


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
#
########################################################################


lowerCAmelCase__    :str       =				1_6
lowerCAmelCase__    :Union[str, Any]       =				3_2

def 			lowerCAmelCase__					(     a__:						Accelerator     ,    a__:						int = 1_6			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     AutoTokenizer.from_pretrained('bert-base-cased'			)
							_UpperCAmelCase											=     load_dataset('glue'     ,    'mrpc'			)

							def tokenize_function(a__:						int			):
														# max_length=None => use the model max length (it's actually the default)
														_UpperCAmelCase											=     tokenizer(examples['sentence1']     ,    examples['sentence2']     ,    truncation=a__     ,    max_length=a__			)
														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():
														_UpperCAmelCase											=     datasets.map(
														    a__     ,    batched=a__     ,    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
							_UpperCAmelCase											=     tokenized_datasets.rename_column('label'     ,    'labels'			)

							def collate_fn(a__:						List[Any]			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_UpperCAmelCase											=     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":
																					_UpperCAmelCase											=     1_6
														elif accelerator.mixed_precision != "no":
																					_UpperCAmelCase											=     8
														else:
																					_UpperCAmelCase											=     None

														return tokenizer.pad(
														    a__     ,    padding='longest'     ,    max_length=a__     ,    pad_to_multiple_of=a__     ,    return_tensors='pt'     ,    )

							# Instantiate dataloaders.
							_UpperCAmelCase											=     DataLoader(
							    tokenized_datasets['train']     ,    shuffle=a__     ,    collate_fn=a__     ,    batch_size=a__			)
							_UpperCAmelCase											=     DataLoader(
							    tokenized_datasets['validation']     ,    shuffle=a__     ,    collate_fn=a__     ,    batch_size=a__			)

							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

		lowerCAmelCase__    :str       =				mocked_dataloaders  # noqa: F811

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						List[str]:


							'''simple docstring'''





							if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,    a__			) == "1":
														_UpperCAmelCase											=     2
							# Initialize accelerator
							_UpperCAmelCase											=     Accelerator(cpu=args.cpu     ,    mixed_precision=args.mixed_precision			)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_UpperCAmelCase											=     config['lr']
							_UpperCAmelCase											=     int(config['num_epochs']			)
							_UpperCAmelCase											=     int(config['seed']			)
							_UpperCAmelCase											=     int(config['batch_size']			)

							_UpperCAmelCase											=     evaluate.load('glue'     ,    'mrpc'			)

							# If the batch size is too big we use gradient accumulation
							_UpperCAmelCase											=     1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_UpperCAmelCase											=     batch_size // MAX_GPU_BATCH_SIZE
														_UpperCAmelCase											=     MAX_GPU_BATCH_SIZE

							set_seed(a__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     get_dataloaders(a__     ,    a__			)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_UpperCAmelCase											=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,    return_dict=a__			)

							# 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).
							_UpperCAmelCase											=     model.to(accelerator.device			)

							# Instantiate optimizer
							_UpperCAmelCase											=     AdamW(params=model.parameters()     ,    lr=a__			)

							# Instantiate scheduler
							_UpperCAmelCase											=     get_linear_schedule_with_warmup(
							    optimizer=a__     ,    num_warmup_steps=1_0_0     ,    num_training_steps=(len(a__			) * 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.
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     accelerator.prepare(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__			)

							# Now we train the model
							for epoch in range(a__			):
														model.train()
														for step, batch in enumerate(a__			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					_UpperCAmelCase											=     model(**a__			)
																					_UpperCAmelCase											=     outputs.loss
																					_UpperCAmelCase											=     loss / gradient_accumulation_steps
																					accelerator.backward(a__			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														_UpperCAmelCase											=     0
														for step, batch in enumerate(a__			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					with torch.no_grad():
																												_UpperCAmelCase											=     model(**a__			)
																					_UpperCAmelCase											=     outputs.logits.argmax(dim=-1			)
																					_UpperCAmelCase						,			_UpperCAmelCase											=     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(a__			) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			_UpperCAmelCase											=     predictions[: len(eval_dataloader.dataset			) - samples_seen]
																																			_UpperCAmelCase											=     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=a__     ,    references=a__     ,    )

														_UpperCAmelCase											=     metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''     ,    a__			)

def 			lowerCAmelCase__					(     )    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     argparse.ArgumentParser(description='Simple example of training script.'			)
							parser.add_argument(
							    '--mixed_precision'     ,    type=a__     ,    default=a__     ,    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.'			)
							_UpperCAmelCase											=     parser.parse_args()
							_UpperCAmelCase											=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							training_function(a__     ,    a__			)


if __name__ == "__main__":
		main()




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase__    :Optional[Any]       =				get_tests_dir('''fixtures''')

class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     mock.Mock()
									_UpperCAmelCase											=     500
									_UpperCAmelCase											=     {}
									_UpperCAmelCase											=     HTTPError
									_UpperCAmelCase											=     {}

									# Download this model to make sure it's in the cache.
									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'		)
									# Under the mock environment we get a 500 error when trying to reach the model.
									with mock.patch('requests.Session.request'     ,       return_value=_SCREAMING_SNAKE_CASE		) as mock_head:
																_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'		)
																# This check we did call the fake head request
																mock_head.assert_called()






		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(
									    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'		)



@is_staging_test
class 						__a (							unittest.TestCase     ):
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     TOKEN
									HfFolder.save_token(_SCREAMING_SNAKE_CASE		)
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Tuple:




									"""simple docstring"""





									try:
																delete_repo(token=cls._token     ,       repo_id='test-feature-extractor'		)
									except HTTPError:
																pass

									try:
																delete_repo(token=cls._token     ,       repo_id='valid_org/test-feature-extractor-org'		)
									except HTTPError:
																pass

									try:
																delete_repo(token=cls._token     ,       repo_id='test-dynamic-feature-extractor'		)
									except HTTPError:
																pass
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)
									feature_extractor.push_to_hub('test-feature-extractor'     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)

									# Reset repo
									delete_repo(token=self._token     ,       repo_id='test-feature-extractor'		)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(
																    _SCREAMING_SNAKE_CASE     ,       repo_id='test-feature-extractor'     ,       push_to_hub=_SCREAMING_SNAKE_CASE     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)
									feature_extractor.push_to_hub('valid_org/test-feature-extractor'     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)

									# Reset repo
									delete_repo(token=self._token     ,       repo_id='valid_org/test-feature-extractor'		)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(
																    _SCREAMING_SNAKE_CASE     ,       repo_id='valid_org/test-feature-extractor-org'     ,       push_to_hub=_SCREAMING_SNAKE_CASE     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									CustomFeatureExtractor.register_for_auto_class()
									_UpperCAmelCase											=     CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)

									feature_extractor.push_to_hub('test-dynamic-feature-extractor'     ,       use_auth_token=self._token		)

									# This has added the proper auto_map field to the config
									self.assertDictEqual(
									    feature_extractor.auto_map     ,       {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}     ,       )

									_UpperCAmelCase											=     AutoFeatureExtractor.from_pretrained(
									    f'''{USER}/test-dynamic-feature-extractor'''     ,       trust_remote_code=_SCREAMING_SNAKE_CASE		)
									# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
									self.assertEqual(new_feature_extractor.__class__.__name__     ,       'CustomFeatureExtractor'		)
