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"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ = 1_0 ):
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or n < 0:
raise ValueError("""Invalid input""" )
_a : str = 1_0**n
_a : Union[str, Any] = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , UpperCamelCase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(10) = }''')
| 324
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif stress < 0:
raise ValueError("""Stress cannot be negative""" )
elif tangential_force < 0:
raise ValueError("""Tangential Force cannot be negative""" )
elif area < 0:
raise ValueError("""Area cannot be negative""" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_snake_case = 5_0000
_snake_case = 5000
_snake_case , _snake_case = os.path.split(__file__)
_snake_case = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
for i in range(UpperCamelCase__ ):
_a : Any = dataset[i]
@get_duration
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
for i in range(0 , len(UpperCamelCase__ ) , UpperCamelCase__ ):
_a : Any = dataset[i : i + batch_size]
@get_duration
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
with dataset.formatted_as(type=UpperCamelCase__ ):
for i in range(UpperCamelCase__ ):
_a : List[Any] = dataset[i]
@get_duration
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
with dataset.formatted_as(type=UpperCamelCase__ ):
for i in range(0 , UpperCamelCase__ , UpperCamelCase__ ):
_a : List[str] = dataset[i : i + batch_size]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Optional[Any] = {"""num examples""": SPEED_TEST_N_EXAMPLES}
_a : List[str] = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
]
_a : Optional[Any] = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("""generating dataset""" )
_a : str = datasets.Features(
{"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
_a : int = generate_example_dataset(
os.path.join(UpperCamelCase__ , """dataset.arrow""" ) , UpperCamelCase__ , num_examples=UpperCamelCase__ , seq_shapes={"""list""": (1_0_0,)} , )
print("""first set of iterations""" )
for func, kwargs in functions:
print(func.__name__ , str(UpperCamelCase__ ) )
_a : Tuple = func(UpperCamelCase__ , **UpperCamelCase__ )
print("""shuffling dataset""" )
_a : str = dataset.shuffle()
print("""Second set of iterations (after shuffling""" )
for func, kwargs in functions_shuffled:
print("""shuffled """ , func.__name__ , str(UpperCamelCase__ ) )
_a : str = func(
UpperCamelCase__ , **UpperCamelCase__ )
with open(UpperCamelCase__ , """wb""" ) as f:
f.write(json.dumps(UpperCamelCase__ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
| 1
|
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
_snake_case = 'facebook/wmt19-en-de'
_snake_case = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_snake_case = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
_snake_case = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
_snake_case = tokenizer(['Making tiny model'], return_tensors='pt')
_snake_case = tiny_model(**batch)
print('test output:', len(outputs.logits[0]))
# Save
_snake_case = 'tiny-wmt19-en-de'
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 324
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 324
| 1
|
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = "x" , UpperCamelCase__ = 1_0**-1_0 , UpperCamelCase__ = 1 , ):
'''simple docstring'''
_a : Tuple = symbols(UpperCamelCase__ )
_a : Union[str, Any] = lambdify(UpperCamelCase__ , UpperCamelCase__ )
_a : List[Any] = lambdify(UpperCamelCase__ , diff(UpperCamelCase__ , UpperCamelCase__ ) )
_a : Union[str, Any] = starting_point
while True:
if diff_function(UpperCamelCase__ ) != 0:
_a : Dict = prev_guess - multiplicity * func(UpperCamelCase__ ) / diff_function(
UpperCamelCase__ )
else:
raise ZeroDivisionError("""Could not find root""" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
_a : Optional[int] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')
# Find root of polynomial
# Find fourth Root of 5
print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}''')
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
F'''{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}''',
)
# Find root of cos(x)
print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')
| 324
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
| 324
| 1
|
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = multiprocessing.Manager()
_a : List[Any] = manager.list()
_a : Union[str, Any] = multiprocessing.Process(target=UpperCamelCase__ , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_a : Optional[Any] = shutil.rmtree
_a : Any = os.rmdir
_a : List[Any] = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_a : str = {}
with swallow_io():
with time_limit(UpperCamelCase__ ):
exec(UpperCamelCase__ , UpperCamelCase__ )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F"""failed: {e}""" )
# Needed for cleaning up.
_a : Union[str, Any] = rmtree
_a : Optional[Any] = rmdir
_a : List[str] = chdir
@contextlib.contextmanager
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
def signal_handler(UpperCamelCase__ , UpperCamelCase__ ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , UpperCamelCase__ )
signal.signal(signal.SIGALRM , UpperCamelCase__ )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = WriteOnlyStringIO()
with contextlib.redirect_stdout(UpperCamelCase__ ):
with contextlib.redirect_stderr(UpperCamelCase__ ):
with redirect_stdin(UpperCamelCase__ ):
yield
@contextlib.contextmanager
def lowerCAmelCase__ ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(UpperCamelCase__ ):
yield dirname
class UpperCamelCase ( snake_case_ ):
pass
class UpperCamelCase ( io.StringIO ):
def _lowercase ( self : str , *UpperCAmelCase__ : Union[str, Any] , **UpperCAmelCase__ : List[str] ) -> List[str]:
raise OSError
def _lowercase ( self : Union[str, Any] , *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : Optional[Any] ) -> Optional[int]:
raise OSError
def _lowercase ( self : str , *UpperCAmelCase__ : str , **UpperCAmelCase__ : List[Any] ) -> List[Any]:
raise OSError
def _lowercase ( self : str , *UpperCAmelCase__ : Union[str, Any] , **UpperCAmelCase__ : Any ) -> str:
return False
class UpperCamelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase : Dict = '''stdin'''
@contextlib.contextmanager
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if root == ".":
yield
return
_a : Tuple = os.getcwd()
os.chdir(UpperCamelCase__ )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__=None ):
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
_a : Optional[Any] = None
_a : List[str] = None
import os
_a : Any = """1"""
_a : Any = None
_a : Optional[Any] = None
_a : List[str] = None
_a : Optional[int] = None
_a : Dict = None
_a : Union[str, Any] = None
_a : int = None
_a : Optional[Any] = None
_a : Any = None
_a : int = None
_a : Any = None
_a : str = None
_a : int = None
_a : Optional[int] = None
_a : str = None
_a : List[Any] = None
_a : Optional[Any] = None
_a : Any = None
_a : str = None
_a : Optional[Any] = None
_a : List[str] = None
_a : Tuple = None
_a : Any = None
_a : Tuple = None
_a : List[Any] = None
_a : int = None
_a : Union[str, Any] = None
import shutil
_a : str = None
_a : str = None
_a : Union[str, Any] = None
import subprocess
_a : List[Any] = None # type: ignore
_a : Optional[Any] = None
import sys
_a : Optional[Any] = None
_a : List[str] = None
_a : List[str] = None
_a : List[Any] = None
_a : Dict = None
| 324
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = '▁'
_snake_case = {'vocab_file': 'sentencepiece.bpe.model'}
_snake_case = {
'vocab_file': {
'facebook/mbart-large-50-one-to-many-mmt': (
'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
),
}
}
_snake_case = {
'facebook/mbart-large-50-one-to-many-mmt': 1024,
}
# fmt: off
_snake_case = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
UpperCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Any = ['''input_ids''', '''attention_mask''']
UpperCamelCase : List[int] = []
UpperCamelCase : List[int] = []
def __init__( self : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Tuple="</s>" , UpperCAmelCase__ : Any="</s>" , UpperCAmelCase__ : Dict="<s>" , UpperCAmelCase__ : str="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : Tuple="<mask>" , UpperCAmelCase__ : Optional[Dict[str, Any]] = None , **UpperCAmelCase__ : Optional[Any] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_a : Tuple = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
_a : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
_a : List[Any] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=UpperCAmelCase__ , tgt_lang=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCAmelCase__ , )
_a : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCAmelCase__ ) )
_a : Tuple = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_a : Any = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_a : Dict = 1
_a : Tuple = len(self.sp_model )
_a : Tuple = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase__ )
}
_a : Optional[int] = {v: k for k, v in self.lang_code_to_id.items()}
_a : int = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_a : Optional[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_a : Union[str, Any] = src_lang if src_lang is not None else """en_XX"""
_a : Optional[Any] = self.lang_code_to_id[self._src_lang]
_a : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowercase ( self : List[Any] ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _lowercase ( self : int ) -> str:
return self._src_lang
@src_lang.setter
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str ) -> None:
_a : List[str] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : str ) -> Dict:
_a : List[Any] = self.__dict__.copy()
_a : Any = None
return state
def __setstate__( self : Tuple , UpperCAmelCase__ : Dict ) -> None:
_a : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_a : Optional[int] = {}
_a : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowercase ( self : List[Any] ) -> Dict:
_a : Optional[int] = {self.convert_ids_to_tokens(UpperCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : str ) -> List[str]:
return self.sp_model.encode(UpperCAmelCase__ , out_type=UpperCAmelCase__ )
def _lowercase ( self : Dict , UpperCAmelCase__ : str ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_a : Union[str, Any] = self.sp_model.PieceToId(UpperCAmelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _lowercase ( self : List[str] , UpperCAmelCase__ : int ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str ) -> str:
_a : str = []
_a : Any = """"""
_a : List[str] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCAmelCase__ ) + token
_a : str = True
_a : Any = []
else:
current_sub_tokens.append(UpperCAmelCase__ )
_a : str = False
out_string += self.sp_model.decode(UpperCAmelCase__ )
return out_string.strip()
def _lowercase ( self : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : int = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCAmelCase__ , """wb""" ) as fi:
_a : Dict = self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase__ )
return (out_vocab_file,)
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None , UpperCAmelCase__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase__ , token_ids_a=UpperCAmelCase__ , already_has_special_tokens=UpperCAmelCase__ )
_a : Tuple = [1] * len(self.prefix_tokens )
_a : Optional[Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(UpperCAmelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(UpperCAmelCase__ )) + ([0] * len(UpperCAmelCase__ )) + suffix_ones
def _lowercase ( self : int , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] , UpperCAmelCase__ : Optional[str] , **UpperCAmelCase__ : Optional[int] ) -> Dict:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
_a : int = src_lang
_a : int = self(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
_a : List[Any] = self.convert_tokens_to_ids(UpperCAmelCase__ )
_a : Optional[Any] = tgt_lang_id
return inputs
def _lowercase ( self : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str = "en_XX" , UpperCAmelCase__ : Optional[List[str]] = None , UpperCAmelCase__ : str = "ro_RO" , **UpperCAmelCase__ : int , ) -> BatchEncoding:
_a : int = src_lang
_a : Optional[Any] = tgt_lang
return super().prepare_seqaseq_batch(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase ( self : List[str] ) -> List[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase ( self : Tuple , UpperCAmelCase__ : str ) -> None:
_a : Optional[Any] = self.lang_code_to_id[src_lang]
_a : List[Any] = [self.cur_lang_code_id]
_a : int = [self.eos_token_id]
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : str ) -> None:
_a : Optional[Any] = self.lang_code_to_id[tgt_lang]
_a : Optional[Any] = [self.cur_lang_code_id]
_a : Optional[int] = [self.eos_token_id]
| 324
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
'cl-tohoku/bert-base-japanese-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
),
'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : int = '''bert'''
def __init__( self : Dict , UpperCAmelCase__ : Optional[Any]=30522 , UpperCAmelCase__ : Optional[int]=768 , UpperCAmelCase__ : Optional[Any]=12 , UpperCAmelCase__ : int=12 , UpperCAmelCase__ : Dict=3072 , UpperCAmelCase__ : Dict="gelu" , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : Optional[int]=0.0_2 , UpperCAmelCase__ : Dict=1E-12 , UpperCAmelCase__ : Dict=0 , UpperCAmelCase__ : Optional[int]="absolute" , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[int]=None , **UpperCAmelCase__ : List[str] , ) -> int:
super().__init__(pad_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Optional[Any] = vocab_size
_a : str = hidden_size
_a : Optional[int] = num_hidden_layers
_a : str = num_attention_heads
_a : Optional[int] = hidden_act
_a : Union[str, Any] = intermediate_size
_a : List[Any] = hidden_dropout_prob
_a : int = attention_probs_dropout_prob
_a : Tuple = max_position_embeddings
_a : Dict = type_vocab_size
_a : Dict = initializer_range
_a : List[Any] = layer_norm_eps
_a : Optional[Any] = position_embedding_type
_a : List[str] = use_cache
_a : Optional[Any] = classifier_dropout
class UpperCamelCase ( snake_case_ ):
@property
def _lowercase ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_a : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_a : Any = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 324
|
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
| 1
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=False ):
'''simple docstring'''
_a : Any = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_a : Tuple = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
_a : Tuple = """"""
else:
_a : int = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_a : int = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
_a : Dict = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : List[str] = in_proj_weight[
: config.hidden_size, :
]
_a : List[Any] = in_proj_bias[: config.hidden_size]
_a : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_a : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_a : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
_a : List[str] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = dct.pop(UpperCamelCase__ )
_a : Union[str, Any] = val
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_a : List[Any] = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = ViTConfig()
_a : Any = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_a : Tuple = True
_a : List[str] = int(vit_name[-1_2:-1_0] )
_a : Dict = int(vit_name[-9:-6] )
else:
_a : Optional[Any] = 1_0_0_0
_a : Any = """huggingface/label-files"""
_a : List[str] = """imagenet-1k-id2label.json"""
_a : Optional[Any] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Tuple = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : List[Any] = idalabel
_a : Union[str, Any] = {v: k for k, v in idalabel.items()}
_a : int = int(vit_name[-6:-4] )
_a : Optional[Any] = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("""tiny""" ):
_a : Any = 1_9_2
_a : str = 7_6_8
_a : List[str] = 1_2
_a : Tuple = 3
elif vit_name[9:].startswith("""small""" ):
_a : int = 3_8_4
_a : Union[str, Any] = 1_5_3_6
_a : Optional[Any] = 1_2
_a : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("""small""" ):
_a : Tuple = 7_6_8
_a : str = 2_3_0_4
_a : Tuple = 8
_a : Optional[int] = 8
elif vit_name[4:].startswith("""base""" ):
pass
elif vit_name[4:].startswith("""large""" ):
_a : int = 1_0_2_4
_a : List[str] = 4_0_9_6
_a : int = 2_4
_a : Dict = 1_6
elif vit_name[4:].startswith("""huge""" ):
_a : int = 1_2_8_0
_a : Any = 5_1_2_0
_a : Any = 3_2
_a : Optional[int] = 1_6
# load original model from timm
_a : Tuple = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_a : Dict = timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase__ )
_a : Optional[int] = create_rename_keys(UpperCamelCase__ , UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_a : List[str] = ViTModel(UpperCamelCase__ ).eval()
else:
_a : Union[str, Any] = ViTForImageClassification(UpperCamelCase__ ).eval()
model.load_state_dict(UpperCamelCase__ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_a : List[str] = DeiTImageProcessor(size=config.image_size )
else:
_a : Optional[Any] = ViTImageProcessor(size=config.image_size )
_a : List[Any] = image_processor(images=prepare_img() , return_tensors="""pt""" )
_a : Any = encoding["""pixel_values"""]
_a : Tuple = model(UpperCamelCase__ )
if base_model:
_a : Optional[int] = timm_model.forward_features(UpperCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase__ , outputs.pooler_output , atol=1e-3 )
else:
_a : int = timm_model(UpperCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase__ , outputs.logits , atol=1e-3 )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_snake_case = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 324
|
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324
| 1
|
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_snake_case = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
print('Googling.....')
_snake_case = F'''https://www.google.com/search?q={query}&num=100'''
_snake_case = requests.get(
url,
headers={'User-Agent': str(UserAgent().random)},
)
try:
_snake_case = (
BeautifulSoup(res.text, 'html.parser')
.find('div', attrs={'class': 'yuRUbf'})
.find('a')
.get('href')
)
except AttributeError:
_snake_case = parse_qs(
BeautifulSoup(res.text, 'html.parser')
.find('div', attrs={'class': 'kCrYT'})
.find('a')
.get('href')
)['url'][0]
webbrowser.open(link)
| 324
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_a : Any = 0
count += depth_first_search(UpperCamelCase__ , row + 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , row - 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col + 1 , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col - 1 , UpperCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
from functools import reduce
_snake_case = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCAmelCase__ ( UpperCamelCase__ = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda UpperCamelCase__ , UpperCamelCase__ : str(int(UpperCamelCase__ ) * int(UpperCamelCase__ ) ) , n[i : i + 1_3] ) )
for i in range(len(UpperCamelCase__ ) - 1_2 ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 324
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MraForMaskedLM',
'MraForMultipleChoice',
'MraForQuestionAnswering',
'MraForSequenceClassification',
'MraForTokenClassification',
'MraLayer',
'MraModel',
'MraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 324
|
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Node | None ) -> List[str]:
_a : int = pos_x
_a : Union[str, Any] = pos_y
_a : Tuple = (pos_y, pos_x)
_a : Tuple = goal_x
_a : int = goal_y
_a : str = parent
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] ) -> List[str]:
_a : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : Optional[int] = [self.start]
_a : Tuple = False
def _lowercase ( self : str ) -> Path | None:
while self.node_queue:
_a : Tuple = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_a : Dict = True
return self.retrace_path(UpperCAmelCase__ )
_a : Tuple = self.get_successors(UpperCAmelCase__ )
for node in successors:
self.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node ) -> list[Node]:
_a : Optional[Any] = []
for action in delta:
_a : str = parent.pos_x + action[1]
_a : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCAmelCase__ , UpperCAmelCase__ , self.target.pos_y , self.target.pos_x , UpperCAmelCase__ ) )
return successors
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Node | None ) -> Path:
_a : Dict = node
_a : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_a : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Dict = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Dict = False
def _lowercase ( self : Any ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_a : List[Any] = self.fwd_bfs.node_queue.pop(0 )
_a : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_a : Optional[int] = True
return self.retrace_bidirectional_path(
UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = current_bwd_node
_a : int = current_fwd_node
_a : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node , UpperCAmelCase__ : Node ) -> Path:
_a : str = self.fwd_bfs.retrace_path(UpperCAmelCase__ )
_a : List[Any] = self.bwd_bfs.retrace_path(UpperCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_a : Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_snake_case = (0, 0)
_snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_snake_case = time.time()
_snake_case = BreadthFirstSearch(init, goal)
_snake_case = bfs.search()
_snake_case = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_snake_case = time.time()
_snake_case = BidirectionalBreadthFirstSearch(init, goal)
_snake_case = bd_bfs.search()
_snake_case = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if inductance <= 0:
raise ValueError("""Inductance cannot be 0 or negative""" )
elif capacitance <= 0:
raise ValueError("""Capacitance cannot be 0 or negative""" )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_snake_case = logging.getLogger(__name__)
_snake_case = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_ )} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
UpperCamelCase : bool = field(default=snake_case_ , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
UpperCamelCase : float = field(
default=0.1_5 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCamelCase : float = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
UpperCamelCase : int = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
UpperCamelCase : int = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = None , ):
'''simple docstring'''
def _dataset(UpperCamelCase__ , UpperCamelCase__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , ref_path=UpperCamelCase__ , )
return LineByLineTextDataset(tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=UpperCamelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(UpperCamelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowerCAmelCase__ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_a , _a , _a : List[str] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , UpperCamelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
_a : str = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
_a : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
_a : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
_a : Optional[Any] = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , )
else:
logger.info("""Training new model from scratch""" )
_a : List[Any] = AutoModelWithLMHead.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
_a : int = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
_a : Optional[Any] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
_a : Optional[Any] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
_a : Optional[int] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , evaluate=UpperCamelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
_a : Any = DataCollatorForPermutationLanguageModeling(
tokenizer=UpperCamelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
_a : Union[str, Any] = DataCollatorForWholeWordMask(
tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
else:
_a : str = DataCollatorForLanguageModeling(
tokenizer=UpperCamelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_a : Union[str, Any] = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , data_collator=UpperCamelCase__ , train_dataset=UpperCamelCase__ , eval_dataset=UpperCamelCase__ , prediction_loss_only=UpperCamelCase__ , )
# Training
if training_args.do_train:
_a : Optional[Any] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=UpperCamelCase__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_a : Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_a : int = trainer.evaluate()
_a : Dict = math.exp(eval_output["""eval_loss"""] )
_a : Union[str, Any] = {"""perplexity""": perplexity}
_a : Optional[Any] = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(UpperCamelCase__ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , UpperCamelCase__ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(UpperCamelCase__ )
return results
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 324
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_snake_case = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_a : Optional[Any] = k.replace(UpperCamelCase__ , UpperCamelCase__ )
return k
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCamelCase__ )
_a : Optional[Any] = PegasusConfig(**UpperCamelCase__ )
_a : Tuple = PegasusForConditionalGeneration(UpperCamelCase__ )
_a : str = torch_model.model.state_dict()
_a : Union[str, Any] = {}
for k, v in tf_weights.items():
_a : Any = rename_state_dict_key(UpperCamelCase__ )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
_a : str = v.T
_a : int = torch.tensor(UpperCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
_a : Union[str, Any] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
_a : str = mapping["""shared.weight"""]
_a : Union[str, Any] = mapping["""shared.weight"""]
_a : Optional[Any] = {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**UpperCamelCase__ )
_a , _a : int = torch_model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : Optional[Any] = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase__ ( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_a : List[Any] = tf.train.list_variables(UpperCamelCase__ )
_a : Optional[int] = {}
_a : Dict = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(UpperCamelCase__ , desc="""converting tf checkpoint to dict""" ):
_a : Optional[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a : str = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
_a : int = array
return tf_weights
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# save tokenizer first
_a : Dict = Path(UpperCamelCase__ ).parent.name
_a : Optional[Any] = task_specific_params[F"""summarization_{dataset}"""]["""max_position_embeddings"""]
_a : Tuple = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=UpperCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCamelCase__ )
# convert model
_a : List[Any] = get_tf_weights_as_numpy(UpperCamelCase__ )
_a : Dict = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
_a : Tuple = task_specific_params
_a : Optional[int] = convert_pegasus(UpperCamelCase__ , UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
_a : Dict = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(UpperCamelCase__ , Path(UpperCamelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_snake_case = parser.parse_args()
if args.save_dir is None:
_snake_case = Path(args.tf_ckpt_path).parent.name
_snake_case = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
_a : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_a : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
_a : Tuple = CLIPTextModel(UpperCAmelCase__ )
_a : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Dict = CLIPTextModelWithProjection(UpperCAmelCase__ )
_a : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=0 ) -> int:
_a : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
_a : Any = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("""mps""" ):
_a : Any = torch.manual_seed(UpperCAmelCase__ )
else:
_a : Tuple = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def _lowercase ( self : Any ) -> List[Any]:
_a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : List[Any] = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Union[str, Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = sd_pipe(**UpperCAmelCase__ ).images
_a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self : Any ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self : Any ) -> Any:
pass
def _lowercase ( self : Tuple ) -> Union[str, Any]:
_a : int = self.get_dummy_components()
_a : Any = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Dict = sd_pipe.to(UpperCAmelCase__ )
_a : List[str] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# forward without prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = 3 * ["""this is a negative prompt"""]
_a : Dict = negative_prompt
_a : Dict = 3 * [inputs["""prompt"""]]
_a : Optional[Any] = sd_pipe(**UpperCAmelCase__ )
_a : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : Union[str, Any] = 3 * ["""this is a negative prompt"""]
_a : int = 3 * [inputs.pop("""prompt""" )]
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : List[str] = sd_pipe.encode_prompt(UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ )
_a : Tuple = sd_pipe(
**UpperCAmelCase__ , prompt_embeds=UpperCAmelCase__ , negative_prompt_embeds=UpperCAmelCase__ , pooled_prompt_embeds=UpperCAmelCase__ , negative_pooled_prompt_embeds=UpperCAmelCase__ , )
_a : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str="cpu" , UpperCAmelCase__ : str=torch.floataa , UpperCAmelCase__ : List[Any]=0 ) -> List[str]:
_a : List[str] = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Union[str, Any] = np.random.RandomState(UpperCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_a : List[Any] = torch.from_numpy(UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
_a : Any = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Union[str, Any] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_inputs(UpperCAmelCase__ )
_a : Tuple = pipe(**UpperCAmelCase__ ).images
_a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_a : int = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 324
| 1
|
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , **UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = [x.strip() for x in open(UpperCamelCase__ ).readlines()]
_a : int = [x.strip() for x in open(UpperCamelCase__ ).readlines()][: len(UpperCamelCase__ )]
_a : int = calculate_rouge(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
if save_path is not None:
save_json(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 324
|
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger()
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : List[nn.Module] = field(default_factory=snake_case_ )
UpperCamelCase : list = field(default_factory=snake_case_ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Tensor ) -> Any:
_a : int = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase__ , nn.Convad ) or isinstance(UpperCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase__ )
def __call__( self : Tuple , UpperCAmelCase__ : Tensor ) -> Tuple:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowercase ( self : Optional[int] ) -> int:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : nn.Module
UpperCamelCase : int = 0
UpperCamelCase : List = field(default_factory=snake_case_ )
UpperCamelCase : List = field(default_factory=snake_case_ )
def __call__( self : Optional[Any] , UpperCAmelCase__ : Tensor ) -> Tuple:
_a : Union[str, Any] = Tracker(self.dest )(UpperCAmelCase__ ).parametrized
_a : List[Any] = Tracker(self.src )(UpperCAmelCase__ ).parametrized
_a : Tuple = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.src_skip , UpperCAmelCase__ ) )
_a : Union[str, Any] = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.dest_skip , UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__ )} operations while"""
f""" destination module has {len(UpperCAmelCase__ )}.""" )
for dest_m, src_m in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
_a : List[str] = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval()
_a : str = ResNetForImageClassification(UpperCamelCase__ ).eval()
_a : List[str] = ModuleTransfer(src=UpperCamelCase__ , dest=UpperCamelCase__ )
_a : List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(UpperCamelCase__ )
assert torch.allclose(from_model(UpperCamelCase__ ) , our_model(UpperCamelCase__ ).logits ), "The model logits don't match the original one."
_a : Dict = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(UpperCamelCase__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=UpperCamelCase__ , )
# we can use the convnext one
_a : Optional[Any] = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=UpperCamelCase__ , )
print(F"""Pushed {checkpoint_name}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
_a : Any = """imagenet-1k-id2label.json"""
_a : Optional[int] = 1_0_0_0
_a : Any = (1, num_labels)
_a : Union[str, Any] = """huggingface/label-files"""
_a : Tuple = num_labels
_a : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Optional[Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Any = idalabel
_a : Tuple = {v: k for k, v in idalabel.items()}
_a : List[str] = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
_a : Union[str, Any] = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
_snake_case = parser.parse_args()
_snake_case = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 324
| 1
|
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Tuple , UpperCAmelCase__ : bool , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[int] = None ) -> List[str]:
super().__init__()
_a : List[Any] = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
_a : Tuple = torch.zeros(UpperCAmelCase__ , UpperCAmelCase__ )
else:
_a : Optional[int] = None
_a : List[Any] = torch.nn.Parameter(UpperCAmelCase__ )
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : VQModel
UpperCamelCase : CLIPTextModel
UpperCamelCase : CLIPTokenizer
UpperCamelCase : TransformeraDModel
UpperCamelCase : LearnedClassifierFreeSamplingEmbeddings
UpperCamelCase : VQDiffusionScheduler
def __init__( self : List[Any] , UpperCAmelCase__ : VQModel , UpperCAmelCase__ : CLIPTextModel , UpperCAmelCase__ : CLIPTokenizer , UpperCAmelCase__ : TransformeraDModel , UpperCAmelCase__ : VQDiffusionScheduler , UpperCAmelCase__ : LearnedClassifierFreeSamplingEmbeddings , ) -> List[Any]:
super().__init__()
self.register_modules(
vqvae=UpperCAmelCase__ , transformer=UpperCAmelCase__ , text_encoder=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , scheduler=UpperCAmelCase__ , learned_classifier_free_sampling_embeddings=UpperCAmelCase__ , )
def _lowercase ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int ) -> List[str]:
_a : Tuple = len(UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else 1
# get prompt text embeddings
_a : List[str] = self.tokenizer(
UpperCAmelCase__ , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
_a : List[str] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_a : int = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
_a : str = text_input_ids[:, : self.tokenizer.model_max_length]
_a : Dict = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
_a : Optional[Any] = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=UpperCAmelCase__ )
# duplicate text embeddings for each generation per prompt
_a : Optional[Any] = prompt_embeds.repeat_interleave(UpperCAmelCase__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
_a : Union[str, Any] = self.learned_classifier_free_sampling_embeddings.embeddings
_a : Any = negative_prompt_embeds.unsqueeze(0 ).repeat(UpperCAmelCase__ , 1 , 1 )
else:
_a : Dict = [""""""] * batch_size
_a : Optional[int] = text_input_ids.shape[-1]
_a : Optional[Any] = self.tokenizer(
UpperCAmelCase__ , padding="""max_length""" , max_length=UpperCAmelCase__ , truncation=UpperCAmelCase__ , return_tensors="""pt""" , )
_a : Any = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
_a : Any = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=UpperCAmelCase__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_a : Any = negative_prompt_embeds.shape[1]
_a : Union[str, Any] = negative_prompt_embeds.repeat(1 , UpperCAmelCase__ , 1 )
_a : Optional[Any] = negative_prompt_embeds.view(batch_size * num_images_per_prompt , UpperCAmelCase__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a : Optional[Any] = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self : Tuple , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 100 , UpperCAmelCase__ : float = 5.0 , UpperCAmelCase__ : float = 1.0 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : str = 1
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Any = len(UpperCAmelCase__ )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__ )}""" )
_a : List[str] = batch_size * num_images_per_prompt
_a : Optional[Any] = guidance_scale > 1.0
_a : int = self._encode_prompt(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(UpperCAmelCase__ )}.""" )
# get the initial completely masked latents unless the user supplied it
_a : Dict = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
_a : List[Any] = self.transformer.num_vector_embeds - 1
_a : str = torch.full(UpperCAmelCase__ , UpperCAmelCase__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"""Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
_a : int = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCAmelCase__ , device=self.device )
_a : Any = self.scheduler.timesteps.to(self.device )
_a : str = latents
for i, t in enumerate(self.progress_bar(UpperCAmelCase__ ) ):
# expand the sample if we are doing classifier free guidance
_a : Any = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
_a : Optional[int] = self.transformer(UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , timestep=UpperCAmelCase__ ).sample
if do_classifier_free_guidance:
_a , _a : Optional[Any] = model_output.chunk(2 )
_a : Optional[int] = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(UpperCAmelCase__ , dim=1 , keepdim=UpperCAmelCase__ )
_a : Optional[int] = self.truncate(UpperCAmelCase__ , UpperCAmelCase__ )
# remove `log(0)`'s (`-inf`s)
_a : Dict = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
_a : Optional[int] = self.scheduler.step(UpperCAmelCase__ , timestep=UpperCAmelCase__ , sample=UpperCAmelCase__ , generator=UpperCAmelCase__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
_a : str = self.vqvae.config.vq_embed_dim
_a : Tuple = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
_a : str = self.vqvae.quantize.get_codebook_entry(UpperCAmelCase__ , shape=UpperCAmelCase__ )
_a : Optional[int] = self.vqvae.decode(UpperCAmelCase__ , force_not_quantize=UpperCAmelCase__ ).sample
_a : int = (image / 2 + 0.5).clamp(0 , 1 )
_a : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a : Any = self.numpy_to_pil(UpperCAmelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : torch.FloatTensor , UpperCAmelCase__ : float ) -> torch.FloatTensor:
_a , _a : Optional[Any] = torch.sort(UpperCAmelCase__ , 1 , descending=UpperCAmelCase__ )
_a : Tuple = torch.exp(UpperCAmelCase__ )
_a : str = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
_a : Any = torch.full_like(keep_mask[:, 0:1, :] , UpperCAmelCase__ )
_a : Tuple = torch.cat((all_true, keep_mask) , dim=1 )
_a : List[str] = keep_mask[:, :-1, :]
_a : str = keep_mask.gather(1 , indices.argsort(1 ) )
_a : Optional[int] = log_p_x_0.clone()
_a : int = -torch.inf # -inf = log(0)
return rv
| 324
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase :
UpperCamelCase : int
UpperCamelCase : TreeNode | None = None
UpperCamelCase : TreeNode | None = None
_snake_case = namedtuple('CoinsDistribResult', 'moves excess')
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(UpperCamelCase__ ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(UpperCamelCase__ ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(UpperCamelCase__ ) != count_coins(UpperCamelCase__ ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(UpperCamelCase__ ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
_a , _a : Any = get_distrib(node.left )
_a , _a : List[Any] = get_distrib(node.right )
_a : str = 1 - left_distrib_excess
_a : Union[str, Any] = 1 - right_distrib_excess
_a : Optional[int] = (
left_distrib_moves
+ right_distrib_moves
+ abs(UpperCamelCase__ )
+ abs(UpperCamelCase__ )
)
_a : List[Any] = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(UpperCamelCase__ , UpperCamelCase__ )
return get_distrib(UpperCamelCase__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
| 1
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
_snake_case = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Tuple = '''deformable_detr'''
UpperCamelCase : Tuple = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : int , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[Any]=3 , UpperCAmelCase__ : int=300 , UpperCAmelCase__ : Dict=1024 , UpperCAmelCase__ : List[Any]=6 , UpperCAmelCase__ : str=1024 , UpperCAmelCase__ : Any=8 , UpperCAmelCase__ : Dict=6 , UpperCAmelCase__ : Union[str, Any]=1024 , UpperCAmelCase__ : int=8 , UpperCAmelCase__ : int=0.0 , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Dict="relu" , UpperCAmelCase__ : Any=256 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : Any=0.0 , UpperCAmelCase__ : str=0.0 , UpperCAmelCase__ : Optional[int]=0.0_2 , UpperCAmelCase__ : Tuple=1.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : str=False , UpperCAmelCase__ : str="sine" , UpperCAmelCase__ : Union[str, Any]="resnet50" , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : List[Any]=False , UpperCAmelCase__ : str=4 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : Optional[Any]=300 , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Optional[int]=1 , UpperCAmelCase__ : Dict=5 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : List[str]=1 , UpperCAmelCase__ : Any=1 , UpperCAmelCase__ : List[str]=5 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : Any=0.2_5 , UpperCAmelCase__ : Any=False , **UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_a : Tuple = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Any = backbone_config.get("""model_type""" )
_a : str = CONFIG_MAPPING[backbone_model_type]
_a : List[str] = config_class.from_dict(UpperCAmelCase__ )
_a : Union[str, Any] = use_timm_backbone
_a : List[Any] = backbone_config
_a : Optional[Any] = num_channels
_a : str = num_queries
_a : List[str] = max_position_embeddings
_a : str = d_model
_a : Union[str, Any] = encoder_ffn_dim
_a : Union[str, Any] = encoder_layers
_a : List[Any] = encoder_attention_heads
_a : Optional[Any] = decoder_ffn_dim
_a : List[Any] = decoder_layers
_a : Dict = decoder_attention_heads
_a : Optional[int] = dropout
_a : List[str] = attention_dropout
_a : List[Any] = activation_dropout
_a : Optional[int] = activation_function
_a : int = init_std
_a : Tuple = init_xavier_std
_a : List[Any] = encoder_layerdrop
_a : List[str] = auxiliary_loss
_a : Any = position_embedding_type
_a : Dict = backbone
_a : Dict = use_pretrained_backbone
_a : Union[str, Any] = dilation
# deformable attributes
_a : int = num_feature_levels
_a : Dict = encoder_n_points
_a : Any = decoder_n_points
_a : str = two_stage
_a : List[Any] = two_stage_num_proposals
_a : int = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
_a : List[str] = class_cost
_a : str = bbox_cost
_a : Dict = giou_cost
# Loss coefficients
_a : Union[str, Any] = mask_loss_coefficient
_a : str = dice_loss_coefficient
_a : Any = bbox_loss_coefficient
_a : Union[str, Any] = giou_loss_coefficient
_a : List[Any] = eos_coefficient
_a : List[Any] = focal_alpha
_a : Dict = disable_custom_kernels
super().__init__(is_encoder_decoder=UpperCAmelCase__ , **UpperCAmelCase__ )
@property
def _lowercase ( self : Any ) -> int:
return self.encoder_attention_heads
@property
def _lowercase ( self : List[Any] ) -> int:
return self.d_model
def _lowercase ( self : List[Any] ) -> List[str]:
_a : int = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
_a : Optional[int] = self.backbone_config.to_dict()
_a : Union[str, Any] = self.__class__.model_type
return output
| 324
|
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_snake_case = logging.getLogger(__name__)
_snake_case = 'pytorch_model.bin'
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The name of the task to train on.'''} , )
UpperCamelCase : Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Random seed for initialization.'''} , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_a : Union[str, Any] = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_a : Any = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
_a : str = dataset.sort("""probability""" , reverse=UpperCamelCase__ )
_a : Any = dataset.select(range(UpperCamelCase__ ) )
_a : Tuple = dataset.remove_columns(["""label""", """probability"""] )
_a : Optional[Any] = dataset.rename_column("""prediction""" , """label""" )
_a : Dict = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
_a : Union[str, Any] = dataset.shuffle(seed=args.seed )
_a : Optional[int] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_a : Dict = STModelArguments(model_name_or_path=UpperCamelCase__ )
_a : Union[str, Any] = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
_a : Any = STTrainingArguments(output_dir=UpperCamelCase__ )
_a : Any = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
_a : Union[str, Any] = {}
_a : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_a : int = args.train_file
_a : List[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_a : Union[str, Any] = args.eval_file
for key in data_files:
_a : Optional[Any] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_a : str = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_a : Tuple = F"""{args.output_dir}/self-train_iter-{{}}""".format
_a : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : str = None
_a : int = None
_a : str = 0
_a : List[Any] = False
# Show the progress bar
_a : List[Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_a : Union[str, Any] = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_a : str = os.path.join(UpperCamelCase__ , """stage-1""" )
_a : Tuple = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
_a : int = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_a : Dict = os.path.join(UpperCamelCase__ , """best-checkpoint""" )
_a : List[str] = os.path.join(UpperCamelCase__ , """stage-2""" )
# Update arguments_dict
_a : int = model_path
_a : Dict = data_files["""train"""]
_a : int = current_output_dir
_a : Any = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , UpperCamelCase__ )
_a : List[Any] = iteration
_a : int = data_dir_format(iteration + 1 )
_a : Dict = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , """best-checkpoint""" ) )
_a : Union[str, Any] = config.idalabel
_a : Any = os.path.join(UpperCamelCase__ , """eval_results_best-checkpoint.json""" )
_a : Any = os.path.join(UpperCamelCase__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , """r""" ) as f:
_a : Tuple = float(json.load(UpperCamelCase__ )[args.eval_metric] )
_a : Dict = os.path.join(UpperCamelCase__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
_a : List[Any] = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_a : Any = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : List[str] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_a : Any = eval_result
if best_iteration is None:
_a : Union[str, Any] = new_iteration
_a : str = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_a : Union[str, Any] = new_iteration
_a : List[str] = new_eval_result
_a : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_a : Tuple = new_iteration
_a : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_a : Union[str, Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , UpperCamelCase__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
| 324
| 1
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Union[str, Any] = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : Union[str, Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Optional[Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
_a : List[Any] = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : Dict = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : Optional[Any] = 2
# New Code #
_a : Union[str, Any] = int(args.gradient_accumulation_steps )
_a : str = int(args.local_sgd_steps )
# Initialize accelerator
_a : Dict = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=UpperCamelCase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : List[Any] = config["""lr"""]
_a : List[Any] = int(config["""num_epochs"""] )
_a : int = int(config["""seed"""] )
_a : int = int(config["""batch_size"""] )
_a : int = evaluate.load("""glue""" , """mrpc""" )
set_seed(UpperCamelCase__ )
_a , _a : str = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : Any = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : Tuple = model.to(accelerator.device )
# Instantiate optimizer
_a : int = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[Any] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Any = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
with LocalSGD(
accelerator=UpperCamelCase__ , model=UpperCamelCase__ , local_sgd_steps=UpperCamelCase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(UpperCamelCase__ ):
_a : str = model(**UpperCamelCase__ )
_a : Dict = output.loss
accelerator.backward(UpperCamelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : List[Any] = model(**UpperCamelCase__ )
_a : List[str] = outputs.logits.argmax(dim=-1 )
_a , _a : Optional[int] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Any = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=UpperCamelCase__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=UpperCamelCase__ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Union[str, Any] = parser.parse_args()
_a : Union[str, Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_snake_case = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
},
}
_snake_case = {
'camembert-base': 512,
}
_snake_case = '▁'
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = VOCAB_FILES_NAMES
UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Dict = ['''input_ids''', '''attention_mask''']
UpperCamelCase : Optional[Any] = CamembertTokenizer
def __init__( self : int , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]="<s>" , UpperCAmelCase__ : Optional[int]="</s>" , UpperCAmelCase__ : Tuple="</s>" , UpperCAmelCase__ : Tuple="<s>" , UpperCAmelCase__ : Tuple="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : int="<mask>" , UpperCAmelCase__ : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a : List[Any] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : int = vocab_file
_a : int = False if not self.vocab_file else True
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a : List[Any] = [self.cls_token_id]
_a : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Union[str, Any] = [self.sep_token_id]
_a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[str] = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int ) -> None:
_a : Optional[Any] = value
_a : Node | None = None
_a : Node | None = None
class UpperCamelCase :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Node ) -> None:
_a : Union[str, Any] = tree
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node | None ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : Dict ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 32 , UpperCAmelCase__ : Optional[Any]=PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , **UpperCAmelCase__ : List[str] , ) -> None:
_a : int = do_resize
_a : Union[str, Any] = do_rescale
_a : Any = size_divisor
_a : Any = resample
super().__init__(**UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[Any] ) -> np.ndarray:
_a , _a : Tuple = get_image_size(UpperCAmelCase__ )
# Rounds the height and width down to the closest multiple of size_divisor
_a : Optional[Any] = height // size_divisor * size_divisor
_a : Union[str, Any] = width // size_divisor * size_divisor
_a : Any = resize(UpperCAmelCase__ , (new_h, new_w) , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
return image
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[int] ) -> np.ndarray:
return rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[TensorType, str]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> BatchFeature:
_a : Dict = do_resize if do_resize is not None else self.do_resize
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : str = size_divisor if size_divisor is not None else self.size_divisor
_a : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_a : Tuple = [to_numpy_array(UpperCAmelCase__ ) for img in images]
if do_resize:
_a : Optional[int] = [self.resize(UpperCAmelCase__ , size_divisor=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : str = [self.rescale(UpperCAmelCase__ , scale=1 / 255 ) for image in images]
_a : Any = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Optional[int] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
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"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
_snake_case = random.Random()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=1.0 , UpperCamelCase__=None , UpperCamelCase__=None ):
'''simple docstring'''
if rng is None:
_a : Union[str, Any] = global_rng
_a : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase ( unittest.TestCase ):
def __init__( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str=7 , UpperCAmelCase__ : Tuple=400 , UpperCAmelCase__ : List[str]=2000 , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : int=16000 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : str=True , ) -> Optional[int]:
_a : Optional[int] = parent
_a : Optional[int] = batch_size
_a : str = min_seq_length
_a : Union[str, Any] = max_seq_length
_a : List[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_a : Dict = feature_size
_a : int = padding_value
_a : Any = sampling_rate
_a : str = return_attention_mask
_a : Any = do_normalize
def _lowercase ( self : Tuple ) -> Optional[Any]:
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _lowercase ( self : List[str] , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Union[str, Any]=False ) -> str:
def _flatten(UpperCAmelCase__ : str ):
return list(itertools.chain(*UpperCAmelCase__ ) )
if equal_length:
_a : str = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
_a : List[str] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_a : Tuple = [np.asarray(UpperCAmelCase__ ) for x in speech_inputs]
return speech_inputs
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : Tuple = WavaVecaFeatureExtractor
def _lowercase ( self : Dict ) -> List[str]:
_a : Tuple = WavaVecaFeatureExtractionTester(self )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Optional[int] ) -> Optional[int]:
self.assertTrue(np.all(np.mean(UpperCAmelCase__ , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase__ , axis=0 ) - 1 ) < 1E-3 ) )
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
# Tests that all call wrap to encode_plus and batch_encode_plus
_a : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_a : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_a : List[Any] = [np.asarray(UpperCAmelCase__ ) for speech_input in speech_inputs]
# Test not batched input
_a : Tuple = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values
_a : str = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1E-3 ) )
# Test batched
_a : int = feat_extract(UpperCAmelCase__ , return_tensors="""np""" ).input_values
_a : Optional[int] = feat_extract(UpperCAmelCase__ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
self.assertTrue(np.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_a : Optional[Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_a : int = np.asarray(UpperCAmelCase__ )
_a : Tuple = feat_extract(UpperCAmelCase__ , return_tensors="""np""" ).input_values
_a : List[Any] = feat_extract(UpperCAmelCase__ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
self.assertTrue(np.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1E-3 ) )
def _lowercase ( self : Tuple ) -> Tuple:
_a : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_a : int = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_a : Optional[Any] = ["""longest""", """max_length""", """do_not_pad"""]
_a : Dict = [None, 1600, None]
for max_length, padding in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Any = feat_extract(UpperCAmelCase__ , padding=UpperCAmelCase__ , max_length=UpperCAmelCase__ , return_tensors="""np""" )
_a : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def _lowercase ( self : List[str] ) -> str:
_a : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_a : List[str] = range(800 , 1400 , 200 )
_a : int = [floats_list((1, x) )[0] for x in lengths]
_a : str = ["""longest""", """max_length""", """do_not_pad"""]
_a : Optional[int] = [None, 1600, None]
for max_length, padding in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : List[Any] = feat_extract(UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding=UpperCAmelCase__ )
_a : Tuple = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def _lowercase ( self : int ) -> Dict:
_a : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_a : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_a : Optional[Any] = feat_extract(
UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=1000 , padding="""max_length""" , return_tensors="""np""" )
_a : str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_a : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_a : Tuple = feat_extract(
UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=1000 , padding="""longest""" , return_tensors="""np""" )
_a : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
_a : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_a : Optional[int] = feat_extract(
UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=2000 , padding="""longest""" , return_tensors="""np""" )
_a : str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
@require_torch
def _lowercase ( self : str ) -> Optional[int]:
import torch
_a : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_a : str = np.random.rand(100 ).astype(np.floataa )
_a : Optional[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_a : Tuple = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
_a : Tuple = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
_a : Optional[Any] = WavaVecaConfig.from_pretrained(UpperCAmelCase__ )
_a : List[str] = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == """layer""" )
| 324
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"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
_a : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase ( self : Dict ) -> Dict:
_a : str = self.dummy_uncond_unet
_a : Optional[int] = KarrasVeScheduler()
_a : List[str] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : int = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Tuple = torch.manual_seed(0 )
_a : int = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : int = image[0, -3:, -3:, -1]
_a : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
_a : Optional[Any] = """google/ncsnpp-celebahq-256"""
_a : Any = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : Dict = KarrasVeScheduler()
_a : int = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[int] = torch.manual_seed(0 )
_a : Tuple = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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"""simple docstring"""
from typing import Any
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if not input_list:
return []
_a : Optional[int] = [input_list.count(UpperCamelCase__ ) for value in input_list]
_a : Tuple = max(UpperCamelCase__ ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(UpperCamelCase__ ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
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| 1
|
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : jnp.ndarray
@flax_register_to_config
class UpperCamelCase ( nn.Module , snake_case_ , snake_case_ ):
UpperCamelCase : int = 32
UpperCamelCase : int = 4
UpperCamelCase : int = 4
UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
UpperCamelCase : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
UpperCamelCase : Union[bool, Tuple[bool]] = False
UpperCamelCase : Tuple[int] = (320, 640, 1_280, 1_280)
UpperCamelCase : int = 2
UpperCamelCase : Union[int, Tuple[int]] = 8
UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
UpperCamelCase : int = 1_280
UpperCamelCase : float = 0.0
UpperCamelCase : bool = False
UpperCamelCase : jnp.dtype = jnp.floataa
UpperCamelCase : bool = True
UpperCamelCase : int = 0
UpperCamelCase : bool = False
def _lowercase ( self : Any , UpperCAmelCase__ : jax.random.KeyArray ) -> FrozenDict:
# init input tensors
_a : str = (1, self.in_channels, self.sample_size, self.sample_size)
_a : Optional[int] = jnp.zeros(UpperCAmelCase__ , dtype=jnp.floataa )
_a : str = jnp.ones((1,) , dtype=jnp.intaa )
_a : List[str] = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_a , _a : List[Any] = jax.random.split(UpperCAmelCase__ )
_a : str = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )["params"]
def _lowercase ( self : int ) -> List[str]:
_a : Union[str, Any] = self.block_out_channels
_a : Any = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_a : Optional[int] = self.num_attention_heads or self.attention_head_dim
# input
_a : Union[str, Any] = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_a : Tuple = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_a : Optional[Any] = FlaxTimestepEmbedding(UpperCAmelCase__ , dtype=self.dtype )
_a : Any = self.only_cross_attention
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Dict = (only_cross_attention,) * len(self.down_block_types )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : List[str] = (num_attention_heads,) * len(self.down_block_types )
# down
_a : List[str] = []
_a : Optional[Any] = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_a : List[str] = output_channel
_a : List[Any] = block_out_channels[i]
_a : List[str] = i == len(UpperCAmelCase__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_a : Dict = FlaxCrossAttnDownBlockaD(
in_channels=UpperCAmelCase__ , out_channels=UpperCAmelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_a : Optional[Any] = FlaxDownBlockaD(
in_channels=UpperCAmelCase__ , out_channels=UpperCAmelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(UpperCAmelCase__ )
_a : Optional[int] = down_blocks
# mid
_a : Optional[Any] = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
_a : int = []
_a : str = list(reversed(UpperCAmelCase__ ) )
_a : Union[str, Any] = list(reversed(UpperCAmelCase__ ) )
_a : str = list(reversed(UpperCAmelCase__ ) )
_a : Optional[int] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_a : int = output_channel
_a : List[str] = reversed_block_out_channels[i]
_a : Optional[Any] = reversed_block_out_channels[min(i + 1 , len(UpperCAmelCase__ ) - 1 )]
_a : Optional[int] = i == len(UpperCAmelCase__ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_a : Optional[int] = FlaxCrossAttnUpBlockaD(
in_channels=UpperCAmelCase__ , out_channels=UpperCAmelCase__ , prev_output_channel=UpperCAmelCase__ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_a : Tuple = FlaxUpBlockaD(
in_channels=UpperCAmelCase__ , out_channels=UpperCAmelCase__ , prev_output_channel=UpperCAmelCase__ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(UpperCAmelCase__ )
_a : Optional[Any] = output_channel
_a : Tuple = up_blocks
# out
_a : Tuple = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_a : List[str] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Tuple , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : bool = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
# 1. time
if not isinstance(UpperCAmelCase__ , jnp.ndarray ):
_a : int = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(UpperCAmelCase__ , jnp.ndarray ) and len(timesteps.shape ) == 0:
_a : Tuple = timesteps.astype(dtype=jnp.floataa )
_a : Any = jnp.expand_dims(UpperCAmelCase__ , 0 )
_a : Optional[int] = self.time_proj(UpperCAmelCase__ )
_a : Optional[int] = self.time_embedding(UpperCAmelCase__ )
# 2. pre-process
_a : Tuple = jnp.transpose(UpperCAmelCase__ , (0, 2, 3, 1) )
_a : Tuple = self.conv_in(UpperCAmelCase__ )
# 3. down
_a : int = (sample,)
for down_block in self.down_blocks:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a , _a : Optional[Any] = down_block(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , deterministic=not train )
else:
_a , _a : int = down_block(UpperCAmelCase__ , UpperCAmelCase__ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_a : str = ()
for down_block_res_sample, down_block_additional_residual in zip(
UpperCAmelCase__ , UpperCAmelCase__ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_a : int = new_down_block_res_samples
# 4. mid
_a : Union[str, Any] = self.mid_block(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_a : str = down_block_res_samples[-(self.layers_per_block + 1) :]
_a : Optional[Any] = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Union[str, Any] = up_block(
UpperCAmelCase__ , temb=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , res_hidden_states_tuple=UpperCAmelCase__ , deterministic=not train , )
else:
_a : Tuple = up_block(UpperCAmelCase__ , temb=UpperCAmelCase__ , res_hidden_states_tuple=UpperCAmelCase__ , deterministic=not train )
# 6. post-process
_a : Dict = self.conv_norm_out(UpperCAmelCase__ )
_a : int = nn.silu(UpperCAmelCase__ )
_a : List[str] = self.conv_out(UpperCAmelCase__ )
_a : Any = jnp.transpose(UpperCAmelCase__ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=UpperCAmelCase__ )
| 324
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = {}
_a : List[str] = job["""started_at"""]
_a : List[str] = job["""completed_at"""]
_a : List[Any] = date_parser.parse(UpperCamelCase__ )
_a : Union[str, Any] = date_parser.parse(UpperCamelCase__ )
_a : Tuple = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_a : List[Any] = start
_a : Dict = end
_a : Any = duration_in_min
return job_info
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=None ):
'''simple docstring'''
_a : Optional[int] = None
if token is not None:
_a : Dict = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}
_a : List[Any] = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
_a : List[str] = requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).json()
_a : int = {}
try:
job_time.update({job["""name"""]: extract_time_from_single_job(UpperCamelCase__ ) for job in result["""jobs"""]} )
_a : str = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0 )
for i in range(UpperCamelCase__ ):
_a : Optional[int] = requests.get(url + F"""&page={i + 2}""" , headers=UpperCamelCase__ ).json()
job_time.update({job["""name"""]: extract_time_from_single_job(UpperCamelCase__ ) for job in result["""jobs"""]} )
return job_time
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
_snake_case = parser.parse_args()
_snake_case = get_job_time(args.workflow_run_id)
_snake_case = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
| 1
|
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
_snake_case = 6_37_81_37.0
_snake_case = 6_35_67_52.31_42_45
_snake_case = 637_8137
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Any = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_a : Optional[int] = atan((1 - flattening) * tan(radians(UpperCamelCase__ ) ) )
_a : Optional[int] = atan((1 - flattening) * tan(radians(UpperCamelCase__ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_a : Tuple = haversine_distance(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_a : Tuple = (b_lata + b_lata) / 2
_a : List[Any] = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_a : Optional[int] = (sin(UpperCamelCase__ ) ** 2) * (cos(UpperCamelCase__ ) ** 2)
_a : Dict = cos(sigma / 2 ) ** 2
_a : List[Any] = (sigma - sin(UpperCamelCase__ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_a : List[str] = (cos(UpperCamelCase__ ) ** 2) * (sin(UpperCamelCase__ ) ** 2)
_a : Optional[Any] = sin(sigma / 2 ) ** 2
_a : str = (sigma + sin(UpperCamelCase__ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 324
| 1
|
"""simple docstring"""
import random
class UpperCamelCase :
@staticmethod
def _lowercase ( UpperCAmelCase__ : str ) -> tuple[list[int], list[int]]:
_a : Union[str, Any] = [ord(UpperCAmelCase__ ) for i in text]
_a : Any = []
_a : Union[str, Any] = []
for i in plain:
_a : Optional[Any] = random.randint(1 , 300 )
_a : Dict = (i + k) * k
cipher.append(UpperCAmelCase__ )
key.append(UpperCAmelCase__ )
return cipher, key
@staticmethod
def _lowercase ( UpperCAmelCase__ : list[int] , UpperCAmelCase__ : list[int] ) -> str:
_a : Optional[int] = []
for i in range(len(UpperCAmelCase__ ) ):
_a : str = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(UpperCAmelCase__ ) )
return "".join(UpperCAmelCase__ )
if __name__ == "__main__":
_snake_case , _snake_case = Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k))
| 324
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
| 324
| 1
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : str = len(UpperCamelCase__ )
print("""The following activities are selected:""" )
# The first activity is always selected
_a : List[Any] = 0
print(UpperCamelCase__ , end=""",""" )
# Consider rest of the activities
for j in range(UpperCamelCase__ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(UpperCamelCase__ , end=""",""" )
_a : Union[str, Any] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = [1, 3, 0, 5, 8, 5]
_snake_case = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 324
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
| 1
|
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for param in module.parameters():
_a : Optional[Any] = False
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : int = """cuda""" if torch.cuda.is_available() else """cpu"""
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_a : List[str] = """mps"""
if device == "mps":
print(
"""WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
""" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
""" with generations.""" )
return device
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : int = plt.imshow(UpperCamelCase__ )
fig.axes.get_xaxis().set_visible(UpperCamelCase__ )
fig.axes.get_yaxis().set_visible(UpperCamelCase__ )
plt.show()
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = datetime.now()
_a : Tuple = current_time.strftime("""%H:%M:%S""" )
return timestamp
| 324
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : int = '''lxmert'''
UpperCamelCase : Tuple = {}
def __init__( self : Dict , UpperCAmelCase__ : Union[str, Any]=30522 , UpperCAmelCase__ : str=768 , UpperCAmelCase__ : Any=12 , UpperCAmelCase__ : Dict=9500 , UpperCAmelCase__ : Tuple=1600 , UpperCAmelCase__ : int=400 , UpperCAmelCase__ : List[str]=3072 , UpperCAmelCase__ : Optional[Any]="gelu" , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : List[Any]=0.1 , UpperCAmelCase__ : int=512 , UpperCAmelCase__ : str=2 , UpperCAmelCase__ : Union[str, Any]=0.0_2 , UpperCAmelCase__ : Optional[int]=1E-12 , UpperCAmelCase__ : int=9 , UpperCAmelCase__ : Union[str, Any]=5 , UpperCAmelCase__ : Optional[Any]=5 , UpperCAmelCase__ : Tuple=2048 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Union[str, Any]=6.6_7 , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : List[Any]=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=True , **UpperCAmelCase__ : List[Any] , ) -> Optional[int]:
_a : Optional[int] = vocab_size
_a : List[str] = hidden_size
_a : Any = num_attention_heads
_a : str = hidden_act
_a : Union[str, Any] = intermediate_size
_a : int = hidden_dropout_prob
_a : List[str] = attention_probs_dropout_prob
_a : List[str] = max_position_embeddings
_a : Dict = type_vocab_size
_a : int = initializer_range
_a : Union[str, Any] = layer_norm_eps
_a : Optional[int] = num_qa_labels
_a : int = num_object_labels
_a : List[Any] = num_attr_labels
_a : List[Any] = l_layers
_a : Tuple = x_layers
_a : Union[str, Any] = r_layers
_a : Dict = visual_feat_dim
_a : Optional[int] = visual_pos_dim
_a : Dict = visual_loss_normalizer
_a : Any = task_matched
_a : int = task_mask_lm
_a : Any = task_obj_predict
_a : int = task_qa
_a : Union[str, Any] = visual_obj_loss
_a : str = visual_attr_loss
_a : List[Any] = visual_feat_loss
_a : Optional[int] = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**UpperCAmelCase__ )
| 324
|
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
| 1
|
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_snake_case = logging.get_logger(__name__)
_snake_case = {
'tensor(bool)': np.bool_,
'tensor(int8)': np.inta,
'tensor(uint8)': np.uinta,
'tensor(int16)': np.intaa,
'tensor(uint16)': np.uintaa,
'tensor(int32)': np.intaa,
'tensor(uint32)': np.uintaa,
'tensor(int64)': np.intaa,
'tensor(uint64)': np.uintaa,
'tensor(float16)': np.floataa,
'tensor(float)': np.floataa,
'tensor(double)': np.floataa,
}
class UpperCamelCase :
def __init__( self : Dict , UpperCAmelCase__ : List[Any]=None , **UpperCAmelCase__ : int ) -> str:
logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future.""" )
_a : Optional[Any] = model
_a : List[Any] = kwargs.get("""model_save_dir""" , UpperCAmelCase__ )
_a : List[str] = kwargs.get("""latest_model_name""" , UpperCAmelCase__ )
def __call__( self : Optional[int] , **UpperCAmelCase__ : List[Any] ) -> Optional[Any]:
_a : str = {k: np.array(UpperCAmelCase__ ) for k, v in kwargs.items()}
return self.model.run(UpperCAmelCase__ , UpperCAmelCase__ )
@staticmethod
def _lowercase ( UpperCAmelCase__ : Union[str, Path] , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[Any]=None ) -> Tuple:
if provider is None:
logger.info("""No onnxruntime provider specified, using CPUExecutionProvider""" )
_a : Any = """CPUExecutionProvider"""
return ort.InferenceSession(UpperCAmelCase__ , providers=[provider] , sess_options=UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Union[str, Path] , UpperCAmelCase__ : Optional[str] = None , **UpperCAmelCase__ : Optional[Any] ) -> int:
_a : Union[str, Any] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
_a : List[Any] = self.model_save_dir.joinpath(self.latest_model_name )
_a : Any = Path(UpperCAmelCase__ ).joinpath(UpperCAmelCase__ )
try:
shutil.copyfile(UpperCAmelCase__ , UpperCAmelCase__ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
_a : List[Any] = self.model_save_dir.joinpath(UpperCAmelCase__ )
if src_path.exists():
_a : str = Path(UpperCAmelCase__ ).joinpath(UpperCAmelCase__ )
try:
shutil.copyfile(UpperCAmelCase__ , UpperCAmelCase__ )
except shutil.SameFileError:
pass
def _lowercase ( self : Tuple , UpperCAmelCase__ : Union[str, os.PathLike] , **UpperCAmelCase__ : str , ) -> Tuple:
if os.path.isfile(UpperCAmelCase__ ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
# saving model weights/files
self._save_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
@classmethod
def _lowercase ( cls : Any , UpperCAmelCase__ : Union[str, Path] , UpperCAmelCase__ : Optional[Union[bool, str, None]] = None , UpperCAmelCase__ : Optional[Union[str, None]] = None , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : Optional[str] = None , UpperCAmelCase__ : Optional[str] = None , UpperCAmelCase__ : Optional[str] = None , UpperCAmelCase__ : Optional["ort.SessionOptions"] = None , **UpperCAmelCase__ : Optional[Any] , ) -> Tuple:
_a : Dict = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(UpperCAmelCase__ ):
_a : Union[str, Any] = OnnxRuntimeModel.load_model(
os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , provider=UpperCAmelCase__ , sess_options=UpperCAmelCase__ )
_a : Optional[int] = Path(UpperCAmelCase__ )
# load model from hub
else:
# download model
_a : Union[str, Any] = hf_hub_download(
repo_id=UpperCAmelCase__ , filename=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , revision=UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , force_download=UpperCAmelCase__ , )
_a : Optional[int] = Path(UpperCAmelCase__ ).parent
_a : Union[str, Any] = Path(UpperCAmelCase__ ).name
_a : int = OnnxRuntimeModel.load_model(UpperCAmelCase__ , provider=UpperCAmelCase__ , sess_options=UpperCAmelCase__ )
return cls(model=UpperCAmelCase__ , **UpperCAmelCase__ )
@classmethod
def _lowercase ( cls : Tuple , UpperCAmelCase__ : Union[str, Path] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[str] = None , UpperCAmelCase__ : Optional[str] = None , **UpperCAmelCase__ : Union[str, Any] , ) -> str:
_a : str = None
if len(str(UpperCAmelCase__ ).split("""@""" ) ) == 2:
_a , _a : List[str] = model_id.split("""@""" )
return cls._from_pretrained(
model_id=UpperCAmelCase__ , revision=UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , force_download=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 324
|
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = '▁'
_snake_case = {'vocab_file': 'sentencepiece.bpe.model'}
_snake_case = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
),
}
}
_snake_case = {
'facebook/nllb-200-distilled-600M': 1024,
}
# fmt: off
_snake_case = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : str = VOCAB_FILES_NAMES
UpperCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = ['''input_ids''', '''attention_mask''']
UpperCamelCase : List[int] = []
UpperCamelCase : List[int] = []
def __init__( self : Optional[int] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any]="<s>" , UpperCAmelCase__ : Union[str, Any]="</s>" , UpperCAmelCase__ : List[Any]="</s>" , UpperCAmelCase__ : Any="<s>" , UpperCAmelCase__ : Tuple="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : Optional[int]="<mask>" , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Optional[Dict[str, Any]] = None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : List[str]=False , **UpperCAmelCase__ : List[Any] , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a : Union[str, Any] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
_a : Any = {} if sp_model_kwargs is None else sp_model_kwargs
_a : str = legacy_behaviour
super().__init__(
bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , src_lang=UpperCAmelCase__ , tgt_lang=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCAmelCase__ ) )
_a : Optional[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
_a : List[str] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_a : int = 1
_a : List[str] = len(self.sp_model )
_a : List[str] = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase__ )
}
_a : int = {v: k for k, v in self.lang_code_to_id.items()}
_a : Optional[int] = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_a : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_a : Optional[Any] = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
_a : List[str] = src_lang if src_lang is not None else """eng_Latn"""
_a : List[Any] = self.lang_code_to_id[self._src_lang]
_a : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : str ) -> Dict:
_a : List[Any] = self.__dict__.copy()
_a : Any = None
_a : Any = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[Any] , UpperCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
_a : Tuple = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_a : Tuple = {}
_a : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def _lowercase ( self : List[str] ) -> Any:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _lowercase ( self : Optional[Any] ) -> str:
return self._src_lang
@src_lang.setter
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : str ) -> None:
_a : List[str] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None , UpperCAmelCase__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase__ , token_ids_a=UpperCAmelCase__ , already_has_special_tokens=UpperCAmelCase__ )
_a : Any = [1] * len(self.prefix_tokens )
_a : Union[str, Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(UpperCAmelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(UpperCAmelCase__ )) + ([0] * len(UpperCAmelCase__ )) + suffix_ones
def _lowercase ( self : Any , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Optional[Any] = [self.sep_token_id]
_a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] , UpperCAmelCase__ : Optional[str] , **UpperCAmelCase__ : Optional[Any] ) -> Optional[int]:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
_a : List[Any] = src_lang
_a : int = self(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
_a : str = self.convert_tokens_to_ids(UpperCAmelCase__ )
_a : List[str] = tgt_lang_id
return inputs
def _lowercase ( self : Dict ) -> Dict:
_a : Optional[Any] = {self.convert_ids_to_tokens(UpperCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : str ) -> List[str]:
return self.sp_model.encode(UpperCAmelCase__ , out_type=UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_a : Any = self.sp_model.PieceToId(UpperCAmelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : Any ) -> Optional[Any]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Tuple = """""".join(UpperCAmelCase__ ).replace(UpperCAmelCase__ , """ """ ).strip()
return out_string
def _lowercase ( self : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[str] = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCAmelCase__ , """wb""" ) as fi:
_a : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase__ )
return (out_vocab_file,)
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str = "eng_Latn" , UpperCAmelCase__ : Optional[List[str]] = None , UpperCAmelCase__ : str = "fra_Latn" , **UpperCAmelCase__ : Union[str, Any] , ) -> BatchEncoding:
_a : Dict = src_lang
_a : Optional[Any] = tgt_lang
return super().prepare_seqaseq_batch(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Any ) -> str:
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] ) -> None:
_a : Optional[Any] = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
_a : Any = []
_a : Dict = [self.eos_token_id, self.cur_lang_code]
else:
_a : str = [self.cur_lang_code]
_a : Any = [self.eos_token_id]
def _lowercase ( self : Tuple , UpperCAmelCase__ : str ) -> None:
_a : int = self.lang_code_to_id[lang]
if self.legacy_behaviour:
_a : List[str] = []
_a : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
else:
_a : List[Any] = [self.cur_lang_code]
_a : Optional[int] = [self.eos_token_id]
| 324
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_a : Any = 0
count += depth_first_search(UpperCamelCase__ , row + 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , row - 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col + 1 , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col - 1 , UpperCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : int = '''audio-spectrogram-transformer'''
def __init__( self : int , UpperCAmelCase__ : str=768 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : str=12 , UpperCAmelCase__ : Optional[int]=3072 , UpperCAmelCase__ : Dict="gelu" , UpperCAmelCase__ : Tuple=0.0 , UpperCAmelCase__ : str=0.0 , UpperCAmelCase__ : Tuple=0.0_2 , UpperCAmelCase__ : Any=1E-12 , UpperCAmelCase__ : Tuple=16 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Any=10 , UpperCAmelCase__ : Union[str, Any]=10 , UpperCAmelCase__ : List[str]=1024 , UpperCAmelCase__ : Any=128 , **UpperCAmelCase__ : Tuple , ) -> List[str]:
super().__init__(**UpperCAmelCase__ )
_a : str = hidden_size
_a : Tuple = num_hidden_layers
_a : int = num_attention_heads
_a : int = intermediate_size
_a : Optional[int] = hidden_act
_a : Union[str, Any] = hidden_dropout_prob
_a : int = attention_probs_dropout_prob
_a : List[Any] = initializer_range
_a : Any = layer_norm_eps
_a : int = patch_size
_a : int = qkv_bias
_a : Any = frequency_stride
_a : Union[str, Any] = time_stride
_a : Optional[int] = max_length
_a : int = num_mel_bins
| 324
|
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Node | None ) -> List[str]:
_a : int = pos_x
_a : Union[str, Any] = pos_y
_a : Tuple = (pos_y, pos_x)
_a : Tuple = goal_x
_a : int = goal_y
_a : str = parent
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] ) -> List[str]:
_a : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : Optional[int] = [self.start]
_a : Tuple = False
def _lowercase ( self : str ) -> Path | None:
while self.node_queue:
_a : Tuple = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_a : Dict = True
return self.retrace_path(UpperCAmelCase__ )
_a : Tuple = self.get_successors(UpperCAmelCase__ )
for node in successors:
self.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node ) -> list[Node]:
_a : Optional[Any] = []
for action in delta:
_a : str = parent.pos_x + action[1]
_a : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCAmelCase__ , UpperCAmelCase__ , self.target.pos_y , self.target.pos_x , UpperCAmelCase__ ) )
return successors
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Node | None ) -> Path:
_a : Dict = node
_a : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_a : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Dict = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Dict = False
def _lowercase ( self : Any ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_a : List[Any] = self.fwd_bfs.node_queue.pop(0 )
_a : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_a : Optional[int] = True
return self.retrace_bidirectional_path(
UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = current_bwd_node
_a : int = current_fwd_node
_a : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node , UpperCAmelCase__ : Node ) -> Path:
_a : str = self.fwd_bfs.retrace_path(UpperCAmelCase__ )
_a : List[Any] = self.bwd_bfs.retrace_path(UpperCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_a : Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_snake_case = (0, 0)
_snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_snake_case = time.time()
_snake_case = BreadthFirstSearch(init, goal)
_snake_case = bfs.search()
_snake_case = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_snake_case = time.time()
_snake_case = BidirectionalBreadthFirstSearch(init, goal)
_snake_case = bd_bfs.search()
_snake_case = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
|
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_snake_case = logging.getLogger(__name__)
_snake_case = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_ )} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
UpperCamelCase : bool = field(default=snake_case_ , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
UpperCamelCase : float = field(
default=0.1_5 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCamelCase : float = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
UpperCamelCase : int = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
UpperCamelCase : int = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = None , ):
'''simple docstring'''
def _dataset(UpperCamelCase__ , UpperCamelCase__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , ref_path=UpperCamelCase__ , )
return LineByLineTextDataset(tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=UpperCamelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(UpperCamelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowerCAmelCase__ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_a , _a , _a : List[str] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , UpperCamelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
_a : str = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
_a : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
_a : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
_a : Optional[Any] = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , )
else:
logger.info("""Training new model from scratch""" )
_a : List[Any] = AutoModelWithLMHead.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
_a : int = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
_a : Optional[Any] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
_a : Optional[Any] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
_a : Optional[int] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , evaluate=UpperCamelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
_a : Any = DataCollatorForPermutationLanguageModeling(
tokenizer=UpperCamelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
_a : Union[str, Any] = DataCollatorForWholeWordMask(
tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
else:
_a : str = DataCollatorForLanguageModeling(
tokenizer=UpperCamelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_a : Union[str, Any] = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , data_collator=UpperCamelCase__ , train_dataset=UpperCamelCase__ , eval_dataset=UpperCamelCase__ , prediction_loss_only=UpperCamelCase__ , )
# Training
if training_args.do_train:
_a : Optional[Any] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=UpperCamelCase__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_a : Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_a : int = trainer.evaluate()
_a : Dict = math.exp(eval_output["""eval_loss"""] )
_a : Union[str, Any] = {"""perplexity""": perplexity}
_a : Optional[Any] = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(UpperCamelCase__ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , UpperCamelCase__ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(UpperCamelCase__ )
return results
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
| 324
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_snake_case = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_a : Optional[Any] = k.replace(UpperCamelCase__ , UpperCamelCase__ )
return k
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCamelCase__ )
_a : Optional[Any] = PegasusConfig(**UpperCamelCase__ )
_a : Tuple = PegasusForConditionalGeneration(UpperCamelCase__ )
_a : str = torch_model.model.state_dict()
_a : Union[str, Any] = {}
for k, v in tf_weights.items():
_a : Any = rename_state_dict_key(UpperCamelCase__ )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
_a : str = v.T
_a : int = torch.tensor(UpperCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
_a : Union[str, Any] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
_a : str = mapping["""shared.weight"""]
_a : Union[str, Any] = mapping["""shared.weight"""]
_a : Optional[Any] = {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**UpperCamelCase__ )
_a , _a : int = torch_model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : Optional[Any] = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase__ ( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_a : List[Any] = tf.train.list_variables(UpperCamelCase__ )
_a : Optional[int] = {}
_a : Dict = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(UpperCamelCase__ , desc="""converting tf checkpoint to dict""" ):
_a : Optional[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a : str = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
_a : int = array
return tf_weights
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# save tokenizer first
_a : Dict = Path(UpperCamelCase__ ).parent.name
_a : Optional[Any] = task_specific_params[F"""summarization_{dataset}"""]["""max_position_embeddings"""]
_a : Tuple = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=UpperCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCamelCase__ )
# convert model
_a : List[Any] = get_tf_weights_as_numpy(UpperCamelCase__ )
_a : Dict = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
_a : Tuple = task_specific_params
_a : Optional[int] = convert_pegasus(UpperCamelCase__ , UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
_a : Dict = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(UpperCamelCase__ , Path(UpperCamelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_snake_case = parser.parse_args()
if args.save_dir is None:
_snake_case = Path(args.tf_ckpt_path).parent.name
_snake_case = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 324
| 1
|
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 324
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
_a : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_a : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
_a : Tuple = CLIPTextModel(UpperCAmelCase__ )
_a : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Dict = CLIPTextModelWithProjection(UpperCAmelCase__ )
_a : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=0 ) -> int:
_a : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
_a : Any = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("""mps""" ):
_a : Any = torch.manual_seed(UpperCAmelCase__ )
else:
_a : Tuple = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def _lowercase ( self : Any ) -> List[Any]:
_a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : List[Any] = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Union[str, Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = sd_pipe(**UpperCAmelCase__ ).images
_a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self : Any ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self : Any ) -> Any:
pass
def _lowercase ( self : Tuple ) -> Union[str, Any]:
_a : int = self.get_dummy_components()
_a : Any = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Dict = sd_pipe.to(UpperCAmelCase__ )
_a : List[str] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# forward without prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = 3 * ["""this is a negative prompt"""]
_a : Dict = negative_prompt
_a : Dict = 3 * [inputs["""prompt"""]]
_a : Optional[Any] = sd_pipe(**UpperCAmelCase__ )
_a : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : Union[str, Any] = 3 * ["""this is a negative prompt"""]
_a : int = 3 * [inputs.pop("""prompt""" )]
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : List[str] = sd_pipe.encode_prompt(UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ )
_a : Tuple = sd_pipe(
**UpperCAmelCase__ , prompt_embeds=UpperCAmelCase__ , negative_prompt_embeds=UpperCAmelCase__ , pooled_prompt_embeds=UpperCAmelCase__ , negative_pooled_prompt_embeds=UpperCAmelCase__ , )
_a : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str="cpu" , UpperCAmelCase__ : str=torch.floataa , UpperCAmelCase__ : List[Any]=0 ) -> List[str]:
_a : List[str] = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Union[str, Any] = np.random.RandomState(UpperCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_a : List[Any] = torch.from_numpy(UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
_a : Any = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Union[str, Any] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_inputs(UpperCAmelCase__ )
_a : Tuple = pipe(**UpperCAmelCase__ ).images
_a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_a : int = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 324
| 1
|
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''input_ids''', '''attention_mask''']
def __init__( self : Tuple , UpperCAmelCase__ : Union[str, Any]="</s>" , UpperCAmelCase__ : Optional[Any]="<unk>" , UpperCAmelCase__ : Any="<pad>" , UpperCAmelCase__ : Any=125 , UpperCAmelCase__ : Optional[int]=None , **UpperCAmelCase__ : Any , ) -> None:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_a : Union[str, Any] = [f"""<extra_id_{i}>""" for i in range(UpperCAmelCase__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_a : List[str] = len(set(filter(lambda UpperCAmelCase__ : bool("""extra_id""" in str(UpperCAmelCase__ ) ) , UpperCAmelCase__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
""" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
""" extra_ids tokens""" )
_a : int = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else pad_token
_a : int = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else eos_token
_a : List[str] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else unk_token
super().__init__(
eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , extra_ids=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : Optional[int] = extra_ids
_a : Optional[Any] = 2**8 # utf is 8 bits
# define special tokens dict
_a : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
_a : Union[str, Any] = len(self.special_tokens_encoder )
_a : int = len(UpperCAmelCase__ )
for i, token in enumerate(UpperCAmelCase__ ):
_a : Optional[int] = self.vocab_size + i - n
_a : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def _lowercase ( self : Any ) -> Tuple:
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None , UpperCAmelCase__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase__ , token_ids_a=UpperCAmelCase__ , already_has_special_tokens=UpperCAmelCase__ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCAmelCase__ )) + [1]
return ([0] * len(UpperCAmelCase__ )) + [1] + ([0] * len(UpperCAmelCase__ )) + [1]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] ) -> List[int]:
if len(UpperCAmelCase__ ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
""" eos tokens being added.""" )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Any = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : int = self._add_eos_if_not_present(UpperCAmelCase__ )
if token_ids_a is None:
return token_ids_a
else:
_a : Union[str, Any] = self._add_eos_if_not_present(UpperCAmelCase__ )
return token_ids_a + token_ids_a
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str ) -> List[str]:
_a : List[str] = [chr(UpperCAmelCase__ ) for i in text.encode("""utf-8""" )]
return tokens
def _lowercase ( self : Any , UpperCAmelCase__ : Optional[int] ) -> Union[str, Any]:
if token in self.special_tokens_encoder:
_a : List[Any] = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
_a : Optional[Any] = self.added_tokens_encoder[token]
elif len(UpperCAmelCase__ ) != 1:
_a : Dict = self.unk_token_id
else:
_a : int = ord(UpperCAmelCase__ ) + self._num_special_tokens
return token_id
def _lowercase ( self : Tuple , UpperCAmelCase__ : Optional[Any] ) -> int:
if index in self.special_tokens_decoder:
_a : int = self.special_tokens_decoder[index]
else:
_a : Optional[Any] = chr(index - self._num_special_tokens )
return token
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : List[str] ) -> Optional[Any]:
_a : Union[str, Any] = B""""""
for token in tokens:
if token in self.special_tokens_decoder:
_a : Dict = self.special_tokens_decoder[token].encode("""utf-8""" )
elif token in self.added_tokens_decoder:
_a : str = self.special_tokens_decoder[token].encode("""utf-8""" )
elif token in self.special_tokens_encoder:
_a : Optional[int] = token.encode("""utf-8""" )
elif token in self.added_tokens_encoder:
_a : List[Any] = token.encode("""utf-8""" )
else:
_a : int = bytes([ord(UpperCAmelCase__ )] )
bstring += tok_string
_a : List[str] = bstring.decode("""utf-8""" , errors="""ignore""" )
return string
def _lowercase ( self : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
return ()
| 324
|
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger()
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : List[nn.Module] = field(default_factory=snake_case_ )
UpperCamelCase : list = field(default_factory=snake_case_ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Tensor ) -> Any:
_a : int = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase__ , nn.Convad ) or isinstance(UpperCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase__ )
def __call__( self : Tuple , UpperCAmelCase__ : Tensor ) -> Tuple:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowercase ( self : Optional[int] ) -> int:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : nn.Module
UpperCamelCase : int = 0
UpperCamelCase : List = field(default_factory=snake_case_ )
UpperCamelCase : List = field(default_factory=snake_case_ )
def __call__( self : Optional[Any] , UpperCAmelCase__ : Tensor ) -> Tuple:
_a : Union[str, Any] = Tracker(self.dest )(UpperCAmelCase__ ).parametrized
_a : List[Any] = Tracker(self.src )(UpperCAmelCase__ ).parametrized
_a : Tuple = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.src_skip , UpperCAmelCase__ ) )
_a : Union[str, Any] = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.dest_skip , UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__ )} operations while"""
f""" destination module has {len(UpperCAmelCase__ )}.""" )
for dest_m, src_m in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
_a : List[str] = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval()
_a : str = ResNetForImageClassification(UpperCamelCase__ ).eval()
_a : List[str] = ModuleTransfer(src=UpperCamelCase__ , dest=UpperCamelCase__ )
_a : List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(UpperCamelCase__ )
assert torch.allclose(from_model(UpperCamelCase__ ) , our_model(UpperCamelCase__ ).logits ), "The model logits don't match the original one."
_a : Dict = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(UpperCamelCase__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=UpperCamelCase__ , )
# we can use the convnext one
_a : Optional[Any] = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=UpperCamelCase__ , )
print(F"""Pushed {checkpoint_name}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
_a : Any = """imagenet-1k-id2label.json"""
_a : Optional[int] = 1_0_0_0
_a : Any = (1, num_labels)
_a : Union[str, Any] = """huggingface/label-files"""
_a : Tuple = num_labels
_a : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Optional[Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Any = idalabel
_a : Tuple = {v: k for k, v in idalabel.items()}
_a : List[str] = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
_a : Union[str, Any] = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
_snake_case = parser.parse_args()
_snake_case = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 324
| 1
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 324
| 1
|
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = prime_factors(UpperCamelCase__ )
if is_square_free(UpperCamelCase__ ):
return -1 if len(UpperCamelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
_a : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase ( self : Dict ) -> Dict:
_a : str = self.dummy_uncond_unet
_a : Optional[int] = KarrasVeScheduler()
_a : List[str] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : int = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Tuple = torch.manual_seed(0 )
_a : int = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : int = image[0, -3:, -3:, -1]
_a : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
_a : Optional[Any] = """google/ncsnpp-celebahq-256"""
_a : Any = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : Dict = KarrasVeScheduler()
_a : int = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[int] = torch.manual_seed(0 )
_a : Tuple = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 324
|
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_snake_case = logging.getLogger(__name__)
_snake_case = 'pytorch_model.bin'
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The name of the task to train on.'''} , )
UpperCamelCase : Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Random seed for initialization.'''} , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_a : Union[str, Any] = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_a : Any = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
_a : str = dataset.sort("""probability""" , reverse=UpperCamelCase__ )
_a : Any = dataset.select(range(UpperCamelCase__ ) )
_a : Tuple = dataset.remove_columns(["""label""", """probability"""] )
_a : Optional[Any] = dataset.rename_column("""prediction""" , """label""" )
_a : Dict = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
_a : Union[str, Any] = dataset.shuffle(seed=args.seed )
_a : Optional[int] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_a : Dict = STModelArguments(model_name_or_path=UpperCamelCase__ )
_a : Union[str, Any] = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
_a : Any = STTrainingArguments(output_dir=UpperCamelCase__ )
_a : Any = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
_a : Union[str, Any] = {}
_a : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_a : int = args.train_file
_a : List[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_a : Union[str, Any] = args.eval_file
for key in data_files:
_a : Optional[Any] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_a : str = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_a : Tuple = F"""{args.output_dir}/self-train_iter-{{}}""".format
_a : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : str = None
_a : int = None
_a : str = 0
_a : List[Any] = False
# Show the progress bar
_a : List[Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_a : Union[str, Any] = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_a : str = os.path.join(UpperCamelCase__ , """stage-1""" )
_a : Tuple = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
_a : int = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_a : Dict = os.path.join(UpperCamelCase__ , """best-checkpoint""" )
_a : List[str] = os.path.join(UpperCamelCase__ , """stage-2""" )
# Update arguments_dict
_a : int = model_path
_a : Dict = data_files["""train"""]
_a : int = current_output_dir
_a : Any = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , UpperCamelCase__ )
_a : List[Any] = iteration
_a : int = data_dir_format(iteration + 1 )
_a : Dict = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , """best-checkpoint""" ) )
_a : Union[str, Any] = config.idalabel
_a : Any = os.path.join(UpperCamelCase__ , """eval_results_best-checkpoint.json""" )
_a : Any = os.path.join(UpperCamelCase__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , """r""" ) as f:
_a : Tuple = float(json.load(UpperCamelCase__ )[args.eval_metric] )
_a : Dict = os.path.join(UpperCamelCase__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
_a : List[Any] = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_a : Any = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : List[str] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_a : Any = eval_result
if best_iteration is None:
_a : Union[str, Any] = new_iteration
_a : str = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_a : Union[str, Any] = new_iteration
_a : List[str] = new_eval_result
_a : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_a : Tuple = new_iteration
_a : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_a : Union[str, Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , UpperCamelCase__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
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"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=snake_case_ ):
UpperCamelCase : str = ['''speech''']
def __init__( self : Optional[Any] , *UpperCAmelCase__ : List[Any] , **UpperCAmelCase__ : List[Any] ) -> List[str]:
requires_backends(self , ["""speech"""] )
class UpperCamelCase ( metaclass=snake_case_ ):
UpperCamelCase : Union[str, Any] = ['''speech''']
def __init__( self : List[str] , *UpperCAmelCase__ : Union[str, Any] , **UpperCAmelCase__ : List[Any] ) -> List[Any]:
requires_backends(self , ["""speech"""] )
| 324
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_snake_case = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
},
}
_snake_case = {
'camembert-base': 512,
}
_snake_case = '▁'
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = VOCAB_FILES_NAMES
UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Dict = ['''input_ids''', '''attention_mask''']
UpperCamelCase : Optional[Any] = CamembertTokenizer
def __init__( self : int , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]="<s>" , UpperCAmelCase__ : Optional[int]="</s>" , UpperCAmelCase__ : Tuple="</s>" , UpperCAmelCase__ : Tuple="<s>" , UpperCAmelCase__ : Tuple="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : int="<mask>" , UpperCAmelCase__ : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a : List[Any] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : int = vocab_file
_a : int = False if not self.vocab_file else True
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a : List[Any] = [self.cls_token_id]
_a : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Union[str, Any] = [self.sep_token_id]
_a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[str] = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 324
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
'uclanlp/visualbert-vqa-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
'uclanlp/visualbert-vcr-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Union[str, Any] = '''visual_bert'''
def __init__( self : str , UpperCAmelCase__ : Optional[int]=30522 , UpperCAmelCase__ : List[str]=768 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : int=12 , UpperCAmelCase__ : List[str]=3072 , UpperCAmelCase__ : List[Any]="gelu" , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : int=512 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : List[Any]=0.0_2 , UpperCAmelCase__ : int=1E-12 , UpperCAmelCase__ : Any=False , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=1 , UpperCAmelCase__ : int=0 , UpperCAmelCase__ : Optional[int]=2 , **UpperCAmelCase__ : List[Any] , ) -> Dict:
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
_a : List[str] = vocab_size
_a : Any = max_position_embeddings
_a : Union[str, Any] = hidden_size
_a : int = visual_embedding_dim
_a : List[str] = num_hidden_layers
_a : str = num_attention_heads
_a : Union[str, Any] = intermediate_size
_a : Tuple = hidden_act
_a : Optional[int] = hidden_dropout_prob
_a : List[str] = attention_probs_dropout_prob
_a : Dict = initializer_range
_a : Optional[Any] = type_vocab_size
_a : Dict = layer_norm_eps
_a : Dict = bypass_transformer
_a : Optional[Any] = special_visual_initialize
| 324
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 32 , UpperCAmelCase__ : Optional[Any]=PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , **UpperCAmelCase__ : List[str] , ) -> None:
_a : int = do_resize
_a : Union[str, Any] = do_rescale
_a : Any = size_divisor
_a : Any = resample
super().__init__(**UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[Any] ) -> np.ndarray:
_a , _a : Tuple = get_image_size(UpperCAmelCase__ )
# Rounds the height and width down to the closest multiple of size_divisor
_a : Optional[Any] = height // size_divisor * size_divisor
_a : Union[str, Any] = width // size_divisor * size_divisor
_a : Any = resize(UpperCAmelCase__ , (new_h, new_w) , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
return image
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[int] ) -> np.ndarray:
return rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[TensorType, str]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> BatchFeature:
_a : Dict = do_resize if do_resize is not None else self.do_resize
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : str = size_divisor if size_divisor is not None else self.size_divisor
_a : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_a : Tuple = [to_numpy_array(UpperCAmelCase__ ) for img in images]
if do_resize:
_a : Optional[int] = [self.resize(UpperCAmelCase__ , size_divisor=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : str = [self.rescale(UpperCAmelCase__ , scale=1 / 255 ) for image in images]
_a : Any = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Optional[int] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
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|
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_snake_case = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
_snake_case = concatenate_datasets
_snake_case = DownloadConfig
_snake_case = DownloadManager
_snake_case = DownloadMode
_snake_case = DownloadConfig
_snake_case = DownloadMode
_snake_case = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 324
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
_a : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase ( self : Dict ) -> Dict:
_a : str = self.dummy_uncond_unet
_a : Optional[int] = KarrasVeScheduler()
_a : List[str] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : int = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Tuple = torch.manual_seed(0 )
_a : int = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : int = image[0, -3:, -3:, -1]
_a : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
_a : Optional[Any] = """google/ncsnpp-celebahq-256"""
_a : Any = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : Dict = KarrasVeScheduler()
_a : int = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[int] = torch.manual_seed(0 )
_a : Tuple = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
# For consistency across different places the DisjunctiveConstraint is called,
# dc.token_ids is a list of integers. It is also initialized only by integers.
_a : Optional[Any] = [[1, 2, 4], [1, 2, 3, 4]]
_a : Optional[int] = DisjunctiveConstraint(UpperCAmelCase__ )
self.assertTrue(isinstance(dc.token_ids , UpperCAmelCase__ ) )
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def _lowercase ( self : str ) -> Optional[Any]:
# We can't have constraints that are complete subsets of another. This leads to a preverse
# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
_a : List[str] = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(UpperCAmelCase__ ):
DisjunctiveConstraint(UpperCAmelCase__ ) # fails here
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
_a : int = [[1, 2, 3], [1, 2, 4]]
_a : List[Any] = DisjunctiveConstraint(UpperCAmelCase__ )
_a , _a , _a : int = dc.update(1 )
_a : str = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
_a , _a , _a : Optional[int] = dc.update(2 )
_a : Optional[Any] = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
_a , _a , _a : List[str] = dc.update(3 )
_a : Tuple = stepped is True and completed is True and reset is False
self.assertTrue(UpperCAmelCase__ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def _lowercase ( self : Union[str, Any] ) -> int:
_a : List[Any] = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
_a : Dict = DisjunctiveConstraint(UpperCAmelCase__ )
_a , _a , _a : str = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
_a , _a , _a : str = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
_a , _a , _a : Any = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
_a , _a , _a : Any = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
_a , _a , _a : List[str] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
_a , _a , _a : Dict = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
_a , _a , _a : Dict = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 324
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , ):
'''simple docstring'''
_a : Optional[Any] = cipher_alphabet or [chr(UpperCamelCase__ ) for i in range(9_7 , 1_2_3 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
_a : Optional[int] = {
"""a""": 0.08_497,
"""b""": 0.01_492,
"""c""": 0.02_202,
"""d""": 0.04_253,
"""e""": 0.11_162,
"""f""": 0.02_228,
"""g""": 0.02_015,
"""h""": 0.06_094,
"""i""": 0.07_546,
"""j""": 0.00_153,
"""k""": 0.01_292,
"""l""": 0.04_025,
"""m""": 0.02_406,
"""n""": 0.06_749,
"""o""": 0.07_507,
"""p""": 0.01_929,
"""q""": 0.00_095,
"""r""": 0.07_587,
"""s""": 0.06_327,
"""t""": 0.09_356,
"""u""": 0.02_758,
"""v""": 0.00_978,
"""w""": 0.02_560,
"""x""": 0.00_150,
"""y""": 0.01_994,
"""z""": 0.00_077,
}
else:
# Custom frequencies dictionary
_a : str = frequencies_dict
if not case_sensitive:
_a : List[Any] = ciphertext.lower()
# Chi squared statistic values
_a : dict[int, tuple[float, str]] = {}
# cycle through all of the shifts
for shift in range(len(UpperCamelCase__ ) ):
_a : List[str] = """"""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
_a : Dict = (alphabet_letters.index(letter.lower() ) - shift) % len(
UpperCamelCase__ )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
_a : Tuple = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
_a : Any = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
_a : Dict = decrypted_with_shift.lower().count(UpperCamelCase__ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_a : int = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_a : int = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
_a : Optional[int] = decrypted_with_shift.count(UpperCamelCase__ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_a : Optional[int] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_a : List[Any] = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
_a : Any = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(UpperCamelCase__ ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
_a : int = min(
UpperCamelCase__ , key=UpperCamelCase__ , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
_a
) , (
_a
) ,
) : Optional[Any] = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
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"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
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"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
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|
"""simple docstring"""
_snake_case = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_snake_case = [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 324
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
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|
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class UpperCamelCase ( unittest.TestCase ):
UpperCamelCase : Optional[Any] = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
UpperCamelCase : List[str] = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def _lowercase ( self : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Any ) -> Optional[Any]:
_a : Optional[Any] = AudioClassificationPipeline(model=UpperCAmelCase__ , feature_extractor=UpperCAmelCase__ )
# test with a raw waveform
_a : Tuple = np.zeros((34000,) )
_a : Optional[Any] = np.zeros((14000,) )
return audio_classifier, [audioa, audio]
def _lowercase ( self : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ) -> int:
_a , _a : Union[str, Any] = examples
_a : Optional[Any] = audio_classifier(UpperCAmelCase__ )
# by default a model is initialized with num_labels=2
self.assertEqual(
UpperCAmelCase__ , [
{"""score""": ANY(UpperCAmelCase__ ), """label""": ANY(UpperCAmelCase__ )},
{"""score""": ANY(UpperCAmelCase__ ), """label""": ANY(UpperCAmelCase__ )},
] , )
_a : List[str] = audio_classifier(UpperCAmelCase__ , top_k=1 )
self.assertEqual(
UpperCAmelCase__ , [
{"""score""": ANY(UpperCAmelCase__ ), """label""": ANY(UpperCAmelCase__ )},
] , )
self.run_torchaudio(UpperCAmelCase__ )
@require_torchaudio
def _lowercase ( self : str , UpperCAmelCase__ : List[str] ) -> List[Any]:
import datasets
# test with a local file
_a : Any = datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
_a : int = dataset[0]["""audio"""]["""array"""]
_a : List[Any] = audio_classifier(UpperCAmelCase__ )
self.assertEqual(
UpperCAmelCase__ , [
{"""score""": ANY(UpperCAmelCase__ ), """label""": ANY(UpperCAmelCase__ )},
{"""score""": ANY(UpperCAmelCase__ ), """label""": ANY(UpperCAmelCase__ )},
] , )
@require_torch
def _lowercase ( self : Tuple ) -> Any:
_a : str = """anton-l/wav2vec2-random-tiny-classifier"""
_a : str = pipeline("""audio-classification""" , model=UpperCAmelCase__ )
_a : Optional[int] = np.ones((8000,) )
_a : Any = audio_classifier(UpperCAmelCase__ , top_k=4 )
_a : int = [
{"""score""": 0.0_8_4_2, """label""": """no"""},
{"""score""": 0.0_8_3_8, """label""": """up"""},
{"""score""": 0.0_8_3_7, """label""": """go"""},
{"""score""": 0.0_8_3_4, """label""": """right"""},
]
_a : List[Any] = [
{"""score""": 0.0_8_4_5, """label""": """stop"""},
{"""score""": 0.0_8_4_4, """label""": """on"""},
{"""score""": 0.0_8_4_1, """label""": """right"""},
{"""score""": 0.0_8_3_4, """label""": """left"""},
]
self.assertIn(nested_simplify(UpperCAmelCase__ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
_a : Tuple = {"""array""": np.ones((8000,) ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
_a : Tuple = audio_classifier(UpperCAmelCase__ , top_k=4 )
self.assertIn(nested_simplify(UpperCAmelCase__ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def _lowercase ( self : str ) -> List[Any]:
import datasets
_a : List[Any] = """superb/wav2vec2-base-superb-ks"""
_a : List[str] = pipeline("""audio-classification""" , model=UpperCAmelCase__ )
_a : Optional[Any] = datasets.load_dataset("""anton-l/superb_dummy""" , """ks""" , split="""test""" )
_a : List[Any] = np.array(dataset[3]["""speech"""] , dtype=np.floataa )
_a : List[str] = audio_classifier(UpperCAmelCase__ , top_k=4 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ , decimals=3 ) , [
{"""score""": 0.9_8_1, """label""": """go"""},
{"""score""": 0.0_0_7, """label""": """up"""},
{"""score""": 0.0_0_6, """label""": """_unknown_"""},
{"""score""": 0.0_0_1, """label""": """down"""},
] , )
@require_tf
@unittest.skip("""Audio classification is not implemented for TF""" )
def _lowercase ( self : Tuple ) -> Union[str, Any]:
pass
| 324
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
| 1
|
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# word like '180' or '身高' or '神'
for char in word:
_a : str = ord(UpperCamelCase__ )
if not _is_chinese_char(UpperCamelCase__ ):
return 0
return 1
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Any = set()
for token in tokens:
_a : List[str] = len(UpperCamelCase__ ) > 1 and is_chinese(UpperCamelCase__ )
if chinese_word:
word_set.add(UpperCamelCase__ )
_a : Optional[int] = list(UpperCamelCase__ )
return word_list
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
_a : Union[str, Any] = max([len(UpperCamelCase__ ) for w in chinese_word_set] )
_a : Tuple = bert_tokens
_a , _a : List[str] = 0, len(UpperCamelCase__ )
while start < end:
_a : Optional[Any] = True
if is_chinese(bert_word[start] ):
_a : List[Any] = min(end - start , UpperCamelCase__ )
for i in range(UpperCamelCase__ , 1 , -1 ):
_a : Optional[Any] = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_a : Tuple = """##""" + bert_word[j]
_a : Any = start + i
_a : Tuple = False
break
if single_word:
start += 1
return bert_word
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[Any] = []
for i in range(0 , len(UpperCamelCase__ ) , 1_0_0 ):
_a : Optional[int] = ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
_a : Dict = [get_chinese_word(UpperCamelCase__ ) for r in res]
ltp_res.extend(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
_a : Optional[Any] = []
for i in range(0 , len(UpperCamelCase__ ) , 1_0_0 ):
_a : Optional[Any] = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=5_1_2 )
bert_res.extend(res["""input_ids"""] )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
_a : int = []
for input_ids, chinese_word in zip(UpperCamelCase__ , UpperCamelCase__ ):
_a : Any = []
for id in input_ids:
_a : List[str] = bert_tokenizer._convert_id_to_token(UpperCamelCase__ )
input_tokens.append(UpperCamelCase__ )
_a : int = add_sub_symbol(UpperCamelCase__ , UpperCamelCase__ )
_a : Optional[Any] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(UpperCamelCase__ ):
if token[:2] == "##":
_a : Dict = token[2:]
# save chinese tokens' pos
if len(UpperCamelCase__ ) == 1 and _is_chinese_char(ord(UpperCamelCase__ ) ):
ref_id.append(UpperCamelCase__ )
ref_ids.append(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
return ref_ids
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
_a : Optional[Any] = f.readlines()
_a : List[str] = [line.strip() for line in data if len(UpperCamelCase__ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_a : int = LTP(args.ltp ) # faster in GPU device
_a : Any = BertTokenizer.from_pretrained(args.bert )
_a : Union[str, Any] = prepare_ref(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
_a : int = [json.dumps(UpperCamelCase__ ) + """\n""" for ref in ref_ids]
f.writelines(UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
)
parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')
_snake_case = parser.parse_args()
main(args)
| 324
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
| 1
|
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 324
|
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# Initialise PyTorch model
_a : Any = BertConfig.from_json_file(UpperCamelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
_a : str = BertForPreTraining(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 324
|
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
_a , _a : str = array[indexa], array[indexa]
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if length > 1:
_a : Optional[Any] = int(length / 2 )
for i in range(UpperCamelCase__ , low + middle ):
comp_and_swap(UpperCamelCase__ , UpperCamelCase__ , i + middle , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , low + middle , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if length > 1:
_a : List[str] = int(length / 2 )
bitonic_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , 1 )
bitonic_sort(UpperCamelCase__ , low + middle , UpperCamelCase__ , 0 )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = input('Enter numbers separated by a comma:\n').strip()
_snake_case = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 324
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_a : Any = 0
count += depth_first_search(UpperCamelCase__ , row + 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , row - 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col + 1 , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col - 1 , UpperCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : int = '''facebook/bart-large-mnli'''
UpperCamelCase : List[Any] = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
UpperCamelCase : Any = '''text_classifier'''
UpperCamelCase : List[str] = AutoTokenizer
UpperCamelCase : Dict = AutoModelForSequenceClassification
UpperCamelCase : List[Any] = ['''text''', ['''text''']]
UpperCamelCase : Dict = ['''text''']
def _lowercase ( self : Optional[int] ) -> Dict:
super().setup()
_a : Tuple = self.model.config
_a : int = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("""entail""" ):
_a : List[Any] = int(UpperCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""" )
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any] ) -> int:
_a : Optional[int] = labels
return self.pre_processor(
[text] * len(UpperCAmelCase__ ) , [f"""This example is {label}""" for label in labels] , return_tensors="""pt""" , padding="""max_length""" , )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Any ) -> str:
_a : str = outputs.logits
_a : Optional[int] = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 324
|
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Node | None ) -> List[str]:
_a : int = pos_x
_a : Union[str, Any] = pos_y
_a : Tuple = (pos_y, pos_x)
_a : Tuple = goal_x
_a : int = goal_y
_a : str = parent
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] ) -> List[str]:
_a : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : Optional[int] = [self.start]
_a : Tuple = False
def _lowercase ( self : str ) -> Path | None:
while self.node_queue:
_a : Tuple = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_a : Dict = True
return self.retrace_path(UpperCAmelCase__ )
_a : Tuple = self.get_successors(UpperCAmelCase__ )
for node in successors:
self.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node ) -> list[Node]:
_a : Optional[Any] = []
for action in delta:
_a : str = parent.pos_x + action[1]
_a : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCAmelCase__ , UpperCAmelCase__ , self.target.pos_y , self.target.pos_x , UpperCAmelCase__ ) )
return successors
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Node | None ) -> Path:
_a : Dict = node
_a : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_a : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Dict = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Dict = False
def _lowercase ( self : Any ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_a : List[Any] = self.fwd_bfs.node_queue.pop(0 )
_a : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_a : Optional[int] = True
return self.retrace_bidirectional_path(
UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = current_bwd_node
_a : int = current_fwd_node
_a : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node , UpperCAmelCase__ : Node ) -> Path:
_a : str = self.fwd_bfs.retrace_path(UpperCAmelCase__ )
_a : List[Any] = self.bwd_bfs.retrace_path(UpperCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_a : Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_snake_case = (0, 0)
_snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_snake_case = time.time()
_snake_case = BreadthFirstSearch(init, goal)
_snake_case = bfs.search()
_snake_case = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_snake_case = time.time()
_snake_case = BidirectionalBreadthFirstSearch(init, goal)
_snake_case = bd_bfs.search()
_snake_case = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 324
| 1
|
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
_snake_case = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'
_snake_case = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n'
_snake_case = r'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
def _lowercase ( self : List[Any] ) -> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""] , )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple=False ) -> int:
_a : Tuple = spearmanr(UpperCAmelCase__ , UpperCAmelCase__ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 324
|
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_snake_case = logging.getLogger(__name__)
_snake_case = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_ )} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
UpperCamelCase : bool = field(default=snake_case_ , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
UpperCamelCase : float = field(
default=0.1_5 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCamelCase : float = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
UpperCamelCase : int = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
UpperCamelCase : int = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = None , ):
'''simple docstring'''
def _dataset(UpperCamelCase__ , UpperCamelCase__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , ref_path=UpperCamelCase__ , )
return LineByLineTextDataset(tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=UpperCamelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(UpperCamelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowerCAmelCase__ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_a , _a , _a : List[str] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , UpperCamelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
_a : str = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
_a : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
_a : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
_a : Optional[Any] = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , )
else:
logger.info("""Training new model from scratch""" )
_a : List[Any] = AutoModelWithLMHead.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
_a : int = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
_a : Optional[Any] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
_a : Optional[Any] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
_a : Optional[int] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , evaluate=UpperCamelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
_a : Any = DataCollatorForPermutationLanguageModeling(
tokenizer=UpperCamelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
_a : Union[str, Any] = DataCollatorForWholeWordMask(
tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
else:
_a : str = DataCollatorForLanguageModeling(
tokenizer=UpperCamelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_a : Union[str, Any] = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , data_collator=UpperCamelCase__ , train_dataset=UpperCamelCase__ , eval_dataset=UpperCamelCase__ , prediction_loss_only=UpperCamelCase__ , )
# Training
if training_args.do_train:
_a : Optional[Any] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=UpperCamelCase__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_a : Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_a : int = trainer.evaluate()
_a : Dict = math.exp(eval_output["""eval_loss"""] )
_a : Union[str, Any] = {"""perplexity""": perplexity}
_a : Optional[Any] = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(UpperCamelCase__ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , UpperCamelCase__ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(UpperCamelCase__ )
return results
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_snake_case = logging.get_logger(__name__)
_snake_case = {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
),
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = '''longformer'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[List[int], int] = 512 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 0 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 30522 , UpperCAmelCase__ : int = 768 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : int = 3072 , UpperCAmelCase__ : str = "gelu" , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : float = 0.0_2 , UpperCAmelCase__ : float = 1E-12 , UpperCAmelCase__ : bool = False , **UpperCAmelCase__ : Optional[int] , ) -> Dict:
super().__init__(pad_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
_a : str = attention_window
_a : int = sep_token_id
_a : List[str] = bos_token_id
_a : int = eos_token_id
_a : Optional[int] = vocab_size
_a : List[Any] = hidden_size
_a : Any = num_hidden_layers
_a : Any = num_attention_heads
_a : str = hidden_act
_a : Optional[int] = intermediate_size
_a : Any = hidden_dropout_prob
_a : int = attention_probs_dropout_prob
_a : str = max_position_embeddings
_a : Tuple = type_vocab_size
_a : int = initializer_range
_a : int = layer_norm_eps
_a : Optional[Any] = onnx_export
class UpperCamelCase ( snake_case_ ):
def __init__( self : Dict , UpperCAmelCase__ : "PretrainedConfig" , UpperCAmelCase__ : str = "default" , UpperCAmelCase__ : "List[PatchingSpec]" = None ) -> Dict:
super().__init__(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = True
@property
def _lowercase ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_a : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_a : str = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def _lowercase ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
_a : int = super().outputs
if self.task == "default":
_a : Union[str, Any] = {0: """batch"""}
return outputs
@property
def _lowercase ( self : Union[str, Any] ) -> float:
return 1E-4
@property
def _lowercase ( self : Tuple ) -> int:
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def _lowercase ( self : Any , UpperCAmelCase__ : "PreTrainedTokenizerBase" , UpperCAmelCase__ : int = -1 , UpperCAmelCase__ : int = -1 , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
_a : str = super().generate_dummy_inputs(
preprocessor=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , seq_length=UpperCAmelCase__ , is_pair=UpperCAmelCase__ , framework=UpperCAmelCase__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
_a : Optional[Any] = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
_a : Tuple = 1
return inputs
| 324
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_snake_case = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_a : Optional[Any] = k.replace(UpperCamelCase__ , UpperCamelCase__ )
return k
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCamelCase__ )
_a : Optional[Any] = PegasusConfig(**UpperCamelCase__ )
_a : Tuple = PegasusForConditionalGeneration(UpperCamelCase__ )
_a : str = torch_model.model.state_dict()
_a : Union[str, Any] = {}
for k, v in tf_weights.items():
_a : Any = rename_state_dict_key(UpperCamelCase__ )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
_a : str = v.T
_a : int = torch.tensor(UpperCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
_a : Union[str, Any] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
_a : str = mapping["""shared.weight"""]
_a : Union[str, Any] = mapping["""shared.weight"""]
_a : Optional[Any] = {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**UpperCamelCase__ )
_a , _a : int = torch_model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : Optional[Any] = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase__ ( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_a : List[Any] = tf.train.list_variables(UpperCamelCase__ )
_a : Optional[int] = {}
_a : Dict = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(UpperCamelCase__ , desc="""converting tf checkpoint to dict""" ):
_a : Optional[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a : str = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
_a : int = array
return tf_weights
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# save tokenizer first
_a : Dict = Path(UpperCamelCase__ ).parent.name
_a : Optional[Any] = task_specific_params[F"""summarization_{dataset}"""]["""max_position_embeddings"""]
_a : Tuple = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=UpperCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCamelCase__ )
# convert model
_a : List[Any] = get_tf_weights_as_numpy(UpperCamelCase__ )
_a : Dict = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
_a : Tuple = task_specific_params
_a : Optional[int] = convert_pegasus(UpperCamelCase__ , UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
_a : Dict = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(UpperCamelCase__ , Path(UpperCamelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_snake_case = parser.parse_args()
if args.save_dir is None:
_snake_case = Path(args.tf_ckpt_path).parent.name
_snake_case = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 324
| 1
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_snake_case = 25_0004
_snake_case = 25_0020
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : List[Any] = MBartTokenizer
UpperCamelCase : str = MBartTokenizerFast
UpperCamelCase : Dict = True
UpperCamelCase : Optional[int] = True
def _lowercase ( self : Tuple ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
_a : Tuple = MBartTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : int ) -> Optional[Any]:
_a : Union[str, Any] = MBartTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
_a : List[str] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_a : int = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_a : Tuple = tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_a : Optional[int] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def _lowercase ( self : int ) -> str:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_a : List[Any] = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : Dict = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : str = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Optional[int] = tempfile.mkdtemp()
_a : str = tokenizer_r.save_pretrained(UpperCAmelCase__ )
_a : str = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
_a : Optional[Any] = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Checks everything loads correctly in the same way
_a : str = tokenizer_r.from_pretrained(UpperCAmelCase__ )
_a : List[Any] = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase__ )
# Save tokenizer rust, legacy_format=True
_a : Union[str, Any] = tempfile.mkdtemp()
_a : List[Any] = tokenizer_r.save_pretrained(UpperCAmelCase__ , legacy_format=UpperCAmelCase__ )
_a : List[str] = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Checks everything loads correctly in the same way
_a : Dict = tokenizer_r.from_pretrained(UpperCAmelCase__ )
_a : List[str] = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
shutil.rmtree(UpperCAmelCase__ )
# Save tokenizer rust, legacy_format=False
_a : List[Any] = tempfile.mkdtemp()
_a : Optional[int] = tokenizer_r.save_pretrained(UpperCAmelCase__ , legacy_format=UpperCAmelCase__ )
_a : List[str] = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_a : Any = tokenizer_r.from_pretrained(UpperCAmelCase__ )
_a : List[Any] = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
shutil.rmtree(UpperCAmelCase__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( unittest.TestCase ):
UpperCamelCase : Tuple = '''facebook/mbart-large-en-ro'''
UpperCamelCase : Optional[Any] = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
UpperCamelCase : Dict = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
UpperCamelCase : Union[str, Any] = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def _lowercase ( cls : int ) -> Tuple:
_a : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
_a : Optional[Any] = 1
return cls
def _lowercase ( self : int ) -> Tuple:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 250020 )
def _lowercase ( self : Union[str, Any] ) -> List[str]:
_a : Tuple = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> Dict:
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
_a : str = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
_a : Any = self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
_a : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def _lowercase ( self : Tuple ) -> str:
_a : Dict = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , UpperCAmelCase__ )
_a : Any = 10
_a : Tuple = self.tokenizer(UpperCAmelCase__ , max_length=UpperCAmelCase__ , truncation=UpperCAmelCase__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCAmelCase__ )
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : List[str] ) -> Optional[int]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [250026, 250001] )
def _lowercase ( self : Optional[int] ) -> List[str]:
_a : Any = tempfile.mkdtemp()
_a : Optional[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase__ )
_a : Any = MBartTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCAmelCase__ )
@require_torch
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
_a : Optional[int] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors="""pt""" )
_a : List[Any] = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Optional[int] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
_a : Optional[int] = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
_a : Optional[int] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def _lowercase ( self : Dict ) -> Dict:
_a : Optional[int] = self.tokenizer(self.src_text , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=3 , return_tensors="""pt""" )
_a : List[Any] = self.tokenizer(
text_target=self.tgt_text , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__ , max_length=10 , return_tensors="""pt""" )
_a : str = targets["""input_ids"""]
_a : List[str] = shift_tokens_right(UpperCAmelCase__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : int = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[62, 3034, 2, 250004]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 250001,
} , )
| 324
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
_a : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_a : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
_a : Tuple = CLIPTextModel(UpperCAmelCase__ )
_a : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Dict = CLIPTextModelWithProjection(UpperCAmelCase__ )
_a : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=0 ) -> int:
_a : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
_a : Any = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("""mps""" ):
_a : Any = torch.manual_seed(UpperCAmelCase__ )
else:
_a : Tuple = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def _lowercase ( self : Any ) -> List[Any]:
_a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : List[Any] = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Union[str, Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = sd_pipe(**UpperCAmelCase__ ).images
_a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self : Any ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self : Any ) -> Any:
pass
def _lowercase ( self : Tuple ) -> Union[str, Any]:
_a : int = self.get_dummy_components()
_a : Any = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Dict = sd_pipe.to(UpperCAmelCase__ )
_a : List[str] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# forward without prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = 3 * ["""this is a negative prompt"""]
_a : Dict = negative_prompt
_a : Dict = 3 * [inputs["""prompt"""]]
_a : Optional[Any] = sd_pipe(**UpperCAmelCase__ )
_a : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : Union[str, Any] = 3 * ["""this is a negative prompt"""]
_a : int = 3 * [inputs.pop("""prompt""" )]
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : List[str] = sd_pipe.encode_prompt(UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ )
_a : Tuple = sd_pipe(
**UpperCAmelCase__ , prompt_embeds=UpperCAmelCase__ , negative_prompt_embeds=UpperCAmelCase__ , pooled_prompt_embeds=UpperCAmelCase__ , negative_pooled_prompt_embeds=UpperCAmelCase__ , )
_a : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str="cpu" , UpperCAmelCase__ : str=torch.floataa , UpperCAmelCase__ : List[Any]=0 ) -> List[str]:
_a : List[str] = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Union[str, Any] = np.random.RandomState(UpperCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_a : List[Any] = torch.from_numpy(UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
_a : Any = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Union[str, Any] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_inputs(UpperCAmelCase__ )
_a : Tuple = pipe(**UpperCAmelCase__ ).images
_a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_a : int = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 324
| 1
|
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
_snake_case = 'docs/source/en/_toctree.yml'
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = defaultdict(UpperCamelCase__ )
_a : Optional[Any] = []
_a : str = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(UpperCamelCase__ )
_a : List[Any] = new_doc_list
_a : Dict = [key for key, value in counts.items() if value > 1]
_a : Any = []
for duplicate_key in duplicates:
_a : Optional[int] = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(UpperCamelCase__ ) > 1:
raise ValueError(
F"""{duplicate_key} is present several times in the documentation table of content at """
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
_a : Optional[Any] = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(UpperCamelCase__ ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(UpperCamelCase__ )
# Sort
return overview_doc
def lowerCAmelCase__ ( UpperCamelCase__=False ):
'''simple docstring'''
with open(UpperCamelCase__ , encoding="""utf-8""" ) as f:
_a : Dict = yaml.safe_load(f.read() )
# Get to the API doc
_a : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a : Optional[Any] = content[api_idx]["""sections"""]
# Then to the model doc
_a : str = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_a : List[Any] = api_doc[scheduler_idx]["""sections"""]
_a : List[str] = clean_doc_toc(UpperCamelCase__ )
_a : Optional[int] = False
if new_scheduler_doc != scheduler_doc:
_a : List[str] = True
if overwrite:
_a : Tuple = new_scheduler_doc
if diff:
if overwrite:
_a : Optional[int] = api_doc
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowerCAmelCase__ ( UpperCamelCase__=False ):
'''simple docstring'''
with open(UpperCamelCase__ , encoding="""utf-8""" ) as f:
_a : Optional[int] = yaml.safe_load(f.read() )
# Get to the API doc
_a : Union[str, Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a : Any = content[api_idx]["""sections"""]
# Then to the model doc
_a : Tuple = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_a : Optional[int] = False
_a : Dict = api_doc[pipeline_idx]["""sections"""]
_a : List[str] = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_a : Any = pipeline_doc["""section"""]
_a : Tuple = clean_doc_toc(UpperCamelCase__ )
if overwrite:
_a : Optional[int] = new_sub_pipeline_doc
new_pipeline_docs.append(UpperCamelCase__ )
# sort overall pipeline doc
_a : Optional[Any] = clean_doc_toc(UpperCamelCase__ )
if new_pipeline_docs != pipeline_docs:
_a : Optional[int] = True
if overwrite:
_a : Optional[Any] = new_pipeline_docs
if diff:
if overwrite:
_a : Any = api_doc
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_snake_case = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 324
|
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger()
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : List[nn.Module] = field(default_factory=snake_case_ )
UpperCamelCase : list = field(default_factory=snake_case_ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Tensor ) -> Any:
_a : int = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase__ , nn.Convad ) or isinstance(UpperCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase__ )
def __call__( self : Tuple , UpperCAmelCase__ : Tensor ) -> Tuple:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowercase ( self : Optional[int] ) -> int:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : nn.Module
UpperCamelCase : int = 0
UpperCamelCase : List = field(default_factory=snake_case_ )
UpperCamelCase : List = field(default_factory=snake_case_ )
def __call__( self : Optional[Any] , UpperCAmelCase__ : Tensor ) -> Tuple:
_a : Union[str, Any] = Tracker(self.dest )(UpperCAmelCase__ ).parametrized
_a : List[Any] = Tracker(self.src )(UpperCAmelCase__ ).parametrized
_a : Tuple = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.src_skip , UpperCAmelCase__ ) )
_a : Union[str, Any] = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.dest_skip , UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__ )} operations while"""
f""" destination module has {len(UpperCAmelCase__ )}.""" )
for dest_m, src_m in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
_a : List[str] = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval()
_a : str = ResNetForImageClassification(UpperCamelCase__ ).eval()
_a : List[str] = ModuleTransfer(src=UpperCamelCase__ , dest=UpperCamelCase__ )
_a : List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(UpperCamelCase__ )
assert torch.allclose(from_model(UpperCamelCase__ ) , our_model(UpperCamelCase__ ).logits ), "The model logits don't match the original one."
_a : Dict = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(UpperCamelCase__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=UpperCamelCase__ , )
# we can use the convnext one
_a : Optional[Any] = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=UpperCamelCase__ , )
print(F"""Pushed {checkpoint_name}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
_a : Any = """imagenet-1k-id2label.json"""
_a : Optional[int] = 1_0_0_0
_a : Any = (1, num_labels)
_a : Union[str, Any] = """huggingface/label-files"""
_a : Tuple = num_labels
_a : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Optional[Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Any = idalabel
_a : Tuple = {v: k for k, v in idalabel.items()}
_a : List[str] = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
_a : Union[str, Any] = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
_snake_case = parser.parse_args()
_snake_case = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['Mask2FormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'Mask2FormerForUniversalSegmentation',
'Mask2FormerModel',
'Mask2FormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 324
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 324
| 1
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : List[str] = ['''pixel_values''']
def __init__( self : Dict , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : int = 0.9 , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , **UpperCAmelCase__ : str , ) -> None:
super().__init__(**UpperCAmelCase__ )
_a : int = size if size is not None else {"""shortest_edge""": 224}
_a : int = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_a : str = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_a : int = get_size_dict(UpperCAmelCase__ , param_name="""crop_size""" )
_a : str = do_resize
_a : Optional[int] = size
_a : List[Any] = crop_pct
_a : List[Any] = resample
_a : Any = do_center_crop
_a : List[str] = crop_size
_a : Dict = do_rescale
_a : Union[str, Any] = rescale_factor
_a : List[str] = do_normalize
_a : int = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_a : Optional[Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _lowercase ( self : List[Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : List[Any] , ) -> np.ndarray:
_a : int = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
if crop_pct is not None:
if "shortest_edge" in size:
_a : List[Any] = int(size["""shortest_edge"""] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
_a : List[Any] = int(size["""height"""] / crop_pct )
else:
_a : str = (int(size["""height"""] / crop_pct ), int(size["""width"""] / crop_pct ))
else:
raise ValueError("""Invalid size for resize: {}""".format(UpperCAmelCase__ ) )
_a : str = get_resize_output_image_size(UpperCAmelCase__ , size=UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
else:
if "shortest_edge" in size:
_a : str = get_resize_output_image_size(UpperCAmelCase__ , size=size["""shortest_edge"""] , default_to_square=UpperCAmelCase__ )
elif "height" in size and "width" in size:
_a : Union[str, Any] = (size["""height"""], size["""width"""])
else:
raise ValueError("""Invalid size for resize: {}""".format(UpperCAmelCase__ ) )
return resize(UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Any , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : str , ) -> np.ndarray:
_a : Optional[int] = get_size_dict(UpperCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(UpperCAmelCase__ , size=(size["""height"""], size["""width"""]) , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Union[int, float] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Union[str, Any] , ) -> Union[str, Any]:
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Dict , ) -> np.ndarray:
return normalize(UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : int = None , UpperCAmelCase__ : PILImageResampling = None , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : float = None , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> PIL.Image.Image:
_a : int = do_resize if do_resize is not None else self.do_resize
_a : Dict = crop_pct if crop_pct is not None else self.crop_pct
_a : Any = resample if resample is not None else self.resample
_a : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_a : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
_a : Any = image_mean if image_mean is not None else self.image_mean
_a : Tuple = image_std if image_std is not None else self.image_std
_a : Optional[Any] = size if size is not None else self.size
_a : Dict = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_a : Any = crop_size if crop_size is not None else self.crop_size
_a : List[Any] = get_size_dict(UpperCAmelCase__ , param_name="""crop_size""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_pct is None:
raise ValueError("""Crop_pct must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_a : Optional[Any] = [to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_resize:
_a : int = [self.resize(image=UpperCAmelCase__ , size=UpperCAmelCase__ , crop_pct=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_center_crop:
_a : List[Any] = [self.center_crop(image=UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : Any = [self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_normalize:
_a : List[str] = [self.normalize(image=UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ ) for image in images]
_a : Union[str, Any] = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_snake_case = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_snake_case = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def lowerCAmelCase__ ( ):
'''simple docstring'''
from timeit import timeit
print("""Without Numpy""" )
print(
timeit(
"""euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""" , number=1_0_0_0_0 , globals=globals() , ) )
print("""With Numpy""" )
print(
timeit(
"""euclidean_distance([1, 2, 3], [4, 5, 6])""" , number=1_0_0_0_0 , globals=globals() , ) )
benchmark()
| 324
|
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_snake_case = logging.getLogger(__name__)
_snake_case = 'pytorch_model.bin'
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The name of the task to train on.'''} , )
UpperCamelCase : Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Random seed for initialization.'''} , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_a : Union[str, Any] = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_a : Any = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
_a : str = dataset.sort("""probability""" , reverse=UpperCamelCase__ )
_a : Any = dataset.select(range(UpperCamelCase__ ) )
_a : Tuple = dataset.remove_columns(["""label""", """probability"""] )
_a : Optional[Any] = dataset.rename_column("""prediction""" , """label""" )
_a : Dict = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
_a : Union[str, Any] = dataset.shuffle(seed=args.seed )
_a : Optional[int] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_a : Dict = STModelArguments(model_name_or_path=UpperCamelCase__ )
_a : Union[str, Any] = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
_a : Any = STTrainingArguments(output_dir=UpperCamelCase__ )
_a : Any = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
_a : Union[str, Any] = {}
_a : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_a : int = args.train_file
_a : List[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_a : Union[str, Any] = args.eval_file
for key in data_files:
_a : Optional[Any] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_a : str = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_a : Tuple = F"""{args.output_dir}/self-train_iter-{{}}""".format
_a : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : str = None
_a : int = None
_a : str = 0
_a : List[Any] = False
# Show the progress bar
_a : List[Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_a : Union[str, Any] = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_a : str = os.path.join(UpperCamelCase__ , """stage-1""" )
_a : Tuple = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
_a : int = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_a : Dict = os.path.join(UpperCamelCase__ , """best-checkpoint""" )
_a : List[str] = os.path.join(UpperCamelCase__ , """stage-2""" )
# Update arguments_dict
_a : int = model_path
_a : Dict = data_files["""train"""]
_a : int = current_output_dir
_a : Any = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , UpperCamelCase__ )
_a : List[Any] = iteration
_a : int = data_dir_format(iteration + 1 )
_a : Dict = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , """best-checkpoint""" ) )
_a : Union[str, Any] = config.idalabel
_a : Any = os.path.join(UpperCamelCase__ , """eval_results_best-checkpoint.json""" )
_a : Any = os.path.join(UpperCamelCase__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , """r""" ) as f:
_a : Tuple = float(json.load(UpperCamelCase__ )[args.eval_metric] )
_a : Dict = os.path.join(UpperCamelCase__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
_a : List[Any] = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_a : Any = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : List[str] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_a : Any = eval_result
if best_iteration is None:
_a : Union[str, Any] = new_iteration
_a : str = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_a : Union[str, Any] = new_iteration
_a : List[str] = new_eval_result
_a : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_a : Tuple = new_iteration
_a : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_a : Union[str, Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , UpperCamelCase__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
| 324
| 1
|
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
_snake_case = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
_snake_case = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
_snake_case = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
def _lowercase ( self : Tuple ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _lowercase ( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[Any]=None ) -> Optional[int]:
return {
"matthews_correlation": float(matthews_corrcoef(UpperCAmelCase__ , UpperCAmelCase__ , sample_weight=UpperCAmelCase__ ) ),
}
| 324
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_snake_case = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
},
}
_snake_case = {
'camembert-base': 512,
}
_snake_case = '▁'
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = VOCAB_FILES_NAMES
UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Dict = ['''input_ids''', '''attention_mask''']
UpperCamelCase : Optional[Any] = CamembertTokenizer
def __init__( self : int , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]="<s>" , UpperCAmelCase__ : Optional[int]="</s>" , UpperCAmelCase__ : Tuple="</s>" , UpperCAmelCase__ : Tuple="<s>" , UpperCAmelCase__ : Tuple="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : int="<mask>" , UpperCAmelCase__ : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a : List[Any] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : int = vocab_file
_a : int = False if not self.vocab_file else True
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a : List[Any] = [self.cls_token_id]
_a : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Union[str, Any] = [self.sep_token_id]
_a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[str] = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 324
| 1
|
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_snake_case = logging.getLogger(__name__)
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : int = argparse.ArgumentParser(
description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" )
parser.add_argument(
"""--dataset_name""" , type=UpperCamelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
parser.add_argument(
"""--dataset_config""" , type=UpperCamelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" )
parser.add_argument(
"""--tokenizer_name_or_path""" , type=UpperCamelCase__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , )
parser.add_argument(
"""--shard_size""" , type=UpperCamelCase__ , default=1_0_0_0 , help="""Number of entries to go in a single shard.""" , )
parser.add_argument("""--split""" , type=UpperCamelCase__ , default="""train""" , choices=["""train""", """test""", """validation"""] )
parser.add_argument(
"""--limit""" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="""Limit the number of shards (used for debugging).""" , )
parser.add_argument(
"""--max_length""" , type=UpperCamelCase__ , default=5_1_2 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum"""
""" sequence length that is a multiple of 8.""" , )
parser.add_argument(
"""--output_dir""" , default="""tf-tpu""" , type=UpperCamelCase__ , help="""Output directory where the TFRecord shards will be saved. If the"""
""" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"""
""" shards will be directly saved to a Google Cloud Storage bucket.""" , )
_a : List[str] = parser.parse_args()
return args
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
def fn(UpperCamelCase__ ):
return tokenizer(examples["""text"""] )
return fn
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : str = []
for i in range(len(tokenized_data["""input_ids"""] ) ):
_a : List[Any] = {
"""input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ),
"""attention_mask""": tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ),
}
_a : str = tf.train.Features(feature=UpperCamelCase__ )
_a : List[Any] = tf.train.Example(features=UpperCamelCase__ )
_a : List[Any] = example.SerializeToString()
records.append(UpperCamelCase__ )
return records
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
_a : List[str] = min(len(UpperCamelCase__ ) , args.limit )
_a : Optional[int] = dataset.select(range(UpperCamelCase__ ) )
print(F"""Limiting the dataset to {args.limit} entries.""" )
_a : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
_a : Optional[Any] = os.path.join(args.output_dir , args.split )
if not os.path.exists(UpperCamelCase__ ):
os.makedirs(UpperCamelCase__ )
else:
_a : Optional[int] = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
_a : Optional[Any] = tokenize_function(UpperCamelCase__ )
_a : List[str] = dataset.map(UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=4 , remove_columns=["""text"""] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(UpperCamelCase__ ):
# Concatenate all texts.
_a : Dict = {k: sum(examples[k] , [] ) for k in examples.keys()}
_a : str = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
_a : Dict = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
_a : Optional[int] = {
k: [t[i : i + args.max_length] for i in range(0 , UpperCamelCase__ , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
_a : List[Any] = dataset_tokenized.map(UpperCamelCase__ , batched=UpperCamelCase__ , batch_size=1_0_0_0 , num_proc=4 )
_a : Any = 0
_a : Union[str, Any] = 0
for shard in range(0 , len(UpperCamelCase__ ) , args.shard_size ):
_a : Optional[int] = grouped_dataset[shard : shard + args.shard_size]
_a : int = len(dataset_snapshot["""input_ids"""] )
_a : Union[str, Any] = os.path.join(UpperCamelCase__ , F"""dataset-{shard_count}-{records_containing}.tfrecord""" )
_a : str = get_serialized_examples(UpperCamelCase__ )
with tf.io.TFRecordWriter(UpperCamelCase__ ) as out_file:
for i in range(len(UpperCamelCase__ ) ):
_a : Any = serialized_examples[i]
out_file.write(UpperCamelCase__ )
print("""Wrote file {} containing {} records""".format(UpperCamelCase__ , UpperCamelCase__ ) )
shard_count += 1
total_records += records_containing
with open(F"""split-{args.split}-records-count.txt""" , """w""" ) as f:
print(F"""Total {args.split} records: {total_records}""" , file=UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = parse_args()
main(args)
| 324
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 32 , UpperCAmelCase__ : Optional[Any]=PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , **UpperCAmelCase__ : List[str] , ) -> None:
_a : int = do_resize
_a : Union[str, Any] = do_rescale
_a : Any = size_divisor
_a : Any = resample
super().__init__(**UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[Any] ) -> np.ndarray:
_a , _a : Tuple = get_image_size(UpperCAmelCase__ )
# Rounds the height and width down to the closest multiple of size_divisor
_a : Optional[Any] = height // size_divisor * size_divisor
_a : Union[str, Any] = width // size_divisor * size_divisor
_a : Any = resize(UpperCAmelCase__ , (new_h, new_w) , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
return image
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[int] ) -> np.ndarray:
return rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[TensorType, str]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> BatchFeature:
_a : Dict = do_resize if do_resize is not None else self.do_resize
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : str = size_divisor if size_divisor is not None else self.size_divisor
_a : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_a : Tuple = [to_numpy_array(UpperCAmelCase__ ) for img in images]
if do_resize:
_a : Optional[int] = [self.resize(UpperCAmelCase__ , size_divisor=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : str = [self.rescale(UpperCAmelCase__ , scale=1 / 255 ) for image in images]
_a : Any = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Optional[int] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
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"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
_a : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase ( self : Dict ) -> Dict:
_a : str = self.dummy_uncond_unet
_a : Optional[int] = KarrasVeScheduler()
_a : List[str] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : int = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Tuple = torch.manual_seed(0 )
_a : int = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : int = image[0, -3:, -3:, -1]
_a : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
_a : Optional[Any] = """google/ncsnpp-celebahq-256"""
_a : Any = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : Dict = KarrasVeScheduler()
_a : int = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[int] = torch.manual_seed(0 )
_a : Tuple = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 324
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"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase ( snake_case_ , snake_case_ , snake_case_ ):
UpperCamelCase : List[Any] = [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
@register_to_config
def __init__( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int = 50257 , UpperCAmelCase__ : int = 1024 , UpperCAmelCase__ : int = 768 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : str = "gelu_new" , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 1E-5 , UpperCAmelCase__ : float = 0.0_2 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : bool = False , ) -> List[Any]:
super().__init__()
_a : Any = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
_a : str = prefix_inner_dim
_a : int = prefix_hidden_dim
_a : int = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_a : List[str] = (
nn.Linear(self.prefix_hidden_dim , UpperCAmelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_a : Dict = GPTaConfig(
vocab_size=UpperCAmelCase__ , n_positions=UpperCAmelCase__ , n_embd=UpperCAmelCase__ , n_layer=UpperCAmelCase__ , n_head=UpperCAmelCase__ , n_inner=UpperCAmelCase__ , activation_function=UpperCAmelCase__ , resid_pdrop=UpperCAmelCase__ , embd_pdrop=UpperCAmelCase__ , attn_pdrop=UpperCAmelCase__ , layer_norm_epsilon=UpperCAmelCase__ , initializer_range=UpperCAmelCase__ , scale_attn_weights=UpperCAmelCase__ , use_cache=UpperCAmelCase__ , scale_attn_by_inverse_layer_idx=UpperCAmelCase__ , reorder_and_upcast_attn=UpperCAmelCase__ , )
_a : Optional[Any] = GPTaLMHeadModel(UpperCAmelCase__ )
def _lowercase ( self : str , UpperCAmelCase__ : torch.Tensor , UpperCAmelCase__ : torch.Tensor , UpperCAmelCase__ : Optional[torch.Tensor] = None , UpperCAmelCase__ : Optional[torch.Tensor] = None , ) -> Tuple:
_a : Union[str, Any] = self.transformer.transformer.wte(UpperCAmelCase__ )
_a : List[Any] = self.encode_prefix(UpperCAmelCase__ )
_a : Dict = self.decode_prefix(UpperCAmelCase__ )
_a : int = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_a : Optional[Any] = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_a : Any = torch.cat((dummy_token, input_ids) , dim=1 )
_a : Union[str, Any] = self.transformer(inputs_embeds=UpperCAmelCase__ , labels=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def _lowercase ( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : torch.device ) -> torch.Tensor:
return torch.zeros(UpperCAmelCase__ , self.prefix_length , dtype=torch.intaa , device=UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[str] ) -> Optional[Any]:
return self.encode_prefix(UpperCAmelCase__ )
@torch.no_grad()
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ) -> Dict:
_a : Dict = torch.split(UpperCAmelCase__ , 1 , dim=0 )
_a : Dict = []
_a : Optional[int] = []
for feature in features:
_a : List[str] = self.decode_prefix(feature.to(UpperCAmelCase__ ) ) # back to the clip feature
# Only support beam search for now
_a , _a : Optional[int] = self.generate_beam(
input_embeds=UpperCAmelCase__ , device=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_a : List[str] = torch.stack(UpperCAmelCase__ )
_a : int = torch.stack(UpperCAmelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def _lowercase ( self : Any , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : int = 5 , UpperCAmelCase__ : int = 67 , UpperCAmelCase__ : float = 1.0 , UpperCAmelCase__ : Optional[int] = None , ) -> List[str]:
_a : Dict = eos_token_id
_a : Tuple = None
_a : Tuple = None
_a : str = torch.ones(UpperCAmelCase__ , device=UpperCAmelCase__ , dtype=torch.int )
_a : List[Any] = torch.zeros(UpperCAmelCase__ , device=UpperCAmelCase__ , dtype=torch.bool )
if input_embeds is not None:
_a : List[Any] = input_embeds
else:
_a : List[str] = self.transformer.transformer.wte(UpperCAmelCase__ )
for i in range(UpperCAmelCase__ ):
_a : Optional[Any] = self.transformer(inputs_embeds=UpperCAmelCase__ )
_a : Tuple = outputs.logits
_a : List[Any] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_a : Dict = logits.softmax(-1 ).log()
if scores is None:
_a , _a : Any = logits.topk(UpperCAmelCase__ , -1 )
_a : str = generated.expand(UpperCAmelCase__ , *generated.shape[1:] )
_a , _a : Union[str, Any] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_a : List[str] = next_tokens
else:
_a : Optional[Any] = tokens.expand(UpperCAmelCase__ , *tokens.shape[1:] )
_a : Optional[int] = torch.cat((tokens, next_tokens) , dim=1 )
else:
_a : Optional[int] = -float(np.inf )
_a : List[Any] = 0
_a : List[str] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_a : Optional[Any] = scores_sum / seq_lengths[:, None]
_a , _a : Optional[int] = scores_sum_average.view(-1 ).topk(UpperCAmelCase__ , -1 )
_a : Optional[int] = next_tokens // scores_sum.shape[1]
_a : Tuple = seq_lengths[next_tokens_source]
_a : Dict = next_tokens % scores_sum.shape[1]
_a : int = next_tokens.unsqueeze(1 )
_a : Optional[Any] = tokens[next_tokens_source]
_a : str = torch.cat((tokens, next_tokens) , dim=1 )
_a : int = generated[next_tokens_source]
_a : List[str] = scores_sum_average * seq_lengths
_a : Tuple = is_stopped[next_tokens_source]
_a : Union[str, Any] = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_a : Tuple = torch.cat((generated, next_token_embed) , dim=1 )
_a : Optional[int] = is_stopped + next_tokens.eq(UpperCAmelCase__ ).squeeze()
if is_stopped.all():
break
_a : Tuple = scores / seq_lengths
_a : Any = scores.argsort(descending=UpperCAmelCase__ )
# tokens tensors are already padded to max_seq_length
_a : Union[str, Any] = [tokens[i] for i in order]
_a : int = torch.stack(UpperCAmelCase__ , dim=0 )
_a : Optional[Any] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 324
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"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
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|
"""simple docstring"""
from torch import nn
class UpperCamelCase ( nn.Module ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict ) -> int:
super().__init__()
_a : List[str] = class_size
_a : Union[str, Any] = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
_a : Dict = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : str ) -> Optional[Any]:
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
_a : Optional[int] = self.mlp(UpperCAmelCase__ )
return logits
| 324
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
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|
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# getting number of pixels in the image
_a , _a : Tuple = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
_a : int = [2_5_5, 2_5_5, 2_5_5] - img[i][j]
return img
if __name__ == "__main__":
# read original image
_snake_case = imread('image_data/lena.jpg', 1)
# convert to its negative
_snake_case = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
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|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_a : Optional[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(UpperCamelCase__ )
# Let's go
_a : int = parser.parse_args()
if not hasattr(UpperCamelCase__ , """func""" ):
parser.print_help()
exit(1 )
# Run
_a : Dict = args.func(UpperCamelCase__ )
service.run()
if __name__ == "__main__":
main()
| 324
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 324
| 1
|
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : Dict = ReformerTokenizer
UpperCamelCase : int = ReformerTokenizerFast
UpperCamelCase : Dict = True
UpperCamelCase : Dict = False
UpperCamelCase : str = True
def _lowercase ( self : Any ) -> str:
super().setUp()
_a : int = ReformerTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : Optional[int] ) -> List[Any]:
_a : Any = """<s>"""
_a : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] ) -> Optional[int]:
_a : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1000 )
def _lowercase ( self : List[str] ) -> str:
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _lowercase ( self : Dict ) -> str:
if not self.test_rust_tokenizer:
return
_a : Dict = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : Dict = """I was born in 92000, and this is falsé."""
_a : Dict = tokenizer.tokenize(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : str = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Any = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : Optional[int]=15 ) -> Union[str, Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : Any = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
# Simple input
_a : Optional[Any] = """This is a simple input"""
_a : List[str] = ["""This is a simple input 1""", """This is a simple input 2"""]
_a : Union[str, Any] = ("""This is a simple input""", """This is a pair""")
_a : List[Any] = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(UpperCAmelCase__ , tokenizer_r.encode , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(UpperCAmelCase__ , tokenizer_r.encode_plus , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(
UpperCAmelCase__ , tokenizer_r.batch_encode_plus , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" , )
# Pair input
self.assertRaises(UpperCAmelCase__ , tokenizer_r.encode , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(UpperCAmelCase__ , tokenizer_r.encode_plus , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(
UpperCAmelCase__ , tokenizer_r.batch_encode_plus , UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="""max_length""" , )
def _lowercase ( self : Any ) -> List[Any]:
pass
def _lowercase ( self : str ) -> str:
_a : List[Any] = ReformerTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
_a : List[str] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [285, 46, 10, 170, 382] , )
_a : int = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_a : str = tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def _lowercase ( self : str ) -> Dict:
return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )
@slow
def _lowercase ( self : List[str] ) -> List[Any]:
_a : int = """Hello World!"""
_a : Optional[int] = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@slow
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
_a : int = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
_a : Tuple = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@require_torch
@slow
def _lowercase ( self : int ) -> Dict:
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_a : int = list(self.big_tokenizer.get_vocab().keys() )[:10]
_a : Any = """ """.join(UpperCAmelCase__ )
_a : Tuple = self.big_tokenizer.encode_plus(UpperCAmelCase__ , return_tensors="""pt""" )
_a : List[str] = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" )
_a : int = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_a : int = encoded_sequence["""input_ids"""].shape
_a : int = ReformerModel(UpperCAmelCase__ )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**UpperCAmelCase__ )
model(**UpperCAmelCase__ )
@slow
def _lowercase ( self : str ) -> int:
# fmt: off
_a : List[Any] = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_a : str = [
"""This is a very simple sentence.""",
"""The quick brown fox jumps over the lazy dog.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=UpperCAmelCase__ , sequences=UpperCAmelCase__ , )
| 324
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
| 324
| 1
|
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(UpperCamelCase__ ):
return ext
raise Exception(
F"""Unable to determine file format from file extension {path}. """
F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
_a : Optional[Any] = try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
_a : List[Any] = PipelineDataFormat.from_str(
format=UpperCamelCase__ , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(UpperCamelCase__ , UpperCamelCase__ )
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Pipeline , UpperCAmelCase__ : PipelineDataFormat ) -> List[Any]:
_a : Any = nlp
_a : List[str] = reader
@staticmethod
def _lowercase ( UpperCAmelCase__ : ArgumentParser ) -> Optional[Any]:
_a : Any = parser.add_parser("""run""" , help="""Run a pipeline through the CLI""" )
run_parser.add_argument("""--task""" , choices=get_supported_tasks() , help="""Task to run""" )
run_parser.add_argument("""--input""" , type=UpperCAmelCase__ , help="""Path to the file to use for inference""" )
run_parser.add_argument("""--output""" , type=UpperCAmelCase__ , help="""Path to the file that will be used post to write results.""" )
run_parser.add_argument("""--model""" , type=UpperCAmelCase__ , help="""Name or path to the model to instantiate.""" )
run_parser.add_argument("""--config""" , type=UpperCAmelCase__ , help="""Name or path to the model's config to instantiate.""" )
run_parser.add_argument(
"""--tokenizer""" , type=UpperCAmelCase__ , help="""Name of the tokenizer to use. (default: same as the model name)""" )
run_parser.add_argument(
"""--column""" , type=UpperCAmelCase__ , help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" , )
run_parser.add_argument(
"""--format""" , type=UpperCAmelCase__ , default="""infer""" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="""Input format to read from""" , )
run_parser.add_argument(
"""--device""" , type=UpperCAmelCase__ , default=-1 , help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" , )
run_parser.add_argument("""--overwrite""" , action="""store_true""" , help="""Allow overwriting the output file.""" )
run_parser.set_defaults(func=UpperCAmelCase__ )
def _lowercase ( self : str ) -> Tuple:
_a , _a : int = self._nlp, []
for entry in self._reader:
_a : Optional[int] = nlp(**UpperCAmelCase__ ) if self._reader.is_multi_columns else nlp(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
outputs.append(UpperCAmelCase__ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
_a : List[str] = self._reader.save_binary(UpperCAmelCase__ )
logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
else:
self._reader.save(UpperCAmelCase__ )
| 324
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
| 1
|
"""simple docstring"""
from PIL import Image
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = image.size
_a : List[str] = 0
_a : Union[str, Any] = image.load()
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
_a : Any = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(UpperCamelCase__ ):
for i in range(UpperCamelCase__ ):
_a : Dict = 2_5_5 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open('path_to_image').convert('L'))
image.save('output_image_path')
| 324
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
| 1
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_snake_case = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_a : Optional[Any] = k.replace(UpperCamelCase__ , UpperCamelCase__ )
return k
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCamelCase__ )
_a : Optional[Any] = PegasusConfig(**UpperCamelCase__ )
_a : Tuple = PegasusForConditionalGeneration(UpperCamelCase__ )
_a : str = torch_model.model.state_dict()
_a : Union[str, Any] = {}
for k, v in tf_weights.items():
_a : Any = rename_state_dict_key(UpperCamelCase__ )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
_a : str = v.T
_a : int = torch.tensor(UpperCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
_a : Union[str, Any] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
_a : str = mapping["""shared.weight"""]
_a : Union[str, Any] = mapping["""shared.weight"""]
_a : Optional[Any] = {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**UpperCamelCase__ )
_a , _a : int = torch_model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : Optional[Any] = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase__ ( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_a : List[Any] = tf.train.list_variables(UpperCamelCase__ )
_a : Optional[int] = {}
_a : Dict = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(UpperCamelCase__ , desc="""converting tf checkpoint to dict""" ):
_a : Optional[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a : str = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
_a : int = array
return tf_weights
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# save tokenizer first
_a : Dict = Path(UpperCamelCase__ ).parent.name
_a : Optional[Any] = task_specific_params[F"""summarization_{dataset}"""]["""max_position_embeddings"""]
_a : Tuple = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=UpperCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCamelCase__ )
# convert model
_a : List[Any] = get_tf_weights_as_numpy(UpperCamelCase__ )
_a : Dict = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
_a : Tuple = task_specific_params
_a : Optional[int] = convert_pegasus(UpperCamelCase__ , UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
_a : Dict = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(UpperCamelCase__ , Path(UpperCamelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_snake_case = parser.parse_args()
if args.save_dir is None:
_snake_case = Path(args.tf_ckpt_path).parent.name
_snake_case = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 324
|
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
| 1
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
_a : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
_a : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
_a : List[Any] = """xvjiarui/stable-diffusion-2-inpainting"""
_a , _a : Tuple = FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase__ , safety_checker=UpperCAmelCase__ )
_a : str = """Face of a yellow cat, high resolution, sitting on a park bench"""
_a : List[Any] = jax.random.PRNGKey(0 )
_a : Any = 50
_a : Dict = jax.device_count()
_a : Optional[int] = num_samples * [prompt]
_a : int = num_samples * [init_image]
_a : Dict = num_samples * [mask_image]
_a , _a , _a : str = pipeline.prepare_inputs(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# shard inputs and rng
_a : Any = replicate(UpperCAmelCase__ )
_a : Optional[Any] = jax.random.split(UpperCAmelCase__ , jax.device_count() )
_a : Tuple = shard(UpperCAmelCase__ )
_a : List[str] = shard(UpperCAmelCase__ )
_a : Any = shard(UpperCAmelCase__ )
_a : List[str] = pipeline(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , jit=UpperCAmelCase__ )
_a : List[Any] = output.images.reshape(UpperCAmelCase__ , 512 , 512 , 3 )
_a : Union[str, Any] = images[0, 253:256, 253:256, -1]
_a : Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_a : Union[str, Any] = jnp.array(
[0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 324
|
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 32 , UpperCAmelCase__ : Optional[Any]=PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , **UpperCAmelCase__ : List[str] , ) -> None:
_a : int = do_resize
_a : Union[str, Any] = do_rescale
_a : Any = size_divisor
_a : Any = resample
super().__init__(**UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[Any] ) -> np.ndarray:
_a , _a : Tuple = get_image_size(UpperCAmelCase__ )
# Rounds the height and width down to the closest multiple of size_divisor
_a : Optional[Any] = height // size_divisor * size_divisor
_a : Union[str, Any] = width // size_divisor * size_divisor
_a : Any = resize(UpperCAmelCase__ , (new_h, new_w) , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
return image
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[int] ) -> np.ndarray:
return rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[TensorType, str]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> BatchFeature:
_a : Dict = do_resize if do_resize is not None else self.do_resize
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : str = size_divisor if size_divisor is not None else self.size_divisor
_a : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_a : Tuple = [to_numpy_array(UpperCAmelCase__ ) for img in images]
if do_resize:
_a : Optional[int] = [self.resize(UpperCAmelCase__ , size_divisor=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : str = [self.rescale(UpperCAmelCase__ , scale=1 / 255 ) for image in images]
_a : Any = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Optional[int] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_a : Any = 0
count += depth_first_search(UpperCamelCase__ , row + 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , row - 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col + 1 , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col - 1 , UpperCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
set_seed(770)
_snake_case = {
'c_attn': 'att_proj',
'c_proj': 'out_proj',
'c_fc': 'in_proj',
'transformer.': '',
'h.': 'layers.',
'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2',
'ln_f': 'layernorm_final',
'wpe': 'position_embeds_layer',
'wte': 'input_embeds_layer',
}
_snake_case = {
'text_small': {
'repo_id': 'suno/bark',
'file_name': 'text.pt',
},
'coarse_small': {
'repo_id': 'suno/bark',
'file_name': 'coarse.pt',
},
'fine_small': {
'repo_id': 'suno/bark',
'file_name': 'fine.pt',
},
'text': {
'repo_id': 'suno/bark',
'file_name': 'text_2.pt',
},
'coarse': {
'repo_id': 'suno/bark',
'file_name': 'coarse_2.pt',
},
'fine': {
'repo_id': 'suno/bark',
'file_name': 'fine_2.pt',
},
}
_snake_case = os.path.dirname(os.path.abspath(__file__))
_snake_case = os.path.join(os.path.expanduser('~'), '.cache')
_snake_case = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=False ):
'''simple docstring'''
_a : Tuple = model_type
if use_small:
key += "_small"
return os.path.join(UpperCamelCase__ , REMOTE_MODEL_PATHS[key]["""file_name"""] )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
hf_hub_download(repo_id=UpperCamelCase__ , filename=UpperCamelCase__ , local_dir=UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__="text" ):
'''simple docstring'''
if model_type == "text":
_a : Optional[Any] = BarkSemanticModel
_a : Union[str, Any] = BarkSemanticConfig
_a : Dict = BarkSemanticGenerationConfig
elif model_type == "coarse":
_a : int = BarkCoarseModel
_a : Union[str, Any] = BarkCoarseConfig
_a : str = BarkCoarseGenerationConfig
elif model_type == "fine":
_a : Dict = BarkFineModel
_a : List[Any] = BarkFineConfig
_a : str = BarkFineGenerationConfig
else:
raise NotImplementedError()
_a : int = F"""{model_type}_small""" if use_small else model_type
_a : Optional[int] = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(UpperCamelCase__ ):
logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
_download(model_info["""repo_id"""] , model_info["""file_name"""] )
_a : List[Any] = torch.load(UpperCamelCase__ , map_location=UpperCamelCase__ )
# this is a hack
_a : Union[str, Any] = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
_a : Any = model_args["""vocab_size"""]
_a : Optional[int] = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
_a : Dict = model_args.pop("""n_head""" )
_a : str = model_args.pop("""n_embd""" )
_a : int = model_args.pop("""n_layer""" )
_a : Any = ConfigClass(**checkpoint["""model_args"""] )
_a : Optional[Any] = ModelClass(config=UpperCamelCase__ )
_a : List[str] = GenerationConfigClass()
_a : Union[str, Any] = model_generation_config
_a : Tuple = checkpoint["""model"""]
# fixup checkpoint
_a : str = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(UpperCamelCase__ ):
# replace part of the key with corresponding layer name in HF implementation
_a : List[Any] = k[len(UpperCamelCase__ ) :]
for old_layer_name in new_layer_name_dict:
_a : Dict = new_k.replace(UpperCamelCase__ , new_layer_name_dict[old_layer_name] )
_a : Optional[Any] = state_dict.pop(UpperCamelCase__ )
_a : Dict = set(state_dict.keys() ) - set(model.state_dict().keys() )
_a : Tuple = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
_a : List[Any] = set(model.state_dict().keys() ) - set(state_dict.keys() )
_a : int = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
if len(UpperCamelCase__ ) != 0:
raise ValueError(F"""extra keys found: {extra_keys}""" )
if len(UpperCamelCase__ ) != 0:
raise ValueError(F"""missing keys: {missing_keys}""" )
model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : int = model.num_parameters(exclude_embeddings=UpperCamelCase__ )
_a : Union[str, Any] = checkpoint["""best_val_loss"""].item()
logger.info(F"""model loaded: {round(n_params/1e6 , 1 )}M params, {round(UpperCamelCase__ , 3 )} loss""" )
model.eval()
model.to(UpperCamelCase__ )
del checkpoint, state_dict
return model
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__="text" ):
'''simple docstring'''
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
_a : Any = """cpu""" # do conversion on cpu
_a : Union[str, Any] = _get_ckpt_path(UpperCamelCase__ , use_small=UpperCamelCase__ )
_a : Any = _load_model(UpperCamelCase__ , UpperCamelCase__ , model_type=UpperCamelCase__ , use_small=UpperCamelCase__ )
# load bark initial model
_a : List[str] = _bark_load_model(UpperCamelCase__ , """cpu""" , model_type=UpperCamelCase__ , use_small=UpperCamelCase__ )
if model_type == "text":
_a : Any = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=UpperCamelCase__ ) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""" )
# check if same output as the bark model
_a : Dict = 5
_a : List[str] = 1_0
if model_type in ["text", "coarse"]:
_a : List[Any] = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
_a : Dict = bark_model(UpperCamelCase__ )[0]
_a : Any = model(UpperCamelCase__ )
# take last logits
_a : List[Any] = output_new_model_total.logits[:, [-1], :]
else:
_a : Optional[Any] = 3
_a : int = 8
_a : List[str] = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
_a : Union[str, Any] = model(UpperCamelCase__ , UpperCamelCase__ )
_a : Any = bark_model(UpperCamelCase__ , UpperCamelCase__ )
_a : List[Any] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("""initial and new outputs are not equal""" )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
_a : List[str] = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
_a : List[Any] = BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase__ , """config.json""" ) )
_a : Optional[int] = BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase__ , """config.json""" ) )
_a : int = BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase__ , """config.json""" ) )
_a : Optional[Any] = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )
_a : Any = BarkSemanticModel.from_pretrained(UpperCamelCase__ )
_a : Any = BarkCoarseModel.from_pretrained(UpperCamelCase__ )
_a : List[str] = BarkFineModel.from_pretrained(UpperCamelCase__ )
_a : List[Any] = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )
_a : Union[str, Any] = BarkConfig.from_sub_model_configs(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
_a : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
_a : List[str] = BarkModel(UpperCamelCase__ )
_a : int = semantic
_a : Dict = coarseAcoustic
_a : Union[str, Any] = fineAcoustic
_a : str = codec
_a : Optional[Any] = bark_generation_config
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
bark.save_pretrained(UpperCamelCase__ , repo_id=UpperCamelCase__ , push_to_hub=UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('model_type', type=str, help='text, coarse or fine.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')
_snake_case = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 324
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
| 1
|
"""simple docstring"""
_snake_case = [
(1000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : int = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
_a : Optional[Any] = 0
_a : Optional[Any] = 0
while place < len(UpperCamelCase__ ):
if (place + 1 < len(UpperCamelCase__ )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = []
for arabic, roman in ROMAN:
((_a) , (_a)) : Dict = divmod(UpperCamelCase__ , UpperCamelCase__ )
result.append(roman * factor )
if number == 0:
break
return "".join(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Node | None ) -> List[str]:
_a : int = pos_x
_a : Union[str, Any] = pos_y
_a : Tuple = (pos_y, pos_x)
_a : Tuple = goal_x
_a : int = goal_y
_a : str = parent
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] ) -> List[str]:
_a : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : Optional[int] = [self.start]
_a : Tuple = False
def _lowercase ( self : str ) -> Path | None:
while self.node_queue:
_a : Tuple = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_a : Dict = True
return self.retrace_path(UpperCAmelCase__ )
_a : Tuple = self.get_successors(UpperCAmelCase__ )
for node in successors:
self.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node ) -> list[Node]:
_a : Optional[Any] = []
for action in delta:
_a : str = parent.pos_x + action[1]
_a : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCAmelCase__ , UpperCAmelCase__ , self.target.pos_y , self.target.pos_x , UpperCAmelCase__ ) )
return successors
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Node | None ) -> Path:
_a : Dict = node
_a : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_a : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Dict = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Dict = False
def _lowercase ( self : Any ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_a : List[Any] = self.fwd_bfs.node_queue.pop(0 )
_a : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_a : Optional[int] = True
return self.retrace_bidirectional_path(
UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = current_bwd_node
_a : int = current_fwd_node
_a : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node , UpperCAmelCase__ : Node ) -> Path:
_a : str = self.fwd_bfs.retrace_path(UpperCAmelCase__ )
_a : List[Any] = self.bwd_bfs.retrace_path(UpperCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_a : Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_snake_case = (0, 0)
_snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_snake_case = time.time()
_snake_case = BreadthFirstSearch(init, goal)
_snake_case = bfs.search()
_snake_case = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_snake_case = time.time()
_snake_case = BidirectionalBreadthFirstSearch(init, goal)
_snake_case = bd_bfs.search()
_snake_case = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
|
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_snake_case = logging.getLogger(__name__)
_snake_case = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_ )} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
UpperCamelCase : bool = field(default=snake_case_ , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
UpperCamelCase : float = field(
default=0.1_5 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCamelCase : float = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
UpperCamelCase : int = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
UpperCamelCase : int = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = None , ):
'''simple docstring'''
def _dataset(UpperCamelCase__ , UpperCamelCase__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , ref_path=UpperCamelCase__ , )
return LineByLineTextDataset(tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=UpperCamelCase__ , file_path=UpperCamelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=UpperCamelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(UpperCamelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowerCAmelCase__ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_a , _a , _a : List[str] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , UpperCamelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
_a : str = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
_a : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
_a : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_a : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
_a : Optional[Any] = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , )
else:
logger.info("""Training new model from scratch""" )
_a : List[Any] = AutoModelWithLMHead.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
_a : int = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
_a : Optional[Any] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
_a : Optional[Any] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
_a : Optional[int] = (
get_dataset(UpperCamelCase__ , tokenizer=UpperCamelCase__ , evaluate=UpperCamelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
_a : Any = DataCollatorForPermutationLanguageModeling(
tokenizer=UpperCamelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
_a : Union[str, Any] = DataCollatorForWholeWordMask(
tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
else:
_a : str = DataCollatorForLanguageModeling(
tokenizer=UpperCamelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_a : Union[str, Any] = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , data_collator=UpperCamelCase__ , train_dataset=UpperCamelCase__ , eval_dataset=UpperCamelCase__ , prediction_loss_only=UpperCamelCase__ , )
# Training
if training_args.do_train:
_a : Optional[Any] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=UpperCamelCase__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_a : Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_a : int = trainer.evaluate()
_a : Dict = math.exp(eval_output["""eval_loss"""] )
_a : Union[str, Any] = {"""perplexity""": perplexity}
_a : Optional[Any] = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(UpperCamelCase__ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , UpperCamelCase__ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(UpperCamelCase__ )
return results
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
_snake_case = pytest.mark.integration
_snake_case = {'comet'}
_snake_case = importlib.util.find_spec('fairseq') is not None
_snake_case = {'code_eval'}
_snake_case = os.name == 'nt'
_snake_case = {'bertscore', 'frugalscore', 'perplexity'}
_snake_case = importlib.util.find_spec('transformers') is not None
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
@wraps(UpperCamelCase__ )
def wrapper(self , UpperCamelCase__ ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , UpperCamelCase__ )
return wrapper
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
@wraps(UpperCamelCase__ )
def wrapper(self , UpperCamelCase__ ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , UpperCamelCase__ )
return wrapper
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
@wraps(UpperCamelCase__ )
def wrapper(self , UpperCamelCase__ ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , UpperCamelCase__ )
return wrapper
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Any = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
snake_case_ , snake_case_ , snake_case_ )
@local
class UpperCamelCase ( parameterized.TestCase ):
UpperCamelCase : Optional[Any] = {}
UpperCamelCase : Optional[Any] = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""" )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str ) -> Union[str, Any]:
_a : List[str] = """[...]"""
_a : Optional[int] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , UpperCAmelCase__ ) ).module_path )
_a : int = datasets.load.import_main_class(metric_module.__name__ , dataset=UpperCAmelCase__ )
# check parameters
_a : Any = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(UpperCAmelCase__ , metric_module.__name__ ):
with self.use_local_metrics():
try:
_a : int = doctest.testmod(UpperCAmelCase__ , verbose=UpperCAmelCase__ , raise_on_error=UpperCAmelCase__ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def _lowercase ( self : Tuple , UpperCAmelCase__ : Optional[Any] ) -> int:
_a : Union[str, Any] = """[...]"""
_a : int = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , UpperCAmelCase__ ) ).module_path )
# run doctest
with self.use_local_metrics():
_a : List[str] = doctest.testmod(UpperCAmelCase__ , verbose=UpperCAmelCase__ , raise_on_error=UpperCAmelCase__ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def _lowercase ( self : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] ) -> Optional[int]:
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCAmelCase__ ):
yield
else:
yield
@contextmanager
def _lowercase ( self : Optional[int] ) -> str:
def load_local_metric(UpperCAmelCase__ : List[Any] , *UpperCAmelCase__ : Any , **UpperCAmelCase__ : List[Any] ):
return load_metric(os.path.join("""metrics""" , UpperCAmelCase__ ) , *UpperCAmelCase__ , **UpperCAmelCase__ )
with patch("""datasets.load_metric""" ) as mock_load_metric:
_a : List[str] = load_local_metric
yield
@classmethod
def _lowercase ( cls : Tuple , UpperCAmelCase__ : List[str] ) -> int:
def wrapper(UpperCAmelCase__ : int ):
_a : Union[str, Any] = contextmanager(UpperCAmelCase__ )
_a : Dict = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class UpperCamelCase ( snake_case_ ):
def _lowercase ( self : int , UpperCAmelCase__ : Optional[int] ) -> Union[str, Any]:
assert len(input_dict["""input_ids"""] ) == 2
return np.array([1.0_3, 1.0_4] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
_a : int = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
import torch
def bert_cos_score_idf(UpperCamelCase__ , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase__ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
_a : List[Any] = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
def load_from_checkpoint(UpperCamelCase__ ):
class UpperCamelCase :
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : Dict , *UpperCAmelCase__ : List[str] , **UpperCAmelCase__ : List[str] ) -> Tuple:
assert len(UpperCAmelCase__ ) == 2
_a : Optional[Any] = [0.1_9, 0.9_2]
return scores, sum(UpperCAmelCase__ ) / len(UpperCAmelCase__ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
_a : Optional[int] = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
_a : Union[str, Any] = load_from_checkpoint
yield
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
_a : Tuple = """ERROR"""
_a : Tuple = F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(UpperCamelCase__ , match=re.escape(UpperCamelCase__ ) ):
metric.compute(predictions=[] , references=[] , scheme=UpperCamelCase__ )
| 324
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_snake_case = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_a : Optional[Any] = k.replace(UpperCamelCase__ , UpperCamelCase__ )
return k
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = DEFAULTS.copy()
cfg_kwargs.update(UpperCamelCase__ )
_a : Optional[Any] = PegasusConfig(**UpperCamelCase__ )
_a : Tuple = PegasusForConditionalGeneration(UpperCamelCase__ )
_a : str = torch_model.model.state_dict()
_a : Union[str, Any] = {}
for k, v in tf_weights.items():
_a : Any = rename_state_dict_key(UpperCamelCase__ )
if new_k not in sd:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
_a : str = v.T
_a : int = torch.tensor(UpperCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
_a : Union[str, Any] = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
_a : str = mapping["""shared.weight"""]
_a : Union[str, Any] = mapping["""shared.weight"""]
_a : Optional[Any] = {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**UpperCamelCase__ )
_a , _a : int = torch_model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_a : Optional[Any] = [
k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase__ ( UpperCamelCase__="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_a : List[Any] = tf.train.list_variables(UpperCamelCase__ )
_a : Optional[int] = {}
_a : Dict = ["""Adafactor""", """global_step"""]
for name, shape in tqdm(UpperCamelCase__ , desc="""converting tf checkpoint to dict""" ):
_a : Optional[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a : str = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
_a : int = array
return tf_weights
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# save tokenizer first
_a : Dict = Path(UpperCamelCase__ ).parent.name
_a : Optional[Any] = task_specific_params[F"""summarization_{dataset}"""]["""max_position_embeddings"""]
_a : Tuple = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=UpperCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(UpperCamelCase__ )
# convert model
_a : List[Any] = get_tf_weights_as_numpy(UpperCamelCase__ )
_a : Dict = task_specific_params[F"""summarization_{dataset}"""]
if dataset == "large":
_a : Tuple = task_specific_params
_a : Optional[int] = convert_pegasus(UpperCamelCase__ , UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
_a : Dict = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(UpperCamelCase__ , Path(UpperCamelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_snake_case = parser.parse_args()
if args.save_dir is None:
_snake_case = Path(args.tf_ckpt_path).parent.name
_snake_case = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 324
| 1
|
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : int = ['''pixel_values''']
def __init__( self : List[Any] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Dict[str, int]] = None , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , **UpperCAmelCase__ : int , ) -> None:
super().__init__(**UpperCAmelCase__ )
_a : Any = size if size is not None else {"""shortest_edge""": 256}
_a : List[str] = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_a : List[Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_a : int = get_size_dict(UpperCAmelCase__ , param_name="""crop_size""" )
_a : Any = do_resize
_a : Any = size
_a : Union[str, Any] = resample
_a : int = do_center_crop
_a : Optional[Any] = crop_size
_a : Optional[int] = do_rescale
_a : List[str] = rescale_factor
_a : Any = do_normalize
_a : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a : List[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Any , ) -> np.ndarray:
_a : Optional[Any] = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
_a : Union[str, Any] = get_resize_output_image_size(UpperCAmelCase__ , size=size["""shortest_edge"""] , default_to_square=UpperCAmelCase__ )
return resize(UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Dict , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Dict[str, int] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : int , ) -> np.ndarray:
_a : List[Any] = get_size_dict(UpperCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(UpperCAmelCase__ , size=(size["""height"""], size["""width"""]) , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Tuple ) -> np.ndarray:
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Union[float, List[float]] , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Tuple , ) -> np.ndarray:
return normalize(UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : PILImageResampling = None , UpperCAmelCase__ : bool = None , UpperCAmelCase__ : Dict[str, int] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[float, List[float]]] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : Optional[Any] , ) -> int:
_a : Tuple = do_resize if do_resize is not None else self.do_resize
_a : Any = size if size is not None else self.size
_a : int = get_size_dict(UpperCAmelCase__ , default_to_square=UpperCAmelCase__ )
_a : int = resample if resample is not None else self.resample
_a : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_a : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
_a : Union[str, Any] = get_size_dict(UpperCAmelCase__ , param_name="""crop_size""" )
_a : Any = do_rescale if do_rescale is not None else self.do_rescale
_a : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_a : Any = do_normalize if do_normalize is not None else self.do_normalize
_a : Any = image_mean if image_mean is not None else self.image_mean
_a : Optional[int] = image_std if image_std is not None else self.image_std
_a : Dict = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_a : Dict = [to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_resize:
_a : Dict = [self.resize(image=UpperCAmelCase__ , size=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_center_crop:
_a : Optional[Any] = [self.center_crop(image=UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : Any = [self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_normalize:
_a : Any = [self.normalize(image=UpperCAmelCase__ , mean=UpperCAmelCase__ , std=UpperCAmelCase__ ) for image in images]
_a : int = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Tuple = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Tuple] = None ) -> int:
_a : List[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase__ ):
_a : Optional[int] = target_sizes.numpy()
_a : Optional[Any] = []
for idx in range(len(UpperCAmelCase__ ) ):
_a : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=UpperCAmelCase__ )
_a : Dict = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase__ )
else:
_a : List[str] = logits.argmax(dim=1 )
_a : Tuple = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 324
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
_a : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_a : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
_a : Tuple = CLIPTextModel(UpperCAmelCase__ )
_a : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Dict = CLIPTextModelWithProjection(UpperCAmelCase__ )
_a : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=0 ) -> int:
_a : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
_a : Any = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("""mps""" ):
_a : Any = torch.manual_seed(UpperCAmelCase__ )
else:
_a : Tuple = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def _lowercase ( self : Any ) -> List[Any]:
_a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : List[Any] = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Union[str, Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = sd_pipe(**UpperCAmelCase__ ).images
_a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self : Any ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self : Any ) -> Any:
pass
def _lowercase ( self : Tuple ) -> Union[str, Any]:
_a : int = self.get_dummy_components()
_a : Any = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Dict = sd_pipe.to(UpperCAmelCase__ )
_a : List[str] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# forward without prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = 3 * ["""this is a negative prompt"""]
_a : Dict = negative_prompt
_a : Dict = 3 * [inputs["""prompt"""]]
_a : Optional[Any] = sd_pipe(**UpperCAmelCase__ )
_a : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : Union[str, Any] = 3 * ["""this is a negative prompt"""]
_a : int = 3 * [inputs.pop("""prompt""" )]
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : List[str] = sd_pipe.encode_prompt(UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ )
_a : Tuple = sd_pipe(
**UpperCAmelCase__ , prompt_embeds=UpperCAmelCase__ , negative_prompt_embeds=UpperCAmelCase__ , pooled_prompt_embeds=UpperCAmelCase__ , negative_pooled_prompt_embeds=UpperCAmelCase__ , )
_a : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str="cpu" , UpperCAmelCase__ : str=torch.floataa , UpperCAmelCase__ : List[Any]=0 ) -> List[str]:
_a : List[str] = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Union[str, Any] = np.random.RandomState(UpperCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_a : List[Any] = torch.from_numpy(UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
_a : Any = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Union[str, Any] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_inputs(UpperCAmelCase__ )
_a : Tuple = pipe(**UpperCAmelCase__ ).images
_a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_a : int = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : int = 2
_a : List[str] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(UpperCamelCase__ )
if n > 1:
factors.append(UpperCamelCase__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger()
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : List[nn.Module] = field(default_factory=snake_case_ )
UpperCamelCase : list = field(default_factory=snake_case_ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Tensor ) -> Any:
_a : int = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase__ , nn.Convad ) or isinstance(UpperCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase__ )
def __call__( self : Tuple , UpperCAmelCase__ : Tensor ) -> Tuple:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowercase ( self : Optional[int] ) -> int:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class UpperCamelCase :
UpperCamelCase : nn.Module
UpperCamelCase : nn.Module
UpperCamelCase : int = 0
UpperCamelCase : List = field(default_factory=snake_case_ )
UpperCamelCase : List = field(default_factory=snake_case_ )
def __call__( self : Optional[Any] , UpperCAmelCase__ : Tensor ) -> Tuple:
_a : Union[str, Any] = Tracker(self.dest )(UpperCAmelCase__ ).parametrized
_a : List[Any] = Tracker(self.src )(UpperCAmelCase__ ).parametrized
_a : Tuple = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.src_skip , UpperCAmelCase__ ) )
_a : Union[str, Any] = list(filter(lambda UpperCAmelCase__ : type(UpperCAmelCase__ ) not in self.dest_skip , UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__ )} operations while"""
f""" destination module has {len(UpperCAmelCase__ )}.""" )
for dest_m, src_m in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
_a : List[str] = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ ).eval()
_a : str = ResNetForImageClassification(UpperCamelCase__ ).eval()
_a : List[str] = ModuleTransfer(src=UpperCamelCase__ , dest=UpperCamelCase__ )
_a : List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(UpperCamelCase__ )
assert torch.allclose(from_model(UpperCamelCase__ ) , our_model(UpperCamelCase__ ).logits ), "The model logits don't match the original one."
_a : Dict = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(UpperCamelCase__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=UpperCamelCase__ , )
# we can use the convnext one
_a : Optional[Any] = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=UpperCamelCase__ , )
print(F"""Pushed {checkpoint_name}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
_a : Any = """imagenet-1k-id2label.json"""
_a : Optional[int] = 1_0_0_0
_a : Any = (1, num_labels)
_a : Union[str, Any] = """huggingface/label-files"""
_a : Tuple = num_labels
_a : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Optional[Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Any = idalabel
_a : Tuple = {v: k for k, v in idalabel.items()}
_a : List[str] = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
_a : Union[str, Any] = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
_snake_case = parser.parse_args()
_snake_case = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 324
| 1
|
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_snake_case = 'src/diffusers'
_snake_case = '.'
# This is to make sure the diffusers module imported is the one in the repo.
_snake_case = importlib.util.spec_from_file_location(
'diffusers',
os.path.join(DIFFUSERS_PATH, '__init__.py'),
submodule_search_locations=[DIFFUSERS_PATH],
)
_snake_case = spec.loader.load_module()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return line.startswith(UpperCamelCase__ ) or len(UpperCamelCase__ ) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""" , UpperCamelCase__ ) is not None
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = object_name.split(""".""" )
_a : int = 0
# First let's find the module where our object lives.
_a : List[str] = parts[i]
while i < len(UpperCamelCase__ ) and not os.path.isfile(os.path.join(UpperCamelCase__ , F"""{module}.py""" ) ):
i += 1
if i < len(UpperCamelCase__ ):
_a : List[Any] = os.path.join(UpperCamelCase__ , parts[i] )
if i >= len(UpperCamelCase__ ):
raise ValueError(F"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(UpperCamelCase__ , F"""{module}.py""" ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_a : str = f.readlines()
# Now let's find the class / func in the code!
_a : List[str] = """"""
_a : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCamelCase__ ) and re.search(RF"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(UpperCamelCase__ ):
raise ValueError(F""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
_a : Dict = line_index
while line_index < len(UpperCamelCase__ ) and _should_continue(lines[line_index] , UpperCamelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_a : List[str] = lines[start_index:line_index]
return "".join(UpperCamelCase__ )
_snake_case = re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_snake_case = re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
_snake_case = re.compile(r'<FILL\s+[^>]*>')
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[Any] = code.split("""\n""" )
_a : Optional[int] = 0
while idx < len(UpperCamelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCamelCase__ ):
return re.search(R"""^(\s*)\S""" , lines[idx] ).groups()[0]
return ""
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[Any] = len(get_indent(UpperCamelCase__ ) ) > 0
if has_indent:
_a : str = F"""class Bla:\n{code}"""
_a : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=UpperCamelCase__ )
_a : Optional[int] = black.format_str(UpperCamelCase__ , mode=UpperCamelCase__ )
_a , _a : Optional[Any] = style_docstrings_in_code(UpperCamelCase__ )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__=False ):
'''simple docstring'''
with open(UpperCamelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_a : str = f.readlines()
_a : List[str] = []
_a : Optional[int] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCamelCase__ ):
_a : int = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
_a , _a , _a : Union[str, Any] = search.groups()
_a : Optional[int] = find_code_in_diffusers(UpperCamelCase__ )
_a : Tuple = get_indent(UpperCamelCase__ )
_a : Any = line_index + 1 if indent == theoretical_indent else line_index + 2
_a : Optional[int] = theoretical_indent
_a : Any = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
_a : Optional[Any] = True
while line_index < len(UpperCamelCase__ ) and should_continue:
line_index += 1
if line_index >= len(UpperCamelCase__ ):
break
_a : Dict = lines[line_index]
_a : Dict = _should_continue(UpperCamelCase__ , UpperCamelCase__ ) and re.search(F"""^{indent}# End copy""" , UpperCamelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_a : Dict = lines[start_index:line_index]
_a : Tuple = """""".join(UpperCamelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
_a : Union[str, Any] = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(UpperCamelCase__ ) is None]
_a : str = """\n""".join(UpperCamelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCamelCase__ ) > 0:
_a : str = replace_pattern.replace("""with""" , """""" ).split(""",""" )
_a : Tuple = [_re_replace_pattern.search(UpperCamelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
_a , _a , _a : List[str] = pattern.groups()
_a : Tuple = re.sub(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if option.strip() == "all-casing":
_a : str = re.sub(obja.lower() , obja.lower() , UpperCamelCase__ )
_a : Optional[int] = re.sub(obja.upper() , obja.upper() , UpperCamelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
_a : Union[str, Any] = blackify(lines[start_index - 1] + theoretical_code )
_a : str = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
_a : Union[str, Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
_a : Tuple = start_index + 1
if overwrite and len(UpperCamelCase__ ) > 0:
# Warn the user a file has been modified.
print(F"""Detected changes, rewriting {filename}.""" )
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(UpperCamelCase__ )
return diffs
def lowerCAmelCase__ ( UpperCamelCase__ = False ):
'''simple docstring'''
_a : List[Any] = glob.glob(os.path.join(UpperCamelCase__ , """**/*.py""" ) , recursive=UpperCamelCase__ )
_a : List[str] = []
for filename in all_files:
_a : Any = is_copy_consistent(UpperCamelCase__ , UpperCamelCase__ )
diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(UpperCamelCase__ ) > 0:
_a : List[str] = """\n""".join(UpperCamelCase__ )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_snake_case = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 324
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 324
| 1
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class UpperCamelCase :
UpperCamelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCamelCase : Optional[str] = field(default=snake_case_ , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCamelCase : Optional[str] = field(default=snake_case_ , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCamelCase : Optional[float] = field(
default=0.1_5 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCamelCase : Optional[int] = field(
default=snake_case_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase : Optional[int] = field(
default=snake_case_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowercase ( self : Optional[int] ) -> int:
_a : int = {}
if self.train_dir is not None:
_a : str = self.train_dir
if self.validation_dir is not None:
_a : List[str] = self.validation_dir
_a : List[Any] = data_files if data_files else None
@dataclass
class UpperCamelCase :
UpperCamelCase : str = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCamelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase : str = field(default=snake_case_ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCamelCase : float = field(
default=0.7_5 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCamelCase : bool = field(
default=snake_case_ , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Any = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowerCAmelCase__ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_a , _a , _a : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_a , _a , _a : str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , UpperCamelCase__ , UpperCamelCase__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_a : Tuple = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase__ )
transformers.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
_a : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_a : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
_a : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_a : Optional[int] = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCamelCase__ ) and data_args.train_val_split > 0.0:
_a : Any = ds["""train"""].train_test_split(data_args.train_val_split )
_a : Any = split["""train"""]
_a : List[str] = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_a : List[str] = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
_a : Union[str, Any] = ViTMAEConfig.from_pretrained(model_args.config_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
_a : List[str] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
_a : Dict = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_a : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
_a : Dict = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
_a : List[Any] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_a : Optional[int] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
_a : int = ViTMAEForPreTraining(UpperCamelCase__ )
if training_args.do_train:
_a : Optional[Any] = ds["""train"""].column_names
else:
_a : int = ds["""validation"""].column_names
if data_args.image_column_name is not None:
_a : Tuple = data_args.image_column_name
elif "image" in column_names:
_a : str = """image"""
elif "img" in column_names:
_a : Any = """img"""
else:
_a : Any = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_a : str = image_processor.size["""shortest_edge"""]
else:
_a : str = (image_processor.size["""height"""], image_processor.size["""width"""])
_a : Tuple = Compose(
[
Lambda(lambda UpperCamelCase__ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(UpperCamelCase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(UpperCamelCase__ ):
_a : int = [transforms(UpperCamelCase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
_a : str = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(UpperCamelCase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
_a : int = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(UpperCamelCase__ )
# Compute absolute learning rate
_a : Optional[int] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_a : Dict = training_args.base_learning_rate * total_train_batch_size / 2_5_6
# Initialize our trainer
_a : Union[str, Any] = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , )
# Training
if training_args.do_train:
_a : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
_a : int = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_a : int = last_checkpoint
_a : int = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_a : int = trainer.evaluate()
trainer.log_metrics("""eval""" , UpperCamelCase__ )
trainer.save_metrics("""eval""" , UpperCamelCase__ )
# Write model card and (optionally) push to hub
_a : str = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase__ )
else:
trainer.create_model_card(**UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 324
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline
UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
_a : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_a : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_a : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_a : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
_a : Tuple = CLIPTextModel(UpperCAmelCase__ )
_a : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Dict = CLIPTextModelWithProjection(UpperCAmelCase__ )
_a : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=UpperCAmelCase__ )
_a : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=0 ) -> int:
_a : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
_a : Any = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("""mps""" ):
_a : Any = torch.manual_seed(UpperCAmelCase__ )
else:
_a : Tuple = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def _lowercase ( self : Any ) -> List[Any]:
_a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : List[Any] = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Union[str, Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = sd_pipe(**UpperCAmelCase__ ).images
_a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self : Any ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self : Any ) -> Any:
pass
def _lowercase ( self : Tuple ) -> Union[str, Any]:
_a : int = self.get_dummy_components()
_a : Any = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase__ )
_a : Dict = sd_pipe.to(UpperCAmelCase__ )
_a : List[str] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# forward without prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : List[str] = 3 * ["""this is a negative prompt"""]
_a : Dict = negative_prompt
_a : Dict = 3 * [inputs["""prompt"""]]
_a : Optional[Any] = sd_pipe(**UpperCAmelCase__ )
_a : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_a : int = self.get_dummy_inputs(UpperCAmelCase__ )
_a : Union[str, Any] = 3 * ["""this is a negative prompt"""]
_a : int = 3 * [inputs.pop("""prompt""" )]
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : List[str] = sd_pipe.encode_prompt(UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ )
_a : Tuple = sd_pipe(
**UpperCAmelCase__ , prompt_embeds=UpperCAmelCase__ , negative_prompt_embeds=UpperCAmelCase__ , pooled_prompt_embeds=UpperCAmelCase__ , negative_pooled_prompt_embeds=UpperCAmelCase__ , )
_a : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : str="cpu" , UpperCAmelCase__ : str=torch.floataa , UpperCAmelCase__ : List[Any]=0 ) -> List[str]:
_a : List[str] = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
_a : Union[str, Any] = np.random.RandomState(UpperCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_a : List[Any] = torch.from_numpy(UpperCAmelCase__ ).to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
_a : Any = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Union[str, Any] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : List[str] = self.get_inputs(UpperCAmelCase__ )
_a : Tuple = pipe(**UpperCAmelCase__ ).images
_a : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_a : int = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 324
|
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_snake_case = logging.getLogger(__name__)
_snake_case = 'pytorch_model.bin'
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase : str = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The name of the task to train on.'''} , )
UpperCamelCase : Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class UpperCamelCase :
UpperCamelCase : str = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
UpperCamelCase : Optional[str] = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
UpperCamelCase : Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
UpperCamelCase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
UpperCamelCase : Optional[int] = dataclasses.field(
default=snake_case_ , metadata={'''help''': '''Random seed for initialization.'''} , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_a : Union[str, Any] = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_a : Any = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
_a : str = dataset.sort("""probability""" , reverse=UpperCamelCase__ )
_a : Any = dataset.select(range(UpperCamelCase__ ) )
_a : Tuple = dataset.remove_columns(["""label""", """probability"""] )
_a : Optional[Any] = dataset.rename_column("""prediction""" , """label""" )
_a : Dict = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
_a : Union[str, Any] = dataset.shuffle(seed=args.seed )
_a : Optional[int] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_a : Dict = STModelArguments(model_name_or_path=UpperCamelCase__ )
_a : Union[str, Any] = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
_a : Any = STTrainingArguments(output_dir=UpperCamelCase__ )
_a : Any = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
_a : Union[str, Any] = {}
_a : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_a : int = args.train_file
_a : List[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_a : Union[str, Any] = args.eval_file
for key in data_files:
_a : Optional[Any] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_a : str = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_a : Tuple = F"""{args.output_dir}/self-train_iter-{{}}""".format
_a : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : str = None
_a : int = None
_a : str = 0
_a : List[Any] = False
# Show the progress bar
_a : List[Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_a : Union[str, Any] = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_a : str = os.path.join(UpperCamelCase__ , """stage-1""" )
_a : Tuple = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
_a : int = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_a : Dict = os.path.join(UpperCamelCase__ , """best-checkpoint""" )
_a : List[str] = os.path.join(UpperCamelCase__ , """stage-2""" )
# Update arguments_dict
_a : int = model_path
_a : Dict = data_files["""train"""]
_a : int = current_output_dir
_a : Any = os.path.join(UpperCamelCase__ , """best-checkpoint""" , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , UpperCamelCase__ )
_a : List[Any] = iteration
_a : int = data_dir_format(iteration + 1 )
_a : Dict = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , """best-checkpoint""" ) )
_a : Union[str, Any] = config.idalabel
_a : Any = os.path.join(UpperCamelCase__ , """eval_results_best-checkpoint.json""" )
_a : Any = os.path.join(UpperCamelCase__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , """r""" ) as f:
_a : Tuple = float(json.load(UpperCamelCase__ )[args.eval_metric] )
_a : Dict = os.path.join(UpperCamelCase__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
_a : List[Any] = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_a : Any = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
_a : List[str] = os.path.join(UpperCamelCase__ , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_a : Any = eval_result
if best_iteration is None:
_a : Union[str, Any] = new_iteration
_a : str = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_a : Union[str, Any] = new_iteration
_a : List[str] = new_eval_result
_a : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_a : Tuple = new_iteration
_a : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_a : Union[str, Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , UpperCamelCase__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(UpperCamelCase__ , """eval_results_best-iteration.json""" ) , )
| 324
| 1
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class UpperCamelCase :
def __init__( self : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple=3 , UpperCAmelCase__ : Any=7 , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : List[Any]=True , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Union[str, Any]=99 , UpperCAmelCase__ : List[Any]=32 , UpperCAmelCase__ : List[str]=5 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : Any=37 , UpperCAmelCase__ : Dict="gelu" , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : int=512 , UpperCAmelCase__ : Dict=16 , UpperCAmelCase__ : Optional[Any]=2 , UpperCAmelCase__ : int=0.0_2 , UpperCAmelCase__ : Tuple=3 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : Optional[int]=None , ) -> Any:
_a : Optional[Any] = parent
_a : List[str] = batch_size
_a : Union[str, Any] = seq_length
_a : Optional[int] = is_training
_a : List[Any] = use_input_mask
_a : List[Any] = use_token_type_ids
_a : List[Any] = use_labels
_a : Optional[int] = vocab_size
_a : str = hidden_size
_a : Any = num_hidden_layers
_a : List[Any] = num_attention_heads
_a : List[str] = intermediate_size
_a : str = hidden_act
_a : Union[str, Any] = hidden_dropout_prob
_a : Dict = attention_probs_dropout_prob
_a : Optional[int] = max_position_embeddings
_a : Union[str, Any] = type_vocab_size
_a : List[Any] = type_sequence_label_size
_a : Union[str, Any] = initializer_range
_a : Dict = num_labels
_a : Dict = num_choices
_a : int = scope
def _lowercase ( self : Tuple ) -> List[Any]:
_a : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : Tuple = None
if self.use_input_mask:
_a : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_a : List[str] = None
_a : Any = None
_a : Optional[int] = None
_a : Tuple = None
if self.use_labels:
_a : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_a : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self : Union[str, Any] ) -> Tuple:
return FalconConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=UpperCAmelCase__ , )
def _lowercase ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] ) -> Union[str, Any]:
_a : Optional[Any] = FalconModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Tuple = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ )
_a : Optional[int] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict , ) -> Union[str, Any]:
_a : List[str] = True
_a : Dict = FalconModel(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , )
_a : Optional[Any] = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , )
_a : str = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict , ) -> Tuple:
_a : List[Any] = FalconForCausalLM(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : Any , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , ) -> int:
_a : Dict = True
_a : List[Any] = True
_a : int = FalconForCausalLM(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
# first forward pass
_a : Tuple = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , use_cache=UpperCAmelCase__ , )
_a : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a : List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a : str = torch.cat([input_ids, next_tokens] , dim=-1 )
_a : Dict = torch.cat([input_mask, next_mask] , dim=-1 )
_a : List[Any] = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , )["""hidden_states"""][0]
_a : int = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , past_key_values=UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , )["""hidden_states"""][0]
# select random slice
_a : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a : str = output_from_no_past[:, -3:, random_slice_idx].detach()
_a : List[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1E-3 ) )
def _lowercase ( self : str ) -> int:
_a : List[str] = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : Optional[Any] = config_and_inputs
_a : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Any = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase : Optional[int] = (FalconForCausalLM,) if is_torch_available() else ()
UpperCamelCase : Dict = (
{
'''feature-extraction''': FalconModel,
'''text-classification''': FalconForSequenceClassification,
'''text-generation''': FalconForCausalLM,
'''question-answering''': FalconForQuestionAnswering,
'''token-classification''': FalconForTokenClassification,
'''zero-shot''': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase : List[Any] = False
UpperCamelCase : str = False
def _lowercase ( self : str ) -> Dict:
_a : Optional[int] = FalconModelTester(self )
_a : Tuple = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def _lowercase ( self : Optional[Any] ) -> Tuple:
self.config_tester.run_common_tests()
def _lowercase ( self : Union[str, Any] ) -> Any:
_a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def _lowercase ( self : int ) -> Tuple:
_a , *_a : int = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
_a : Optional[Any] = alibi
self.model_tester.create_and_check_model(UpperCAmelCase__ , *UpperCAmelCase__ )
def _lowercase ( self : Any ) -> Any:
_a , _a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_a : str = 3
_a : List[str] = input_dict["""input_ids"""]
_a : Union[str, Any] = input_ids.ne(1 ).to(UpperCAmelCase__ )
_a : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a : Any = FalconForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowercase ( self : List[Any] ) -> str:
_a , _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
_a : Any = 3
_a : str = """single_label_classification"""
_a : Union[str, Any] = input_dict["""input_ids"""]
_a : List[Any] = input_ids.ne(1 ).to(UpperCAmelCase__ )
_a : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a : Tuple = FalconForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowercase ( self : Optional[Any] ) -> List[Any]:
_a , _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
_a : List[Any] = input_dict["""input_ids"""]
_a : Union[str, Any] = FalconForCausalLM(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : List[str] = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__ )
_a : Optional[Any] = input_ids.shape[0]
_a : Any = model._convert_to_rw_cache(result.past_key_values )
_a : Dict = model._convert_cache_to_standard_format(UpperCAmelCase__ , UpperCAmelCase__ )
for layer in range(len(UpperCAmelCase__ ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def _lowercase ( self : Union[str, Any] ) -> int:
_a , _a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_a : List[Any] = 3
_a : Any = """multi_label_classification"""
_a : int = input_dict["""input_ids"""]
_a : List[str] = input_ids.ne(1 ).to(UpperCAmelCase__ )
_a : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a : Union[str, Any] = FalconForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Any = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowercase ( self : Optional[int] ) -> Any:
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
_a , _a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(UpperCAmelCase__ , """use_cache""" ):
return
_a : Optional[Any] = model_class(UpperCAmelCase__ ).to(UpperCAmelCase__ )
if "use_cache" not in inputs:
_a : Tuple = True
_a : str = model(**UpperCAmelCase__ )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
_a : Tuple = (
getattr(UpperCAmelCase__ , """decoder_layers""" , UpperCAmelCase__ )
or getattr(UpperCAmelCase__ , """num_decoder_layers""" , UpperCAmelCase__ )
or config.num_hidden_layers
)
_a : Union[str, Any] = getattr(UpperCAmelCase__ , """num_kv_heads""" , config.num_attention_heads )
_a : Optional[Any] = getattr(UpperCAmelCase__ , """d_model""" , config.hidden_size )
_a : Optional[int] = embed_dim // num_attention_heads
_a : Dict = outputs["""past_key_values"""]
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
_a , _a : Union[str, Any] = inputs["""input_ids"""].shape
for i in range(UpperCAmelCase__ ):
if config.new_decoder_architecture:
_a : Optional[Any] = config.num_attention_heads
elif config.multi_query:
_a : Any = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class UpperCamelCase ( unittest.TestCase ):
@slow
def _lowercase ( self : Any ) -> Tuple:
_a : Any = AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
_a : List[Any] = FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
model.eval()
model.to(UpperCAmelCase__ )
_a : str = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(UpperCAmelCase__ )
_a : Optional[int] = (
"""My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
)
_a : Optional[Any] = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=19 )
_a : Dict = tokenizer.batch_decode(UpperCAmelCase__ )[0]
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : str ) -> Dict:
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
_a : Optional[int] = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
_a : int = FalconForCausalLM.from_pretrained(UpperCAmelCase__ )
model.eval()
model.to(UpperCAmelCase__ )
_a : Any = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(UpperCAmelCase__ )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=4 )
model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=4 )
model.generate(**UpperCAmelCase__ , num_beams=2 , max_new_tokens=4 )
@slow
def _lowercase ( self : Union[str, Any] ) -> List[str]:
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
_a : Union[str, Any] = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
_a : Tuple = FalconForCausalLM.from_pretrained(UpperCAmelCase__ )
model.eval()
model.to(device=UpperCAmelCase__ )
_a : Optional[int] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(UpperCAmelCase__ )
# Test results are the same with and without cache
_a : str = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=20 , use_cache=UpperCAmelCase__ )
_a : Union[str, Any] = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=20 , use_cache=UpperCAmelCase__ )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 324
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_snake_case = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
},
}
_snake_case = {
'camembert-base': 512,
}
_snake_case = '▁'
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = VOCAB_FILES_NAMES
UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Dict = ['''input_ids''', '''attention_mask''']
UpperCamelCase : Optional[Any] = CamembertTokenizer
def __init__( self : int , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]="<s>" , UpperCAmelCase__ : Optional[int]="</s>" , UpperCAmelCase__ : Tuple="</s>" , UpperCAmelCase__ : Tuple="<s>" , UpperCAmelCase__ : Tuple="<unk>" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : int="<mask>" , UpperCAmelCase__ : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a : List[Any] = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : int = vocab_file
_a : int = False if not self.vocab_file else True
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a : List[Any] = [self.cls_token_id]
_a : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Union[str, Any] = [self.sep_token_id]
_a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : List[str] = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 324
| 1
|
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'spiece.model'}
_snake_case = {
'vocab_file': {
'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
}
}
class UpperCamelCase ( snake_case_ ):
def __init__( self : str , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any]=False , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Any="<s>" , UpperCAmelCase__ : int="</s>" , UpperCAmelCase__ : Union[str, Any]="<unk>" , UpperCAmelCase__ : str="<sep>" , UpperCAmelCase__ : str="<pad>" , UpperCAmelCase__ : Optional[int]="<cls>" , UpperCAmelCase__ : Tuple="<mask>" , UpperCAmelCase__ : Dict=["<eop>", "<eod>"] , UpperCAmelCase__ : Optional[Dict[str, Any]] = None , **UpperCAmelCase__ : Optional[Any] , ) -> None:
_a : Dict = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else mask_token
_a : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=UpperCAmelCase__ , remove_space=UpperCAmelCase__ , keep_accents=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCAmelCase__ , )
_a : Union[str, Any] = 3
_a : Dict = do_lower_case
_a : int = remove_space
_a : Union[str, Any] = keep_accents
_a : Any = vocab_file
_a : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCAmelCase__ )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"""You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
"""See https://pypi.org/project/jieba/ for installation.""" )
_a : List[str] = jieba
_a : int = str.maketrans(""" \n""" , """\u2582\u2583""" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def _lowercase ( self : Dict ) -> str:
return len(self.sp_model )
def _lowercase ( self : Tuple ) -> str:
_a : Optional[int] = {self.convert_ids_to_tokens(UpperCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[Any] ) -> List[Any]:
_a : Tuple = self.__dict__.copy()
_a : Optional[Any] = None
return state
def __setstate__( self : Union[str, Any] , UpperCAmelCase__ : str ) -> List[Any]:
_a : int = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_a : List[str] = {}
_a : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : int ) -> Optional[Any]:
if self.remove_space:
_a : Any = """ """.join(inputs.strip().split() )
else:
_a : Union[str, Any] = inputs
_a : Optional[int] = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
_a : List[Any] = unicodedata.normalize("""NFKD""" , UpperCAmelCase__ )
_a : Union[str, Any] = """""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__ )] )
if self.do_lower_case:
_a : Optional[int] = outputs.lower()
return outputs
def _lowercase ( self : Dict , UpperCAmelCase__ : str ) -> List[str]:
_a : Union[str, Any] = self.preprocess_text(UpperCAmelCase__ )
_a : Optional[int] = self.sp_model.encode(UpperCAmelCase__ , out_type=UpperCAmelCase__ )
_a : Optional[int] = []
for piece in pieces:
if len(UpperCAmelCase__ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
_a : Any = self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__ , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_a : Tuple = cur_pieces[1:]
else:
_a : int = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(UpperCAmelCase__ )
else:
new_pieces.append(UpperCAmelCase__ )
return new_pieces
def _lowercase ( self : Dict , UpperCAmelCase__ : List[str] ) -> Dict:
return self.sp_model.PieceToId(UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Tuple ) -> Dict:
return self.sp_model.IdToPiece(UpperCAmelCase__ )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Optional[int] ) -> Dict:
_a : Any = """""".join(UpperCAmelCase__ ).replace(UpperCAmelCase__ , """ """ ).strip()
return out_string
def _lowercase ( self : Tuple , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : str = [self.sep_token_id]
_a : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None , UpperCAmelCase__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase__ , token_ids_a=UpperCAmelCase__ , already_has_special_tokens=UpperCAmelCase__ )
if token_ids_a is not None:
return ([0] * len(UpperCAmelCase__ )) + [1] + ([0] * len(UpperCAmelCase__ )) + [1, 1]
return ([0] * len(UpperCAmelCase__ )) + [1, 1]
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Any = [self.sep_token_id]
_a : Optional[int] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _lowercase ( self : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : Tuple = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCAmelCase__ , """wb""" ) as fi:
_a : Any = self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase__ )
return (out_vocab_file,)
def _lowercase ( self : List[Any] , *UpperCAmelCase__ : Any , **UpperCAmelCase__ : str ) -> Union[str, Any]:
_a : List[str] = super()._decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
_a : List[Any] = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" )
return text
| 324
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = ['''pixel_values''']
def __init__( self : Any , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 32 , UpperCAmelCase__ : Optional[Any]=PILImageResampling.BILINEAR , UpperCAmelCase__ : bool = True , **UpperCAmelCase__ : List[str] , ) -> None:
_a : int = do_resize
_a : Union[str, Any] = do_rescale
_a : Any = size_divisor
_a : Any = resample
super().__init__(**UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[Any] ) -> np.ndarray:
_a , _a : Tuple = get_image_size(UpperCAmelCase__ )
# Rounds the height and width down to the closest multiple of size_divisor
_a : Optional[Any] = height // size_divisor * size_divisor
_a : Union[str, Any] = width // size_divisor * size_divisor
_a : Any = resize(UpperCAmelCase__ , (new_h, new_w) , resample=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
return image
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[ChannelDimension] = None , **UpperCAmelCase__ : Optional[int] ) -> np.ndarray:
return rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[Union[TensorType, str]] = None , UpperCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase__ : int , ) -> BatchFeature:
_a : Dict = do_resize if do_resize is not None else self.do_resize
_a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
_a : str = size_divisor if size_divisor is not None else self.size_divisor
_a : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_a : List[str] = make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_a : Tuple = [to_numpy_array(UpperCAmelCase__ ) for img in images]
if do_resize:
_a : Optional[int] = [self.resize(UpperCAmelCase__ , size_divisor=UpperCAmelCase__ , resample=UpperCAmelCase__ ) for image in images]
if do_rescale:
_a : str = [self.rescale(UpperCAmelCase__ , scale=1 / 255 ) for image in images]
_a : Any = [to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
_a : Optional[int] = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 324
| 1
|
"""simple docstring"""
import math
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 0 , UpperCamelCase__ = 0 ):
'''simple docstring'''
_a : List[Any] = end or len(UpperCamelCase__ )
for i in range(UpperCamelCase__ , UpperCamelCase__ ):
_a : int = i
_a : Tuple = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_a : Any = array[temp_index - 1]
temp_index -= 1
_a : List[Any] = temp_index_value
return array
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): # Max Heap
'''simple docstring'''
_a : Union[str, Any] = index
_a : Union[str, Any] = 2 * index + 1 # Left Node
_a : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_a : Dict = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_a : Dict = right_index
if largest != index:
_a , _a : Optional[int] = array[largest], array[index]
heapify(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = len(UpperCamelCase__ )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for i in range(n - 1 , 0 , -1 ):
_a , _a : List[Any] = array[0], array[i]
heapify(UpperCamelCase__ , 0 , UpperCamelCase__ )
return array
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : str = low
_a : List[Any] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_a , _a : int = array[j], array[i]
i += 1
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if len(UpperCamelCase__ ) == 0:
return array
_a : str = 2 * math.ceil(math.loga(len(UpperCamelCase__ ) ) )
_a : Any = 1_6
return intro_sort(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase__ )
max_depth -= 1
_a : Dict = median_of_a(UpperCamelCase__ , UpperCamelCase__ , start + ((end - start) // 2) + 1 , end - 1 )
_a : Optional[Any] = partition(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
intro_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
_a : List[str] = p
return insertion_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = input('Enter numbers separated by a comma : ').strip()
_snake_case = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 324
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
_a : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def _lowercase ( self : Dict ) -> Dict:
_a : str = self.dummy_uncond_unet
_a : Optional[int] = KarrasVeScheduler()
_a : List[str] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : int = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Tuple = torch.manual_seed(0 )
_a : int = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : int = image[0, -3:, -3:, -1]
_a : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : Tuple ) -> List[str]:
_a : Optional[Any] = """google/ncsnpp-celebahq-256"""
_a : Any = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : Dict = KarrasVeScheduler()
_a : int = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[int] = torch.manual_seed(0 )
_a : Tuple = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Optional[int] = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 324
| 1
|
"""simple docstring"""
from __future__ import annotations
_snake_case = tuple[int, int, int]
_snake_case = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_snake_case = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection --------------------------
# rotors --------------------------
_snake_case = 'EGZWVONAHDCLFQMSIPJBYUKXTR'
_snake_case = 'FOBHMDKEXQNRAULPGSJVTYICZW'
_snake_case = 'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
_snake_case = {
'A': 'N',
'N': 'A',
'B': 'O',
'O': 'B',
'C': 'P',
'P': 'C',
'D': 'Q',
'Q': 'D',
'E': 'R',
'R': 'E',
'F': 'S',
'S': 'F',
'G': 'T',
'T': 'G',
'H': 'U',
'U': 'H',
'I': 'V',
'V': 'I',
'J': 'W',
'W': 'J',
'K': 'X',
'X': 'K',
'L': 'Y',
'Y': 'L',
'M': 'Z',
'Z': 'M',
}
# -------------------------- extra rotors --------------------------
_snake_case = 'RMDJXFUWGISLHVTCQNKYPBEZOA'
_snake_case = 'SGLCPQWZHKXAREONTFBVIYJUDM'
_snake_case = 'HVSICLTYKQUBXDWAJZOMFGPREN'
_snake_case = 'RZWQHFMVDBKICJLNTUXAGYPSOE'
_snake_case = 'LFKIJODBEGAMQPXVUHYSTCZRWN'
_snake_case = 'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(UpperCamelCase__ ) )) < 3:
_a : Tuple = F"""Please use 3 unique rotors (not {unique_rotsel})"""
raise Exception(UpperCamelCase__ )
# Checks if rotor positions are valid
_a , _a , _a : int = rotpos
if not 0 < rotorposa <= len(UpperCamelCase__ ):
_a : List[str] = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
raise ValueError(UpperCamelCase__ )
if not 0 < rotorposa <= len(UpperCamelCase__ ):
_a : Optional[Any] = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(UpperCamelCase__ )
if not 0 < rotorposa <= len(UpperCamelCase__ ):
_a : Optional[Any] = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(UpperCamelCase__ )
# Validates string and returns dict
_a : Union[str, Any] = _plugboard(UpperCamelCase__ )
return rotpos, rotsel, pbdict
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_a : Dict = F"""Plugboard setting isn't type string ({type(UpperCamelCase__ )})"""
raise TypeError(UpperCamelCase__ )
elif len(UpperCamelCase__ ) % 2 != 0:
_a : Any = F"""Odd number of symbols ({len(UpperCamelCase__ )})"""
raise Exception(UpperCamelCase__ )
elif pbstring == "":
return {}
pbstring.replace(""" """ , """""" )
# Checks if all characters are unique
_a : int = set()
for i in pbstring:
if i not in abc:
_a : Optional[Any] = F"""'{i}' not in list of symbols"""
raise Exception(UpperCamelCase__ )
elif i in tmppbl:
_a : List[str] = F"""Duplicate symbol ({i})"""
raise Exception(UpperCamelCase__ )
else:
tmppbl.add(UpperCamelCase__ )
del tmppbl
# Created the dictionary
_a : int = {}
for j in range(0 , len(UpperCamelCase__ ) - 1 , 2 ):
_a : Any = pbstring[j + 1]
_a : Optional[Any] = pbstring[j]
return pb
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = (rotora, rotora, rotora) , UpperCamelCase__ = "" , ):
'''simple docstring'''
_a : List[Any] = text.upper()
_a , _a , _a : Optional[Any] = _validator(
UpperCamelCase__ , UpperCamelCase__ , plugb.upper() )
_a , _a , _a : Tuple = rotor_position
_a , _a , _a : Optional[int] = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_a : Any = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_a : Union[str, Any] = plugboard[symbol]
# rotor ra --------------------------
_a : Optional[Any] = abc.index(UpperCamelCase__ ) + rotorposa
_a : List[Any] = rotora[index % len(UpperCamelCase__ )]
# rotor rb --------------------------
_a : Tuple = abc.index(UpperCamelCase__ ) + rotorposa
_a : Union[str, Any] = rotora[index % len(UpperCamelCase__ )]
# rotor rc --------------------------
_a : str = abc.index(UpperCamelCase__ ) + rotorposa
_a : str = rotora[index % len(UpperCamelCase__ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_a : Tuple = reflector[symbol]
# 2nd rotors
_a : Optional[int] = abc[rotora.index(UpperCamelCase__ ) - rotorposa]
_a : Tuple = abc[rotora.index(UpperCamelCase__ ) - rotorposa]
_a : str = abc[rotora.index(UpperCamelCase__ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_a : int = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(UpperCamelCase__ ):
_a : Tuple = 0
rotorposa += 1
if rotorposa >= len(UpperCamelCase__ ):
_a : int = 0
rotorposa += 1
if rotorposa >= len(UpperCamelCase__ ):
_a : Union[str, Any] = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(UpperCamelCase__ )
return "".join(UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = 'This is my Python script that emulates the Enigma machine from WWII.'
_snake_case = (1, 1, 1)
_snake_case = 'pictures'
_snake_case = (rotora, rotora, rotora)
_snake_case = enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))
| 324
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 1_6 ):
'''simple docstring'''
_a : str = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_a : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : Tuple = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : int = 1_6
elif accelerator.mixed_precision != "no":
_a : int = 8
else:
_a : str = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_a : List[str] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
_a : str = 2
# Initialize accelerator
_a : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Any = config["""lr"""]
_a : Union[str, Any] = int(config["""num_epochs"""] )
_a : str = int(config["""seed"""] )
_a : List[Any] = int(config["""batch_size"""] )
_a : Tuple = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_a : Optional[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_a : str = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
_a , _a : Optional[int] = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : List[str] = model.to(accelerator.device )
# Instantiate optimizer
_a : List[str] = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_a , _a , _a , _a , _a : Optional[Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : Optional[Any] = model(**UpperCamelCase__ )
_a : str = outputs.loss
_a : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_a : Union[str, Any] = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Dict = model(**UpperCamelCase__ )
_a : Optional[Any] = outputs.logits.argmax(dim=-1 )
_a , _a : int = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_a : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_a : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_a : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_a : Optional[Any] = parser.parse_args()
_a : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 324
| 1
|
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_snake_case = re.compile(r'\b(a|an|the)\b', re.UNICODE)
_snake_case = None
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=UpperCamelCase__ , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=UpperCamelCase__ , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
_a : List[str] = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
def remove_articles(UpperCamelCase__ ):
return ARTICLES_REGEX.sub(""" """ , UpperCamelCase__ )
def white_space_fix(UpperCamelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase__ ):
_a : List[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if not s:
return []
return normalize_answer(UpperCamelCase__ ).split()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return int(normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ ) )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = get_tokens(UpperCamelCase__ )
_a : str = get_tokens(UpperCamelCase__ )
_a : Optional[Any] = collections.Counter(UpperCamelCase__ ) & collections.Counter(UpperCamelCase__ )
_a : Optional[Any] = sum(common.values() )
if len(UpperCamelCase__ ) == 0 or len(UpperCamelCase__ ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
_a : Dict = 1.0 * num_same / len(UpperCamelCase__ )
_a : Optional[Any] = 1.0 * num_same / len(UpperCamelCase__ )
_a : str = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : int = {}
_a : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
_a : Union[str, Any] = qa["""id"""]
_a : Tuple = [t for t in qa["""answers"""]["""text"""] if normalize_answer(UpperCamelCase__ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
_a : int = [""""""]
if qid not in preds:
print(F"""Missing prediction for {qid}""" )
continue
_a : Optional[Any] = preds[qid]
# Take max over all gold answers
_a : int = max(compute_exact(UpperCamelCase__ , UpperCamelCase__ ) for a in gold_answers )
_a : Dict = max(compute_fa(UpperCamelCase__ , UpperCamelCase__ ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = {}
for qid, s in scores.items():
_a : str = na_probs[qid] > na_prob_thresh
if pred_na:
_a : Dict = float(not qid_to_has_ans[qid] )
else:
_a : List[str] = s
return new_scores
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None ):
'''simple docstring'''
if not qid_list:
_a : Optional[int] = len(UpperCamelCase__ )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
_a : Dict = len(UpperCamelCase__ )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
for k in new_eval:
_a : Optional[Any] = new_eval[k]
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
plt.step(UpperCamelCase__ , UpperCamelCase__ , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(UpperCamelCase__ , UpperCamelCase__ , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(UpperCamelCase__ )
plt.savefig(UpperCamelCase__ )
plt.clf()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ):
'''simple docstring'''
_a : List[Any] = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : na_probs[k] )
_a : Dict = 0.0
_a : Dict = 1.0
_a : Any = 0.0
_a : Union[str, Any] = [1.0]
_a : str = [0.0]
_a : Union[str, Any] = 0.0
for i, qid in enumerate(UpperCamelCase__ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
_a : Any = true_pos / float(i + 1 )
_a : Optional[int] = true_pos / float(UpperCamelCase__ )
if i == len(UpperCamelCase__ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(UpperCamelCase__ )
recalls.append(UpperCamelCase__ )
if out_image:
plot_pr_curve(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if out_image_dir and not os.path.exists(UpperCamelCase__ ):
os.makedirs(UpperCamelCase__ )
_a : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
_a : List[str] = make_precision_recall_eval(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , out_image=os.path.join(UpperCamelCase__ , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
_a : Union[str, Any] = make_precision_recall_eval(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , out_image=os.path.join(UpperCamelCase__ , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
_a : List[Any] = {k: float(UpperCamelCase__ ) for k, v in qid_to_has_ans.items()}
_a : List[Any] = make_precision_recall_eval(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , out_image=os.path.join(UpperCamelCase__ , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(UpperCamelCase__ , UpperCamelCase__ , """pr_exact""" )
merge_eval(UpperCamelCase__ , UpperCamelCase__ , """pr_f1""" )
merge_eval(UpperCamelCase__ , UpperCamelCase__ , """pr_oracle""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if not qid_list:
return
_a : Union[str, Any] = [na_probs[k] for k in qid_list]
_a : int = np.ones_like(UpperCamelCase__ ) / float(len(UpperCamelCase__ ) )
plt.hist(UpperCamelCase__ , weights=UpperCamelCase__ , bins=2_0 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(F"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(UpperCamelCase__ , F"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
_a : Tuple = num_no_ans
_a : int = cur_score
_a : str = 0.0
_a : Dict = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : na_probs[k] )
for i, qid in enumerate(UpperCamelCase__ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
_a : List[Any] = scores[qid]
else:
if preds[qid]:
_a : Optional[Any] = -1
else:
_a : Optional[Any] = 0
cur_score += diff
if cur_score > best_score:
_a : List[str] = cur_score
_a : Dict = na_probs[qid]
return 100.0 * best_score / len(UpperCamelCase__ ), best_thresh
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Optional[int] = find_best_thresh(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
_a , _a : List[str] = find_best_thresh(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
_a : Dict = best_exact
_a : str = exact_thresh
_a : Dict = best_fa
_a : str = fa_thresh
def lowerCAmelCase__ ( ):
'''simple docstring'''
with open(OPTS.data_file ) as f:
_a : str = json.load(UpperCamelCase__ )
_a : Tuple = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
_a : Union[str, Any] = json.load(UpperCamelCase__ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
_a : Optional[int] = json.load(UpperCamelCase__ )
else:
_a : int = {k: 0.0 for k in preds}
_a : str = make_qid_to_has_ans(UpperCamelCase__ ) # maps qid to True/False
_a : Dict = [k for k, v in qid_to_has_ans.items() if v]
_a : List[str] = [k for k, v in qid_to_has_ans.items() if not v]
_a , _a : int = get_raw_scores(UpperCamelCase__ , UpperCamelCase__ )
_a : List[str] = apply_no_ans_threshold(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , OPTS.na_prob_thresh )
_a : List[str] = apply_no_ans_threshold(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , OPTS.na_prob_thresh )
_a : str = make_eval_dict(UpperCamelCase__ , UpperCamelCase__ )
if has_ans_qids:
_a : Dict = make_eval_dict(UpperCamelCase__ , UpperCamelCase__ , qid_list=UpperCamelCase__ )
merge_eval(UpperCamelCase__ , UpperCamelCase__ , """HasAns""" )
if no_ans_qids:
_a : Any = make_eval_dict(UpperCamelCase__ , UpperCamelCase__ , qid_list=UpperCamelCase__ )
merge_eval(UpperCamelCase__ , UpperCamelCase__ , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , OPTS.out_image_dir )
histogram_na_prob(UpperCamelCase__ , UpperCamelCase__ , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(UpperCamelCase__ , UpperCamelCase__ , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
else:
print(json.dumps(UpperCamelCase__ , indent=2 ) )
if __name__ == "__main__":
_snake_case = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| 324
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# Base Case
if curr_ind == len(UpperCamelCase__ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(UpperCamelCase__ ) ):
if valid_connection(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# Insert current vertex into path as next transition
_a : int = next_ver
# Validate created path
if util_hamilton_cycle(UpperCamelCase__ , UpperCamelCase__ , curr_ind + 1 ):
return True
# Backtrack
_a : Tuple = -1
return False
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = 0 ):
'''simple docstring'''
_a : int = [-1] * (len(UpperCamelCase__ ) + 1)
# initialize start and end of path with starting index
_a : str = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(UpperCamelCase__ , UpperCamelCase__ , 1 ) else []
| 324
|
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
_snake_case = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
_snake_case = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : str = CamembertTokenizer
UpperCamelCase : List[Any] = CamembertTokenizerFast
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = True
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] ) -> Tuple:
_a : Optional[Any] = """<pad>"""
_a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def _lowercase ( self : List[str] ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _lowercase ( self : Union[str, Any] ) -> str:
_a : Tuple = CamembertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
_a : List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_a : Any = """I was born in 92000, and this is falsé."""
_a : Union[str, Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Tuple = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_a : List[str] = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
_a : int = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> List[str]:
if not self.test_rust_tokenizer:
return
_a : Optional[int] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : List[Any] = """I was born in 92000, and this is falsé."""
_a : List[str] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : Optional[Any] = tokenizer.encode(UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def _lowercase ( self : Tuple ) -> List[Any]:
# fmt: off
_a : Dict = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_a : Union[str, Any] = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=UpperCAmelCase__ , )
| 324
| 1
|
"""simple docstring"""
import numpy as np
from PIL import Image
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = np.array(UpperCamelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not a square matrix""" )
_a : Tuple = 0
_a : Tuple = 0
_a : Dict = 0
_a : int = 0
# compute the shape of the output matrix
_a : Dict = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
_a : Any = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
_a : Optional[Any] = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
_a : Optional[int] = 0
_a : Dict = 0
return updated_arr
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = np.array(UpperCamelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not a square matrix""" )
_a : int = 0
_a : Dict = 0
_a : Dict = 0
_a : Any = 0
# compute the shape of the output matrix
_a : Optional[Any] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
_a : Optional[Any] = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
_a : Optional[int] = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
_a : int = 0
_a : Union[str, Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='avgpooling', verbose=True)
# Loading the image
_snake_case = Image.open('path_to_image')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 324
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
_snake_case = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCamelCase__ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_a : Optional[int] = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_a : List[Any] = collections.defaultdict(UpperCamelCase__ )
_a : List[str] = collections.defaultdict(UpperCamelCase__ )
_a : Tuple = collections.defaultdict(UpperCamelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(UpperCamelCase__ ):
_a : str = None
if _re_tf_models.match(UpperCamelCase__ ) is not None:
_a : List[Any] = tf_models
_a : int = _re_tf_models.match(UpperCamelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCamelCase__ ) is not None:
_a : Any = flax_models
_a : Any = _re_flax_models.match(UpperCamelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCamelCase__ ) is not None:
_a : int = pt_models
_a : int = _re_pt_models.match(UpperCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCamelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
_a : Optional[int] = True
break
# Try again after removing the last word in the name
_a : List[Any] = """""".join(camel_case_split(UpperCamelCase__ )[:-1] )
_a : Optional[int] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_a : Dict = list(UpperCamelCase__ )
all_models.sort()
_a : str = {"""model_type""": all_models}
_a : List[Any] = [pt_models[t] for t in all_models]
_a : str = [tf_models[t] for t in all_models]
_a : Optional[int] = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_a : str = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_a : List[str] = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_a : str = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_a : int = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_a : int = """AutoTokenizer"""
_a : Any = [processors[t] for t in all_models]
return pd.DataFrame(UpperCamelCase__ )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_a : List[Any] = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_a : Union[str, Any] = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
continue
# First extract all model_names
_a : str = []
for name in getattr(UpperCamelCase__ , UpperCamelCase__ ).values():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
model_names.append(UpperCamelCase__ )
else:
model_names.extend(list(UpperCamelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = get_frameworks_table()
_a : Optional[Any] = Dataset.from_pandas(UpperCamelCase__ )
_a : Any = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=UpperCamelCase__ )
_a : List[Any] = Dataset.from_json(UpperCamelCase__ )
_a : List[str] = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(UpperCamelCase__ ) )
}
_a : str = update_pipeline_and_auto_class_table(UpperCamelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_a : int = sorted(table.keys() )
_a : Union[str, Any] = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_a : Dict = Dataset.from_pandas(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(UpperCamelCase__ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(UpperCamelCase__ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_a : List[str] = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_a : Optional[Any] = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=UpperCamelCase__ , repo_type="""dataset""" , token=UpperCamelCase__ , commit_message=UpperCamelCase__ , )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_a : Any = transformers_module.pipelines.SUPPORTED_TASKS
_a : List[str] = []
for key in pipeline_tasks:
if key not in in_table:
_a : Tuple = pipeline_tasks[key]["""pt"""]
if isinstance(UpperCamelCase__ , (list, tuple) ):
_a : Dict = model[0]
_a : List[str] = model.__name__
if model not in in_table.values():
missing.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
_a : Union[str, Any] = """, """.join(UpperCamelCase__ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
_snake_case = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 324
| 1
|
"""simple docstring"""
from copy import deepcopy
class UpperCamelCase :
def __init__( self : int , UpperCAmelCase__ : list[int] | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if arr is None and size is not None:
_a : Dict = size
_a : Tuple = [0] * size
elif arr is not None:
self.init(UpperCAmelCase__ )
else:
raise ValueError("""Either arr or size must be specified""" )
def _lowercase ( self : Tuple , UpperCAmelCase__ : list[int] ) -> None:
_a : Optional[int] = len(UpperCAmelCase__ )
_a : int = deepcopy(UpperCAmelCase__ )
for i in range(1 , self.size ):
_a : str = self.next_(UpperCAmelCase__ )
if j < self.size:
self.tree[j] += self.tree[i]
def _lowercase ( self : Optional[Any] ) -> list[int]:
_a : int = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
_a : Optional[int] = self.next_(UpperCAmelCase__ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _lowercase ( UpperCAmelCase__ : int ) -> int:
return index + (index & (-index))
@staticmethod
def _lowercase ( UpperCAmelCase__ : int ) -> int:
return index - (index & (-index))
def _lowercase ( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> None:
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
_a : List[str] = self.next_(UpperCAmelCase__ )
def _lowercase ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> None:
self.add(UpperCAmelCase__ , value - self.get(UpperCAmelCase__ ) )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : int ) -> int:
if right == 0:
return 0
_a : int = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
_a : Union[str, Any] = self.prev(UpperCAmelCase__ )
return result
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
return self.prefix(UpperCAmelCase__ ) - self.prefix(UpperCAmelCase__ )
def _lowercase ( self : int , UpperCAmelCase__ : int ) -> int:
return self.query(UpperCAmelCase__ , index + 1 )
def _lowercase ( self : Tuple , UpperCAmelCase__ : int ) -> int:
value -= self.tree[0]
if value < 0:
return -1
_a : Any = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
_a : str = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_a : Dict = DatasetInfosDict.from_directory(UpperCamelCase__ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = str(UpperCamelCase__ )
dataset_info.write_to_directory(UpperCamelCase__ )
_a : Any = DatasetInfo.from_directory(UpperCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase__ , """dataset_info.json""" ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Dict = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
_a : int = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_a : List[str] = yaml.safe_dump(UpperCamelCase__ )
_a : Optional[int] = yaml.safe_load(UpperCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = DatasetInfo()
_a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=4_2 ),
"""v2""": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = str(UpperCamelCase__ )
dataset_infos_dict.write_to_directory(UpperCamelCase__ )
_a : List[Any] = DatasetInfosDict.from_directory(UpperCamelCase__ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_a : str = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_a : Dict = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase__ , """README.md""" ) )
| 324
| 1
|
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return (data["data"], data["target"])
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(UpperCamelCase__ , UpperCamelCase__ )
# Predict target for test data
_a : Union[str, Any] = xgb.predict(UpperCamelCase__ )
_a : Optional[int] = predictions.reshape(len(UpperCamelCase__ ) , 1 )
return predictions
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Optional[Any] = fetch_california_housing()
_a , _a : List[str] = data_handling(UpperCamelCase__ )
_a , _a , _a , _a : Optional[int] = train_test_split(
UpperCamelCase__ , UpperCamelCase__ , test_size=0.25 , random_state=1 )
_a : str = xgboost(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Error printing
print(F"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__ , UpperCamelCase__ )}""" )
print(F"""Mean Square Error : {mean_squared_error(UpperCamelCase__ , UpperCamelCase__ )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 324
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase , snake_case_ ):
def _lowercase ( self : int ) -> int:
_a : Optional[Any] = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self : List[str] ) -> Union[str, Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : str = self.tool("""hey""" )
_a : List[str] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
_a : int = self.tool("""hey""" )
_a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 324
| 1
|
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 324
|
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCamelCase ( snake_case_ ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : str ) -> int:
_a : str = parent
_a : Union[str, Any] = config_class
_a : List[Any] = has_text_modality
_a : List[Any] = kwargs
_a : List[Any] = common_properties
def _lowercase ( self : int ) -> Tuple:
_a : List[str] = self.config_class(**self.inputs_dict )
_a : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) , msg=f"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase__ ):
try:
setattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase__ ):
try:
_a : Optional[int] = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase__ , UpperCAmelCase__ ) , UpperCAmelCase__ , msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__ , UpperCAmelCase__ )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
_a : List[str] = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase__ )
def _lowercase ( self : int ) -> List[str]:
_a : Optional[Any] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = os.path.join(UpperCAmelCase__ , """config.json""" )
config_first.to_json_file(UpperCAmelCase__ )
_a : List[str] = self.config_class.from_json_file(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Union[str, Any] ) -> Dict:
_a : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase__ )
_a : Dict = self.config_class.from_pretrained(UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : Dict ) -> Tuple:
_a : List[Any] = self.config_class(**self.inputs_dict )
_a : Any = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a : List[Any] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
config_first.save_pretrained(UpperCAmelCase__ )
_a : List[Any] = self.config_class.from_pretrained(UpperCAmelCase__ , subfolder=UpperCAmelCase__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Tuple = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
_a : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowercase ( self : Tuple ) -> List[str]:
if self.config_class.is_composition:
return
_a : str = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[Any]:
_a : Dict = copy.deepcopy(UpperCAmelCase__ )
_a : Any = self.config_class(**UpperCAmelCase__ )
_a : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase__ , UpperCAmelCase__ ) != value:
wrong_values.append((key, getattr(UpperCAmelCase__ , UpperCAmelCase__ ), value) )
if len(UpperCAmelCase__ ) > 0:
_a : List[Any] = """\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""" )
def _lowercase ( self : int ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 324
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
_snake_case = {
'vocab_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
},
'tokenizer_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
},
}
_snake_case = {
'google/fnet-base': 512,
'google/fnet-large': 512,
}
_snake_case = '▁'
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
UpperCamelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : str = ['''input_ids''', '''token_type_ids''']
UpperCamelCase : List[str] = FNetTokenizer
def __init__( self : Dict , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Optional[int]=False , UpperCAmelCase__ : int=True , UpperCAmelCase__ : List[Any]=True , UpperCAmelCase__ : List[Any]="<unk>" , UpperCAmelCase__ : Optional[Any]="[SEP]" , UpperCAmelCase__ : Optional[Any]="<pad>" , UpperCAmelCase__ : List[Any]="[CLS]" , UpperCAmelCase__ : List[Any]="[MASK]" , **UpperCAmelCase__ : Union[str, Any] , ) -> Optional[Any]:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_a : List[Any] = (
AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ , normalized=UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
else mask_token
)
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , remove_space=UpperCAmelCase__ , keep_accents=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
_a : List[str] = do_lower_case
_a : List[Any] = remove_space
_a : List[str] = keep_accents
_a : List[Any] = vocab_file
_a : Optional[Any] = False if not self.vocab_file else True
def _lowercase ( self : Any , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : int = [self.sep_token_id]
_a : Any = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowercase ( self : Tuple , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
_a : Any = [self.sep_token_id]
_a : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : Any = os.path.join(
UpperCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 324
|
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_snake_case = HUGGINGFACE_HUB_CACHE
_snake_case = 'config.json'
_snake_case = 'diffusion_pytorch_model.bin'
_snake_case = 'diffusion_flax_model.msgpack'
_snake_case = 'model.onnx'
_snake_case = 'diffusion_pytorch_model.safetensors'
_snake_case = 'weights.pb'
_snake_case = 'https://huggingface.co'
_snake_case = default_cache_path
_snake_case = 'diffusers_modules'
_snake_case = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_snake_case = ['fp16', 'non-ema']
_snake_case = '.self_attn'
| 324
| 1
|
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 324
|
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324
| 1
|
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase :
def __init__( self : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any]=13 , UpperCAmelCase__ : Dict=30 , UpperCAmelCase__ : List[str]=2 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Dict=32 , UpperCAmelCase__ : str=5 , UpperCAmelCase__ : Tuple=4 , UpperCAmelCase__ : Tuple=37 , UpperCAmelCase__ : Tuple="gelu" , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Dict=10 , UpperCAmelCase__ : Tuple=0.0_2 , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : int=2 , ) -> Dict:
_a : int = parent
_a : Optional[Any] = batch_size
_a : Tuple = image_size
_a : List[str] = patch_size
_a : Tuple = num_channels
_a : List[str] = is_training
_a : List[Any] = use_labels
_a : List[Any] = hidden_size
_a : int = num_hidden_layers
_a : Optional[Any] = num_attention_heads
_a : List[str] = intermediate_size
_a : Optional[Any] = hidden_act
_a : Tuple = hidden_dropout_prob
_a : Optional[int] = attention_probs_dropout_prob
_a : List[Any] = type_sequence_label_size
_a : Dict = initializer_range
_a : Optional[int] = scope
_a : Dict = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_a : List[Any] = (image_size // patch_size) ** 2
_a : List[Any] = num_patches + 1
def _lowercase ( self : int ) -> Optional[Any]:
_a : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[int] = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _lowercase ( self : List[str] ) -> str:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ) -> str:
_a : Dict = ViTModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Optional[int] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : str ) -> Tuple:
_a : Dict = ViTForMaskedImageModeling(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Optional[int] = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_a : List[str] = 1
_a : Optional[Any] = ViTForMaskedImageModeling(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a : int = model(UpperCAmelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _lowercase ( self : str , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[Any] ) -> List[Any]:
_a : Union[str, Any] = self.type_sequence_label_size
_a : int = ViTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_a : Union[str, Any] = 1
_a : List[str] = ViTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a : Optional[int] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowercase ( self : int ) -> Union[str, Any]:
_a : Any = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) ,
) : List[Any] = config_and_inputs
_a : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Any = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
UpperCamelCase : List[str] = (
{'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase : Dict = True
UpperCamelCase : Optional[Any] = False
UpperCamelCase : Optional[int] = False
UpperCamelCase : Optional[int] = False
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
_a : Optional[Any] = ViTModelTester(self )
_a : int = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def _lowercase ( self : Optional[int] ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def _lowercase ( self : Tuple ) -> Tuple:
pass
def _lowercase ( self : Dict ) -> int:
_a , _a : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[Any] = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__ , nn.Linear ) )
def _lowercase ( self : Dict ) -> Tuple:
_a , _a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : List[Any] = model_class(UpperCAmelCase__ )
_a : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> str:
_a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def _lowercase ( self : Optional[int] ) -> Dict:
_a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] ) -> List[Any]:
_a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def _lowercase ( self : List[Any] ) -> Optional[Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Optional[Any] = ViTModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class UpperCamelCase ( unittest.TestCase ):
@cached_property
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def _lowercase ( self : Dict ) -> Optional[int]:
_a : Tuple = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(UpperCAmelCase__ )
_a : Union[str, Any] = self.default_image_processor
_a : str = prepare_img()
_a : Optional[Any] = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
_a : List[str] = model(**UpperCAmelCase__ )
# verify the logits
_a : Optional[Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
_a : str = torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
@slow
def _lowercase ( self : Dict ) -> int:
# ViT models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions. The DINO model by Facebook AI leverages this
# to visualize self-attention on higher resolution images.
_a : Any = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(UpperCAmelCase__ )
_a : Optional[int] = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=480 )
_a : str = prepare_img()
_a : Dict = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" )
_a : List[Any] = inputs.pixel_values.to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
_a : Dict = model(UpperCAmelCase__ , interpolate_pos_encoding=UpperCAmelCase__ )
# verify the logits
_a : Union[str, Any] = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase__ )
_a : Optional[int] = torch.tensor(
[[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def _lowercase ( self : Dict ) -> Union[str, Any]:
_a : List[str] = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
_a : str = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" )
_a : List[str] = inputs.pixel_values.to(UpperCAmelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
_a : List[str] = model(UpperCAmelCase__ )
| 324
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a , _a : Dict = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_a : Any = 0
count += depth_first_search(UpperCamelCase__ , row + 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , row - 1 , UpperCamelCase__ , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col + 1 , UpperCamelCase__ )
count += depth_first_search(UpperCamelCase__ , UpperCamelCase__ , col - 1 , UpperCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 324
| 1
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = abs(UpperCamelCase__ )
_a : Tuple = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = abs(UpperCamelCase__ )
return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0 )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return sum(int(UpperCamelCase__ ) for c in str(abs(UpperCamelCase__ ) ) )
def lowerCAmelCase__ ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(UpperCamelCase__ , UpperCamelCase__ ) -> None:
_a : Optional[int] = F"""{func.__name__}({value})"""
_a : int = timeit(F"""__main__.{call}""" , setup="""import __main__""" )
print(F"""{call:56} = {func(UpperCamelCase__ )} -- {timing:.4f} seconds""" )
for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(UpperCamelCase__ , UpperCamelCase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 324
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['ConditionalDetrFeatureExtractor']
_snake_case = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324
|
"""simple docstring"""
from __future__ import annotations
import time
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Node | None ) -> List[str]:
_a : int = pos_x
_a : Union[str, Any] = pos_y
_a : Tuple = (pos_y, pos_x)
_a : Tuple = goal_x
_a : int = goal_y
_a : str = parent
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : tuple[int, int] ) -> List[str]:
_a : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCAmelCase__ )
_a : Optional[int] = [self.start]
_a : Tuple = False
def _lowercase ( self : str ) -> Path | None:
while self.node_queue:
_a : Tuple = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_a : Dict = True
return self.retrace_path(UpperCAmelCase__ )
_a : Tuple = self.get_successors(UpperCAmelCase__ )
for node in successors:
self.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node ) -> list[Node]:
_a : Optional[Any] = []
for action in delta:
_a : str = parent.pos_x + action[1]
_a : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCAmelCase__ , UpperCAmelCase__ , self.target.pos_y , self.target.pos_x , UpperCAmelCase__ ) )
return successors
def _lowercase ( self : List[Any] , UpperCAmelCase__ : Node | None ) -> Path:
_a : Dict = node
_a : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_a : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ) -> Any:
_a : Dict = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = BreadthFirstSearch(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Dict = False
def _lowercase ( self : Any ) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_a : List[Any] = self.fwd_bfs.node_queue.pop(0 )
_a : Union[str, Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_a : Optional[int] = True
return self.retrace_bidirectional_path(
UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = current_bwd_node
_a : int = current_fwd_node
_a : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCAmelCase__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCAmelCase__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCAmelCase__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Node , UpperCAmelCase__ : Node ) -> Path:
_a : str = self.fwd_bfs.retrace_path(UpperCAmelCase__ )
_a : List[Any] = self.bwd_bfs.retrace_path(UpperCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_a : Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_snake_case = (0, 0)
_snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_snake_case = time.time()
_snake_case = BreadthFirstSearch(init, goal)
_snake_case = bfs.search()
_snake_case = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_snake_case = time.time()
_snake_case = BidirectionalBreadthFirstSearch(init, goal)
_snake_case = bd_bfs.search()
_snake_case = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 324
| 1
|
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