 0.42899439, 0.38660881], [ 0.79973811, 0.13178682, 0.08550351], [-0.15651935, -0.44360259, 0.76910877]]) b2 = np.array([ 0.15624429, 0.02294923, -0.0341086 ]) w4 = np.array([[-0.31521443], [-0.38626176], [ 0.52667892]]) b4 = np.array([-0.02922216]) def compute_output(dat, w0, b0, w2, b2, w4, b4): m0 = np.dot(dat, w0) + b0 m0 = leakyrelu(m0, 0.1) m2 = np.dot(m0, w2) + b2 m2 = leakyrelu(m2, 0.1) m4 = np.dot(m2, w4) + b4 return m4 def leakyrelu(x, alpha): return np.maximum(x, alpha * x) def _compute_gb_acura(accel, speed): # linearly extrap below v1 using v1 and v2 data v1 = 5. v2 = 10. dat = np.array([accel, speed]) if speed > 5.: m4 = compute_output(dat, w0, b0, w2, b2, w4, b4) else: dat[1] = v1 m4v1 = compute_output(dat, w0, b0, w2, b2, w4, b4) dat[1] = v2 m4v2 = compute_output(dat, w0, b0, w2, b2, w4, b4) m4 = (speed - v1) * (m4v2 - m4v1) / (v2 - v1) + m4v1 return float(m4) return _compute_gb_acura class CarInterface(CarInterfaceBase): def __init__(self, CP, CarController, CarState): super().__init__(CP, CarController, CarState) self.last_enable_pressed = 0 self.last_enable_sent = 0 if self.CS.CP.carFingerprint == CAR.ACURA_ILX: self.compute_gb = get_compute_gb_acura() else: self.compute_gb = compute_gb_honda @staticmethod def compute_gb(accel, speed): # pylint: disable=method-hidden raise NotImplementedError @staticmethod def calc_accel_override(a_ego, a_target, v_ego, v_target): # normalized max accel. Allowing max accel at low speed causes speed overshoots max_accel_bp = [10, 20] # m/s max_accel_v = [0.714, 1.0] # unit of max accel max_accel = interp(v_ego, max_accel_bp, max_accel_v) # limit the pcm accel cmd if: # - v_ego exceeds v_target, or # - a_ego exceeds a_target and v_ego is close to v_target eA = a_ego - a_target valuesA = [1.0, 0.1] bpA = [0.3, 1.1] eV = v_ego - v_target valuesV = [1.0, 0.1] bpV = [0.0, 0.5] valuesRangeV = [1., 0.] bpRangeV = [-1., 0.] # only limit if v_ego is close to v_target speedLimiter = interp(eV, bpV, valuesV) accelLimiter = max(interp(eA, bpA, valuesA), interp(eV, bpRangeV, valuesRangeV)) # accelOverride is more or less the max throttle allowed to pcm: usually set to a constant # unless aTargetMax is very high and then we scale with it; this help in quicker restart return float(max(max_accel, a_target / A_ACC_MAX)) * min(speedLimiter, accelLimiter) @staticmethod def get_params(candidate, fingerprint=gen_empty_fingerprint(), car_fw=[]): # pylint: disable=dangerous-default-value ret = CarInterfaceBase.get_std_params(candidate, fingerprint) ret.carName = "honda" if candidate in HONDA_BOSCH: ret.safetyModel = car.CarParams.SafetyModel.hondaBoschHarness ret.enableCamera = True ret.radarOffCan = True ret.openpilotLongitudinalControl = False else: ret.safetyModel = car.CarParams.SafetyModel.hondaNidec ret.enableCamera = True ret.enableGasInterceptor = 0x201 in fingerprint[0] ret.openpilotLongitudinalControl = ret.enableCamera cloudlog.warning("ECU Camera Simulated: %r", ret.enableCamera) cloudlog.warning("ECU Gas Interceptor: %r", ret.enableGasInterceptor) ret.enableCruise = not ret.enableGasInterceptor ret.communityFeature = ret.enableGasInterceptor # Certain Hondas have an extra steering sensor at the bottom of the steering rack, # which improves controls quality as it removes the steering column torsion from feedback. # Tire stiffness factor fictitiously lower if it includes the steering column torsion effect. # For modeling details, see p.198-200 in "The Science of Vehicle Dynamics (2014), M. Guiggiani" ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0], [0]] ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[0.], [0.]] ret.lateralTuning.pid.kf = 0.00006 # conservative feed-forward eps_modified = False for fw in car_fw: if fw.ecu == "eps" and b"," in fw.fwVersion: eps_modified = True if candidate == CAR.CIVIC: stop_and_go = True ret.mass = CivicParams.MASS ret.wheelbase = CivicParams.WHEELBASE ret.centerToFront = CivicParams.CENTER_TO_FRONT ret.steerRatio = 15.38 # 10.93 is end-to-end spec if eps_modified: # stock request input values: 0x0000, 0x00DE, 0x014D, 0x01EF, 0x0290, 0x0377, 0x0454, 0x0610, 0x06EE # stock request output values: 0x0000, 0x0917, 0x0DC5, 0x1017, 0x119F, 0x140B, 0x1680, 0x1680, 0x1680 # modified request output values: 0x0000, 0x0917, 0x0DC5, 0x1017, 0x119F, 0x140B, 0x1680, 0x2880, 0x3180 # stock filter output values: 0x009F, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108 # modified filter output values: 0x009F, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0400, 0x0480 # note: max request allowed is 4096, but request is capped at 3840 in firmware, so modifications result in 2x max ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 2560, 8000], [0, 2560, 3840]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3], [0.1]] else: ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 2560], [0, 2560]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[1.1], [0.33]] tire_stiffness_factor = 1. ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [3.6, 2.4, 1.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.54, 0.36] elif candidate in (CAR.CIVIC_BOSCH, CAR.CIVIC_BOSCH_DIESEL): stop_and_go = True ret.mass = CivicParams.MASS ret.wheelbase = CivicParams.WHEELBASE ret.centerToFront = CivicParams.CENTER_TO_FRONT ret.steerRatio = 15.38 # 10.93 is end-to-end spec if eps_modified: ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 2566, 8000], [0, 2566, 3840]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.4], [0.12]] else: ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8], [0.24]] tire_stiffness_factor = 1. ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate in (CAR.ACCORD, CAR.ACCORD_15, CAR.ACCORDH): stop_and_go = True if not candidate == CAR.ACCORDH: # Hybrid uses same brake msg as hatch ret.safetyParam = 1 # Accord(ICE), CRV 5G, and RDX 3G use an alternate user brake msg ret.mass = 3279. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.83 ret.centerToFront = ret.wheelbase * 0.39 ret.steerRatio = 16.33 # 11.82 is spec end-to-end ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.8467 ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] if eps_modified: ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3], [0.09]] else: ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6], [0.18]] elif candidate == CAR.ACURA_ILX: stop_and_go = False ret.mass = 3095. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.67 ret.centerToFront = ret.wheelbase * 0.37 ret.steerRatio = 18.61 # 15.3 is spec end-to-end ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 3840], [0, 3840]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.72 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8], [0.24]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate in (CAR.CRV, CAR.CRV_EU): stop_and_go = False ret.mass = 3572. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.62 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 16.89 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 1000], [0, 1000]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.444 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8], [0.24]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.CRV_5G: stop_and_go = True ret.safetyParam = 1 # Accord(ICE), CRV 5G, and RDX 3G use an alternate user brake msg ret.mass = 3410. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.66 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 16.0 # 12.3 is spec end-to-end if eps_modified: # stock request input values: 0x0000, 0x00DB, 0x01BB, 0x0296, 0x0377, 0x0454, 0x0532, 0x0610, 0x067F # stock request output values: 0x0000, 0x0500, 0x0A15, 0x0E6D, 0x1100, 0x1200, 0x129A, 0x134D, 0x1400 # modified request output values: 0x0000, 0x0500, 0x0A15, 0x0E6D, 0x1100, 0x1200, 0x1ACD, 0x239A, 0x2800 ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 2560, 10000], [0, 2560, 3840]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.21], [0.07]] else: ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 3840], [0, 3840]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.64], [0.192]] tire_stiffness_factor = 0.677 ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.CRV_HYBRID: stop_and_go = True ret.safetyParam = 1 # Accord(ICE), CRV 5G, and RDX 3G use an alternate user brake msg ret.mass = 1667. + STD_CARGO_KG # mean of 4 models in kg ret.wheelbase = 2.66 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 16.0 # 12.3 is spec end-to-end ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.677 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6], [0.18]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.FIT: stop_and_go = False ret.mass = 2644. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.53 ret.centerToFront = ret.wheelbase * 0.39 ret.steerRatio = 13.06 ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.75 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.2], [0.05]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.HRV: stop_and_go = False ret.mass = 3125 * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.61 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 15.2 ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] tire_stiffness_factor = 0.5 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.16], [0.025]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.ACURA_RDX: stop_and_go = False ret.mass = 3935. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.68 ret.centerToFront = ret.wheelbase * 0.38 ret.steerRatio = 15.0 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 1000], [0, 1000]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.444 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8], [0.24]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.ACURA_RDX_3G: stop_and_go = True ret.safetyParam = 1 # Accord(ICE), CRV 5G, and RDX 3G use an alternate user brake msg ret.mass = 4068. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.75 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 11.95 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 3840], [0, 3840]] ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6], [0.18]] tire_stiffness_factor = 0.677 ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.ODYSSEY: stop_and_go = False ret.mass = 4471. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 3.00 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 14.35 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.82 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.28], [0.08]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.ODYSSEY_CHN: stop_and_go = False ret.mass = 1849.2 + STD_CARGO_KG # mean of 4 models in kg ret.wheelbase = 2.90 ret.centerToFront = ret.wheelbase * 0.41 # from CAR.ODYSSEY ret.steerRatio = 14.35 ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 32767], [0, 32767]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.82 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.28], [0.08]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate in (CAR.PILOT, CAR.PILOT_2019): stop_and_go = False ret.mass = 4204. * CV.LB_TO_KG + STD_CARGO_KG # average weight ret.wheelbase = 2.82 ret.centerToFront = ret.wheelbase * 0.428 ret.steerRatio = 17.25 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.444 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.38], [0.11]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.RIDGELINE: stop_and_go = False ret.mass = 4515. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 3.18 ret.centerToFront = ret.wheelbase * 0.41 ret.steerRatio = 15.59 # as spec ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.444 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.38], [0.11]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] elif candidate == CAR.INSIGHT: stop_and_go = True ret.mass = 2987. * CV.LB_TO_KG + STD_CARGO_KG ret.wheelbase = 2.7 ret.centerToFront = ret.wheelbase * 0.39 ret.steerRatio = 15.0 # 12.58 is spec end-to-end ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096], [0, 4096]] # TODO: determine if there is a dead zone at the top end tire_stiffness_factor = 0.82 ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6], [0.18]] ret.longitudinalTuning.kpBP = [0., 5., 35.] ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5] ret.longitudinalTuning.kiBP = [0., 35.] ret.longitudinalTuning.kiV = [0.18, 0.12] else: raise ValueError("unsupported car %s" % candidate) # min speed to enable ACC. if car can do stop and go, then set enabling speed # to a negative value, so it won't matter. Otherwise, add 0.5 mph margin to not # conflict with PCM acc ret.minEnableSpeed = -1. if (stop_and_go or ret.enableGasInterceptor) else 25.5 * CV.MPH_TO_MS # TODO: get actual value, for now starting with reasonable value for # civic and scaling by mass and wheelbase ret.rotationalInertia = scale_rot_inertia(ret.mass, ret.wheelbase) # TODO: start from empirically derived lateral slip stiffness for the civic and scale by # mass and CG position, so all cars will have approximately similar dyn behaviors ret.tireStiffnessFront, ret.tireStiffnessRear = scale_tire_stiffness(ret.mass, ret.wheelbase, ret.centerToFront, tire_stiffness_factor=tire_stiffness_factor) ret.gasMaxBP = [0.] # m/s ret.gasMaxV = [0.6] if ret.enableGasInterceptor else [0.] # max gas allowed ret.brakeMaxBP = [5., 20.] # m/s ret.brakeMaxV = [1., 0.8] # max brake allowed ret.stoppingControl = True ret.startAccel = 0.5 ret.steerActuatorDelay = 0.1 ret.steerRateCost = 0.5 ret.steerLimitTimer = 0.8 return ret # returns a car.CarState def update(self, c, can_strings): # ******************* do can recv ******************* self.cp.update_strings(can_strings) self.cp_cam.update_strings(can_strings) if self.cp_body: self.cp_body.update_strings(can_strings) ret = self.CS.update(self.cp, self.cp_cam, self.cp_body) ret.canValid = self.cp.can_valid and self.cp_cam.can_valid and (self.cp_body is None or self.cp_body.can_valid) ret.yawRate = self.VM.yaw_rate(ret.steeringAngleDeg * CV.DEG_TO_RAD, ret.vEgo) # FIXME: read sendcan for brakelights brakelights_threshold = 0.02 if self.CS.CP.carFingerprint == CAR.CIVIC else 0.1 ret.brakeLights = bool(self.CS.brake_switch or c.actuators.brake > brakelights_threshold) buttonEvents = [] if self.CS.cruise_buttons != self.CS.prev_cruise_buttons: be = car.CarState.ButtonEvent.new_message() be.type = ButtonType.unknown if self.CS.cruise_buttons != 0: be.pressed = True but = self.CS.cruise_buttons else: be.pressed = False but = self.CS.prev_cruise_buttons if but == CruiseButtons.RES_ACCEL: be.type = ButtonType.accelCruise elif but == CruiseButtons.DECEL_SET: be.type = ButtonType.decelCruise elif but == CruiseButtons.CANCEL: be.type = ButtonType.cancel elif but == CruiseButtons.MAIN: be.type = ButtonType.altButton3 buttonEvents.append(be) if self.CS.cruise_setting != self.CS.prev_cruise_setting: be = car.CarState.ButtonEvent.new_message() be.type = ButtonType.unknown if self.CS.cruise_setting != 0: be.pressed = True but = self.CS.cruise_setting else: be.pressed = False but = self.CS.prev_cruise_setting if but == 1: be.type = ButtonType.altButton1 # TODO: more buttons? buttonEvents.append(be) ret.buttonEvents = buttonEvents # events events = self.create_common_events(ret, pcm_enable=False) if self.CS.brake_error: events.add(EventName.brakeUnavailable) if self.CS.brake_hold and self.CS.CP.openpilotLongitudinalControl: events.add(EventName.brakeHold) if self.CS.park_brake: events.add(EventName.parkBrake) if self.CP.enableCruise and ret.vEgo < self.CP.minEnableSpeed: events.add(EventName.belowEngageSpeed) # it can happen that car cruise disables while comma system is enabled: need to # keep braking if needed or if the speed is very low if self.CP.enableCruise and not ret.cruiseState.enabled \ and (c.actuators.brake <= 0. or not self.CP.openpilotLongitudinalControl): # non loud alert if cruise disables below 25mph as expected (+ a little margin) if ret.vEgo < self.CP.minEnableSpeed + 2.: events.add(EventName.speedTooLow) else: events.add(EventName.cruiseDisabled) if self.CS.CP.minEnableSpeed > 0 and ret.vEgo < 0.001: events.add(EventName.manualRestart) cur_time = self.frame * DT_CTRL enable_pressed = False # handle button presses for b in ret.buttonEvents: # do enable on both accel and decel buttons if b.type in [ButtonType.accelCruise, ButtonType.decelCruise] and not b.pressed: self.last_enable_pressed = cur_time enable_pressed = True # do disable on button down if b.type == "cancel" and b.pressed: events.add(EventName.buttonCancel) if self.CP.enableCruise: # KEEP THIS EVENT LAST! send enable event if button is pressed and there are # NO_ENTRY events, so controlsd will display alerts. Also not send enable events # too close in time, so a no_entry will not be followed by another one. # TODO: button press should be the only thing that triggers enable if ((cur_time - self.last_enable_pressed) < 0.2 and (cur_time - self.last_enable_sent) > 0.2 and ret.cruiseState.enabled) or \ (enable_pressed and events.any(ET.NO_ENTRY)): events.add(EventName.buttonEnable) self.last_enable_sent = cur_time elif enable_pressed: events.add(EventName.buttonEnable) ret.events = events.to_msg() self.CS.out = ret.as_reader() return self.CS.out # pass in a car.CarControl # to be called @ 100hz def apply(self, c): if c.hudControl.speedVisible: hud_v_cruise = c.hudControl.setSpeed * CV.MS_TO_KPH else: hud_v_cruise = 255 pcm_accel = int(clip(c.cruiseControl.accelOverride, 0, 1) * 0xc6) can_sends = self.CC.update(c.enabled, self.CS, self.frame, c.actuators, c.cruiseControl.speedOverride, c.cruiseControl.override, c.cruiseControl.cancel, pcm_accel, hud_v_cruise, c.hudControl.lanesVisible, hud_show_car=c.hudControl.leadVisible, hud_alert=c.hudControl.visualAlert) self.frame += 1 return can_sends

43.940972

144

0.654326

860589ec69c6aef2f6ac55e687ee0ca53be43059

178,837

py

Python

pytorch/test/quantization/test_quantized_op.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

[ "MIT" ]

null

pytorch/test/quantization/test_quantized_op.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

[ "MIT" ]

null

pytorch/test/quantization/test_quantized_op.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

[ "MIT" ]

null

from __future__ import division from builtins import round import itertools import numpy as np import sys import unittest import torch from torch import _VF import torch.jit import torch.nn.functional as F from torch.nn.modules.utils import _single, _pair from hypothesis import settings, HealthCheck from hypothesis import assume, given, note from hypothesis import strategies as st import torch.testing._internal.hypothesis_utils as hu hu.assert_deadline_disabled() from torch.testing._internal.common_utils import TestCase from torch.testing._internal.common_quantization import skipIfNoFBGEMM from torch.testing._internal.common_quantized import _quantize, _dequantize, _calculate_dynamic_qparams, \ override_quantized_engine, supported_qengines, override_qengines np_dtype = { torch.quint8 : np.uint8, torch.qint8 : np.int8, torch.qint32 : np.int32 } # Make sure we won't have overflows from vpmaddubsw instruction used in FBGEMM. # On the current Intel x86 architecture, we need to utilize vpmaddubsw instruction # for the 8-bit int multiplication. This instruction vertically multiplies each # unsigned 8-bit integer from a with the corresponding signed 8-bit integer from # b, producing intermediate signed 16-bit integers. This function modifies the # weights to eliminate the overflow on the signed 16-bit integers. def avoid_vpmaddubsw_overflow_linear( batch_size, input_channels, output_channels, X, X_min, X_max, W, W_min, W_max ): for i, j in np.ndindex((batch_size, output_channels)): for k in range(0, input_channels // 2 * 2, 2): x0 = X[i, k] - X_min x1 = X[i, k + 1] - X_min w0 = W[j, k] - 128 - W_min w1 = W[j, k + 1] - 128 - W_min if x0 * w0 + x1 * w1 < -(1 << 15): w1_adjusted = (-(1 << 15) - float(x0) * w0) / x1 W[j, k + 1] = int(w1_adjusted) + 128 + W_min elif x0 * w0 + x1 * w1 > (1 << 15) - 1: w1_adjusted = ((1 << 15) - 1 - float(x0) * w0) / x1 W[j, k + 1] = int(w1_adjusted) + 128 + W_min # Go through the same loop again to double check we don't have any overflow for i, j in np.ndindex((batch_size, output_channels)): for k in range(0, input_channels // 2 * 2, 2): x0 = X[i, k] - X_min x1 = X[i, k + 1] - X_min w0 = W[j, k] - 128 - W_min w1 = W[j, k + 1] - 128 - W_min assert -(1 << 15) <= x0 * w0 + x1 * w1 < (1 << 15) # Reference quantized Linear operator def qlinear_ref(X_q, X_scale, X_zp, W_q, W_scale, W_zp, b_q, Y_scale, Y_zp): X_q = np.reshape(X_q, (-1, X_q.shape[X_q.ndim - 1])) row_offsets_ref = X_q.sum(axis=1).astype(np.int32).reshape((-1, 1)) col_offsets_ref = W_q.sum(axis=1).astype(np.int32).reshape((1, -1)) assert X_q.ndim == 2 batch_size, input_channels = X_q.shape Prod_XqWq_ref = ( np.matmul(X_q.astype(np.int32), W_q.astype(np.int32).T) - W_zp * row_offsets_ref - X_zp * col_offsets_ref + input_channels * X_zp * W_zp ) if b_q is not None: Prod_XqWq_ref += b_q Y_q_ref = _quantize(Prod_XqWq_ref, Y_scale / (X_scale * W_scale), Y_zp) return Y_q_ref """Computes the output shape given pooling parameters.""" def pool_output_shape(input_size, kernel_size, padding, stride, dilation, ceiling_mode=False): if stride is None: stride = kernel_size output_size = ( (input_size + 2 * padding - dilation * (kernel_size - 1) - 1 + (stride - 1 if ceiling_mode else 0)) // stride + 1) if (padding > 0 and ((output_size - 1) * stride >= input_size + padding)): output_size += 1 return output_size """ Util for creating a random tensor and quantization params when Hypothesis is undesirable. """ def _get_random_tensor_and_q_params(shapes, rand_scale, torch_type): X = (torch.rand(*shapes, dtype=torch.float) - 0.5) * rand_scale # Calculate reasonable quantization params min_val = torch.min(X) max_val = torch.max(X) if torch_type == torch.qint32: X_zero_point = int(torch.randint(-1 * (2 ** 31), 2 ** 31 - 1, (1,))) num_bins = 2 ** 32 X_scale = float(max_val - min_val) / num_bins elif torch_type == torch.qint8: X_zero_point = int(torch.randint(-128, 127, (1,))) num_bins = 2 ** 8 X_scale = float(max_val - min_val) / num_bins else: # torch.quint8 X_zero_point = 127 num_bins = 2 ** 8 X_scale = float(max_val - min_val) / num_bins if X_scale == 0: X_scale = 1e-10 return X, X_scale, X_zero_point class TestQuantizedOps(TestCase): """Helper function to test quantized activation functions.""" def _test_activation_function(self, X, fn_name, test_configs): r""" When writing a unit test for the activation function, instead of specifying the test routines only applicable to the activation function itself, you utilize the _test_activation_function that provides general testing. To utilize the helper function, a test config must be provided. A test config is a list that contains metadata about the quantized activation functions that will be tested and how the tests need to be set up; it allows simpler and more concise unit tests to be written by specifying the configurations needed and calling the provided helper function _test_activation_function. Inside the list, each config (as a dictionary) represents a suite of tests that assert the correctness of various quantization functions. You can check out the test_qrelu, test_qrelu6, test_qsigmoid, and test_qhardsigmoid for how their test configs are specified. Here's a list of the fields that can be included in a test config: quantized_fn: a list of the quantized functions to be tested reference_fn: the original reference function to be called on the the dequantized X inplace_kwarg: the additional inplace keyword argument to test in-place for each test entry in ops_under_test, it must have at least the fields for quantized_fn and reference_fn. If inplace_kwarg is missing, the quantized function is assumed to be either inplace by default or the test is not testing an inplace function. output_range: the output range the operator will map to. By default, if it is no specified, the range will not be controlled and depend on Xmin and Xmax. change_zero_point: a boolean flag indicating if the zero point parameter should be determined based on torch_type during quantization (see sigmoid/hardsigmoid for examples). By default, if it is not specified, change_zero_point is assumed to be False and zero point will just take on the default value from X. """ # Retrives the default parameters from X. X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X) # Quantizes the reference to account for max error. # q_min and q_max only depend on the initial torch_type. q_min, q_max = torch.iinfo(torch_type).min, torch.iinfo(torch_type).max for op_group in test_configs: ref_op = op_group['reference_fn'] for q_op in op_group['quantized_fn']: # Quantizes and dequantizes to account for max error. qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) dqX = qX.dequantize() dqY_hat = ref_op(dqX.clone()) # Retrieves the inplace keyword arguments # some functions require inplace=True to test in-place. inplace_kwarg = op_group.get('inplace_kwarg', dict()) # Adjusts output_scale if needed. # The output_scale determines the quantization scale for functions that # have a constrained output range. e.x. sigmoid ranges from 0 to 1. output_scale = scale if 'output_range' in op_group: (f_min, f_max) = op_group['output_range'] output_scale = (f_max - f_min) / (q_max - q_min + 1.0) # Adjusts output_zero_point if needed (see explanation for the # change_zero_point parameter above). # output_zero_point determines the additional offset that will be # added to a scaled value during quantization. if op_group.get('change_zero_point', False): output_zero_point = 0 if torch_type == torch.qint32 else q_min else: output_zero_point = zero_point # Quantizes the dequantized version of Y_hat. qY_hat = torch.quantize_per_tensor(dqY_hat, scale=output_scale, zero_point=output_zero_point, dtype=torch_type) # Finds qY using in-place or non-in-place quantized operators. qY = q_op(qX, **inplace_kwarg) self.assertEqual(qY, qY_hat, msg='{} - {} failed: ({} vs. {})'.format( fn_name, q_op, qY, qY_hat )) """Tests the correctness of the quantized::relu op.""" @override_qengines @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams())) def test_qrelu(self, X): relu_test_configs = [ { 'quantized_fn': [ torch.relu, torch.relu_, torch.nn.functional.relu, torch.nn.quantized.functional.relu, ], 'reference_fn': torch.nn.functional.relu }, { 'quantized_fn': [ torch.nn.functional.relu, torch.nn.quantized.functional.relu, ], 'reference_fn': torch.nn.functional.relu, 'inplace_kwarg': { 'inplace': True } } ] self._test_activation_function(X, 'relu', relu_test_configs) """Tests the correctness of the quantized::relu6 op.""" @override_qengines @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams())) def test_qrelu6(self, X): relu6_test_configs = [ { 'quantized_fn': [ torch.ops.quantized.relu6, torch.nn.quantized.ReLU6(inplace=False), torch.nn.quantized.ReLU6(inplace=True) ], 'reference_fn': torch.nn.functional.relu6 } ] self._test_activation_function(X, 'relu6', relu6_test_configs) """Tests the correctness of the quantized::sigmoid op.""" @override_qengines @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams())) def test_qsigmoid(self, X): sigmoid_test_configs = [ { 'quantized_fn': [ torch.sigmoid ], 'reference_fn': torch.sigmoid, 'output_range': (0.0, 1.0), 'change_zero_point': True } ] self._test_activation_function(X, 'sigmoid', sigmoid_test_configs) """Tests the correctness of the quantized::hardsigmoid op.""" @override_qengines @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams())) def test_qhardsigmoid(self, X): hardsigmoid_test_configs = [ { 'quantized_fn': [ torch.nn.quantized.functional.hardsigmoid ], 'reference_fn': torch.nn.functional.hardsigmoid, 'output_range': (0.0, 1.0), 'change_zero_point': True } ] self._test_activation_function(X, 'hardsigmoid', hardsigmoid_test_configs) """Tests the correctness of the quantized::relu op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams()), alpha=st.floats(0.0, 1.0, allow_nan=False, allow_infinity=False)) def test_qrelu_leaky(self, X, alpha): X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) dqX = qX.dequantize() # torch.nn.functional op = torch.nn.functional.leaky_relu dqY = op(dqX, negative_slope=alpha) qY = torch.quantize_per_tensor(dqY, scale=scale, zero_point=zero_point, dtype=torch_type) qY_hat = op(qX, negative_slope=alpha) self.assertEqual(qY.dequantize(), qY_hat.dequantize(), msg="F.leaky_relu failed ({} vs {})".format(qY, qY_hat)) """Tests the correctness of the quantized::elu op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), elements=hu.floats(-1e3, 1e3, allow_nan=False, allow_infinity=False), qparams=hu.qparams()), alpha=st.floats(0.01, 10.0, allow_nan=False, allow_infinity=False)) def test_qelu(self, X, alpha): X, (scale, zero_point, torch_type) = X output_scale = 0.5 output_zero_point = 1 X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # calculate ELU(dqX) and quantize dqX = qX.dequantize() dqY_hat = dqX.clone() dqY_hat = torch.nn.functional.elu(dqX, alpha) qY_hat = torch.quantize_per_tensor(dqY_hat, scale=output_scale, zero_point=output_zero_point, dtype=torch_type) qY = torch.nn.quantized.functional.elu(qX, output_scale, output_zero_point, alpha=alpha) self.assertEqual(qY, qY_hat, msg="F.elu failed ({} vs {})".format(qY, qY_hat)) """Tests the correctness of the quantized::celu op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), elements=hu.floats(-1e2, 1e2, allow_nan=False, allow_infinity=False), qparams=hu.qparams(scale_max=9.999999747378752e-06)), alpha=st.floats(0.01, 100.0, allow_nan=False, allow_infinity=False)) def test_qcelu(self, X, alpha): X, (scale, zero_point, torch_type) = X output_scale = 0.5 output_zero_point = 1 X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # calculate CELU(dqX) and quantize dqX = qX.dequantize() dqY_hat = torch.nn.functional.celu(dqX, alpha) qY_hat = torch.quantize_per_tensor(dqY_hat, scale=output_scale, zero_point=output_zero_point, dtype=torch_type) # test regular qY = torch.ops.quantized.celu(qX, output_scale, output_zero_point, alpha=alpha) self.assertEqual(qY, qY_hat, msg="F.celu failed ({} vs {})".format(qY, qY_hat)) """Tests the correctness of the quantized::qlayer_norm op.""" @skipIfNoFBGEMM def test_qlayer_norm(self): # hypothesis is flaky for this test, create test cases manually side_lens = (1, 8, 11) torch_types = (torch.qint8, torch.quint8) y_scales = (0.1, 4.23) y_zero_points = (0, 1) channels_last_list = (True, False) affine_list = (True, False) combined = [side_lens, torch_types, y_scales, y_zero_points, channels_last_list, affine_list] test_cases = itertools.product(*combined) with override_quantized_engine("fbgemm"): for test_case in test_cases: side_len, torch_type, Y_scale, Y_zero_point, channels_last, \ affine = test_case shapes = [side_len] * 4 # In the FP kernel, mean and variance are calculated in floating point. # In the quantized kernel, they are calculated in integer arithmetic. # Because of this, the numerics do not always match exactly which is # expected and acceptable. We do two things to allow this failure # in this test: # 1. do not use Hypothesis to generate the input tensor. Hypothesis # favors homogeneous inputs in its search strategies which isn't # representative of the inputs we care about, and tends to maximize # this particular numerics difference. # 2. allow a small % of off by Y_scale errors. Even when the # variance of the input is high, there can be off by one errors # in the result if the input value happens to fall exactly on # the bin boundary of the output scale. # # If we want the numerics to match we could switch to calculating # mean+var in floating point in the future, at the cost of speed. X, X_scale, X_zero_point = \ _get_random_tensor_and_q_params(shapes, 1.0, torch_type) qX = torch.quantize_per_tensor(X, scale=X_scale, zero_point=X_zero_point, dtype=torch_type) if channels_last: qX = qX.contiguous(memory_format=torch.channels_last) dqX = qX.dequantize() # Enforce non-homogeneous inputs enough_unique_vals_in_each_layer = sum( 1 if ( dqX[i].shape[0] < 5 or float(torch.unique(dqX[i]).shape[0]) / dqX[i].shape[0] > 0.01 ) else 0 for i in range(dqX.shape[0]) ) == dqX.shape[0] assume(enough_unique_vals_in_each_layer) # Initialize the weights non-randomly for reproducibility, to avoid # flaky tests if affine: weight = torch.ones(*qX.size()[1:], dtype=torch.float) * 0.5 bias = torch.ones(*qX.size()[1:], dtype=torch.float) * 1 else: weight = None bias = None epsilon = 1e-5 qY = torch.ops.quantized.layer_norm( qX, qX.size()[1:], weight=weight, bias=bias, eps=epsilon, output_scale=Y_scale, output_zero_point=Y_zero_point) Y_hat = F.layer_norm( dqX, dqX.size()[1:], weight=weight, bias=bias, eps=epsilon) qY_hat = torch.quantize_per_tensor( Y_hat, scale=Y_scale, zero_point=Y_zero_point, dtype=torch_type) # Due to the numerics difference mentioned above between calculating # the variance in float vs int, the results can still be slightly # different. dqY = qY.dequantize() dqY_hat = qY_hat.dequantize() diff = dqY - dqY_hat # off-by-one errors are magnitude of Y_scale num_diff = torch.sum(diff > Y_scale * 1.0001) pct_diff = float(num_diff) / (diff.numel() + 1e-5) num_diff_off_by_one = torch.sum((diff > 0) * (diff <= Y_scale)) pct_diff_off_by_one = float(num_diff_off_by_one) / (diff.numel() + 1e-5) self.assertTrue(pct_diff < 1e-6) self.assertTrue(pct_diff_off_by_one < 0.01) """Tests the correctness of the quantized::qnnpack_tanh op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams())) def test_qtanh(self, X): # Note: QNNPACK is tested separately in TestQNNPackOps X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X) Y = torch.tanh(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # Quantize the reference to account for max error. # Note that the output scale has +1, because we use scale of 2.0/2^BITS # in the implementations. f_min, f_max = -1.0, 1.0 q_min, q_max = torch.iinfo(torch_type).min, torch.iinfo(torch_type).max output_scale = (f_max - f_min) / (q_max - q_min + 1.0) output_zero_point = int(round((q_max + q_min) / 2.0)) qY = torch.quantize_per_tensor(Y, scale=output_scale, zero_point=output_zero_point, dtype=torch_type) qY_hat = torch.tanh(qX) self.assertEqual(qY, qY_hat, msg="TanH failed: {} vs. {}".format(qY, qY_hat)) """Tests the correctness of the quantized::threshold op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), elements=hu.floats(-1e3, 1e3, allow_nan=False, allow_infinity=False), qparams=hu.qparams()), threshold=hu.floats(-1e3, 1e3, allow_nan=False, allow_infinity=False), value=hu.floats(-1e3, 1e3, allow_nan=False, allow_infinity=False)) def test_qthreshold(self, X, threshold, value): X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # calculate threshold(dqX) and quantize dqX = qX.dequantize() dqY_hat = dqX.clone() dqY_hat = torch.nn.functional.threshold(dqY_hat, threshold, value) qY_hat = torch.quantize_per_tensor(dqY_hat, scale=scale, zero_point=zero_point, dtype=torch_type) ops_under_test = { 'native': torch.threshold, 'nn.functional': torch.nn.functional.threshold, 'nn.quantized.functional': torch.nn.quantized.functional.threshold } for name, op in ops_under_test.items(): qY = op(qX, threshold, value) self.assertEqual(qY, qY_hat, msg="{} qthreshold failed".format(name)) """Tests the correctness of the quantized::clamp op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 8, 1, 8, max_numel=10**5), elements=hu.floats(-1e6, 1e6, allow_nan=False), qparams=hu.qparams()), min_val=hu.floats(-1e6, 1e6, allow_nan=False), max_val=hu.floats(-1e6, 1e6, allow_nan=False)) def test_qclamp(self, X, min_val, max_val): X, (scale, zero_point, torch_type) = X assume(min_val <= max_val) Y = X.copy() Y[Y < min_val] = min_val Y[Y > max_val] = max_val qY = torch.quantize_per_tensor(torch.from_numpy(Y), scale=scale, zero_point=zero_point, dtype=torch_type) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) ops_under_test = { 'ops.quantized': torch.ops.quantized.clamp, } for name, op in ops_under_test.items(): qY_hat = op(qX, min_val, max_val) self.assertEqual(qY, qY_hat, msg="{} qclamp failed".format(name)) """Tests the correctness of the quantized::hardtanh op.""" @skipIfNoFBGEMM @given(X=hu.tensor(shapes=hu.array_shapes(1, 8, 1, 8, max_numel=10**5), elements=hu.floats(-1e6, 1e6, allow_nan=False, allow_infinity=False), qparams=hu.qparams()), min_val=hu.floats(-1e6, 1e6, allow_nan=False, allow_infinity=False), max_val=hu.floats(-1e6, 1e6, allow_nan=False, allow_infinity=False)) def test_hardtanh(self, X, min_val, max_val): with override_quantized_engine('fbgemm'): X, (scale, zero_point, torch_type) = X assume(min_val <= max_val) Y = X.copy() Y[Y < min_val] = min_val Y[Y > max_val] = max_val qY = torch.quantize_per_tensor(torch.from_numpy(Y), scale=scale, zero_point=zero_point, dtype=torch_type) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) ops_under_test = { 'nn.quantized.functional.hardtanh': torch.nn.quantized.functional.hardtanh, } for name, op in ops_under_test.items(): qY_hat = op(qX, min_val, max_val) self.assertEqual(qY, qY_hat, msg="{} hardtanh failed".format(name)) ops_under_test_inplace = { 'inplace nn.quantized.functional.hardtanh': torch.nn.quantized.functional.hardtanh, } for name, op_ in ops_under_test_inplace.items(): qY_hat = qX.clone() op_(qY_hat, min_val, max_val, inplace=True) self.assertEqual(qY, qY_hat, msg="{} hardtanh failed".format(name)) """Tests the correctness of the quantized::hardswish op.""" @override_qengines def test_hardswish(self): max_sides = (3, 5) side_lens = (1, 7, 8) torch_types = (torch.quint8, torch.qint8) y_scales = (0.1, 4.23) y_zero_points = (0, 1) combined = [max_sides, side_lens, torch_types, y_scales, y_zero_points] test_cases = itertools.product(*combined) for test_case in test_cases: max_side, side_len, torch_type, Y_scale, Y_zero_point = test_case if torch.backends.quantized.engine == 'qnnpack' and torch_type != torch.quint8: continue shapes = [side_len] * max_side X, X_scale, X_zero_point = \ _get_random_tensor_and_q_params(shapes, 2.0, torch_type) qX = torch.quantize_per_tensor(X, scale=X_scale, zero_point=X_zero_point, dtype=torch_type) dqX = qX.dequantize() dqY_hat = F.hardswish(dqX) qY_hat = torch.quantize_per_tensor(dqY_hat, scale=Y_scale, zero_point=Y_zero_point, dtype=torch_type) qY = torch.nn.quantized.functional.hardswish( qX, scale=Y_scale, zero_point=Y_zero_point) self.assertEqual( qY, qY_hat, msg="Hardswish failed: {} vs {}, {}".format(qY, qY_hat, torch.backends.quantized.engine)) """Tests the correctness of the scalar addition.""" @unittest.skip("Failing on MacOS") @given(A=hu.tensor(shapes=hu.array_shapes(1, 4, 1, 5), elements=hu.floats(-1e6, 1e6, allow_nan=False), qparams=hu.qparams()), b=hu.floats(-1e6, 1e6, allow_nan=False, allow_infinity=False)) def test_qadd_scalar_relu(self, A, b): import copy add_scalar = torch.ops.quantized.add_scalar add_scalar_relu = torch.ops.quantized.add_scalar_relu A, (scale, zero_point, dtype) = A A = A.astype(np.float32) qA = torch.quantize_per_tensor(torch.from_numpy(A), scale, zero_point, dtype) C = qA.dequantize() + round(b / scale) * scale C_relu = copy.deepcopy(C) C_relu[C_relu < 0] = 0 C_hat = add_scalar(qA, b) C_ref = torch.quantize_per_tensor(C, C_hat.q_scale(), C_hat.q_zero_point(), dtype) C_relu_hat = add_scalar_relu(qA, b) C_relu_ref = torch.quantize_per_tensor( C_relu, C_relu_hat.q_scale(), C_relu_hat.q_zero_point(), dtype) self.assertEqual(C_ref.dequantize(), C_hat.dequantize(), msg="Scalar add results don't match:\ {} vs {}".format(C_ref.dequantize(), C_hat.dequantize())) self.assertEqual(C_relu_ref.dequantize(), C_relu_hat.dequantize(), msg="Scalar add relu results don't match:\ {} vs {}".format(C_relu_ref.dequantize(), C_relu_hat.dequantize())) """Tests the correctness of the add and add_relu op.""" def test_qadd_relu_same_qparams(self): for dtype in [torch.quint8, torch.qint8, torch.qint32]: add_relu = torch.ops.quantized.add_relu add = torch.ops.quantized.add add_out = torch.ops.quantized.add_out add_relu_out = torch.ops.quantized.add_relu_out # NB: This is a strange size so that we exercise both the vectorized # implementation (64-element chunks at at time) as well as the scalar # implementation A = torch.arange(-128, 130, dtype=torch.float) B = torch.arange(-128, 130, dtype=torch.float) scale = 2.0 zero_point = 127 qA = torch.quantize_per_tensor(A, scale=scale, zero_point=zero_point, dtype=dtype) qB = torch.quantize_per_tensor(B, scale=scale, zero_point=zero_point, dtype=dtype) # Add ReLU ground truth C = (qA.dequantize() + qB.dequantize()).numpy() qC = _quantize(C, scale, zero_point, dtype=np_dtype[dtype]) qC_hat = add(qA, qB, scale=scale, zero_point=zero_point) np.testing.assert_equal(qC, qC_hat.int_repr(), "Quantized addition failed.") qC_out_hat = torch._empty_affine_quantized(qC.shape, scale=scale, zero_point=zero_point, dtype=dtype) add_out(qA, qB, out=qC_out_hat) self.assertEqual(qC_hat, qC_out_hat, msg="Add.out failed") # Add + ReLU ground truth Crelu = C.copy() Crelu[C < 0] = 0 qCrelu = _quantize(Crelu, scale, zero_point, dtype=np_dtype[dtype]) qCrelu_hat = add_relu(qA, qB, scale=scale, zero_point=zero_point) np.testing.assert_equal(qCrelu, qCrelu_hat.int_repr(), "Quantized addition with ReLU failed.") qCrelu_out_hat = torch._empty_affine_quantized(qCrelu.shape, scale=scale, zero_point=zero_point, dtype=dtype) add_relu_out(qA, qB, out=qCrelu_out_hat) self.assertEqual(qCrelu_hat, qCrelu_out_hat, msg="AddReLU.out failed") """Tests the correctness of the add and add_relu op.""" def test_qadd_relu_different_qparams(self): for dtype in [torch.quint8, torch.qint8, torch.qint32]: add_relu = torch.ops.quantized.add_relu add = torch.ops.quantized.add add_out = torch.ops.quantized.add_out add_relu_out = torch.ops.quantized.add_relu_out # NB: This is a strange size so that we exercise both the vectorized # implementation (64-element chunks at at time) as well as the scalar # implementation A = torch.arange(-128, 130, dtype=torch.float) B = torch.arange(-128, 130, dtype=torch.float) scale_A = 3.0 zero_point_A = 7 scale_B = 5.0 zero_point_B = 127 scale_C = 0.5 zero_point_C = 5 qA = torch.quantize_per_tensor(A, scale=scale_A, zero_point=zero_point_A, dtype=dtype) qB = torch.quantize_per_tensor(B, scale=scale_B, zero_point=zero_point_B, dtype=dtype) # Add ground truth C = (qA.dequantize() + qB.dequantize()).numpy() qC = _quantize(C, scale_C, zero_point_C, dtype=np_dtype[dtype]) qC_hat = add(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qC, qC_hat.int_repr(), "Quantized addition failed.") qC_out_hat = torch._empty_affine_quantized(qC.shape, scale=scale_C, zero_point=zero_point_C, dtype=dtype) add_out(qA, qB, out=qC_out_hat) self.assertEqual(qC_hat, qC_out_hat, msg="Add.out failed") # Add + ReLU ground truth Crelu = C.copy() Crelu[C < 0] = 0 qCrelu = _quantize(Crelu, scale_C, zero_point_C, dtype=np_dtype[dtype]) qCrelu_hat = add_relu(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qCrelu, qCrelu_hat.int_repr(), "Quantized addition with ReLU failed.") qCrelu_out_hat = torch._empty_affine_quantized(qCrelu.shape, scale=scale_C, zero_point=zero_point_C, dtype=dtype) add_relu_out(qA, qB, out=qCrelu_out_hat) self.assertEqual(qCrelu_hat, qCrelu_out_hat, msg="AddReLU.out failed") """Tests the correctness of the mul and mul_relu op.""" def test_qmul_relu_same_qparams(self): for dtype in [torch.quint8, torch.qint8, torch.qint32]: mul_relu = torch.ops.quantized.mul_relu mul = torch.ops.quantized.mul mul_out = torch.ops.quantized.mul_out mul_relu_out = torch.ops.quantized.mul_relu_out A = torch.arange(-100, 100, dtype=torch.float) B = torch.arange(-100, 100, dtype=torch.float) scale = 2.0 zero_point = 127 qA = torch.quantize_per_tensor(A, scale=scale, zero_point=zero_point, dtype=dtype) qB = torch.quantize_per_tensor(B, scale=scale, zero_point=zero_point, dtype=dtype) # mul ReLU ground truth C = (qA.dequantize() * qB.dequantize()).numpy() qC = _quantize(C, scale, zero_point, dtype=np_dtype[dtype]) qC_hat = mul(qA, qB, scale=scale, zero_point=zero_point) np.testing.assert_equal(qC, qC_hat.int_repr(), "Quantized mulition failed.") qC_out_hat = torch._empty_affine_quantized(qC.shape, scale=scale, zero_point=zero_point, dtype=dtype) mul_out(qA, qB, out=qC_out_hat) self.assertEqual(qC_hat, qC_out_hat, msg="mul.out failed") # mul + ReLU ground truth Crelu = C.copy() Crelu[C < 0] = 0 qCrelu = _quantize(Crelu, scale, zero_point, dtype=np_dtype[dtype]) qCrelu_hat = mul_relu(qA, qB, scale=scale, zero_point=zero_point) np.testing.assert_equal(qCrelu, qCrelu_hat.int_repr(), "Quantized mulition with ReLU failed.") qCrelu_out_hat = torch._empty_affine_quantized(qCrelu.shape, scale=scale, zero_point=zero_point, dtype=dtype) mul_relu_out(qA, qB, out=qCrelu_out_hat) self.assertEqual(qCrelu_hat, qCrelu_out_hat, msg="mulReLU.out failed") # Scalar multiplication for b in B: C_ref = qA.dequantize().numpy() * b.item() qC_hat = torch.ops.quantized.mul_scalar(qA, b.item()) self.assertEqual(C_ref, qC_hat.dequantize()) # Scalar multiplication + relu for b in B: C_ref = qA.dequantize().numpy() * b.item() C_ref[C_ref < 0] = 0 qC_hat = torch.ops.quantized.mul_scalar_relu(qA, b.item()) self.assertEqual(C_ref, qC_hat.dequantize()) """Tests the correctness of the mul and mul_relu op.""" def test_qmul_relu_different_qparams(self): for dtype in [torch.quint8, torch.qint8, torch.qint32]: mul_relu = torch.ops.quantized.mul_relu mul = torch.ops.quantized.mul mul_out = torch.ops.quantized.mul_out mul_relu_out = torch.ops.quantized.mul_relu_out A = torch.arange(-100, 100, dtype=torch.float) B = torch.arange(-100, 100, dtype=torch.float) scale_A = 3.0 zero_point_A = 7 scale_B = 5.0 zero_point_B = 127 scale_C = 0.5 zero_point_C = 5 qA = torch.quantize_per_tensor(A, scale=scale_A, zero_point=zero_point_A, dtype=dtype) qB = torch.quantize_per_tensor(B, scale=scale_B, zero_point=zero_point_B, dtype=dtype) # mul ground truth C = (qA.dequantize() * qB.dequantize()).numpy() qC = _quantize(C, scale_C, zero_point_C, dtype=np_dtype[dtype]) qC_hat = mul(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qC, qC_hat.int_repr(), "Quantized multiplication failed.") qC_out_hat = torch._empty_affine_quantized(qC.shape, scale=scale_C, zero_point=zero_point_C, dtype=dtype) mul_out(qA, qB, out=qC_out_hat) self.assertEqual(qC_hat, qC_out_hat, msg="mul.out failed") # mul + ReLU ground truth Crelu = C.copy() Crelu[C < 0] = 0 qCrelu = _quantize(Crelu, scale_C, zero_point_C, dtype=np_dtype[dtype]) qCrelu_hat = mul_relu(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qCrelu, qCrelu_hat.int_repr(), "Quantized multiplication with ReLU failed.") qCrelu_out_hat = torch._empty_affine_quantized(qCrelu.shape, scale=scale_C, zero_point=zero_point_C, dtype=dtype) mul_relu_out(qA, qB, out=qCrelu_out_hat) self.assertEqual(qCrelu_hat, qCrelu_out_hat, msg="mulReLU.out failed") """Tests the correctness of the mul and mul_relu op.""" def test_qmul_broadcast(self): mul_relu = torch.ops.quantized.mul_relu mul = torch.ops.quantized.mul mul_out = torch.ops.quantized.mul_out mul_relu_out = torch.ops.quantized.mul_relu_out # A = torch.arange(-25, 25, dtype=torch.float) # B = torch.arange(-25, 25, dtype=torch.float) A = torch.randn(8, 1, 6, 1) B = torch.randn(7, 1, 5) scale_A = 3.0 zero_point_A = 7 scale_B = 5.0 zero_point_B = 127 scale_C = 0.5 zero_point_C = 5 qA = torch.quantize_per_tensor(A, scale=scale_A, zero_point=zero_point_A, dtype=torch.quint8) qB = torch.quantize_per_tensor(B, scale=scale_B, zero_point=zero_point_B, dtype=torch.quint8) # mul ground truth C = (qA.dequantize() * qB.dequantize()).numpy() qC = _quantize(C, scale_C, zero_point_C) qC_hat = mul(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qC, qC_hat.int_repr(), "Quantized multiplication failed.") """Tests channel shuffle operation on quantized tensors.""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=2, max_side=32, max_numel=10**5), qparams=hu.qparams(dtypes=[torch.quint8])), groups=st.integers(2, 6)) def test_channel_shuffle(self, X, groups): X, (scale, zero_point, torch_type) = X channels = X.shape[-3] iH, iW = X.shape[-2:] assume(channels % groups == 0) a = torch.from_numpy(X) a = torch.rand(a.shape) a_out = torch.nn.functional.channel_shuffle(a, groups) a_ref = torch.quantize_per_tensor(a_out, scale=scale, zero_point=zero_point, dtype=torch_type) a_ref = a_ref.dequantize() qa = torch.quantize_per_tensor(a, scale=scale, zero_point=zero_point, dtype=torch_type) a_hat = torch.nn.functional.channel_shuffle(qa, groups) self.assertEqual(a_ref, a_hat.dequantize(), msg="torch.nn.functional.channel_shuffle results are off") """Tests max pool operation on quantized tensors.""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), kernel=st.sampled_from((3, 5, 7)), stride=st.sampled_from((None, 1, 2)), dilation=st.integers(1, 2), padding=st.integers(0, 2), ceil_mode=st.booleans()) def test_max_pool2d(self, X, kernel, stride, dilation, padding, ceil_mode): X, (scale, zero_point, torch_type) = X # Check constraints assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, dilation, ceil_mode) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation, ceil_mode) assume(oW > 0) a = torch.from_numpy(X) a_pool = torch.nn.functional.max_pool2d(a, kernel_size=kernel, stride=stride, padding=padding, dilation=dilation, ceil_mode=ceil_mode) a_ref = torch.quantize_per_tensor(a_pool, scale=scale, zero_point=zero_point, dtype=torch_type) a_ref = a_ref.dequantize() qa = torch.quantize_per_tensor(a, scale=scale, zero_point=zero_point, dtype=torch_type) ops_under_test = { "torch": torch.max_pool2d, "nn.functional": torch.nn.functional.max_pool2d, "nn.quantized.functional": torch.nn.quantized.functional.max_pool2d } for name, op in ops_under_test.items(): a_hat = op(qa, kernel_size=kernel, stride=stride, padding=padding, dilation=dilation, ceil_mode=ceil_mode) self.assertEqual(a_ref, a_hat.dequantize(), msg="{} results are off".format(name)) # Test the ops.quantized separately, because None is not treated. a_hat = torch.ops.quantized.max_pool2d( qa, kernel_size=_pair(kernel), stride=_pair(kernel if stride is None else stride), padding=_pair(padding), dilation=_pair(dilation), ceil_mode=ceil_mode) self.assertEqual(a_ref, a_hat.dequantize(), msg="ops.quantized.max_pool2d results are off") """Tests max pool operation on NHWC quantized tensors.""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), kernel=st.sampled_from((3, 5, 7)), stride=st.sampled_from((None, 1, 2)), dilation=st.integers(1, 2), padding=st.integers(0, 2), ceil_mode=st.booleans()) def test_max_pool2d_nhwc(self, X, kernel, stride, dilation, padding, ceil_mode): X, (scale, zero_point, torch_type) = X # Ensure we hit the vectorized paths # 176 = 128 + 32 + 16 # 128 hits the interleaved path # 32 hits the non-interleaved path # 16 hits the scalar path if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) # Check constraints assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, dilation, ceil_mode) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation, ceil_mode) assume(oW > 0) X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 1])) a = torch.from_numpy(X_nchw).permute([0, 3, 1, 2]) a_pool = torch.nn.functional.max_pool2d(a, kernel_size=kernel, stride=stride, padding=padding, dilation=dilation, ceil_mode=ceil_mode) a_ref = torch.quantize_per_tensor(a_pool, scale=scale, zero_point=zero_point, dtype=torch_type) a_ref = a_ref.dequantize() qa = torch.quantize_per_tensor(torch.from_numpy(X_nchw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 3, 1, 2]) self.assertTrue(qa.stride() != sorted(qa.stride())) ops_under_test = { "torch": torch.max_pool2d, "nn.functional": torch.nn.functional.max_pool2d, "nn.quantized.functional": torch.nn.quantized.functional.max_pool2d } for name, op in ops_under_test.items(): a_hat = op(qa, kernel_size=kernel, stride=stride, padding=padding, dilation=dilation, ceil_mode=ceil_mode) self.assertTrue(a_hat.stride() != sorted(a_hat.stride())) self.assertEqual(a_ref, a_hat.dequantize(), msg="{} results are off".format(name)) # Test the ops.quantized separately, because None is not treated. a_hat = torch.ops.quantized.max_pool2d( qa, kernel_size=_pair(kernel), stride=_pair(kernel if stride is None else stride), padding=_pair(padding), dilation=_pair(dilation), ceil_mode=ceil_mode) self.assertEqual(a_ref, a_hat.dequantize(), msg="ops.quantized.max_pool2d results are off") @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=4, min_side=5, max_side=10), qparams=hu.qparams(dtypes=torch.quint8)), kernel=st.sampled_from((3, 5)), stride=st.sampled_from((None, 1, 2)), padding=st.integers(0, 2), ceil_mode=st.sampled_from((True, False)), count_include_pad=st.sampled_from((True, False)), divisor_override=st.sampled_from((None, None))) def test_avg_pool2d(self, X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override): """ Note: we currently cannot test the divisor_override, because quantized op will clamp the result within range. However, the float op will not. """ X, (scale, zero_point, torch_type) = X assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, dilation=1) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation=1) assume(oW > 0) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) X = qX.dequantize() # Run reference on float tensor and then quantize the result for comparison X_ref = torch.nn.functional.avg_pool2d( X, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) ops_under_test = { "nn.functional": torch.nn.functional.avg_pool2d, "nn.quantized.functional": torch.nn.quantized.functional.avg_pool2d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): qX_hat = op(qX, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qX_ref = torch.quantize_per_tensor(X_ref, scale=qX_hat.q_scale(), zero_point=qX_hat.q_zero_point(), dtype=torch_type) self.assertEqual(qX_ref.int_repr().to(torch.double), qX_hat.int_repr().to(torch.double), atol=1.0, rtol=0, msg=error_message.format(name, qX_ref.int_repr(), qX_hat.int_repr())) self.assertEqual(scale, qX_hat.q_scale(), msg=error_message.format(name + '.scale', scale, qX_hat.q_scale())) self.assertEqual(zero_point, qX_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, qX_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=5, max_side=10), qparams=hu.qparams(dtypes=torch.qint8)), kernel=st.sampled_from((4, 5)), stride=st.sampled_from((None, 1, 2)), padding=st.integers(0, 2), ceil_mode=st.sampled_from((True, False)), count_include_pad=st.sampled_from((True, False)), divisor_override=st.sampled_from((None, None))) def test_avg_pool2d_nhwc(self, X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override): """ Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result within range. However, the float op will not. 2) we cannot test the qint32, since the float point precision is much lower than int32 for big number, which will make the test be very flaky. """ X, (scale, zero_point, torch_type) = X H, W = X.shape[-2:] if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, dilation=1) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation=1) assume(oW > 0) X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 1])) qX = torch.quantize_per_tensor(torch.from_numpy(X_nchw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 3, 1, 2]) X = qX.dequantize() # Run reference on int_repr + round to avoid double rounding error. X_ref = torch.nn.functional.avg_pool2d( X, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) self.assertTrue(qX.stride() != sorted(qX.stride())) ops_under_test = { "nn.functional": torch.nn.functional.avg_pool2d, "nn.quantized.functional": torch.nn.quantized.functional.avg_pool2d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): X_hat = op(qX, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) self.assertTrue(X_hat.stride() != sorted(X_hat.stride())) qX_ref = torch.quantize_per_tensor(X_ref, scale=X_hat.q_scale(), zero_point=X_hat.q_zero_point(), dtype=torch_type) self.assertEqual(qX_ref.int_repr().to(torch.double), X_hat.int_repr().to(torch.double), atol=1.0, rtol=0, msg=error_message.format(name, qX_ref.int_repr(), X_hat.int_repr())) self.assertEqual(scale, X_hat.q_scale(), msg=error_message.format(name + '.scale', scale, X_hat.q_scale())) self.assertEqual(zero_point, X_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, X_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=5, max_dims=5, min_side=5, max_side=10), qparams=hu.qparams(dtypes=torch.quint8)), kernel=st.sampled_from((3, 5)), stride=st.sampled_from((None, 1, 2)), padding=st.integers(0, 2), ceil_mode=st.sampled_from((True, False)), count_include_pad=st.sampled_from((True, False)), divisor_override=st.sampled_from((None, None))) def test_avg_pool3d(self, X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override): """ Note: we currently cannot test the divisor_override, because quantized op will clamp the result within range. However, the float op will not. """ X, (scale, zero_point, torch_type) = X assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iD, iH, iW = X.shape[-3:] oD = pool_output_shape(iD, kernel, padding, stride, dilation=1) assume(oD > 0) oH = pool_output_shape(iH, kernel, padding, stride, dilation=1) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation=1) assume(oW > 0) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) X = qX.dequantize() # Run reference on float tensor and then quantize the result for comparison X_ref = torch.nn.functional.avg_pool3d( X, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) ops_under_test = { "nn.functional": torch.nn.functional.avg_pool3d, "nn.quantized.functional": torch.nn.quantized.functional.avg_pool3d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): qX_hat = op(qX, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qX_ref = torch.quantize_per_tensor(X_ref, scale=qX_hat.q_scale(), zero_point=qX_hat.q_zero_point(), dtype=torch_type) self.assertEqual(qX_ref.int_repr().to(torch.double), qX_hat.int_repr().to(torch.double), atol=1.0, rtol=0, msg=error_message.format(name, qX_ref.int_repr(), qX_hat.int_repr())) self.assertEqual(scale, qX_hat.q_scale(), msg=error_message.format(name + '.scale', scale, qX_hat.q_scale())) self.assertEqual(zero_point, qX_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, qX_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=5, max_dims=5, min_side=5, max_side=10), qparams=hu.qparams(dtypes=torch.qint8)), kernel=st.sampled_from((4, 5)), stride=st.sampled_from((None, 1, 2)), padding=st.integers(0, 2), ceil_mode=st.sampled_from((True, False)), count_include_pad=st.sampled_from((True, False)), divisor_override=st.sampled_from((None, None))) def test_avg_pool3d_nhwc(self, X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override): """ Note: 1) we currently cannot test the divisor_override, because quantized op will clamp the result within range. However, the float op will not. 2) we cannot test the qint32, since the float point precision is much lower than int32 for big number, which will make the test be very flaky. """ X, (scale, zero_point, torch_type) = X D, H, W = X.shape[-3:] if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iD, iH, iW = X.shape[-3:] oD = pool_output_shape(iD, kernel, padding, stride, dilation=1) assume(oD > 0) oH = pool_output_shape(iH, kernel, padding, stride, dilation=1) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation=1) assume(oW > 0) X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 4, 1])) qX = torch.quantize_per_tensor(torch.from_numpy(X_nchw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 4, 1, 2, 3]) X = qX.dequantize() # Run reference on int_repr + round to avoid double rounding error. X_ref = torch.nn.functional.avg_pool3d( X, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) self.assertTrue(qX.stride() != sorted(qX.stride())) ops_under_test = { "nn.functional": torch.nn.functional.avg_pool3d, "nn.quantized.functional": torch.nn.quantized.functional.avg_pool3d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): X_hat = op(qX, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) self.assertTrue(X_hat.stride() != sorted(X_hat.stride())) qX_ref = torch.quantize_per_tensor(X_ref, scale=X_hat.q_scale(), zero_point=X_hat.q_zero_point(), dtype=torch_type) self.assertEqual(qX_ref.int_repr().to(torch.double), X_hat.int_repr().to(torch.double), atol=1.0, rtol=0, msg=error_message.format(name, qX_ref.int_repr(), X_hat.int_repr())) self.assertEqual(scale, X_hat.q_scale(), msg=error_message.format(name + '.scale', scale, X_hat.q_scale())) self.assertEqual(zero_point, X_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, X_hat.q_zero_point())) """Tests adaptive average pool operation on NHWC quantized tensors.""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams(dtypes=torch.qint8)), output_size_h=st.integers(1, 10), output_size_w=st.integers(1, 10)) def test_adaptive_avg_pool2d_nhwc(self, X, output_size_h, output_size_w): X, (scale, zero_point, torch_type) = X H, W = X.shape[-2:] assume(output_size_h <= H) assume(output_size_w <= W) if output_size_h == output_size_w: output_size = output_size_h else: output_size = (output_size_h, output_size_w) if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) if X.ndim == 4: X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 1])) X = torch.from_numpy(X_nchw).permute([0, 3, 1, 2]) qX = torch.quantize_per_tensor(torch.from_numpy(X_nchw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 3, 1, 2]) else: # ndim == 3 X_nchw = np.ascontiguousarray(X.transpose([1, 2, 0])) X = torch.from_numpy(X_nchw).permute([2, 0, 1]) qX = torch.quantize_per_tensor(torch.from_numpy(X_nchw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([2, 0, 1]) # Run reference on int_repr + round to avoid double rounding error. X_ref = torch.nn.functional.adaptive_avg_pool2d(qX.int_repr().to(torch.double), output_size).round() self.assertTrue(qX.stride() != sorted(qX.stride())) ops_under_test = { "nn.functional": torch.nn.functional.adaptive_avg_pool2d, "nn.quantized.functional": torch.nn.quantized.functional.adaptive_avg_pool2d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): X_hat = op(qX, output_size=output_size) self.assertTrue(X_hat.stride() != sorted(X_hat.stride())) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType(X_ref, X_hat.int_repr(), atol=1.0, rtol=0, msg=error_message.format(name, X_ref, X_hat.int_repr())) self.assertEqual(scale, X_hat.q_scale(), msg=error_message.format(name + '.scale', scale, X_hat.q_scale())) self.assertEqual(zero_point, X_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, X_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=5, min_side=1, max_side=10), qparams=hu.qparams(dtypes=torch.quint8)), output_size_d=st.integers(1, 10), output_size_h=st.integers(1, 10), output_size_w=st.integers(1, 10)) def test_adaptive_avg_pool(self, X, output_size_d, output_size_h, output_size_w): X, (scale, zero_point, torch_type) = X ndim = X.ndim dim_to_check = [] if ndim <= 4: dim_to_check.append(2) if ndim >= 4: dim_to_check.append(3) D, H, W = X.shape[-3:] assume(output_size_d <= D) assume(output_size_h <= H) assume(output_size_w <= W) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) for dim in dim_to_check: if dim == 2: if output_size_h == output_size_w: output_size = output_size_h else: output_size = (output_size_h, output_size_w) elif dim == 3: if output_size_d == output_size_h == output_size_w: output_size = output_size_h else: output_size = (output_size_d, output_size_h, output_size_w) # Run reference on int_repr + round to avoid double rounding error. ref_op = getattr(torch.nn.functional, 'adaptive_avg_pool{}d'.format(dim)) X_ref = ref_op(qX.int_repr().to(torch.float), output_size).round() ops_under_test = { "nn.functional": getattr(torch.nn.functional, 'adaptive_avg_pool{}d'.format(dim)), "nn.quantized.functional": getattr(torch.nn.quantized.functional, 'adaptive_avg_pool{}d'.format(dim)) } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): qX_hat = op(qX, output_size=output_size) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType( X_ref, qX_hat.int_repr(), atol=1.0, rtol=0, msg=error_message.format(name, X_ref, qX_hat)) self.assertEqual( scale, qX_hat.q_scale(), msg=error_message.format(name + '.scale', scale, qX_hat.q_scale())) self.assertEqual( zero_point, qX_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, qX_hat.q_zero_point())) """Tests adaptive average pool operation on NHWC quantized tensors.""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=5, min_side=1, max_side=10), qparams=hu.qparams(dtypes=torch.qint8)), output_size_d=st.integers(1, 10), output_size_h=st.integers(1, 10), output_size_w=st.integers(1, 10)) def test_adaptive_avg_pool3d_ndhwc(self, X, output_size_d, output_size_h, output_size_w): X, (scale, zero_point, torch_type) = X D, H, W = X.shape[-3:] assume(output_size_d <= D) assume(output_size_h <= H) assume(output_size_w <= W) if output_size_d == output_size_h == output_size_w: output_size = output_size_h else: output_size = (output_size_d, output_size_h, output_size_w) if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) if X.ndim == 5: X_ncdhw = np.ascontiguousarray(X.transpose([0, 2, 3, 4, 1])) X = torch.from_numpy(X_ncdhw).permute([0, 4, 1, 2, 3]) qX = torch.quantize_per_tensor(torch.from_numpy(X_ncdhw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 4, 1, 2, 3]) else: # ndim == 4 X_ncdhw = np.ascontiguousarray(X.transpose([1, 2, 3, 0])) X = torch.from_numpy(X_ncdhw).permute([3, 0, 1, 2]) qX = torch.quantize_per_tensor(torch.from_numpy(X_ncdhw), scale=scale, zero_point=zero_point, dtype=torch_type).permute([3, 0, 1, 2]) # Run reference on int_repr + round to avoid double rounding error. X_ref = torch.nn.functional.adaptive_avg_pool3d( qX.int_repr().to(torch.double), output_size).round() self.assertTrue(qX.stride() != sorted(qX.stride())) ops_under_test = { "nn.functional": torch.nn.functional.adaptive_avg_pool3d, "nn.quantized.functional": torch.nn.quantized.functional.adaptive_avg_pool3d } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): X_hat = op(qX, output_size=output_size) self.assertTrue(X_hat.stride() != sorted(X_hat.stride())) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType(X_ref, X_hat.int_repr(), atol=1.0, rtol=0, msg=error_message.format(name, X_ref, X_hat.int_repr())) self.assertEqual(scale, X_hat.q_scale(), msg=error_message.format(name + '.scale', scale, X_hat.q_scale())) self.assertEqual(zero_point, X_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, X_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), k=st.integers(1, 10), dim=st.integers(1, 4), largest=st.booleans(), sorted=st.booleans()) def test_qtopk(self, X, k, dim, largest, sorted): X, (scale, zero_point, torch_type) = X qX = torch.quantize_per_tensor(torch.from_numpy(X), scale, zero_point, torch_type) assume(dim < X.ndim) assume(k < X.shape[dim]) unquantized_out = torch.topk(qX.dequantize(), k, dim=dim, largest=largest, sorted=sorted) values = torch.quantize_per_tensor(torch.from_numpy(X), scale, zero_point, torch_type) indices = torch.tensor(torch.from_numpy(X)).long() quantized_out = torch.topk(qX, k, dim=dim, largest=largest, sorted=sorted) assert(len(unquantized_out) == len(quantized_out)) torch.testing.assert_allclose(quantized_out[0].dequantize(), unquantized_out[0]) torch.testing.assert_allclose(quantized_out[1], unquantized_out[1]) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), k=st.integers(1, 10), dim=st.integers(1, 4), largest=st.booleans(), sorted=st.booleans()) def test_qtopk_nhwc(self, X, k, dim, largest, sorted): # X is NHWC, we permute to view as NCHW but keep NHWC in memory X, (scale, zero_point, torch_type) = X qX = torch.quantize_per_tensor(torch.from_numpy(X), scale, zero_point, torch_type).permute([0, 3, 1, 2]) X = np.transpose(X, [0, 3, 1, 2]) assume(dim < X.ndim) assume(k < X.shape[dim]) unquantized_out = torch.topk(qX.dequantize(), k, dim=dim, largest=largest, sorted=sorted) values = torch.quantize_per_tensor(torch.from_numpy(X), scale, zero_point, torch_type) indices = torch.tensor(torch.from_numpy(X)).long() quantized_out = torch.topk(qX, k, dim=dim, largest=largest, sorted=sorted) assert(len(unquantized_out) == len(quantized_out)) torch.testing.assert_allclose(quantized_out[0].dequantize(), unquantized_out[0]) torch.testing.assert_allclose(quantized_out[1], unquantized_out[1]) """Tests quantize concatenation (both fused and not).""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), num=st.integers(1, 4), dim=st.integers(1, 4), relu=st.booleans()) def test_cat(self, X, num, dim, relu): tensors_q = [] tensors_ref = [] X, (scale, zero_point, torch_type) = X assume(dim < X.ndim) X = torch.from_numpy(X) new_shape = np.array(X.shape) new_shape[dim] = 0 for idx in range(num): tensors_q.append(torch.quantize_per_tensor(X, scale, zero_point, torch_type)) tensors_ref.append(X) new_shape[dim] += tensors_ref[-1].shape[dim] cat_ref = torch.cat(tensors_ref, dim=dim) cat_ref = torch.quantize_per_tensor(cat_ref, scale, zero_point, torch_type) cat_ref = cat_ref.dequantize() if relu: cat_ref = F.relu(cat_ref) q_cat_op = torch.ops.quantized.cat_relu q_cat_out_op = torch.ops.quantized.cat_relu_out else: q_cat_op = torch.ops.quantized.cat q_cat_out_op = torch.ops.quantized.cat_out cat_q = q_cat_op(tensors_q, dim=dim, scale=scale, zero_point=zero_point) cat_q = cat_q.dequantize() np.testing.assert_equal(cat_ref.numpy(), cat_q.numpy()) cat_q_out = torch._empty_affine_quantized( list(new_shape), scale=scale, zero_point=zero_point, dtype=torch_type) q_cat_out_op(tensors_q, dim=dim, out=cat_q_out) cat_q_out = cat_q_out.dequantize() np.testing.assert_equal(cat_ref.numpy(), cat_q_out.numpy()) # Test the cat on per-channel quantized tensor. ch_axis = 1 scales = torch.from_numpy(np.array([1.0] * X.shape[ch_axis])) scales = scales.to(torch.float64) zero_points = torch.from_numpy(np.array([0] * X.shape[ch_axis])) zero_points = zero_points.to(torch.long) tensors_q[0] = torch.quantize_per_channel( X, scales, zero_points, axis=ch_axis, dtype=torch_type) with self.assertRaisesRegex(RuntimeError, "supported.*cat"): cat_q = q_cat_op(tensors_q, dim=ch_axis, scale=scale, zero_point=zero_point) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=5, max_side=10), qparams=hu.qparams()), size=st.sampled_from((1, 3, 5, 10)), mode=st.sampled_from(("bilinear", "nearest")), scale_factor=st.sampled_from((None, 1.5, 2.0)), align_corners=st.sampled_from((True, False)), nhwc_layout=st.sampled_from((True, False))) def test_interpolate(self, X, size, mode, scale_factor, align_corners, nhwc_layout): """ This test cover upsample_nearest2d and upsample_bilinear2d """ X, (scale, zero_point, torch_type) = X H, W = X.shape[-2:] if scale_factor is not None: size = None if mode == "nearest": align_corners = None if nhwc_layout: if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 1])) X = torch.from_numpy(X_nchw).permute([0, 3, 1, 2]) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 3, 1, 2]) else: X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) X_ref = torch.nn.functional.interpolate( qX.int_repr().to(torch.float), size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners) ops_under_test = { "nn.functional": torch.nn.functional.interpolate, "nn.quantized.functional": torch.nn.quantized.functional.interpolate } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): qX_hat = op(qX, size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType(X_ref, qX_hat.int_repr(), atol=1.0, rtol=0, msg="{} results are off: qX_hat={} X_ref={}" .format(name, qX_hat.int_repr(), X_ref)) self.assertEqual(scale, qX_hat.q_scale(), msg=error_message.format(name + '.scale', scale, qX_hat.q_scale())) self.assertEqual(zero_point, qX_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, qX_hat.q_zero_point())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=5, max_dims=5, min_side=5, max_side=10), qparams=hu.qparams()), size=st.sampled_from((1, 3, 5, 5, 10)), scale_factor=st.sampled_from((None, 1.5, 2.0)), align_corners=st.sampled_from((True, False)), nhwc_layout=st.sampled_from((True, False))) def test_interpolate3d(self, X, size, scale_factor, align_corners, nhwc_layout): """ This test cover upsample_nearest2d and upsample_bilinear2d """ X, (scale, zero_point, torch_type) = X D, H, W = X.shape[-3:] mode = "nearest" if scale_factor is not None: size = None if mode == "nearest": align_corners = None if nhwc_layout: if X.shape[1] < 176: X = np.repeat(X, 176 / X.shape[1], 1) X_nchw = np.ascontiguousarray(X.transpose([0, 2, 3, 4, 1])) X = torch.from_numpy(X_nchw).permute([0, 4, 1, 2, 3]) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type).permute([0, 4, 1, 2, 3]) else: X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) X_ref = torch.nn.functional.interpolate( qX.int_repr().to(torch.float), size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners) ops_under_test = { "nn.functional": torch.nn.functional.interpolate, "nn.quantized.functional": torch.nn.quantized.functional.interpolate } error_message = r"Results are off for {}:\n\tExpected:\n{}\n\tGot:\n{}" for name, op in ops_under_test.items(): qX_hat = op(qX, size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType(X_ref, qX_hat.int_repr(), atol=1.0, rtol=0, msg="{} results are off: qX_hat={}, X_ref={}" .format(name, qX_hat.int_repr(), X_ref)) self.assertEqual(scale, qX_hat.q_scale(), msg=error_message.format(name + '.scale', scale, qX_hat.q_scale())) self.assertEqual(zero_point, qX_hat.q_zero_point(), msg=error_message.format(name + '.zero_point', scale, qX_hat.q_zero_point())) """Tests quantize concatenation (both fused and not).""" @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=4, max_dims=4, min_side=1, max_side=10), qparams=hu.qparams()), relu=st.booleans()) def test_cat_nhwc(self, X, relu): # X is NHWC X, (scale, zero_point, torch_type) = X # Tile out X so # channels is > 64 X = np.repeat(X, 70 / X.shape[3], 3) X = torch.from_numpy(np.ascontiguousarray(X)) Y = X.clone() Y = torch.from_numpy(np.ascontiguousarray(Y)) # Here, we quantize and get quantized tensors in NHWC for both dims and strides. The # permute switches it so that the tensor looks like NCHW but it laid out in memory as # NHWC. qX = torch.quantize_per_tensor(X, scale, zero_point, torch_type).permute([0, 3, 1, 2]) qY = torch.quantize_per_tensor(Y, scale, zero_point, torch_type).permute([0, 3, 1, 2]) ref = torch.cat([qX.dequantize(), qY.dequantize()], dim=1) if relu: ref[ref < 0] = 0.0 ref = torch.quantize_per_tensor(ref, scale=scale, zero_point=zero_point, dtype=torch_type) if relu: out = torch.ops.quantized.cat_relu( [qX, qY], dim=1, scale=scale, zero_point=zero_point) else: out = torch.ops.quantized.cat([qX, qY], dim=1, scale=scale, zero_point=zero_point) torch.testing.assert_allclose(out.dequantize(), ref.dequantize()) self.assertNotEqual(out.stride(), sorted(out.stride())) @given(X=hu.tensor(shapes=hu.array_shapes(min_dims=3, max_dims=3, min_side=1, max_side=2), qparams=hu.qparams()), dim=st.integers(1, 2)) def test_mean(self, X, dim): X, (scale, zero_point, torch_type) = X qX = torch.quantize_per_tensor(torch.tensor(X).float(), scale, zero_point, torch_type) Y = torch.mean(qX.dequantize(), dim) Y = torch.quantize_per_tensor(Y, scale, zero_point, torch_type).dequantize() qY = torch.mean(qX, dim) self.assertEqual(Y, qY.dequantize()) """Tests the correctness of the quantized equal op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams()), X2=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams()), X_per_channel=st.booleans(), X2_per_channel=st.booleans()) def test_equal(self, X, X2, X_per_channel, X2_per_channel): X, X_params = X (scale, zero_point, torch_type) = X_params X2, X2_params = X2 (scale2, zero_point2, torch_type2) = X2_params X = torch.from_numpy(X) if X_per_channel: X_scheme = 'per_channel' channels = X.shape[-1] qX = torch.quantize_per_channel( X, scales=torch.tensor([scale] * channels), zero_points=torch.tensor([zero_point] * channels), dtype=torch_type, axis=X.ndim - 1) else: X_scheme = 'per_tensor' qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) X2 = torch.from_numpy(X2) if X2_per_channel: X2_scheme = 'per_channel' channels = X2.shape[-1] qX2 = torch.quantize_per_channel( X2, scales=torch.tensor([scale2] * channels), zero_points=torch.tensor([zero_point2] * channels), dtype=torch_type2, axis=X2.ndim - 1) else: X2_scheme = 'per_tensor' qX2 = torch.quantize_per_tensor(X2, scale=scale2, zero_point=zero_point2, dtype=torch_type2) def equal_ref(qX, qX2): if qX.qscheme() != qX2.qscheme(): return False if qX.shape != qX2.shape: return False if qX.dtype != qX2.dtype: return False if qX.qscheme() == torch.per_tensor_affine: if qX.q_scale() != qX2.q_scale(): return False if qX.q_zero_point() != qX2.q_zero_point(): return False elif qX.qscheme() == torch.per_channel_affine: if (qX.q_per_channel_scales() != qX2.q_per_channel_scales()).any(): return False if (qX.q_per_channel_zero_points() != qX2.q_per_channel_zero_points()).any(): return False else: raise NotImplementedError("Don't know what to do with", qX.qscheme()) if (qX.int_repr().to(float) != qX2.int_repr().to(float)).any(): return False return True self.assertEqual(qX.equal(qX), equal_ref(qX, qX)) self.assertEqual(qX.equal(qX2), equal_ref(qX, qX2)) @skipIfNoFBGEMM def test_group_norm(self): # hypothesis is flaky for this test, create test cases manually batches_list = (1, 7) num_groups_list = (1, 2) channels_per_groups = (1, 2) elements_per_channels = (8, 17) torch_types = (torch.qint8, torch.quint8) y_scales = (0.1, 4.23) y_zero_points = (0, 1) channels_last_list = [True, False] affine_list = [True, False] combined = [batches_list, num_groups_list, channels_per_groups, elements_per_channels, torch_types, y_scales, y_zero_points, channels_last_list, affine_list] test_cases = itertools.product(*combined) with override_quantized_engine("fbgemm"): for test_case in test_cases: batches, num_groups, channels_per_group, elements_per_channel, \ torch_type, Y_scale, Y_zero_point, channels_last, \ affine = test_case num_channels = num_groups * channels_per_group # minimum rank for for channels_last shapes = (batches, num_channels, elements_per_channel, 1) # In the FP kernel, sums and sums of squares are calculated in floating point. # In the int8 and uint8 versions of the quantized kernel, they are # calculated in integer arithmetic (which is exact). # Because of this, the numerics do not always match exactly which is # expected and acceptable. We do the following to allow this failure # in this test: # 1. do not use Hypothesis to generate the input tensor. Hypothesis # favors homogeneous inputs in its search strategies which isn't # representative of the inputs we care about, and tends to maximize # this particular numerics difference. # 2. allow a small % of off by Y_scale errors. Even when the # variance of the input is high, there can be off by one errors # in the result if the input value happens to fall exactly on # the bin boundary of the output scale. # # If we want the numerics to match we could switch to calculating # mean+var in floating point in the future, at the cost of speed. X, X_scale, X_zero_point = \ _get_random_tensor_and_q_params(shapes, 1.0, torch_type) # Initialize the weights non-randomly for reproducibility if affine: weight = torch.ones(num_channels).float() * 0.5 bias = torch.ones(num_channels).float() for i in range(num_channels): weight[i] *= i bias[i] *= i else: weight = None bias = None eps = 0.001 qX = torch.quantize_per_tensor(X, X_scale, X_zero_point, torch_type) if channels_last: qX = qX.contiguous(memory_format=torch.channels_last) dqX = qX.dequantize() # Enforce non-homogeneous inputs for batch_idx in range(batches): for group_idx in range(num_groups): ch_start = group_idx * channels_per_group ch_end = ch_start + channels_per_group group_vals = dqX[batch_idx][ch_start:ch_end] assume( float(torch.unique(group_vals).shape[0]) / group_vals.numel() > 0.01 or group_vals.numel() < 5) qY = torch.ops.quantized.group_norm(qX, num_groups, weight, bias, eps, Y_scale, Y_zero_point) dqY_hat = F.group_norm(dqX, num_groups=num_groups, weight=weight, bias=bias, eps=eps) qY_hat = torch.quantize_per_tensor(dqY_hat, Y_scale, Y_zero_point, torch_type) # Due to the numerics difference mentioned above between calculating # the variance in float vs int, the results can still be slightly # different. dqY = qY.dequantize() dqY_hat = qY_hat.dequantize() diff = dqY - dqY_hat # off-by-one errors are magnitude of Y_scale num_diff = torch.sum(diff > Y_scale * 1.0001) pct_diff = float(num_diff) / (diff.numel() + 1e-5) num_diff_off_by_one = torch.sum((diff > 0) * (diff <= Y_scale)) pct_diff_off_by_one = float(num_diff_off_by_one) / (diff.numel() + 1e-5) self.assertTrue(pct_diff < 1e-6) self.assertTrue(pct_diff_off_by_one < 0.01) @skipIfNoFBGEMM def test_instance_norm(self): max_sides = (4, 5) side_lens = (2, 8, 11) torch_types = (torch.qint8, torch.quint8) y_scales = (0.1, 4.23) y_zero_points = (0, 1) channels_last_list = (True, False) affine_list = (True, False) combined = [side_lens, torch_types, y_scales, y_zero_points, channels_last_list, affine_list] test_cases = itertools.product(*combined) with override_quantized_engine("fbgemm"): for test_case in test_cases: side_len, torch_type, Y_scale, Y_zero_point, channels_last, affine = test_case shapes = [side_len] * 4 # In the FP kernel, sums and sums of squares are calculated in floating point. # In the int8 and uint8 versions of the quantized kernel, they are # calculated in integer arithmetic (which is exact). # Because of this, the numerics do not always match exactly which is # expected and acceptable. We do the following to allow this failure # in this test: # 1. do not use Hypothesis to generate the input tensor. Hypothesis # favors homogeneous inputs in its search strategies which isn't # representative of the inputs we care about, and tends to maximize # this particular numerics difference. # 2. allow a small % of off by Y_scale errors. Even when the # variance of the input is high, there can be off by one errors # in the result if the input value happens to fall exactly on # the bin boundary of the output scale. # # If we want the numerics to match we could switch to calculating # mean+var in floating point in the future, at the cost of speed. X, X_scale, X_zero_point = \ _get_random_tensor_and_q_params(shapes, 1.0, torch_type) num_channels = shapes[1] if affine: weight = torch.rand(num_channels).float() * 0.5 bias = torch.rand(num_channels).float() for i in range(num_channels): weight[i] *= i bias[i] *= i else: weight = None bias = None eps = 0.001 qX = torch.quantize_per_tensor(X, X_scale, X_zero_point, torch_type) if channels_last: qX = qX.contiguous(memory_format=torch.channels_last) dqX = qX.dequantize() # Enforce non-homogeneous inputs batches = shapes[0] for batch_idx in range(batches): for ch_idx in range(num_channels): ch_vals = dqX[batch_idx][ch_idx] assume( float(torch.unique(ch_vals).shape[0]) / ch_vals.numel() > 0.01 or group_vals.numel() < 5) qY = torch.ops.quantized.instance_norm(qX, weight, bias, eps, Y_scale, Y_zero_point) dqY_hat = F.instance_norm(dqX, weight=weight, bias=bias, eps=eps) qY_hat = torch.quantize_per_tensor(dqY_hat, Y_scale, Y_zero_point, torch_type) # Due to the numerics difference mentioned above between calculating # the variance in float vs int, the results can still be slightly # different. dqY = qY.dequantize() dqY_hat = qY_hat.dequantize() diff = dqY - dqY_hat # off-by-one errors are magnitude of Y_scale num_diff = torch.sum(diff > Y_scale * 1.0001) pct_diff = float(num_diff) / (diff.numel() + 1e-5) num_diff_off_by_one = torch.sum((diff > 0) * (diff <= Y_scale)) pct_diff_off_by_one = float(num_diff_off_by_one) / (diff.numel() + 1e-5) self.assertTrue(pct_diff < 1e-6) self.assertTrue(pct_diff_off_by_one < 0.01) @skipIfNoFBGEMM def test_batch_norm_relu(self): # hypothesis too slow for this test, create test cases manually max_sides = (3, 4, 5) side_lens = (1, 8, 11) torch_types = (torch.qint8, torch.quint8) combined = [max_sides, side_lens, torch_types] test_cases = itertools.product(*combined) with override_quantized_engine("fbgemm"): for test_case in test_cases: max_side, side_len, torch_type = test_case Y_zero_point = 1 Y_scale = 0.5 shapes = [side_len] * max_side X, scale_x, zero_point_x = \ _get_random_tensor_and_q_params(shapes, 1.0, torch_type) dtype_x = torch_type c = X.shape[1] mean = torch.rand(c).float() var = torch.rand(c).float() weight = torch.rand(c).float() bias = torch.rand(c).float() eps = 0.001 qx = torch.quantize_per_tensor(X, scale_x, zero_point_x, dtype_x) if len(X.shape) == 3: qy = torch.ops.quantized.batch_norm1d_relu( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) elif len(X.shape) == 4: qy = torch.ops.quantized.batch_norm2d_relu( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) else: qy = torch.ops.quantized.batch_norm3d_relu( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) float_ref = F.batch_norm(qx.dequantize(), weight=weight, bias=bias, running_mean=mean, running_var=var, training=False, momentum=0, eps=eps).numpy() float_ref_relu = float_ref.copy() float_ref_relu[float_ref < 0] = 0 quantize_ref = torch.quantize_per_tensor( torch.from_numpy(float_ref_relu), Y_scale, Y_zero_point, dtype_x) self.assertEqual( qy.int_repr().numpy(), quantize_ref.int_repr().numpy(), msg="{} vs {}".format(qy, quantize_ref)) @skipIfNoFBGEMM def test_batch_norm(self): # hypothesis too slow for this test, create test cases manually max_sides = (3, 4, 5) side_lens = (1, 8, 11) torch_types = (torch.qint8, torch.quint8) combined = [max_sides, side_lens, torch_types] test_cases = itertools.product(*combined) with override_quantized_engine("fbgemm"): for test_case in test_cases: max_side, side_len, torch_type = test_case Y_zero_point = 1 Y_scale = 0.5 shapes = [side_len] * max_side X, scale_x, zero_point_x = \ _get_random_tensor_and_q_params(shapes, 1.0, torch_type) dtype_x = torch_type c = X.shape[1] mean = torch.rand(c).float() var = torch.rand(c).float() weight = torch.rand(c).float() bias = torch.rand(c).float() eps = 0.001 qx = torch.quantize_per_tensor(X, scale_x, zero_point_x, dtype_x) if len(X.shape) == 3: qy = torch.ops.quantized.batch_norm1d( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) if len(X.shape) == 4: qy = torch.ops.quantized.batch_norm2d( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) if len(X.shape) == 5: qy = torch.ops.quantized.batch_norm3d( qx, weight, bias, mean, var, eps, Y_scale, Y_zero_point) float_ref = F.batch_norm(qx.dequantize(), weight=weight, bias=bias, running_mean=mean, running_var=var, training=False, momentum=0, eps=eps) quantize_ref = torch.quantize_per_tensor(float_ref, Y_scale, Y_zero_point, dtype_x) self.assertEqual( qy.int_repr().numpy(), quantize_ref.int_repr().numpy(), msg="{} vs {}".format(qy, quantize_ref)) @override_qengines def test_empty_batch(self): scale = 1.0 zero_point = 0 X = torch.ones((0, 2, 4, 4), dtype=torch.float32) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8) # relu qY = torch.nn.functional.relu(qX) np.testing.assert_equal(qY.size(), qX.size(), "Quantized relu with batch size 0 failed.") # tanh qY = torch.tanh(qX) np.testing.assert_equal(qY.size(), qX.size(), "Quantized tanh with batch size 0 failed.") # sigmoid qY = torch.sigmoid(qX) np.testing.assert_equal(qY.size(), qX.size(), "Quantized sigmoid with batch size 0 failed.") # interpolate op = torch.nn.quantized.functional.interpolate for mode in ["nearest", "bilinear"]: qY = op(qX, scale_factor=2, mode=mode) np.testing.assert_equal(qY.size(), (0, 2, 8, 8), "Quantized interpolate with batch size 0 failed.") # avg_pool kernel = (2, 2) stride = (1, 1) padding = (0, 0) op = torch.nn.quantized.functional.avg_pool2d qY = op(qX, kernel, stride, padding) np.testing.assert_equal(qY.size(), (0, 2, 3, 3), "Quantized avg_pool2d with batch size 0 failed.") # adaptive_avg_pool op = torch.nn.quantized.functional.adaptive_avg_pool2d qY = op(qX, (3, 3)) np.testing.assert_equal(qY.size(), (0, 2, 3, 3), "Quantized adaptive_avg_pool2d with batch size 0 failed.") # max_pool dilation = (1, 1) qY = torch.ops.quantized.max_pool2d(qX, kernel, stride, padding, dilation, ceil_mode=False) oH = pool_output_shape(4, 2, 0, 1, 1) oW = pool_output_shape(4, 2, 0, 1, 1) np.testing.assert_equal(qY.size(), (0, 2, oH, oW), "Quantized maxpool2d with batch size 0 failed.") # hardtanh qY = torch.nn.quantized.functional.hardtanh(qX, -1, 6) np.testing.assert_equal(qY.size(), qX.size(), "Quantized hardtanh with batch size 0 failed.") # mul qY = torch.ops.quantized.mul(qX, qX, 1.0, 0) np.testing.assert_equal(qY.size(), qX.size(), "Quantized mul with batch size 0 failed.") # add qY = torch.ops.quantized.add(qX, qX, 1.0, 0) np.testing.assert_equal(qY.size(), qX.size(), "Quantized addition with batch size 0 failed.") # conv w = torch.randn((2, 2, 2, 2), dtype=torch.float) qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.qint8) bias_float = torch.ones(2, dtype=torch.float) strides = [1, 1] pads = [0, 0] dilations = [1, 1] w_packed = torch.ops.quantized.conv2d_prepack(qw, bias_float, strides, pads, dilations, 1) result = torch.ops.quantized.conv2d(qX, w_packed, 1.0, 0) self.assertEqual(result.shape, (0, 2, 3, 3)) # linear X = torch.ones((0, 2), dtype=torch.float32) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8) w = torch.randn((2, 2), dtype=torch.float) qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.qint8) w_packed = torch.ops.quantized.linear_prepack(qw, bias_float) result = torch.ops.quantized.linear(qX, w_packed, 1.0, 0) self.assertEqual(result.shape, (0, 2)) # dynamic linear result = torch.ops.quantized.linear_dynamic(X, w_packed) self.assertEqual(result.shape, (0, 2)) class TestDynamicQuantizedLinear(TestCase): """Tests the correctness of the dynamic quantized linear and linear_relu op.""" @override_qengines @given( batch_size=st.integers(1, 4), input_channels=st.integers(16, 32), output_channels=st.integers(4, 8), use_bias=st.booleans(), use_relu=st.booleans(), use_multi_dim_input=st.booleans(), use_channelwise=st.booleans(), reduce_range=st.booleans()) def test_qlinear(self, batch_size, input_channels, output_channels, use_bias, use_relu, use_multi_dim_input, use_channelwise, reduce_range): if torch.backends.quantized.engine == 'qnnpack': use_relu = False reduce_range = False qlinear_prepack = torch.ops.quantized.linear_prepack if use_relu: qlinear_dynamic = torch.ops.quantized.linear_relu_dynamic else: qlinear_dynamic = torch.ops.quantized.linear_dynamic if use_multi_dim_input: batch_size *= 3 # Test the multi-dim input tensor X_scale = 1.0 X_zp = 0 X_value_min = 0 X_value_max = 255 if reduce_range: X_value_max = 127 X_q0 = np.round(np.random.rand(batch_size, input_channels) * (X_value_max - X_value_min) + X_value_min).astype(np.uint8) X_q0[0, 0] = X_value_min X_q0[0, 1] = X_value_max # W_scale = 1.0 # W_zp = 0 W_scales = np.ones(output_channels) W_zps = np.zeros(output_channels).astype(np.int) W_value_min = -128 W_value_max = 127 W_q0 = np.round( np.random.rand(output_channels, input_channels) * (W_value_max - W_value_min) + W_value_min ).astype(np.int8) W_q0[0, 0] = W_value_min W_q0[1, 0] = W_value_max b_value_min = -10 b_value_max = 10 b_q0 = np.round( np.random.rand(output_channels) * (b_value_max - b_value_min) + b_value_min ).astype(np.int32) if use_bias else None if torch.backends.quantized.engine == 'fbgemm': avoid_vpmaddubsw_overflow_linear( batch_size, input_channels, output_channels, X_q0, X_value_min, X_value_max, W_q0, W_value_min, W_value_max, ) X_fp32 = torch.from_numpy(_dequantize(X_q0, X_scale, X_zp)).to(dtype=torch.float) if use_multi_dim_input: X_fp32 = X_fp32.view(3, int(batch_size / 3), input_channels) # W_scale, W_zp = _calculate_dynamic_qparams(W_fp32, torch.qint8) # We currently only check the case where W_scale = 1.0, W_zp = 0. if use_channelwise: W_fp32 = torch.from_numpy(_dequantize(W_q0, W_scales.reshape( (-1, 1)), W_zps.reshape((-1, 1)))).to(dtype=torch.float) W_q = torch.quantize_per_channel(W_fp32, scales=torch.from_numpy(W_scales), zero_points=torch.from_numpy(W_zps), axis=0, dtype=torch.qint8) b_fp32 = torch.from_numpy( _dequantize(b_q0, X_scale * W_scales, 0) ).to(dtype=torch.float) if use_bias else None else: W_fp32 = torch.from_numpy(_dequantize( W_q0, W_scales[0], W_zps[0])).to(dtype=torch.float) W_q = torch.quantize_per_tensor(W_fp32, scale=W_scales[0], zero_point=( W_zps[0].astype(int).item()), dtype=torch.qint8) b_fp32 = torch.from_numpy( _dequantize(b_q0, X_scale * int(W_scales[0].item()), 0) ).to(dtype=torch.float) if use_bias else None # Observe X_fp32 and determine X_scale and X_zero_point, this should match # internals of dynamic linear. X_scale, X_zp = _calculate_dynamic_qparams(X_fp32, torch.quint8, reduce_range) X_q = torch.quantize_per_tensor(X_fp32, scale=X_scale, zero_point=X_zp, dtype=torch.quint8) # Weight prepacking operator for dynamic quantized Linear W_prepack = qlinear_prepack(W_q, b_fp32) # Dynamic quantized Linear operator with prepacked weight Y_fp32 = qlinear_dynamic(X_q.dequantize(), W_prepack, reduce_range) # Y_fp32 = qlinear_dynamic(X_fp32, W_prepack, b_fp32) Y_fp32_ref = F.linear(X_q.dequantize(), W_q.dequantize(), b_fp32) # Y_fp32_ref = F.linear(X_fp32, W_fp32, b_fp32) # if use_multi_dim_input: # Y_fp32_ref = Y_fp32_ref.view(3, int(batch_size / 3), output_channels) if use_relu: Y_fp32_ref[Y_fp32_ref < 0.0] = 0.0 self.assertEqual(Y_fp32, Y_fp32_ref, msg="torch.ops.quantized.linear_dynamic results are off") class TestDynamicQuantizedRNNOp(TestCase): """Tests the correctness of the dynamic quantized lstm/gru.""" def _get_rnn_inputs(self, seq_len, num_batches, input_size, hidden_size, num_directions): # For Input (seq_len, batch, input_size) X = torch.randn(seq_len, num_batches, input_size) s, z = _calculate_dynamic_qparams(X, torch.quint8, reduce_range=True) Xq = torch.quantize_per_tensor(X, s, z, torch.quint8) # For H and C: (num_layers(1) * num_directions, batch, hidden_size) if num_directions == 1: H = torch.randn(num_directions, num_batches, hidden_size) C = torch.randn(num_directions, num_batches, hidden_size) else: H = torch.zeros(num_directions, num_batches, hidden_size) C = torch.zeros(num_directions, num_batches, hidden_size) s, z = _calculate_dynamic_qparams(H, torch.quint8, reduce_range=True) Hq = torch.quantize_per_tensor(H, s, z, torch.quint8) s, z = _calculate_dynamic_qparams(C, torch.quint8, reduce_range=True) Cq = torch.quantize_per_tensor(C, s, z, torch.quint8) return Xq, Hq, Cq def _get_rnn_weights_and_bias(self, input_size, hidden_size, num_directions, per_channel_quant, rnn_type): hidden_mult_map = {'LSTM': 4, 'LSTMCell': 4, 'GRU': 3, 'GRUCell': 3, 'RNNTanh': 2, 'RNNReLU': 2} hidden_mult = hidden_mult_map[rnn_type] weights1 = torch.randn(hidden_mult * hidden_size, input_size) weights2 = torch.randn(hidden_mult * hidden_size, hidden_size) scale1 = 0.1 * torch.ones([weights1.size()[0]]) scale2 = 0.3 * torch.ones([weights2.size()[0]]) zero_point1 = torch.zeros(scale1.size()).to(int) zero_point2 = torch.zeros(scale2.size()).to(int) b1 = torch.zeros(hidden_mult * hidden_size) if per_channel_quant: Wq1 = torch.quantize_per_channel(weights1, scale1, zero_point1, 0, torch.qint8) Wq2 = torch.quantize_per_channel(weights2, scale2, zero_point2, 0, torch.qint8) else: Wq1 = torch.quantize_per_tensor(weights1, float(scale1[0]), int(zero_point1[0]), torch.qint8) Wq2 = torch.quantize_per_tensor(weights2, float(scale2[0]), int(zero_point2[0]), torch.qint8) return Wq1, Wq2, b1, b1 @given( num_batches=st.integers(1, 4), input_size=st.integers(16, 32), hidden_size=st.integers(4, 8), num_directions=st.integers(1, 2), per_channel_quant=st.booleans()) @override_qengines def test_qlstmGRU(self, num_batches, input_size, hidden_size, num_directions, per_channel_quant): # We test only for seq length of 1 and num layers of 1 as dynamic quantization occurs multiple times # within the LSTM op and we do not model the quantization between multiple calls of the linear op within the # lstm op seq_len = 1 for rnn_type in ['LSTM', 'GRU']: for dtype in [torch.qint8, torch.float16]: # Fp16 quantization is not supported for qnnpack if torch.backends.quantized.engine == 'qnnpack' and dtype == torch.float16: continue Xq, Hq, Cq = self._get_rnn_inputs(seq_len, num_batches, input_size, hidden_size, num_directions) Wq1, Wq2, b1, b2 = self._get_rnn_weights_and_bias(input_size, hidden_size, num_directions, per_channel_quant, rnn_type) if dtype == torch.qint8: packed_ih = torch.ops.quantized.linear_prepack(Wq1, b1) packed_hh = torch.ops.quantized.linear_prepack(Wq2, b2) cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(packed_ih, packed_hh, b1, b2, True) W_ref1 = Wq1.dequantize() W_ref2 = Wq2.dequantize() else: packed_ih = torch.ops.quantized.linear_prepack_fp16(Wq1.dequantize(), b1) packed_hh = torch.ops.quantized.linear_prepack_fp16(Wq2.dequantize(), b2) cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(packed_ih, packed_hh) W_ref1 = Wq1.dequantize().to(torch.float16).to(torch.float32) W_ref2 = Wq2.dequantize().to(torch.float16).to(torch.float32) if rnn_type == 'LSTM': if num_directions > 1: result_ref = _VF.lstm(Xq.dequantize(), (Hq.dequantize(), Cq.dequantize()), [W_ref1, W_ref2, b1, b2, W_ref1, W_ref2, b1, b2], True, 1, 0, False, num_directions > 1, False) result_dynamic = torch.quantized_lstm(Xq.dequantize(), (Hq.dequantize(), Cq.dequantize()), ([cell_params, cell_params]), True, 1, 0, False, True, False, dtype=torch.qint8, use_dynamic=True) else: result_ref = _VF.lstm(Xq.dequantize(), (Hq.dequantize(), Cq.dequantize()), [W_ref1, W_ref2, b1, b2], True, 1, 0, False, num_directions > 1, False) result_dynamic = torch.quantized_lstm(Xq.dequantize(), (Hq.dequantize(), Cq.dequantize()), ([cell_params]), True, 1, 0, False, num_directions > 1, False, dtype=torch.qint8, use_dynamic=True) if rnn_type == 'GRU': if num_directions > 1: result_ref = _VF.gru(Xq.dequantize(), Hq.dequantize(), [W_ref1, W_ref2, b1, b2, W_ref1, W_ref2, b1, b2], True, 1, 0, False, True, False) result_dynamic = torch.quantized_gru(Xq.dequantize(), Hq.dequantize(), ([cell_params, cell_params]), True, 1, 0, False, True, False) else: result_ref = _VF.gru(Xq.dequantize(), Hq.dequantize(), [W_ref1, W_ref2, b1, b2], True, 1, 0, False, False, False) result_dynamic = torch.quantized_gru(Xq.dequantize(), Hq.dequantize(), ([cell_params]), True, 1, 0, False, False, False) self.assertEqual(result_ref[0], result_dynamic[0], msg="torch.quantized_lstm results are off") @given( num_batches=st.integers(1, 4), input_size=st.integers(16, 32), hidden_size=st.integers(4, 8), per_channel_quant=st.booleans()) @override_qengines def test_qrnncell(self, num_batches, input_size, hidden_size, per_channel_quant): # We test only for seq length of 1 and num layers of 1 as dynamic quantization occurs multiple times # within the LSTM op and we do not model the quantization between multiple calls of the linear op within the # lstm op seq_len = 1 for rnn_type in ['LSTMCell', 'GRUCell', 'RNNTanh', 'RNNReLU']: for dtype in [torch.qint8, torch.float16]: # Fp16 quantization is not supported for qnnpack if torch.backends.quantized.engine == 'qnnpack' and dtype == torch.float16: continue Xq, Hq, Cq = self._get_rnn_inputs(seq_len, num_batches, input_size, hidden_size, 1) Wq1, Wq2, b1, b2 = self._get_rnn_weights_and_bias(input_size, hidden_size, 1, per_channel_quant, rnn_type) if dtype == torch.qint8: packed_ih = torch.ops.quantized.linear_prepack(Wq1, b1) packed_hh = torch.ops.quantized.linear_prepack(Wq2, b2) W_ref1 = Wq1.dequantize() W_ref2 = Wq2.dequantize() else: packed_ih = torch.ops.quantized.linear_prepack_fp16(Wq1.dequantize(), b1) packed_hh = torch.ops.quantized.linear_prepack_fp16(Wq2.dequantize(), b2) W_ref1 = Wq1.dequantize().to(torch.float16).to(torch.float32) W_ref2 = Wq2.dequantize().to(torch.float16).to(torch.float32) state = {'LSTMCell': (Hq.dequantize()[0], Cq.dequantize()[0]), 'GRUCell': Hq.dequantize()[0], 'RNNTanh': Hq.dequantize()[0], 'RNNReLU': Hq.dequantize()[0]} fn_dict = {'LSTMCell': torch._VF.lstm_cell, 'GRUCell': torch._VF.gru_cell, 'RNNTanh': torch._VF.rnn_tanh_cell, 'RNNReLU': torch._VF.rnn_relu_cell} qfn_dict = {'LSTMCell': torch.ops.quantized.quantized_lstm_cell_dynamic, 'GRUCell': torch.ops.quantized.quantized_gru_cell_dynamic, 'RNNTanh': torch.ops.quantized.quantized_rnn_tanh_cell_dynamic, 'RNNReLU': torch.ops.quantized.quantized_rnn_relu_cell_dynamic} W_ref_dict = {torch.float16: (Wq1.dequantize().to(torch.float16).to(torch.float32), Wq2.dequantize().to(torch.float16).to(torch.float32)), torch.qint8: (Wq1.dequantize(), Wq2.dequantize())} result_ref = fn_dict[rnn_type](Xq.dequantize()[0], state[rnn_type], W_ref1, W_ref2, b1, b2) result_dynamic = qfn_dict[rnn_type](Xq.dequantize()[0], state[rnn_type], packed_ih, packed_hh, b1, b2) self.assertEqual(result_ref[0], result_dynamic[0], msg="torch.quantized_rnncell results are off") @skipIfNoFBGEMM @given( batch_size=st.integers(1, 4), input_channels=st.integers(16, 32), output_channels=st.integers(4, 8), ) def test_qlinear_legacy(self, batch_size, input_channels, output_channels): X_scale = 1.0 X_zp = 0 X_value_min = 0 X_value_max = 255 X_q0 = np.round(np.random.rand(batch_size, input_channels) * ( X_value_max - X_value_min) + X_value_min ).astype(np.uint8) X_q0[0, 0] = X_value_min X_q0[0, 1] = X_value_max W_scale = 1.0 W_zp = 0 W_value_min = -128 W_value_max = 127 W_q0 = np.round( np.random.rand(output_channels, input_channels) * (W_value_max - W_value_min) + W_value_min ).astype(np.int8) W_q0[0, 0] = W_value_min W_q0[1, 0] = W_value_max b_value_min = -10 b_value_max = 10 b_q0 = np.round( np.random.rand(output_channels) * (b_value_max - b_value_min) + b_value_min ).astype(np.int32) avoid_vpmaddubsw_overflow_linear( batch_size, input_channels, output_channels, X_q0, X_value_min, X_value_max, W_q0, W_value_min, W_value_max, ) X_fp32 = torch.from_numpy(_dequantize(X_q0, X_scale, X_zp)).to(dtype=torch.float) W_fp32 = torch.from_numpy(_dequantize(W_q0, W_scale, W_zp)).to(dtype=torch.float) b_fp32 = torch.from_numpy( _dequantize(b_q0, X_scale * W_scale, 0) ).to(dtype=torch.float) W_scale, W_zp = _calculate_dynamic_qparams(W_fp32, torch.qint8) W_q = torch.quantize_per_tensor(W_fp32, scale=W_scale, zero_point=W_zp, dtype=torch.qint8) # Observe X_fp32 and determine X_scale and X_zero_point, this should match # internals of dynamic linear. X_scale, X_zp = _calculate_dynamic_qparams(X_fp32, torch.quint8) X_q = torch.quantize_per_tensor(X_fp32, scale=X_scale, zero_point=X_zp, dtype=torch.quint8) W_int8, col_offsets, W_scale, W_zp = torch.fbgemm_linear_quantize_weight(W_q.dequantize()) W_prepack = torch.fbgemm_pack_quantized_matrix(W_int8.clone(), W_int8.size(1), W_int8.size(0)) # Quantized Linear operator with prepacked weight Y_fp32 = torch.fbgemm_linear_int8_weight( X_q.dequantize(), W_q.dequantize(), W_prepack, col_offsets, W_scale, W_zp, b_fp32) Y_fp32_ref = F.linear(X_q.dequantize(), W_q.dequantize(), b_fp32) # Y_fp32_ref = F.linear(X_fp32, W_fp32, b_fp32) self.assertEqual(Y_fp32, Y_fp32_ref, msg="torch.ops.quantized.fbgemm_linear_dynamic results are off") class TestQuantizedLinear(unittest.TestCase): """Tests the correctness of the quantized linear and linear_relu op.""" @given(batch_size=st.integers(1, 4), input_channels=st.integers(16, 32), output_channels=st.integers(4, 8), use_bias=st.booleans(), use_relu=st.booleans(), use_multi_dim_input=st.booleans(), use_channelwise=st.booleans()) @override_qengines def test_qlinear(self, batch_size, input_channels, output_channels, use_bias, use_relu, use_multi_dim_input, use_channelwise): decimal_val = 4 if torch.backends.quantized.engine == 'qnnpack': use_multi_dim_input = False # QNNPACK supports uint8 in the kernels. In the op we shift the int8 # weight values to uint8 to be on par with fbgemm. However, this causes # some rounding issues in rare cases. So, we relax the check to allow # off by one results. decimal_val = 0 qlinear_prepack = torch.ops.quantized.linear_prepack if use_relu: qlinear = torch.ops.quantized.linear_relu else: qlinear = torch.ops.quantized.linear if use_multi_dim_input: batch_size *= 3 # Test the multi-dim input tensor X_scale = 1.5 X_zp = 5 X_value_min = 0 X_value_max = 225 X_q0 = np.round( np.random.rand(batch_size, input_channels) * (X_value_max - X_value_min) + X_value_min ).astype(np.uint8) W_scales = np.random.rand(output_channels) W_zps = np.round(np.random.rand(output_channels) * 100 - 50).astype(np.int) W_value_min = -128 W_value_max = 127 W_q0 = np.round( np.random.rand(output_channels, input_channels) * (W_value_max - W_value_min) + W_value_min ).astype(np.int8) b_value_min = -10 b_value_max = 10 b_q0 = np.round( np.random.rand(output_channels) * (b_value_max - b_value_min) + b_value_min ).astype(np.int32) if use_bias else None avoid_vpmaddubsw_overflow_linear( batch_size, input_channels, output_channels, X_q0, X_value_min, X_value_max, W_q0, W_value_min, W_value_max, ) X = torch.from_numpy(_dequantize( X_q0, X_scale, X_zp)).to(dtype=torch.float) X_q = torch.quantize_per_tensor( X, scale=X_scale, zero_point=X_zp, dtype=torch.quint8) if use_channelwise: W = torch.from_numpy(_dequantize(W_q0, W_scales.reshape( (-1, 1)), W_zps.reshape((-1, 1)))).to(dtype=torch.float) W_q = torch.quantize_per_channel(W, scales=torch.from_numpy(W_scales), zero_points=torch.from_numpy(W_zps), axis=0, dtype=torch.qint8) b = torch.from_numpy(_dequantize( b_q0, X_scale * W_scales, 0)).to(dtype=torch.float) if use_bias else None b_q = torch.quantize_per_channel(b, scales=torch.from_numpy(X_scale * W_scales), zero_points=torch.zeros(output_channels, dtype=torch.long), axis=0, dtype=torch.qint32) if use_bias else None else: W = torch.from_numpy(_dequantize( W_q0, W_scales[0], W_zps[0])).to(dtype=torch.float) W_q = torch.quantize_per_tensor(W, scale=W_scales[0], zero_point=( W_zps[0].astype(int).item()), dtype=torch.qint8) b = torch.from_numpy(_dequantize( b_q0, X_scale * (W_scales[0].item()), 0)).to(dtype=torch.float) if use_bias else None b_q = torch.quantize_per_tensor( b, scale=X_scale * (W_scales[0].item()), zero_point=0, dtype=torch.qint32) if use_bias else None # Compare X_scale * W_scale * input_channels * X_value_max * W_value_max with # Y_scale * 255 (max for uint8). Y_scale = 125.1234 Y_zp = 5 # Weight prepacking operator for quantized Linear float_bias = b if use_bias else None W_prepack = qlinear_prepack(W_q, float_bias) if use_multi_dim_input: X_q = X_q.view(3, int(batch_size / 3), input_channels) # Quantized Linear operator with prepacked weight Y_q = qlinear(X_q, W_prepack, Y_scale, Y_zp) if not use_channelwise: # Test the per-tensor quantization only # Reference quantized Linear operator Y_q_ref = qlinear_ref(X_q0, X_scale, X_zp, W_q0, W_scales[0], W_zps[0], b_q0, Y_scale, Y_zp) if use_relu: Y_q_ref[Y_q_ref < Y_zp] = Y_zp if use_multi_dim_input: Y_q_ref = np.reshape( Y_q_ref, (3, int(batch_size / 3), output_channels)) # Assert equal np.testing.assert_array_almost_equal(Y_q_ref, Y_q.int_repr().numpy(), decimal=decimal_val) # Test both per-tensor and per-channel quantization # Reference quantized result from PyTorch Linear operator W_fp32 = W_q.dequantize().to(dtype=torch.float) X_fp32 = X_q.dequantize().to(dtype=torch.float) b_fp32 = b_q.dequantize().to(dtype=torch.float) if use_bias else None Y_fp32_ref = F.linear(X_fp32, W_fp32, b_fp32) if use_relu: Y_fp32_ref[Y_fp32_ref < 0.0] = 0.0 Y_q_ref2 = torch.quantize_per_tensor( Y_fp32_ref, Y_scale, Y_zp, torch.quint8) # Assert equal np.testing.assert_array_almost_equal( Y_q_ref2.int_repr().numpy(), Y_q.int_repr().numpy(), decimal=decimal_val) """Tests the correctness of the quantized::linear_unpack op.""" @given(W=hu.tensor(shapes=hu.array_shapes(2, 2,), qparams=hu.qparams(dtypes=torch.qint8)), use_channelwise=st.booleans()) @override_qengines def test_qlinear_unpack(self, W, use_channelwise): W, (W_scale, W_zp, torch_type) = W if use_channelwise: output_channels = W.shape[0] W_scales = torch.rand(output_channels).to(torch.double) W_zps = torch.round(torch.rand(output_channels) * 100 - 50).to(torch.int64) qlinear_prepack = torch.ops.quantized.linear_prepack qlinear_unpack = torch.ops.quantized.linear_unpack W = torch.from_numpy(W) if use_channelwise: W_q = torch.quantize_per_channel( W, W_scales, W_zps, 0, dtype=torch_type) else: W_q = torch.quantize_per_tensor(W, scale=W_scale, zero_point=W_zp, dtype=torch_type) # Weight prepacking operator for quantized Linear W_prepack = qlinear_prepack(W_q) # Weight unpack operator for quantized Linear (Used for serialization) W_q_origin = qlinear_unpack(W_prepack)[0] # Assert equal np.testing.assert_equal(W_q.int_repr(), W_q_origin.int_repr().numpy()) if use_channelwise: np.testing.assert_array_almost_equal(np.float32(W_q.q_per_channel_scales().numpy()), np.float32( W_q_origin.q_per_channel_scales().numpy()), decimal=4) np.testing.assert_equal(W_q.q_per_channel_zero_points( ).numpy(), W_q_origin.q_per_channel_zero_points().numpy()) else: np.testing.assert_equal(np.float32( W_q.q_scale()), np.float32(W_q_origin.q_scale())) np.testing.assert_equal( W_q.q_zero_point(), W_q_origin.q_zero_point()) @unittest.skipIf(sys.platform == "darwin", "Known test failure on Mac.") class TestQuantizedEmbeddingBag(TestCase): def _test_embedding_bag_unpack_fn(self, pack_fn, unpack_fn, num_embeddings, embedding_dim, bit_rate): weights = torch.from_numpy((np.random.random_sample(( num_embeddings, embedding_dim)) + 1).astype(np.float32)) w_packed = pack_fn(weights) w_unpacked = unpack_fn(w_packed) # compare against C2 to ensure numerical equivalency. from caffe2.python import core, workspace conversion_op = "FloatToFused8BitRowwiseQuantized" if bit_rate == 4: conversion_op = "FloatToFused4BitRowwiseQuantized" def get_c2_weights(weights): workspace.ResetWorkspace() workspace.FeedBlob("weights", weights) workspace.RunOperatorOnce( core.CreateOperator( conversion_op, ["weights"], ["quantized_weights"] ) ) emb_q = workspace.FetchBlob("quantized_weights") if bit_rate == 4: workspace.RunOperatorOnce( core.CreateOperator( "Fused4BitRowwiseQuantizedToFloat", ["quantized_weights"], ["dequantized_weights"] ) ) dequantized_data = torch.from_numpy(workspace.FetchBlob("dequantized_weights")) else: dequantized_data = torch.ops._caffe2.Fused8BitRowwiseQuantizedToFloat( torch.tensor(emb_q) ) return torch.from_numpy(emb_q), dequantized_data w_packed_c2, w_unpacked_c2 = get_c2_weights(weights) # Compare packed weights against C2. np.testing.assert_equal(w_packed.numpy(), w_packed_c2.numpy()) # Compare unpacked weights against C2 np.testing.assert_equal(w_unpacked.numpy(), w_unpacked_c2.numpy()) """ Tests the correctness of the embedding_bag_8bit pack/unpack op against C2 """ @given(num_embeddings=st.integers(10, 100), embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0),) def test_embedding_bag_byte_unpack(self, num_embeddings, embedding_dim): pack_fn = torch.ops.quantized.embedding_bag_byte_prepack unpack_fn = torch.ops.quantized.embedding_bag_byte_unpack self._test_embedding_bag_unpack_fn(pack_fn, unpack_fn, num_embeddings, embedding_dim, bit_rate=8) """ Tests the correctness of the embedding_bag_4bit pack/unpack op against C2 """ @given(num_embeddings=st.integers(10, 100), embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0),) def test_embedding_bag_4bit_unpack(self, num_embeddings, embedding_dim): pack_fn = torch.ops.quantized.embedding_bag_4bit_prepack unpack_fn = torch.ops.quantized.embedding_bag_4bit_unpack self._test_embedding_bag_unpack_fn(pack_fn, unpack_fn, num_embeddings, embedding_dim, bit_rate=4) def embedding_bag_rowwise_offsets_run( self, bit_rate, num_embeddings, embedding_dim, num_offsets, enable_per_sample_weights, include_last_offset, atol, rtol): pt_op = torch.ops.quantized.embedding_bag_byte_rowwise_offsets pt_prepack_op = torch.ops.quantized.embedding_bag_byte_prepack if bit_rate == 4: pt_op = torch.ops.quantized.embedding_bag_4bit_rowwise_offsets pt_prepack_op = torch.ops.quantized.embedding_bag_4bit_prepack weights = torch.from_numpy((np.random.random_sample(( num_embeddings, embedding_dim)) + 1).astype(np.float32)) max_segments = 5 max_segment_length = 20 num_lengths = np.random.randint(1, max_segments + 1) lengths = np.random.randint(0, max_segment_length + 1, size=num_lengths).astype(np.int32) num_indices = np.sum(lengths) def lengths_to_offsets(t, offset_type=np.int64, use_begin_offset=True): """ Convert lengths to offsets """ tt = np.zeros((t.shape[0] + 1,), dtype=offset_type) tt[1:] = t tt = torch.from_numpy(np.cumsum(tt, dtype=offset_type)) if use_begin_offset: return tt[:-1] return tt[1:] offsets = lengths_to_offsets(lengths) indices = torch.from_numpy(np.random.randint( low=0, high=num_embeddings, size=num_indices, dtype=np.int64)) q_weights = pt_prepack_op(weights) per_sample_weights = torch.from_numpy(np.random.uniform( low=0.01, high=0.5, size=[len(indices)]).astype(np.float32)) if \ enable_per_sample_weights else None if include_last_offset: offsets = torch.cat( (offsets, torch.tensor([indices.size(0)], dtype=torch.long)), 0 ) # Reference result will be the floating point torch.nn.EmbeddingBag. def get_reference_result( num_embeddings, embedding_dim, include_last_offset, weights, per_sample_weights, indices, offsets): embedding_bag = torch.nn.EmbeddingBag( num_embeddings=num_embeddings, embedding_dim=embedding_dim, include_last_offset=include_last_offset, _weight=weights, scale_grad_by_freq=False, mode='sum' ) return embedding_bag(indices, offsets, per_sample_weights=per_sample_weights) reference_result = get_reference_result( num_embeddings, embedding_dim, include_last_offset, weights, per_sample_weights, indices, offsets) result = pt_op( q_weights, indices, offsets, mode=0, per_sample_weights=per_sample_weights, include_last_offset=include_last_offset, ) torch.testing.assert_allclose(reference_result, result, atol=atol, rtol=rtol) """ Tests the correctness of the embedding_bag_8bit quantized operator """ @given(num_embeddings=st.integers(10, 100), embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0), num_offsets=st.integers(1, 20), enable_per_sample_weights=st.booleans(), include_last_offset=st.booleans()) def test_embedding_bag_byte_rowwise_offsets(self, num_embeddings, embedding_dim, num_offsets, enable_per_sample_weights, include_last_offset): self.embedding_bag_rowwise_offsets_run( 8, num_embeddings, embedding_dim, num_offsets, enable_per_sample_weights, include_last_offset, atol=0.005, rtol=1e-3) """ Tests the correctness of the embedding_bag_4bit quantized operator """ @given(num_embeddings=st.integers(10, 100), embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0), num_offsets=st.integers(1, 20), enable_per_sample_weights=st.booleans(), include_last_offset=st.booleans()) def test_embedding_bag_4bit_rowwise_offsets(self, num_embeddings, embedding_dim, num_offsets, enable_per_sample_weights, include_last_offset): self.embedding_bag_rowwise_offsets_run(4, num_embeddings, embedding_dim, num_offsets, enable_per_sample_weights, include_last_offset, atol=0.1, rtol=1e-2) class TestQuantizedConv(unittest.TestCase): def _test_qconv_unpack_impl( self, qconv_prepack_fn, qconv_unpack_fn, inputs, strides, pads, channelwise ): (X_data, W_data, bias_data, groups) = inputs (X, (X_scale, X_zero_point, X_qtype)) = X_data (W, (W_scale, W_zero_point, W_qtype)) = W_data (bias, (bias_scale, bias_zero_point, bias_qtype)) = bias_data if channelwise: output_channels = W.shape[0] W_scale = torch.tensor([W_scale] * output_channels) W_zero_point = torch.tensor([W_zero_point] * output_channels) W = torch.from_numpy(W).float() bias = torch.from_numpy(bias).float() if channelwise: W_q = torch.quantize_per_channel( W, scales=W_scale, zero_points=W_zero_point, axis=0, dtype=W_qtype) else: W_q = torch.quantize_per_tensor( W, scale=W_scale, zero_point=W_zero_point, dtype=W_qtype) if isinstance(strides, int): dilations = [1] else: dilations = (1,) * len(strides) W_packed = qconv_prepack_fn(W_q, bias, strides, pads, dilations, groups) (W_unpacked, bias) = qconv_unpack_fn(W_packed) # Assert equal np.testing.assert_equal(W_q.int_repr().numpy(), W_unpacked.int_repr().numpy()) if channelwise: np.testing.assert_array_almost_equal( np.float32(W_q.q_per_channel_scales().numpy()), np.float32(W_unpacked.q_per_channel_scales().numpy()), decimal=4) np.testing.assert_equal(W_q.q_per_channel_zero_points( ).numpy(), W_unpacked.q_per_channel_zero_points().numpy()) else: np.testing.assert_equal(np.float32( W_q.q_scale()), np.float32(W_unpacked.q_scale())) np.testing.assert_equal( W_q.q_zero_point(), W_unpacked.q_zero_point()) def _make_qconv_tensors( self, batch_size, input_channels_per_group, input_feature_map_shape, output_channels_per_group, groups, kernels, strides, pads, dilations, X_scale, X_zero_point, W_scale, W_zero_point, use_bias, use_channelwise ): input_channels = input_channels_per_group * groups output_channels = output_channels_per_group * groups # Padded input size should be at least as big as dilated kernel kernels = _single(kernels) strides = _single(strides) pads = _single(pads) dilations = _single(dilations) for i in range(len(kernels)): assume(input_feature_map_shape[i] + 2 * pads[i] >= dilations[i] * (kernels[i] - 1) + 1) W_scale = W_scale * output_channels W_zero_point = W_zero_point * output_channels # Resize W_scale and W_zero_points arrays equal to output_channels W_scale = W_scale[:output_channels] W_zero_point = W_zero_point[:output_channels] # For testing, we use small values for weights and for activations # so that no overflow occurs in vpmaddubsw instruction. If the # overflow occurs in qconv implementation and if there is no # overflow # In reference we can't exactly match the results with reference. # Please see the comment in qconv implementation file # aten/src/ATen/native/quantized/cpu/qconv.cpp for more details. (W_value_min, W_value_max) = (-5, 5) # the operator expects them in the format # (output_channels, input_channels/groups, # kernel_d, kernel_h, kernel_w) W_init = torch.randint( W_value_min, W_value_max, (output_channels, input_channels_per_group,) + kernels, ) b_init = torch.randint(0, 10, (output_channels,)) (X_value_min, X_value_max) = (0, 4) X_init = torch.randint( X_value_min, X_value_max, (batch_size, input_channels,) + input_feature_map_shape, ) X = X_scale * (X_init - X_zero_point).float() if use_channelwise: W_shape = (-1, 1) + (1,) * len(kernels) W_scales_tensor = torch.tensor(W_scale, dtype=torch.float) W_zero_points_tensor = torch.tensor(W_zero_point, dtype=torch.float) W = W_scales_tensor.reshape(*W_shape) * ( W_init.float() - W_zero_points_tensor.reshape(*W_shape)).float() b = X_scale * W_scales_tensor * b_init.float() else: W = W_scale[0] * (W_init - W_zero_point[0]).float() b = X_scale * W_scale[0] * b_init.float() X_q = torch.quantize_per_tensor( X, scale=X_scale, zero_point=X_zero_point, dtype=torch.quint8) if use_channelwise: W_q = torch.quantize_per_channel( W, W_scales_tensor, W_zero_points_tensor.long(), 0, dtype=torch.qint8) else: W_q = torch.quantize_per_tensor( W, scale=W_scale[0], zero_point=W_zero_point[0], dtype=torch.qint8) bias_float = b if use_bias else None return (X, W), (X_q, W_q), bias_float def _test_qconv_impl( self, qconv_fn, qconv_prepack_fn, conv_op, batch_size, input_channels_per_group, input_feature_map_shape, output_channels_per_group, groups, kernels, strides, pads, dilations, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise ): (X, W), (X_q, W_q), bias_float = self._make_qconv_tensors( batch_size, input_channels_per_group, input_feature_map_shape, output_channels_per_group, groups, kernels, strides, pads, dilations, X_scale, X_zero_point, W_scale, W_zero_point, use_bias, use_channelwise) # Assign weights conv_op.weight = torch.nn.Parameter(W, requires_grad=False) conv_op.bias = torch.nn.Parameter( bias_float, requires_grad=False) if use_bias else None result_ref = conv_op(X) if use_relu: relu = torch.nn.ReLU() result_ref = relu(result_ref) # Quantize reference results for comparison result_ref_q = torch.quantize_per_tensor( result_ref, scale=Y_scale, zero_point=Y_zero_point, dtype=torch.quint8) W_prepack = qconv_prepack_fn( W_q, bias_float, strides, pads, dilations, groups) Y_q = qconv_fn( X_q, W_prepack, Y_scale, Y_zero_point, ) # Make sure the results match # assert_array_almost_equal compares using the following formula: # abs(desired-actual) < 1.5 * 10**(-decimal) # (https://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.assert_almost_equal.html) # We use decimal = 0 to ignore off-by-1 differences between # reference and test. Off-by-1 differences arise due to the order of # round and zero_point addition operation, i.e., if addition # followed by round is used by reference and round followed by # addition is used by test, the results may differ by 1. # For example, the result of round(2.5) + 1 is 3 while # round(2.5 + 1) is 4 assuming the rounding mode is # round-to-nearest, ties-to-even. np.testing.assert_array_almost_equal( result_ref_q.int_repr().numpy(), Y_q.int_repr().numpy(), decimal=0) """Tests the correctness of quantized convolution op.""" @given(batch_size=st.integers(1, 3), input_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]), height=st.integers(10, 16), width=st.integers(7, 14), output_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]), groups=st.integers(1, 3), kernel_h=st.integers(1, 7), kernel_w=st.integers(1, 7), stride_h=st.integers(1, 2), stride_w=st.integers(1, 2), pad_h=st.integers(0, 2), pad_w=st.integers(0, 2), dilation=st.integers(1, 2), X_scale=st.floats(1.2, 1.6), X_zero_point=st.integers(0, 4), W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2), W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2), Y_scale=st.floats(4.2, 5.6), Y_zero_point=st.integers(0, 4), use_bias=st.booleans(), use_relu=st.sampled_from([False]), use_channelwise=st.booleans()) @override_qengines def test_qconv2d( self, batch_size, input_channels_per_group, height, width, output_channels_per_group, groups, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise, ): input_channels = input_channels_per_group * groups output_channels = output_channels_per_group * groups kernels = (kernel_h, kernel_w) strides = (stride_h, stride_w) pads = (pad_h, pad_w) dilations = (dilation, dilation) qconv = torch.ops.quantized.conv2d if use_relu: qconv = torch.ops.quantized.conv2d_relu qconv_prepack = torch.ops.quantized.conv2d_prepack conv_op = torch.nn.Conv2d( input_channels, output_channels, kernels, strides, pads, dilations, groups, ) self._test_qconv_impl( qconv, qconv_prepack, conv_op, batch_size, input_channels_per_group, (height, width), output_channels_per_group, groups, kernels, strides, pads, dilations, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise) """Tests the correctness of the quantized::qconv_unpack op.""" @given( inputs=hu.tensor_conv( spatial_dim=2, batch_size_range=(1, 3), input_channels_per_group_range=(1, 4), output_channels_per_group_range=(1, 4), feature_map_range=(4, 8), kernel_range=(1, 4), max_groups=4, qparams=[hu.qparams(dtypes=torch.quint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint32, zero_point_min=0, zero_point_max=0)]), stride_h=st.integers(1, 3), stride_w=st.integers(1, 3), pad_h=st.integers(1, 2), pad_w=st.integers(1, 2), channelwise=st.booleans()) @override_qengines def test_qconv_unpack( self, inputs, stride_h, stride_w, pad_h, pad_w, channelwise ): qconv_prepack = torch.ops.quantized.conv2d_prepack qconv_unpack = torch.ops.quantized.conv2d_unpack self._test_qconv_unpack_impl( qconv_prepack, qconv_unpack, inputs, (stride_h, stride_w), (pad_h, pad_w), channelwise) @given( inputs=hu.tensor_conv( spatial_dim=1, batch_size_range=(1, 3), input_channels_per_group_range=(1, 4), output_channels_per_group_range=(1, 4), feature_map_range=(4, 8), kernel_range=(1, 4), max_groups=4, qparams=[hu.qparams(dtypes=torch.quint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint32, zero_point_min=0, zero_point_max=0)]), stride=st.integers(1, 3), pad=st.integers(1, 2), channelwise=st.booleans(), qengine=st.sampled_from(("qnnpack", "fbgemm"))) def test_qconv1d_unpack( self, inputs, stride, pad, channelwise, qengine ): if qengine not in supported_qengines: return if qengine == 'qnnpack': channelwise = False with override_quantized_engine(qengine): qconv_prepack = torch.ops.quantized.conv1d_prepack qconv_unpack = torch.ops.quantized.conv1d_unpack self._test_qconv_unpack_impl( qconv_prepack, qconv_unpack, inputs, [stride], [pad], channelwise) """Tests the correctness of quantized 1D convolution op.""" @given(batch_size=st.integers(1, 6), input_channels_per_group=st.sampled_from((2, 4, 5, 8, 16, 32)), output_channels_per_group=st.sampled_from((2, 4, 5, 8, 16, 32)), groups=st.integers(1, 3), length=st.integers(4, 16), kernel=st.integers(1, 7), stride=st.integers(1, 2), pad=st.integers(0, 2), dilation=st.integers(1, 2), X_scale=st.floats(1.2, 1.6), X_zero_point=st.integers(0, 4), W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2), W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2), Y_scale=st.floats(4.2, 5.6), Y_zero_point=st.integers(0, 4), use_bias=st.booleans(), use_relu=st.booleans(), use_channelwise=st.booleans()) @override_qengines def test_qconv1d( self, batch_size, input_channels_per_group, output_channels_per_group, groups, length, kernel, stride, pad, dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise, ): input_channels = input_channels_per_group * groups output_channels = output_channels_per_group * groups if torch.backends.quantized.engine == 'qnnpack': use_channelwise = False true_conv1d = torch.nn.Conv1d( input_channels, output_channels, kernel, stride, pad, dilation, groups, ) qconv_prepack = torch.ops.quantized.conv1d_prepack qconv = torch.ops.quantized.conv1d if use_relu: qconv = torch.ops.quantized.conv1d_relu self._test_qconv_impl( qconv, qconv_prepack, true_conv1d, batch_size, input_channels_per_group, (length, ), output_channels_per_group, groups, kernel, [stride], [pad], [dilation], X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise ) @given(batch_size=st.integers(1, 4), input_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]), D=st.integers(4, 8), H=st.integers(4, 8), W=st.integers(4, 8), output_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]), groups=st.integers(1, 3), kernel_d=st.integers(1, 4), kernel_h=st.integers(1, 4), kernel_w=st.integers(1, 4), stride_d=st.integers(1, 2), stride_h=st.integers(1, 2), stride_w=st.integers(1, 2), pad_d=st.integers(0, 2), pad_h=st.integers(0, 2), pad_w=st.integers(0, 2), dilation=st.integers(1, 2), X_scale=st.floats(1.2, 1.6), X_zero_point=st.integers(0, 4), W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2), W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2), Y_scale=st.floats(4.2, 5.6), Y_zero_point=st.integers(0, 4), use_bias=st.booleans(), use_relu=st.booleans(), use_channelwise=st.booleans(), qengine=st.sampled_from(("fbgemm",))) def test_qconv3d( self, batch_size, input_channels_per_group, D, H, W, output_channels_per_group, groups, kernel_d, kernel_h, kernel_w, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise, qengine ): if qengine not in supported_qengines: return input_channels = input_channels_per_group * groups output_channels = output_channels_per_group * groups kernels = (kernel_d, kernel_h, kernel_w) strides = (stride_d, stride_h, stride_w) pads = (pad_d, pad_h, pad_w) dilations = (dilation, dilation, dilation) with override_quantized_engine(qengine): qconv = torch.ops.quantized.conv3d if use_relu: qconv = torch.ops.quantized.conv3d_relu qconv_prepack = torch.ops.quantized.conv3d_prepack conv_op = torch.nn.Conv3d( input_channels, output_channels, kernels, strides, pads, dilations, groups, ) self._test_qconv_impl( qconv, qconv_prepack, conv_op, batch_size, input_channels_per_group, (D, H, W), output_channels_per_group, groups, kernels, strides, pads, dilations, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_relu, use_channelwise) """Tests the correctness of the quantized::qconv3d_unpack op.""" @given( inputs=hu.tensor_conv( spatial_dim=3, batch_size_range=(1, 3), input_channels_per_group_range=(1, 3), output_channels_per_group_range=(1, 3), feature_map_range=(3, 6), kernel_range=(1, 3), max_groups=3, qparams=[hu.qparams(dtypes=torch.quint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint8, zero_point_min=0, zero_point_max=0), hu.qparams(dtypes=torch.qint32, zero_point_min=0, zero_point_max=0)]), stride_d=st.integers(1, 2), stride_h=st.integers(1, 2), stride_w=st.integers(1, 2), pad_d=st.integers(1, 2), pad_h=st.integers(1, 2), pad_w=st.integers(1, 2), channelwise=st.booleans(), qengine=st.sampled_from(("fbgemm",))) def test_qconv3d_unpack( self, inputs, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, channelwise, qengine ): if qengine not in supported_qengines: return with override_quantized_engine(qengine): qconv3d_prepack = torch.ops.quantized.conv3d_prepack qconv3d_unpack = torch.ops.quantized.conv3d_unpack self._test_qconv_unpack_impl( qconv3d_prepack, qconv3d_unpack, inputs, (stride_d, stride_h, stride_w), (pad_d, pad_h, pad_w), channelwise) class TestPadding(TestCase): @given(batch_size=st.integers(1, 64), channels=st.integers(1, 64), width=st.integers(16, 128), qtype=st.sampled_from(hu._ALL_QINT_TYPES)) def test_reflection_pad1d(self, batch_size, channels, width, qtype): padding = width // 4 x = torch.arange(batch_size * channels * width).to(torch.float) x = x.resize(batch_size, channels, width) # Per-Tensor test scale, zp = _calculate_dynamic_qparams(x, qtype) qx = torch.quantize_per_tensor(x, scale, zp, qtype) padding_op = torch.nn.ReflectionPad1d(padding) y_ref = padding_op(x) qy_ref = torch.quantize_per_tensor(y_ref, scale, zp, qtype) qy_hat = padding_op(qx) self.assertEqual(qy_ref, qy_hat) @unittest.skipUnless('qnnpack' in supported_qengines, "This Pytorch Build has not been built with or does not support QNNPACK") class TestQNNPackOps(TestCase): """Tests the correctness of the quantized::qnnpack_relu op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams(dtypes=torch.quint8, zero_point_min=0, zero_point_max=0))) def test_qnnpack_relu(self, X): with override_quantized_engine('qnnpack'): X, (scale, zero_point, torch_type) = X relu = torch.nn.functional.relu X = torch.from_numpy(X) Y = X.clone() qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) qY_hat = relu(qX) Y[Y < 0] = 0 qY = torch.quantize_per_tensor(Y, scale=scale, zero_point=zero_point, dtype=torch_type) self.assertEqual(qY, qY_hat) """Tests the correctness of the quantized::qnnpack_tanh op.""" @skipIfNoFBGEMM @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams(dtypes=torch.quint8))) def test_qnnpack_tanh(self, X): # Note: In QNNPACK the output scale and zero_point can only be # 2.0/256, 128 respectively, as it uses a LUT with 256 bins. X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # Floating point reference Y = torch.tanh(X) qY = torch.quantize_per_tensor(Y, scale=1.0 / 128, zero_point=128, dtype=torch.quint8) with override_quantized_engine('fbgemm'): qYserver = torch.tanh(qX) with override_quantized_engine('qnnpack'): qY_hat = torch.tanh(qX) self.assertEqual(qY, qY_hat, msg="QNNPACK TanH failed (FP ref)!") self.assertEqual(qYserver, qY_hat, msg="QNNPACK TanH failed (FBGEMM ref)!") """Tests the correctness of the quantized::qnnpack_sigmoid op.""" @skipIfNoFBGEMM @given(X=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams(dtypes=torch.quint8))) def test_qnnpack_sigmoid(self, X): # Note: In QNNPACK the output scale and zero_point can only be # 1.0/256, 0 respectively, as it uses a LUT with 256 bins. X, (scale, zero_point, torch_type) = X X = torch.from_numpy(X).to(torch.float32) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) # Floating point reference Y = torch.sigmoid(X) qY = torch.quantize_per_tensor(Y, scale=1.0 / 256, zero_point=0, dtype=torch.quint8) with override_quantized_engine('fbgemm'): qYserver = torch.sigmoid(qX) with override_quantized_engine('qnnpack'): qY_hat = torch.sigmoid(qX) self.assertEqual(qY, qY_hat, msg="QNNPACK Sigmoid failed (FP ref)!") self.assertEqual(qYserver, qY_hat, msg="QNNPACK Sigmoid failed (FBGEMM ref)!") @skipIfNoFBGEMM def test_qnnpack_sigmoid_sweep(self): # Input parameters f_min = -4.0 f_max = 4.0 scale = (f_max - f_min) / 256.0 zero_point = 128 dtype = torch.quint8 step = scale / 2.0 x = np.arange(f_min, f_max + step, step) X = torch.from_numpy(x).to(torch.float32) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=dtype) dqX = qX.dequantize() # Floating point reference Y = torch.sigmoid(dqX) qY = torch.quantize_per_tensor(Y, scale=1.0 / 256, zero_point=0, dtype=torch.quint8) with override_quantized_engine('fbgemm'): qYserver = torch.sigmoid(qX) with override_quantized_engine('qnnpack'): qY_hat = torch.sigmoid(qX) self.assertEqual(qY, qY_hat, msg="QNNPACK Sigmoid failed (FP ref)!") self.assertEqual(qYserver, qY_hat, msg="QNNPACK Sigmoid failed (FBGEMM ref)!") """Tests the correctness of the quantized::add (qnnpack) op.""" @settings(suppress_health_check=(HealthCheck.filter_too_much,)) @given(A=hu.tensor(shapes=hu.array_shapes(1, 5, 1, 5), qparams=hu.qparams(dtypes=torch.quint8)), zero_point=st.sampled_from([0, 2, 5, 15, 127]), scale_A=st.sampled_from([0.001, 0.057, 0.889, 12.3]), scale_B=st.sampled_from([0.008, 0.0821, 0.67, 7]), scale_C=st.sampled_from([0.003, 0.07821, 0.457, 7.34]),) def test_qnnpack_add(self, A, zero_point, scale_A, scale_B, scale_C): with override_quantized_engine('qnnpack'): A_temp = A A, (scale_a, zero_point_A, torch_type) = A_temp B, (scale_b, zero_point_B, torch_type) = A_temp A = torch.from_numpy(A) B = torch.from_numpy(B) assume(scale_A // scale_C >= 2**-14) assume(scale_A // scale_C < 2**8) assume(scale_B // scale_C >= 2**-14) assume(scale_B // scale_C < 2**8) zero_point_C = 127 qA = torch.quantize_per_tensor(A, scale=scale_A, zero_point=zero_point, dtype=torch.quint8) qB = torch.quantize_per_tensor(B, scale=scale_B, zero_point=zero_point, dtype=torch.quint8) # Add ground truth C = (qA.dequantize() + qB.dequantize()).numpy() qC = _quantize(C, scale_C, zero_point_C) qC_qnnp = torch.ops.quantized.add(qA, qB, scale_C, zero_point_C) np.testing.assert_equal(qC, qC_qnnp.int_repr(), "Quantized addition failed.") Crelu = C.copy() Crelu[C < 0] = 0 qCrelu = torch.quantize_per_tensor(torch.from_numpy(Crelu), scale_C, zero_point_C, dtype=torch.quint8) qCrelu_hat = torch.ops.quantized.add_relu(qA, qB, scale=scale_C, zero_point=zero_point_C) np.testing.assert_equal(qCrelu.int_repr().numpy(), qCrelu_hat.int_repr(), "Quantized addition with ReLU failed.") """Tests the correctness of quantized::qnnpack_maxpool2d op.""" @given(A=hu.tensor(shapes=hu.array_shapes(4, 4, 3, 5), qparams=hu.qparams(dtypes=torch.quint8)), kernel=st.sampled_from([2, 4]), stride=st.sampled_from([1, 2]), padding=st.sampled_from([1, 2])) def test_qnnpack_maxpool2d(self, A, kernel, stride, padding): import torch.nn.functional as F with override_quantized_engine('qnnpack'): A, (scale, zero_point, torch_type) = A X = torch.from_numpy(A) np_type = np.uint8 dilation = 1 # Check constraints assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, dilation) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, dilation) assume(oW > 0) k = (kernel, kernel) s = (stride, stride) d = (dilation, dilation) p = (padding, padding) q_max_pool = torch.ops.quantized.max_pool2d a = scale * (X - zero_point).to(dtype=torch.float) qa = torch.quantize_per_tensor(a, scale=scale, zero_point=zero_point, dtype=torch_type) a_ref = qa.dequantize() a_pool = F.max_pool2d(a_ref, kernel_size=k, stride=s, padding=p, dilation=d) a_pool_nhwc = a_pool.permute([0, 2, 3, 1]) qa_pool = q_max_pool(qa, k, s, p, d, ceil_mode=False) qa_pool_int = qa_pool.dequantize() np.testing.assert_equal(a_pool.numpy(), qa_pool_int.numpy()) @given(batch_size=st.integers(1, 5), channels=st.sampled_from([2, 4, 5, 8, 16, 32]), height=st.integers(4, 10), width=st.integers(4, 10), kernel=st.integers(2, 5), stride=st.integers(1, 2), padding=st.integers(1, 2), scale=st.floats(0.2, 1.6), zero_point=st.integers(0, 25) ) def test_avg_pool2d( self, batch_size, channels, height, width, kernel, stride, padding, scale, zero_point ): with override_quantized_engine('qnnpack'): import torch.nn.functional as F X_init = torch.from_numpy(np.random.randint( 0, 50, (batch_size, channels, height, width))) X = scale * (X_init - zero_point).to(dtype=torch.float) # Check constraints assume(kernel // 2 >= padding) # Kernel cannot be overhanging! iH, iW = X.shape[-2:] oH = pool_output_shape(iH, kernel, padding, stride, 1) assume(oH > 0) oW = pool_output_shape(iW, kernel, padding, stride, 1) assume(oW > 0) k = (kernel, kernel) s = (stride, stride) p = (padding, padding) q_avg_pool = torch.nn.quantized.functional.avg_pool2d x_q = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8) a_pool = F.avg_pool2d(x_q.dequantize().to(torch.float), kernel_size=k, stride=s, padding=p) qa_pool = q_avg_pool(x_q, k, s, p) # Quantize Ref Output a_pool_q = torch.quantize_per_tensor(a_pool, scale=scale, zero_point=zero_point, dtype=torch.quint8) np.testing.assert_array_almost_equal(a_pool_q.int_repr().numpy(), qa_pool.int_repr().numpy(), decimal=0) @given(batch_size=st.integers(1, 5), channels=st.sampled_from([2, 4, 5, 8, 16, 32]), height=st.integers(4, 20), width=st.integers(4, 20), output_height=st.integers(2, 10), output_width=st.integers(2, 10), scale=st.floats(0.2, 1.6), zero_point=st.integers(0, 25) ) def test_adaptive_avg_pool2d( self, batch_size, channels, height, width, output_height, output_width, scale, zero_point ): with override_quantized_engine('qnnpack'): # Check constraints assume(height >= output_height) assume(width >= output_width) import torch.nn.functional as F X_init = torch.from_numpy(np.random.randint( 0, 50, (batch_size, channels, height, width))) X = scale * (X_init - zero_point).to(dtype=torch.float) iH, iW = X.shape[-2:] q_avg_pool = torch.nn.quantized.functional.adaptive_avg_pool2d x_q = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8) a_pool = F.adaptive_avg_pool2d(x_q.dequantize().to(torch.float), (output_height, output_width)) qa_pool = q_avg_pool(x_q, (output_height, output_width)) # Quantize Ref Output a_pool_q = torch.quantize_per_tensor(a_pool, scale=scale, zero_point=zero_point, dtype=torch.quint8) np.testing.assert_array_almost_equal(a_pool_q.int_repr().numpy(), qa_pool.int_repr().numpy(), decimal=0) @given(batch_size=st.integers(1, 5), channels=st.sampled_from([2, 4, 5, 8, 16, 32]), height=st.integers(4, 10), width=st.integers(4, 10), scale=st.floats(0.02, 2.6), zero_point=st.integers(0, 25)) def test_mean(self, batch_size, channels, height, width, scale, zero_point): with override_quantized_engine('qnnpack'): dim = (2, 3) X_init = torch.from_numpy(np.random.randint( 0, 50, (batch_size, channels, height, width))) X = scale * (X_init - zero_point).to(dtype=torch.float) qX = torch.quantize_per_tensor(X, scale, zero_point, torch.quint8) Y = torch.mean(qX.dequantize(), dim) Y = torch.quantize_per_tensor(Y, scale, zero_point, torch.quint8) qY = torch.mean(qX, dim) np.testing.assert_array_almost_equal(Y.int_repr().numpy(), qY.int_repr().numpy(), decimal=0) """Tests the correctness of the quantized::hardtanh op.""" @given(X=hu.tensor(shapes=hu.array_shapes(1, 8, 1, 8, max_numel=10**5), elements=hu.floats(-1e6, 1e6, allow_nan=False, allow_infinity=False), qparams=hu.qparams(dtypes=torch.quint8)), min_val=hu.floats(-1e6, -9.999999974752427e-07, allow_nan=False, allow_infinity=False), max_val=hu.floats(9.999999974752427e-07, 1e6, allow_nan=False, allow_infinity=False)) def test_hardtanh(self, X, min_val, max_val): if 'qnnpack' not in torch.backends.quantized.supported_engines: return with override_quantized_engine('qnnpack'): X, (scale, zero_point, torch_type) = X assume(min_val <= max_val) Y = X.copy() Y[Y < min_val] = min_val Y[Y > max_val] = max_val qY = torch.quantize_per_tensor(torch.from_numpy(Y), scale=scale, zero_point=zero_point, dtype=torch_type) X = torch.from_numpy(X) qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch_type) qY_hat = torch.nn.quantized.functional.hardtanh(qX, min_val, max_val) self.assertEqual( qY, qY_hat, msg="hardtanh failed:\nactual {}\nexpected {}".format(qY_hat, qY)) """Tests the correctness of the tensor comparators.""" class TestComparatorOps(TestCase): """Tests the element-wise equality ops.""" @given(A=hu.tensor(shapes=((3, 4, 5),), qparams=hu.qparams()), B=hu.tensor(shapes=((5,), (1, 5), (1, 1, 5), (4, 5), (3, 4, 5)), qparams=hu.qparams())) def test_compare_tensor_tensor(self, A, B): A, (scale_a, zero_point_a, dtype_a) = A B, (scale_b, zero_point_b, dtype_b) = B tA = torch.from_numpy(A) tB = torch.from_numpy(B) qA = torch.quantize_per_tensor(tA, scale=scale_a, zero_point=zero_point_a, dtype=dtype_a) qB = torch.quantize_per_tensor(tB, scale=scale_b, zero_point=zero_point_b, dtype=dtype_b) dqA = qA.dequantize() dqB = qB.dequantize() ops_under_test = ('__eq__', '__ne__', '__ge__', '__le__', '__gt__', '__lt__', 'eq', 'ne', 'ge', 'le', 'gt', 'lt') for op in ops_under_test: result_ref = getattr(dqA, op)(dqB) result = getattr(qA, op)(qB) self.assertEqual(result_ref, result, msg="'tensor.{}(tensor)'' failed".format(op)) # Reversed broadcasting. result_ref = getattr(dqB, op)(dqA) result = getattr(qB, op)(qA) self.assertEqual(result_ref, result, msg="'tensor.{}(tensor)'' failed".format(op)) @given(A=hu.tensor(shapes=((3, 4, 5),), qparams=hu.qparams()), b=hu.floats(allow_infinity=False, allow_nan=False)) def test_compare_tensor_scalar(self, A, b): A, (scale_a, zero_point_a, dtype_a) = A tA = torch.from_numpy(A) qA = torch.quantize_per_tensor(tA, scale=scale_a, zero_point=zero_point_a, dtype=dtype_a) dqA = qA.dequantize() ops_under_test_reversible = ('__eq__', '__ne__', '__ge__', '__le__', '__gt__', '__lt__') ops_under_test_nonreversible = ('eq', 'ne', 'ge', 'le', 'gt', 'lt') for op in ops_under_test_reversible: result_ref = getattr(dqA, op)(b) result = getattr(qA, op)(b) note("result_ref 1: {}".format(result_ref)) note("result 1: {}".format(result)) self.assertEqual(result_ref, result, msg="'tensor.{}(scalar)'' failed".format(op)) # Reversed broadcasting. result_ref = getattr(b, op)(dqA) result = getattr(b, op)(qA) note("result_ref 2: {}".format(result_ref)) note("result 2: {}".format(result)) self.assertEqual(result_ref, result, msg="'scalar.{}(tensor)'' failed".format(op)) for op in ops_under_test_nonreversible: result_ref = getattr(dqA, op)(b) result = getattr(qA, op)(b) note("result_ref 3: {}".format(result_ref)) note("result 3: {}".format(result)) self.assertEqual(result_ref, result, msg="'tensor.{}(scalar)'' failed".format(op))

46.754771

124

0.555422

933bb5cf4efa7bb1148bec519683973ebc68c2f0

19,544

py

Python

examples/frameworks/pytorch/pytorch_matplotlib.py

noklam/trains

70536544ed5e2b9aac8576ef2eaaef31c99ca670

[ "Apache-2.0" ]

8

2019-04-24T18:55:50.000Z

2022-03-04T13:38:42.000Z

examples/frameworks/pytorch/pytorch_matplotlib.py

aliceUnhinged613/trains

8ec6bba4d91104a2bdd2e537bec21078529540e0

[ "Apache-2.0" ]

2

2020-07-05T08:28:40.000Z

2020-08-11T13:32:49.000Z

examples/frameworks/pytorch/pytorch_matplotlib.py

aliceUnhinged613/trains

8ec6bba4d91104a2bdd2e537bec21078529540e0

[ "Apache-2.0" ]

6

2021-03-06T03:18:14.000Z

2021-12-14T02:40:12.000Z

# TRAINS - Example of Pytorch and matplotlib integration and reporting # """ Neural Transfer Using PyTorch ============================= **Author**: `Alexis Jacq <https://alexis-jacq.github.io>`_ **Edited by**: `Winston Herring <https://github.com/winston6>`_ Introduction ------------ This tutorial explains how to implement the `Neural-Style algorithm <https://arxiv.org/abs/1508.06576>`__ developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge. Neural-Style, or Neural-Transfer, allows you to take an image and reproduce it with a new artistic style. The algorithm takes three images, an input image, a content-image, and a style-image, and changes the input to resemble the content of the content-image and the artistic style of the style-image. .. figure:: /_static/img/neural-style/neuralstyle.png :alt: content1 """ ###################################################################### # Underlying Principle # -------------------- # # The principle is simple: we define two distances, one for the content # (:math:`D_C`) and one for the style (:math:`D_S`). :math:`D_C` measures how different the content # is between two images while :math:`D_S` measures how different the style is # between two images. Then, we take a third image, the input, and # transform it to minimize both its content-distance with the # content-image and its style-distance with the style-image. Now we can # import the necessary packages and begin the neural transfer. # # Importing Packages and Selecting a Device # ----------------------------------------- # Below is a list of the packages needed to implement the neural transfer. # # - ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for # neural networks with PyTorch) # - ``torch.optim`` (efficient gradient descents) # - ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display # images) # - ``torchvision.transforms`` (transform PIL images into tensors) # - ``torchvision.models`` (train or load pre-trained models) # - ``copy`` (to deep copy the models; system package) from __future__ import print_function import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from PIL import Image import matplotlib.pyplot as plt import torchvision.transforms as transforms import torchvision.models as models import copy from trains import Task task = Task.init(project_name='examples', task_name='pytorch with matplotlib example', task_type=Task.TaskTypes.testing) ###################################################################### # Next, we need to choose which device to run the network on and import the # content and style images. Running the neural transfer algorithm on large # images takes longer and will go much faster when running on a GPU. We can # use ``torch.cuda.is_available()`` to detect if there is a GPU available. # Next, we set the ``torch.device`` for use throughout the tutorial. Also the ``.to(device)`` # method is used to move tensors or modules to a desired device. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ###################################################################### # Loading the Images # ------------------ # # Now we will import the style and content images. The original PIL images have values between 0 and 255, but when # transformed into torch tensors, their values are converted to be between # 0 and 1. The images also need to be resized to have the same dimensions. # An important detail to note is that neural networks from the # torch library are trained with tensor values ranging from 0 to 1. If you # try to feed the networks with 0 to 255 tensor images, then the activated # feature maps will be unable sense the intended content and style. # However, pre-trained networks from the Caffe library are trained with 0 # to 255 tensor images. # # # .. Note:: # Here are links to download the images required to run the tutorial: # `picasso.jpg <https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg>`__ and # `dancing.jpg <https://pytorch.org/tutorials/_static/img/neural-style/dancing.jpg>`__. # Download these two images and add them to a directory # with name ``images`` in your current working directory. # desired size of the output image imsize = 512 if torch.cuda.is_available() else 128 # use small size if no gpu loader = transforms.Compose([ transforms.Resize(imsize), # scale imported image transforms.ToTensor()]) # transform it into a torch tensor def image_loader(image_name): image = Image.open(image_name) # fake batch dimension required to fit network's input dimensions image = loader(image).unsqueeze(0) return image.to(device, torch.float) style_img = image_loader(os.path.join("..", "..", "reporting", "data_samples", "picasso.jpg")) content_img = image_loader(os.path.join("..", "..", "reporting", "data_samples", "dancing.jpg")) assert style_img.size() == content_img.size(), \ "we need to import style and content images of the same size" ###################################################################### # Now, let's create a function that displays an image by reconverting a # copy of it to PIL format and displaying the copy using # ``plt.imshow``. We will try displaying the content and style images # to ensure they were imported correctly. unloader = transforms.ToPILImage() # reconvert into PIL image plt.ion() def imshow(tensor, title=None): image = tensor.cpu().clone() # we clone the tensor to not do changes on it image = image.squeeze(0) # remove the fake batch dimension image = unloader(image) plt.imshow(image) if title is not None: plt.title(title) plt.pause(0.001) # pause a bit so that plots are updated plt.figure() imshow(style_img, title='Style Image') plt.figure() imshow(content_img, title='Content Image') ###################################################################### # Loss Functions # -------------- # Content Loss # ~~~~~~~~~~~~ # # The content loss is a function that represents a weighted version of the # content distance for an individual layer. The function takes the feature # maps :math:`F_{XL}` of a layer :math:`L` in a network processing input :math:`X` and returns the # weighted content distance :math:`w_{CL}.D_C^L(X,C)` between the image :math:`X` and the # content image :math:`C`. The feature maps of the content image(:math:`F_{CL}`) must be # known by the function in order to calculate the content distance. We # implement this function as a torch module with a constructor that takes # :math:`F_{CL}` as an input. The distance :math:`\|F_{XL} - F_{CL}\|^2` is the mean square error # between the two sets of feature maps, and can be computed using ``nn.MSELoss``. # # We will add this content loss module directly after the convolution # layer(s) that are being used to compute the content distance. This way # each time the network is fed an input image the content losses will be # computed at the desired layers and because of auto grad, all the # gradients will be computed. Now, in order to make the content loss layer # transparent we must define a ``forward`` method that computes the content # loss and then returns the layer's input. The computed loss is saved as a # parameter of the module. # class ContentLoss(nn.Module): def __init__(self, target, ): super(ContentLoss, self).__init__() # we 'detach' the target content from the tree used # to dynamically compute the gradient: this is a stated value, # not a variable. Otherwise the forward method of the criterion # will throw an error. self.target = target.detach() def forward(self, input): self.loss = F.mse_loss(input, self.target) return input ###################################################################### # .. Note:: # **Important detail**: although this module is named ``ContentLoss``, it # is not a true PyTorch Loss function. If you want to define your content # loss as a PyTorch Loss function, you have to create a PyTorch autograd function # to recompute/implement the gradient manually in the ``backward`` # method. ###################################################################### # Style Loss # ~~~~~~~~~~ # # The style loss module is implemented similarly to the content loss # module. It will act as a transparent layer in a # network that computes the style loss of that layer. In order to # calculate the style loss, we need to compute the gram matrix :math:`G_{XL}`. A gram # matrix is the result of multiplying a given matrix by its transposed # matrix. In this application the given matrix is a reshaped version of # the feature maps :math:`F_{XL}` of a layer :math:`L`. :math:`F_{XL}` is reshaped to form :math:`\hat{F}_{XL}`, a :math:`K`\ x\ :math:`N` # matrix, where :math:`K` is the number of feature maps at layer :math:`L` and :math:`N` is the # length of any vectorized feature map :math:`F_{XL}^k`. For example, the first line # of :math:`\hat{F}_{XL}` corresponds to the first vectorized feature map :math:`F_{XL}^1`. # # Finally, the gram matrix must be normalized by dividing each element by # the total number of elements in the matrix. This normalization is to # counteract the fact that :math:`\hat{F}_{XL}` matrices with a large :math:`N` dimension yield # larger values in the Gram matrix. These larger values will cause the # first layers (before pooling layers) to have a larger impact during the # gradient descent. Style features tend to be in the deeper layers of the # network so this normalization step is crucial. # def gram_matrix(input): a, b, c, d = input.size() # a=batch size(=1) # b=number of feature maps # (c,d)=dimensions of a f. map (N=c*d) features = input.view(a * b, c * d) # resise F_XL into \hat F_XL G = torch.mm(features, features.t()) # compute the gram product # we 'normalize' the values of the gram matrix # by dividing by the number of element in each feature maps. return G.div(a * b * c * d) ###################################################################### # Now the style loss module looks almost exactly like the content loss # module. The style distance is also computed using the mean square # error between :math:`G_{XL}` and :math:`G_{SL}`. # class StyleLoss(nn.Module): def __init__(self, target_feature): super(StyleLoss, self).__init__() self.target = gram_matrix(target_feature).detach() def forward(self, input): G = gram_matrix(input) self.loss = F.mse_loss(G, self.target) return input ###################################################################### # Importing the Model # ------------------- # # Now we need to import a pre-trained neural network. We will use a 19 # layer VGG network like the one used in the paper. # # PyTorch's implementation of VGG is a module divided into two child # ``Sequential`` modules: ``features`` (containing convolution and pooling layers), # and ``classifier`` (containing fully connected layers). We will use the # ``features`` module because we need the output of the individual # convolution layers to measure content and style loss. Some layers have # different behavior during training than evaluation, so we must set the # network to evaluation mode using ``.eval()``. # cnn = models.vgg19(pretrained=True).features.to(device).eval() ###################################################################### # Additionally, VGG networks are trained on images with each channel # normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. # We will use them to normalize the image before sending it into the network. # cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device) cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device) # create a module to normalize input image so we can easily put it in a # nn.Sequential class Normalization(nn.Module): def __init__(self, mean, std): super(Normalization, self).__init__() # .view the mean and std to make them [C x 1 x 1] so that they can # directly work with image Tensor of shape [B x C x H x W]. # B is batch size. C is number of channels. H is height and W is width. self.mean = torch.tensor(mean).view(-1, 1, 1) self.std = torch.tensor(std).view(-1, 1, 1) def forward(self, img): # normalize img return (img - self.mean) / self.std ###################################################################### # A ``Sequential`` module contains an ordered list of child modules. For # instance, ``vgg19.features`` contains a sequence (Conv2d, ReLU, MaxPool2d, # Conv2d, ReLU...) aligned in the right order of depth. We need to add our # content loss and style loss layers immediately after the convolution # layer they are detecting. To do this we must create a new ``Sequential`` # module that has content loss and style loss modules correctly inserted. # # desired depth layers to compute style/content losses : content_layers_default = ['conv_4'] style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5'] def get_style_model_and_losses(cnn, normalization_mean, normalization_std, style_img, content_img, content_layers=content_layers_default, style_layers=style_layers_default): cnn = copy.deepcopy(cnn) # normalization module normalization = Normalization(normalization_mean, normalization_std).to(device) # just in order to have an iterable access to or list of content/syle # losses content_losses = [] style_losses = [] # assuming that cnn is a nn.Sequential, so we make a new nn.Sequential # to put in modules that are supposed to be activated sequentially model = nn.Sequential(normalization) i = 0 # increment every time we see a conv for layer in cnn.children(): if isinstance(layer, nn.Conv2d): i += 1 name = 'conv_{}'.format(i) elif isinstance(layer, nn.ReLU): name = 'relu_{}'.format(i) # The in-place version doesn't play very nicely with the ContentLoss # and StyleLoss we insert below. So we replace with out-of-place # ones here. layer = nn.ReLU(inplace=False) elif isinstance(layer, nn.MaxPool2d): name = 'pool_{}'.format(i) elif isinstance(layer, nn.BatchNorm2d): name = 'bn_{}'.format(i) else: raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__)) model.add_module(name, layer) if name in content_layers: # add content loss: target = model(content_img).detach() content_loss = ContentLoss(target) model.add_module("content_loss_{}".format(i), content_loss) content_losses.append(content_loss) if name in style_layers: # add style loss: target_feature = model(style_img).detach() style_loss = StyleLoss(target_feature) model.add_module("style_loss_{}".format(i), style_loss) style_losses.append(style_loss) # now we trim off the layers after the last content and style losses for i in range(len(model) - 1, -1, -1): if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss): break model = model[:(i + 1)] return model, style_losses, content_losses ###################################################################### # Next, we select the input image. You can use a copy of the content image # or white noise. # input_img = content_img.clone() # if you want to use white noise instead uncomment the below line: # input_img = torch.randn(content_img.data.size(), device=device) # add the original input image to the figure: plt.figure() imshow(input_img, title='Input Image') ###################################################################### # Gradient Descent # ---------------- # # As Leon Gatys, the author of the algorithm, suggested `here <https://discuss.pytorch.org/t/pytorch-tutorial-for-neural-transfert-of-artistic-style/336/20?u=alexis-jacq>`__, we will use # L-BFGS algorithm to run our gradient descent. Unlike training a network, # we want to train the input image in order to minimise the content/style # losses. We will create a PyTorch L-BFGS optimizer ``optim.LBFGS`` and pass # our image to it as the tensor to optimize. # def get_input_optimizer(input_img): # this line to show that input is a parameter that requires a gradient optimizer = optim.LBFGS([input_img.requires_grad_()]) return optimizer ###################################################################### # Finally, we must define a function that performs the neural transfer. For # each iteration of the networks, it is fed an updated input and computes # new losses. We will run the ``backward`` methods of each loss module to # dynamicaly compute their gradients. The optimizer requires a "closure" # function, which reevaluates the modul and returns the loss. # # We still have one final constraint to address. The network may try to # optimize the input with values that exceed the 0 to 1 tensor range for # the image. We can address this by correcting the input values to be # between 0 to 1 each time the network is run. # def run_style_transfer(cnn, normalization_mean, normalization_std, content_img, style_img, input_img, num_steps=300, style_weight=1000000, content_weight=1): """Run the style transfer.""" print('Building the style transfer model..') model, style_losses, content_losses = get_style_model_and_losses(cnn, normalization_mean, normalization_std, style_img, content_img) optimizer = get_input_optimizer(input_img) print('Optimizing..') run = [0] while run[0] <= num_steps: def closure(): # correct the values of updated input image input_img.data.clamp_(0, 1) optimizer.zero_grad() model(input_img) style_score = 0 content_score = 0 for sl in style_losses: style_score += sl.loss for cl in content_losses: content_score += cl.loss style_score *= style_weight content_score *= content_weight loss = style_score + content_score loss.backward() run[0] += 1 if run[0] % 50 == 0: print("run {}:".format(run)) print('Style Loss : {:4f} Content Loss: {:4f}'.format( style_score.item(), content_score.item())) print() return style_score + content_score optimizer.step(closure) # a last correction... input_img.data.clamp_(0, 1) return input_img ###################################################################### # Finally, we can run the algorithm. # output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std, content_img, style_img, input_img) plt.figure() imshow(output, title='Output Image') # sphinx_gallery_thumbnail_number = 4 plt.ioff() plt.show()

40.463768

186

0.647667

0511512cf9c1f20d93b313eeb13302178d0be3fe

947

py

Python

myclasses/ClassHomework5.py

gurmeetkhehra/python-practice

abeb5586f8c1e673fd8ff312a4ae0941f2a0194b

[ "Apache-2.0" ]

null

myclasses/ClassHomework5.py

gurmeetkhehra/python-practice

abeb5586f8c1e673fd8ff312a4ae0941f2a0194b

[ "Apache-2.0" ]

null

myclasses/ClassHomework5.py

gurmeetkhehra/python-practice

abeb5586f8c1e673fd8ff312a4ae0941f2a0194b

[ "Apache-2.0" ]

null

# 5. Write a Python class Car and all attributes private. Write method to return all individual attributes class Car(): def __init__(self, brand, model, year, color): self.brand = brand self.model = model self.year = year self.color = color def get_car_details(self): print(self.brand) print(self.model) print(self.color) print(self.year) Toyota = Car('Toyota', 'Camry', 2018, 'white') print(Toyota.get_car_details()) class ElectricCar(Car): def __init__(self, brand, model, year, color, battery, charged_capacity): super().__init__(brand, model, year, color) self.battery=battery self.charged_capacity=charged_capacity def get_car_details(self): super().get_car_details() print(self.battery) print(self.charged_capacity) tesla = ElectricCar('Tesla', 'X', 2019, 'Red', 5000, 100) print(tesla.get_car_details())

27.852941

106

0.654699

8953f544bfd4760e29f4de01a9f1ac2eba5e0594

3,195

py

Python

gui/qt/qrwindow.py

stratisproject/electrum

c60fa543418c31ce7f5dcf5aa717d82a5c47e216

[ "MIT" ]

26

2017-06-09T04:13:13.000Z

2021-11-15T11:35:30.000Z

gui/qt/qrwindow.py

stratisproject/electrum

c60fa543418c31ce7f5dcf5aa717d82a5c47e216

[ "MIT" ]

29

2017-05-07T05:08:06.000Z

2021-02-19T13:15:03.000Z

gui/qt/qrwindow.py

stratisproject/electrum

c60fa543418c31ce7f5dcf5aa717d82a5c47e216

[ "MIT" ]

21

2017-05-31T14:24:20.000Z

2021-01-30T17:35:43.000Z

#!/usr/bin/env python # # Electrum - lightweight Bitcoin client # Copyright (C) 2014 Thomas Voegtlin # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import re import platform from decimal import Decimal from urllib import quote from PyQt4.QtGui import * from PyQt4.QtCore import * import PyQt4.QtCore as QtCore import PyQt4.QtGui as QtGui from electrum_stratis_gui.qt.qrcodewidget import QRCodeWidget from electrum_stratis.i18n import _ if platform.system() == 'Windows': MONOSPACE_FONT = 'Lucida Console' elif platform.system() == 'Darwin': MONOSPACE_FONT = 'Monaco' else: MONOSPACE_FONT = 'monospace' column_index = 4 class QR_Window(QWidget): def __init__(self, win): QWidget.__init__(self) self.win = win self.setWindowTitle('Electrum - '+_('Payment Request')) self.setMinimumSize(800, 250) self.address = '' self.label = '' self.amount = 0 self.setFocusPolicy(QtCore.Qt.NoFocus) main_box = QHBoxLayout() self.qrw = QRCodeWidget() main_box.addWidget(self.qrw, 1) vbox = QVBoxLayout() main_box.addLayout(vbox) self.address_label = QLabel("") #self.address_label.setFont(QFont(MONOSPACE_FONT)) vbox.addWidget(self.address_label) self.label_label = QLabel("") vbox.addWidget(self.label_label) self.amount_label = QLabel("") vbox.addWidget(self.amount_label) vbox.addStretch(1) self.setLayout(main_box) def set_content(self, address, amount, message, url): address_text = "%s" % address if address else "" self.address_label.setText(address_text) if amount: amount = self.win.format_amount(amount) amount_text = "%s %s " % (amount, self.win.base_unit()) else: amount_text = '' self.amount_label.setText(amount_text) label_text = "%s" % message if message else "" self.label_label.setText(label_text) self.qrw.setData(url)

33.989362

141

0.694836

f4eda96fc27599aff40f0ee7dfbcf3f031ad9ef8

1,994

py

Python

benchmarks/experimental/benchmark_dataset.py

gautham-kollu/fairscale

9dc1b92ff0897f150f8d0259966ef477ef891883

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

benchmarks/experimental/benchmark_dataset.py

gautham-kollu/fairscale

9dc1b92ff0897f150f8d0259966ef477ef891883

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

benchmarks/experimental/benchmark_dataset.py

gautham-kollu/fairscale

9dc1b92ff0897f150f8d0259966ef477ef891883

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. # # This source code is licensed under the BSD license found in the # LICENSE file in the root directory of this source tree. import torch from torch.utils.data import Dataset # TODO(sidgoyal): Refactor benchmarks to remove this file eventually. def collate_sentences_lm(samples): if len(samples) == 0: return {} id = torch.LongTensor([s["id"] for s in samples]) src_tokens = torch.stack([s["source"] for s in samples], 0) tgt_tokens = torch.stack([s["target"] for s in samples], 0) ntokens = len(samples) * len(samples[0]["target"]) src_lengths = torch.LongTensor([len(samples[0]["source"])] * len(samples)) batch = { "id": id, "nsentences": len(samples), "ntokens": ntokens, "input": src_tokens, "target": tgt_tokens, } return batch class BenchmarkLMDataset(Dataset): """ Dataset to benchmark a translation like seq2seq task. Args: vocab_size (int, optional): size of the vocabulary (default 10000). max_source_positions (int, optional): max number of tokens in the source sentence (default: 1024). total_samples (int, optional): the total number of rows in the dataset (default: 10000). """ def __init__( self, vocab_size=10000, max_source_positions=1024, total_samples=10000, ): self.vocab_size = vocab_size self.max_source_positions = max_source_positions self.total_samples = total_samples self.sizes = [self.max_source_positions] * self.total_samples def __getitem__(self, index): length = self.sizes[index] source = torch.randint(1, self.vocab_size, (length,)) target = source.clone() return { "id": index, "source": source, "target": target, } def __len__(self): return self.total_samples

29.761194

78

0.629388

77736aa1fe9c94bea589eb6e7574d81227dec2e6

1,153

py

Python

recipe_scrapers/thevintagemixer.py

riki900/recipes

7895802f6cf80d14db8465e2f3d3874cec922b5d

[ "MIT" ]

null

recipe_scrapers/thevintagemixer.py

riki900/recipes

7895802f6cf80d14db8465e2f3d3874cec922b5d

[ "MIT" ]

null

recipe_scrapers/thevintagemixer.py

riki900/recipes

7895802f6cf80d14db8465e2f3d3874cec922b5d

[ "MIT" ]

null

from ._abstract import AbstractScraper from ._utils import get_minutes, normalize_string class TheVintageMixer(AbstractScraper): @classmethod def host(self): return 'thevintagemixer.com' def title(self): return self.soup.find( 'div', {'class': 'wprm-recipe-name'} ).get_text() def total_time(self): return get_minutes(self.soup.find( 'meta', {'itemprop': 'totalTime'}).parent ) def yields(self): return 0 # Servings do not exist in this site. def ingredients(self): ingredients = self.soup.findAll( 'li', {'itemprop': "recipeIngredient"} ) return [ normalize_string(ingredient.get_text()) for ingredient in ingredients if len(normalize_string(ingredient.get_text())) > 0 ] def instructions(self): instructions = self.soup.findAll( 'div', {'itemprop': 'recipeInstructions'} ) return '\n'.join([ normalize_string(instruction.get_text()) for instruction in instructions ])

25.065217

63

0.575889

0eee58f996a06392582f59bf111614aa5707cc46

19,146

py

Python

tests/tensorflow/test_nn.py

xnuohz/dgl

115ac0b9a3dbd806cc52f2a428048b79502f2350

[ "Apache-2.0" ]

1

2020-06-04T07:57:12.000Z

tests/tensorflow/test_nn.py

hetong007/dgl

1bfc3118e4a542821c1415e376c026fe1dfd0b59

[ "Apache-2.0" ]

null

tests/tensorflow/test_nn.py

hetong007/dgl

1bfc3118e4a542821c1415e376c026fe1dfd0b59

[ "Apache-2.0" ]

null

import tensorflow as tf from tensorflow.keras import layers import networkx as nx import pytest import dgl import dgl.nn.tensorflow as nn import dgl.function as fn import backend as F from test_utils.graph_cases import get_cases, random_graph, random_bipartite, random_dglgraph from test_utils import parametrize_dtype from copy import deepcopy import numpy as np import scipy as sp def _AXWb(A, X, W, b): X = tf.matmul(X, W) Y = tf.reshape(tf.matmul(A, tf.reshape(X, (X.shape[0], -1))), X.shape) return Y + b @pytest.mark.parametrize('out_dim', [1, 2]) def test_graph_conv(out_dim): g = dgl.DGLGraph(nx.path_graph(3)).to(F.ctx()) ctx = F.ctx() adj = tf.sparse.to_dense(tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))) conv = nn.GraphConv(5, out_dim, norm='none', bias=True) # conv = conv print(conv) # test#1: basic h0 = F.ones((3, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias)) # test#2: more-dim h0 = F.ones((3, 5, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias)) conv = nn.GraphConv(5, out_dim) # conv = conv # test#3: basic h0 = F.ones((3, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 # test#4: basic h0 = F.ones((3, 5, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 conv = nn.GraphConv(5, out_dim) # conv = conv # test#3: basic h0 = F.ones((3, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 # test#4: basic h0 = F.ones((3, 5, 5)) h1 = conv(g, h0) assert len(g.ndata) == 0 assert len(g.edata) == 0 # test rest_parameters # old_weight = deepcopy(conv.weight.data) # conv.reset_parameters() # new_weight = conv.weight.data # assert not F.allclose(old_weight, new_weight) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree', 'dglgraph'])) @pytest.mark.parametrize('norm', ['none', 'both', 'right']) @pytest.mark.parametrize('weight', [True, False]) @pytest.mark.parametrize('bias', [True, False]) @pytest.mark.parametrize('out_dim', [1, 2]) def test_graph_conv2(idtype, g, norm, weight, bias, out_dim): g = g.astype(idtype).to(F.ctx()) conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias) ext_w = F.randn((5, out_dim)) nsrc = g.number_of_src_nodes() ndst = g.number_of_dst_nodes() h = F.randn((nsrc, 5)) h_dst = F.randn((ndst, out_dim)) if weight: h_out = conv(g, h) else: h_out = conv(g, h, weight=ext_w) assert h_out.shape == (ndst, out_dim) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree', 'dglgraph'])) @pytest.mark.parametrize('norm', ['none', 'both', 'right']) @pytest.mark.parametrize('weight', [True, False]) @pytest.mark.parametrize('bias', [True, False]) @pytest.mark.parametrize('out_dim', [1, 2]) def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim): g = g.astype(idtype).to(F.ctx()) conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias) ext_w = F.randn((5, out_dim)) nsrc = g.number_of_src_nodes() ndst = g.number_of_dst_nodes() h = F.randn((nsrc, 5)) h_dst = F.randn((ndst, out_dim)) if weight: h_out = conv(g, (h, h_dst)) else: h_out = conv(g, (h, h_dst), weight=ext_w) assert h_out.shape == (ndst, out_dim) def test_simple_pool(): ctx = F.ctx() g = dgl.DGLGraph(nx.path_graph(15)).to(F.ctx()) sum_pool = nn.SumPooling() avg_pool = nn.AvgPooling() max_pool = nn.MaxPooling() sort_pool = nn.SortPooling(10) # k = 10 print(sum_pool, avg_pool, max_pool, sort_pool) # test#1: basic h0 = F.randn((g.number_of_nodes(), 5)) h1 = sum_pool(g, h0) assert F.allclose(F.squeeze(h1, 0), F.sum(h0, 0)) h1 = avg_pool(g, h0) assert F.allclose(F.squeeze(h1, 0), F.mean(h0, 0)) h1 = max_pool(g, h0) assert F.allclose(F.squeeze(h1, 0), F.max(h0, 0)) h1 = sort_pool(g, h0) assert h1.shape[0] == 1 and h1.shape[1] == 10 * 5 and h1.ndim == 2 # test#2: batched graph g_ = dgl.DGLGraph(nx.path_graph(5)).to(F.ctx()) bg = dgl.batch([g, g_, g, g_, g]) h0 = F.randn((bg.number_of_nodes(), 5)) h1 = sum_pool(bg, h0) truth = tf.stack([F.sum(h0[:15], 0), F.sum(h0[15:20], 0), F.sum(h0[20:35], 0), F.sum(h0[35:40], 0), F.sum(h0[40:55], 0)], 0) assert F.allclose(h1, truth) h1 = avg_pool(bg, h0) truth = tf.stack([F.mean(h0[:15], 0), F.mean(h0[15:20], 0), F.mean(h0[20:35], 0), F.mean(h0[35:40], 0), F.mean(h0[40:55], 0)], 0) assert F.allclose(h1, truth) h1 = max_pool(bg, h0) truth = tf.stack([F.max(h0[:15], 0), F.max(h0[15:20], 0), F.max(h0[20:35], 0), F.max(h0[35:40], 0), F.max(h0[40:55], 0)], 0) assert F.allclose(h1, truth) h1 = sort_pool(bg, h0) assert h1.shape[0] == 5 and h1.shape[1] == 10 * 5 and h1.ndim == 2 def test_glob_att_pool(): g = dgl.DGLGraph(nx.path_graph(10)).to(F.ctx()) gap = nn.GlobalAttentionPooling(layers.Dense(1), layers.Dense(10)) print(gap) # test#1: basic h0 = F.randn((g.number_of_nodes(), 5)) h1 = gap(g, h0) assert h1.shape[0] == 1 and h1.shape[1] == 10 and h1.ndim == 2 # test#2: batched graph bg = dgl.batch([g, g, g, g]) h0 = F.randn((bg.number_of_nodes(), 5)) h1 = gap(bg, h0) assert h1.shape[0] == 4 and h1.shape[1] == 10 and h1.ndim == 2 @pytest.mark.parametrize('O', [1, 2, 8]) def test_rgcn(O): etype = [] g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True).to(F.ctx()) # 5 etypes R = 5 for i in range(g.number_of_edges()): etype.append(i % 5) B = 2 I = 10 rgc_basis = nn.RelGraphConv(I, O, R, "basis", B) rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True) rgc_basis_low.weight = rgc_basis.weight rgc_basis_low.w_comp = rgc_basis.w_comp rgc_basis_low.loop_weight = rgc_basis.loop_weight h = tf.random.normal((100, I)) r = tf.constant(etype) h_new = rgc_basis(g, h, r) h_new_low = rgc_basis_low(g, h, r) assert list(h_new.shape) == [100, O] assert list(h_new_low.shape) == [100, O] assert F.allclose(h_new, h_new_low) if O % B == 0: rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B) rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True) rgc_bdd_low.weight = rgc_bdd.weight rgc_bdd_low.loop_weight = rgc_bdd.loop_weight h = tf.random.normal((100, I)) r = tf.constant(etype) h_new = rgc_bdd(g, h, r) h_new_low = rgc_bdd_low(g, h, r) assert list(h_new.shape) == [100, O] assert list(h_new_low.shape) == [100, O] assert F.allclose(h_new, h_new_low) # with norm norm = tf.zeros((g.number_of_edges(), 1)) rgc_basis = nn.RelGraphConv(I, O, R, "basis", B) rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True) rgc_basis_low.weight = rgc_basis.weight rgc_basis_low.w_comp = rgc_basis.w_comp rgc_basis_low.loop_weight = rgc_basis.loop_weight h = tf.random.normal((100, I)) r = tf.constant(etype) h_new = rgc_basis(g, h, r, norm) h_new_low = rgc_basis_low(g, h, r, norm) assert list(h_new.shape) == [100, O] assert list(h_new_low.shape) == [100, O] assert F.allclose(h_new, h_new_low) if O % B == 0: rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B) rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True) rgc_bdd_low.weight = rgc_bdd.weight rgc_bdd_low.loop_weight = rgc_bdd.loop_weight h = tf.random.normal((100, I)) r = tf.constant(etype) h_new = rgc_bdd(g, h, r, norm) h_new_low = rgc_bdd_low(g, h, r, norm) assert list(h_new.shape) == [100, O] assert list(h_new_low.shape) == [100, O] assert F.allclose(h_new, h_new_low) # id input rgc_basis = nn.RelGraphConv(I, O, R, "basis", B) rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True) rgc_basis_low.weight = rgc_basis.weight rgc_basis_low.w_comp = rgc_basis.w_comp rgc_basis_low.loop_weight = rgc_basis.loop_weight h = tf.constant(np.random.randint(0, I, (100,))) * 1 r = tf.constant(etype) * 1 h_new = rgc_basis(g, h, r) h_new_low = rgc_basis_low(g, h, r) assert list(h_new.shape) == [100, O] assert list(h_new_low.shape) == [100, O] assert F.allclose(h_new, h_new_low) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree'])) @pytest.mark.parametrize('out_dim', [1, 2]) @pytest.mark.parametrize('num_heads', [1, 4]) def test_gat_conv(g, idtype, out_dim, num_heads): g = g.astype(idtype).to(F.ctx()) ctx = F.ctx() gat = nn.GATConv(5, out_dim, num_heads) feat = F.randn((g.number_of_src_nodes(), 5)) h = gat(g, feat) assert h.shape == (g.number_of_dst_nodes(), num_heads, out_dim) _, a = gat(g, feat, get_attention=True) assert a.shape == (g.number_of_edges(), num_heads, 1) # test residual connection gat = nn.GATConv(5, out_dim, num_heads, residual=True) h = gat(g, feat) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree'])) @pytest.mark.parametrize('out_dim', [1, 2]) @pytest.mark.parametrize('num_heads', [1, 4]) def test_gat_conv_bi(g, idtype, out_dim, num_heads): g = g.astype(idtype).to(F.ctx()) ctx = F.ctx() gat = nn.GATConv(5, out_dim, num_heads) feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5))) h = gat(g, feat) assert h.shape == (g.number_of_dst_nodes(), num_heads, out_dim) _, a = gat(g, feat, get_attention=True) assert a.shape == (g.number_of_edges(), num_heads, 1) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'])) @pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn']) @pytest.mark.parametrize('out_dim', [1, 10]) def test_sage_conv(idtype, g, aggre_type, out_dim): g = g.astype(idtype).to(F.ctx()) sage = nn.SAGEConv(5, out_dim, aggre_type) feat = F.randn((g.number_of_src_nodes(), 5)) h = sage(g, feat) assert h.shape[-1] == out_dim @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['bipartite'])) @pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn']) @pytest.mark.parametrize('out_dim', [1, 2]) def test_sage_conv_bi(idtype, g, aggre_type, out_dim): g = g.astype(idtype).to(F.ctx()) dst_dim = 5 if aggre_type != 'gcn' else 10 sage = nn.SAGEConv((10, dst_dim), out_dim, aggre_type) feat = (F.randn((g.number_of_src_nodes(), 10)), F.randn((g.number_of_dst_nodes(), dst_dim))) h = sage(g, feat) assert h.shape[-1] == out_dim assert h.shape[0] == g.number_of_dst_nodes() @parametrize_dtype @pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn']) @pytest.mark.parametrize('out_dim', [1, 2]) def test_sage_conv_bi_empty(idtype, aggre_type, out_dim): # Test the case for graphs without edges g = dgl.heterograph({('_U', '_E', '_V'): ([], [])}, {'_U': 5, '_V': 3}).to(F.ctx()) g = g.astype(idtype).to(F.ctx()) sage = nn.SAGEConv((3, 3), out_dim, 'gcn') feat = (F.randn((5, 3)), F.randn((3, 3))) h = sage(g, feat) assert h.shape[-1] == out_dim assert h.shape[0] == 3 for aggre_type in ['mean', 'pool', 'lstm']: sage = nn.SAGEConv((3, 1), out_dim, aggre_type) feat = (F.randn((5, 3)), F.randn((3, 1))) h = sage(g, feat) assert h.shape[-1] == out_dim assert h.shape[0] == 3 @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['zero-degree'])) @pytest.mark.parametrize('out_dim', [1, 2]) def test_sgc_conv(g, idtype, out_dim): ctx = F.ctx() g = g.astype(idtype).to(ctx) # not cached sgc = nn.SGConv(5, out_dim, 3) feat = F.randn((g.number_of_nodes(), 5)) h = sgc(g, feat) assert h.shape[-1] == out_dim # cached sgc = nn.SGConv(5, out_dim, 3, True) h_0 = sgc(g, feat) h_1 = sgc(g, feat + 1) assert F.allclose(h_0, h_1) assert h_0.shape[-1] == out_dim @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['zero-degree'])) def test_appnp_conv(g, idtype): ctx = F.ctx() g = g.astype(idtype).to(ctx) appnp = nn.APPNPConv(10, 0.1) feat = F.randn((g.number_of_nodes(), 5)) h = appnp(g, feat) assert h.shape[-1] == 5 @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'])) @pytest.mark.parametrize('aggregator_type', ['mean', 'max', 'sum']) def test_gin_conv(g, idtype, aggregator_type): g = g.astype(idtype).to(F.ctx()) ctx = F.ctx() gin = nn.GINConv( tf.keras.layers.Dense(12), aggregator_type ) feat = F.randn((g.number_of_src_nodes(), 5)) h = gin(g, feat) assert h.shape == (g.number_of_dst_nodes(), 12) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['bipartite'])) @pytest.mark.parametrize('aggregator_type', ['mean', 'max', 'sum']) def test_gin_conv_bi(g, idtype, aggregator_type): g = g.astype(idtype).to(F.ctx()) gin = nn.GINConv( tf.keras.layers.Dense(12), aggregator_type ) feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5))) h = gin(g, feat) assert h.shape == (g.number_of_dst_nodes(), 12) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree'])) @pytest.mark.parametrize('out_dim', [1, 2]) def test_edge_conv(g, idtype, out_dim): g = g.astype(idtype).to(F.ctx()) edge_conv = nn.EdgeConv(out_dim) h0 = F.randn((g.number_of_src_nodes(), 5)) h1 = edge_conv(g, h0) assert h1.shape == (g.number_of_dst_nodes(), out_dim) @parametrize_dtype @pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree'])) @pytest.mark.parametrize('out_dim', [1, 2]) def test_edge_conv_bi(g, idtype, out_dim): g = g.astype(idtype).to(F.ctx()) ctx = F.ctx() edge_conv = nn.EdgeConv(out_dim) h0 = F.randn((g.number_of_src_nodes(), 5)) x0 = F.randn((g.number_of_dst_nodes(), 5)) h1 = edge_conv(g, (h0, x0)) assert h1.shape == (g.number_of_dst_nodes(), out_dim) def myagg(alist, dsttype): rst = alist[0] for i in range(1, len(alist)): rst = rst + (i + 1) * alist[i] return rst @parametrize_dtype @pytest.mark.parametrize('agg', ['sum', 'max', 'min', 'mean', 'stack', myagg]) def test_hetero_conv(agg, idtype): g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 0, 2, 1], [1, 2, 1, 3]), ('user', 'plays', 'game'): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]), ('store', 'sells', 'game'): ([0, 0, 1, 1], [0, 3, 1, 2])}, idtype=idtype, device=F.ctx()) conv = nn.HeteroGraphConv({ 'follows': nn.GraphConv(2, 3, allow_zero_in_degree=True), 'plays': nn.GraphConv(2, 4, allow_zero_in_degree=True), 'sells': nn.GraphConv(3, 4, allow_zero_in_degree=True)}, agg) uf = F.randn((4, 2)) gf = F.randn((4, 4)) sf = F.randn((2, 3)) h = conv(g, {'user': uf, 'store': sf, 'game': gf}) assert set(h.keys()) == {'user', 'game'} if agg != 'stack': assert h['user'].shape == (4, 3) assert h['game'].shape == (4, 4) else: assert h['user'].shape == (4, 1, 3) assert h['game'].shape == (4, 2, 4) block = dgl.to_block(g.to(F.cpu()), {'user': [0, 1, 2, 3], 'game': [0, 1, 2, 3], 'store': []}).to(F.ctx()) h = conv(block, ({'user': uf, 'game': gf, 'store': sf}, {'user': uf, 'game': gf, 'store': sf[0:0]})) assert set(h.keys()) == {'user', 'game'} if agg != 'stack': assert h['user'].shape == (4, 3) assert h['game'].shape == (4, 4) else: assert h['user'].shape == (4, 1, 3) assert h['game'].shape == (4, 2, 4) h = conv(block, {'user': uf, 'game': gf, 'store': sf}) assert set(h.keys()) == {'user', 'game'} if agg != 'stack': assert h['user'].shape == (4, 3) assert h['game'].shape == (4, 4) else: assert h['user'].shape == (4, 1, 3) assert h['game'].shape == (4, 2, 4) # test with mod args class MyMod(tf.keras.layers.Layer): def __init__(self, s1, s2): super(MyMod, self).__init__() self.carg1 = 0 self.carg2 = 0 self.s1 = s1 self.s2 = s2 def call(self, g, h, arg1=None, *, arg2=None): if arg1 is not None: self.carg1 += 1 if arg2 is not None: self.carg2 += 1 return tf.zeros((g.number_of_dst_nodes(), self.s2)) mod1 = MyMod(2, 3) mod2 = MyMod(2, 4) mod3 = MyMod(3, 4) conv = nn.HeteroGraphConv({ 'follows': mod1, 'plays': mod2, 'sells': mod3}, agg) mod_args = {'follows' : (1,), 'plays' : (1,)} mod_kwargs = {'sells' : {'arg2' : 'abc'}} h = conv(g, {'user' : uf, 'game': gf, 'store' : sf}, mod_args=mod_args, mod_kwargs=mod_kwargs) assert mod1.carg1 == 1 assert mod1.carg2 == 0 assert mod2.carg1 == 1 assert mod2.carg2 == 0 assert mod3.carg1 == 0 assert mod3.carg2 == 1 @pytest.mark.parametrize('out_dim', [1, 2]) def test_dense_cheb_conv(out_dim): for k in range(3, 4): ctx = F.ctx() g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1, random_state=42)) g = g.to(ctx) adj = tf.sparse.to_dense(tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))) cheb = nn.ChebConv(5, out_dim, k, None, bias=True) dense_cheb = nn.DenseChebConv(5, out_dim, k, bias=True) # init cheb modules feat = F.ones((100, 5)) out_cheb = cheb(g, feat, [2.0]) dense_cheb.W = tf.reshape(cheb.linear.weights[0], (k, 5, out_dim)) if cheb.linear.bias is not None: dense_cheb.bias = cheb.linear.bias out_dense_cheb = dense_cheb(adj, feat, 2.0) print(out_cheb - out_dense_cheb) assert F.allclose(out_cheb, out_dense_cheb) if __name__ == '__main__': test_graph_conv() # test_set2set() test_glob_att_pool() test_simple_pool() # test_set_trans() test_rgcn() # test_tagconv() test_gat_conv() test_sage_conv() test_sgc_conv() test_appnp_conv() test_gin_conv() test_edge_conv() # test_agnn_conv() # test_gated_graph_conv() # test_nn_conv() # test_gmm_conv() # test_dense_graph_conv() # test_dense_sage_conv() test_dense_cheb_conv() # test_sequential()

34.684783

110

0.595007

84c5414277bc1734a47f55cab27ea5504f745f74

310

py

Python

src/rfidam/setup.py

larioandr/thesis-models

ecbc8c01aaeaa69034d6fe1d8577ab655968ea5f

[ "MIT" ]

1

2021-01-17T15:49:03.000Z

src/rfidam/setup.py

larioandr/thesis-models

ecbc8c01aaeaa69034d6fe1d8577ab655968ea5f

[ "MIT" ]

null

src/rfidam/setup.py

larioandr/thesis-models

ecbc8c01aaeaa69034d6fe1d8577ab655968ea5f

[ "MIT" ]

1

2021-03-07T15:31:06.000Z

from setuptools import setup setup( name='rfidam', version='1.0', py_modules=['rfidam'], install_requires=[ 'Click', 'numpy>=1.19.2', ], tests_requires=[ 'pytest', ], entry_points=''' [console_scripts] rfidam=rfidam.main:main ''' )

15.5

31

0.522581

765af8f31a422794d675590ce89e84015b8c7c07

10,487

py

Python

nova/cmd/baremetal_deploy_helper.py

melwitt/nova

6c8706b70c3bb386e01742116306a0a7942956be

[ "Apache-2.0" ]

null

nova/cmd/baremetal_deploy_helper.py

melwitt/nova

6c8706b70c3bb386e01742116306a0a7942956be

[ "Apache-2.0" ]

null

nova/cmd/baremetal_deploy_helper.py

melwitt/nova

6c8706b70c3bb386e01742116306a0a7942956be

[ "Apache-2.0" ]

null

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright (c) 2012 NTT DOCOMO, INC. # 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. """Starter script for Bare-Metal Deployment Service.""" import os import sys import threading import time import cgi import Queue import re import socket import stat from wsgiref import simple_server from nova import config from nova import context as nova_context from nova.openstack.common import log as logging from nova.openstack.common import processutils from nova import utils from nova.virt.baremetal import baremetal_states from nova.virt.baremetal import db QUEUE = Queue.Queue() LOG = logging.getLogger(__name__) # All functions are called from deploy() directly or indirectly. # They are split for stub-out. def discovery(portal_address, portal_port): """Do iSCSI discovery on portal.""" utils.execute('iscsiadm', '-m', 'discovery', '-t', 'st', '-p', '%s:%s' % (portal_address, portal_port), run_as_root=True, check_exit_code=[0]) def login_iscsi(portal_address, portal_port, target_iqn): """Login to an iSCSI target.""" utils.execute('iscsiadm', '-m', 'node', '-p', '%s:%s' % (portal_address, portal_port), '-T', target_iqn, '--login', run_as_root=True, check_exit_code=[0]) # Ensure the login complete time.sleep(3) def logout_iscsi(portal_address, portal_port, target_iqn): """Logout from an iSCSI target.""" utils.execute('iscsiadm', '-m', 'node', '-p', '%s:%s' % (portal_address, portal_port), '-T', target_iqn, '--logout', run_as_root=True, check_exit_code=[0]) def make_partitions(dev, root_mb, swap_mb): """Create partitions for root and swap on a disk device.""" # Lead in with 1MB to allow room for the partition table itself, otherwise # the way sfdisk adjusts doesn't shift the partition up to compensate, and # we lose the space. # http://bazaar.launchpad.net/~ubuntu-branches/ubuntu/raring/util-linux/ # raring/view/head:/fdisk/sfdisk.c#L1940 stdin_command = ('1,%d,83;\n,%d,82;\n0,0;\n0,0;\n' % (root_mb, swap_mb)) utils.execute('sfdisk', '-uM', dev, process_input=stdin_command, run_as_root=True, check_exit_code=[0]) # avoid "device is busy" time.sleep(3) def is_block_device(dev): """Check whether a device is block or not.""" s = os.stat(dev) return stat.S_ISBLK(s.st_mode) def dd(src, dst): """Execute dd from src to dst.""" utils.execute('dd', 'if=%s' % src, 'of=%s' % dst, 'bs=1M', 'oflag=direct', run_as_root=True, check_exit_code=[0]) def mkswap(dev, label='swap1'): """Execute mkswap on a device.""" utils.execute('mkswap', '-L', label, dev, run_as_root=True, check_exit_code=[0]) def block_uuid(dev): """Get UUID of a block device.""" out, _ = utils.execute('blkid', '-s', 'UUID', '-o', 'value', dev, run_as_root=True, check_exit_code=[0]) return out.strip() def switch_pxe_config(path, root_uuid): """Switch a pxe config from deployment mode to service mode.""" with open(path) as f: lines = f.readlines() root = 'UUID=%s' % root_uuid rre = re.compile(r'\$\{ROOT\}') dre = re.compile('^default .*$') with open(path, 'w') as f: for line in lines: line = rre.sub(root, line) line = dre.sub('default boot', line) f.write(line) def notify(address, port): """Notify a node that it becomes ready to reboot.""" s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: s.connect((address, port)) s.send('done') finally: s.close() def get_dev(address, port, iqn, lun): """Returns a device path for given parameters.""" dev = "/dev/disk/by-path/ip-%s:%s-iscsi-%s-lun-%s" \ % (address, port, iqn, lun) return dev def get_image_mb(image_path): """Get size of an image in Megabyte.""" mb = 1024 * 1024 image_byte = os.path.getsize(image_path) # round up size to MB image_mb = int((image_byte + mb - 1) / mb) return image_mb def work_on_disk(dev, root_mb, swap_mb, image_path): """Creates partitions and write an image to the root partition.""" root_part = "%s-part1" % dev swap_part = "%s-part2" % dev if not is_block_device(dev): LOG.warn("parent device '%s' not found", dev) return make_partitions(dev, root_mb, swap_mb) if not is_block_device(root_part): LOG.warn("root device '%s' not found", root_part) return if not is_block_device(swap_part): LOG.warn("swap device '%s' not found", swap_part) return dd(image_path, root_part) mkswap(swap_part) root_uuid = block_uuid(root_part) return root_uuid def deploy(address, port, iqn, lun, image_path, pxe_config_path, root_mb, swap_mb): """All-in-one function to deploy a node.""" dev = get_dev(address, port, iqn, lun) image_mb = get_image_mb(image_path) if image_mb > root_mb: root_mb = image_mb discovery(address, port) login_iscsi(address, port, iqn) try: root_uuid = work_on_disk(dev, root_mb, swap_mb, image_path) except processutils.ProcessExecutionError, err: # Log output if there was a error LOG.error("Cmd : %s" % err.cmd) LOG.error("StdOut : %s" % err.stdout) LOG.error("StdErr : %s" % err.stderr) finally: logout_iscsi(address, port, iqn) switch_pxe_config(pxe_config_path, root_uuid) # Ensure the node started netcat on the port after POST the request. time.sleep(3) notify(address, 10000) class Worker(threading.Thread): """Thread that handles requests in queue.""" def __init__(self): super(Worker, self).__init__() self.setDaemon(True) self.stop = False self.queue_timeout = 1 def run(self): while not self.stop: try: # Set timeout to check self.stop periodically (node_id, params) = QUEUE.get(block=True, timeout=self.queue_timeout) except Queue.Empty: pass else: # Requests comes here from BareMetalDeploy.post() LOG.info(_('start deployment for node %(node_id)s, ' 'params %(params)s') % locals()) context = nova_context.get_admin_context() try: db.bm_node_update(context, node_id, {'task_state': baremetal_states.DEPLOYING}) deploy(**params) except Exception: LOG.exception(_('deployment to node %s failed') % node_id) db.bm_node_update(context, node_id, {'task_state': baremetal_states.DEPLOYFAIL}) else: LOG.info(_('deployment to node %s done') % node_id) db.bm_node_update(context, node_id, {'task_state': baremetal_states.DEPLOYDONE}) class BareMetalDeploy(object): """WSGI server for bare-metal deployment.""" def __init__(self): self.worker = Worker() self.worker.start() def __call__(self, environ, start_response): method = environ['REQUEST_METHOD'] if method == 'POST': return self.post(environ, start_response) else: start_response('501 Not Implemented', [('Content-type', 'text/plain')]) return 'Not Implemented' def post(self, environ, start_response): LOG.info("post: environ=%s", environ) inpt = environ['wsgi.input'] length = int(environ.get('CONTENT_LENGTH', 0)) x = inpt.read(length) q = dict(cgi.parse_qsl(x)) try: node_id = q['i'] deploy_key = q['k'] address = q['a'] port = q.get('p', '3260') iqn = q['n'] lun = q.get('l', '1') except KeyError as e: start_response('400 Bad Request', [('Content-type', 'text/plain')]) return "parameter '%s' is not defined" % e context = nova_context.get_admin_context() d = db.bm_node_get(context, node_id) if d['deploy_key'] != deploy_key: start_response('400 Bad Request', [('Content-type', 'text/plain')]) return 'key is not match' params = {'address': address, 'port': port, 'iqn': iqn, 'lun': lun, 'image_path': d['image_path'], 'pxe_config_path': d['pxe_config_path'], 'root_mb': int(d['root_mb']), 'swap_mb': int(d['swap_mb']), } # Restart worker, if needed if not self.worker.isAlive(): self.worker = Worker() self.worker.start() LOG.info("request is queued: node %s, params %s", node_id, params) QUEUE.put((node_id, params)) # Requests go to Worker.run() start_response('200 OK', [('Content-type', 'text/plain')]) return '' def main(): config.parse_args(sys.argv) logging.setup("nova") global LOG LOG = logging.getLogger('nova.virt.baremetal.deploy_helper') app = BareMetalDeploy() srv = simple_server.make_server('', 10000, app) srv.serve_forever()

32.568323

79

0.575093

36d98648a0c36bd93bf3b48ca406dee51349fcc6

824

py

Python

authentication/auth.py

morfat/djangorest_start

093b6ea878ec51bfc10b99f0801f989d09bc3f88

[ "MIT" ]

1

2017-01-27T13:24:57.000Z

authentication/auth.py

morfat/djangorest_start

093b6ea878ec51bfc10b99f0801f989d09bc3f88

[ "MIT" ]

null

authentication/auth.py

morfat/djangorest_start

093b6ea878ec51bfc10b99f0801f989d09bc3f88

[ "MIT" ]

null

from users.models import User class CustomBackend(object): """authenticate when given email,phone number or secret key and password """ def get_by_email(self,email,password): try: user = User.objects.get(email=email) if password: if user.check_password(password): return user else: return user except User.DoesNotExist: pass def authenticate(self, email=None, password=None, **kwargs): if email: return self.get_by_email(email, password) return None def get_user(self, pk): try: return User.objects.get(pk=pk) except User.DoesNotExist: return None

23.542857

80

0.525485