


import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


_lowercase       :      Any	=	get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowercase       :      Union[str, Any]	=	get_tests_dir("fixtures/vocab.json")
_lowercase       :      int	=	get_tests_dir("fixtures")






class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				0

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""   )
					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						A								=				WavaVecaConfig()
						A								=				AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""   )

						# save in new folder
						model_config.save_pretrained(a__   )
						processor.save_pretrained(a__   )

						A								=				AutoProcessor.from_pretrained(a__   )

					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					with tempfile.TemporaryDirectory() as tmpdirname:
						# copy relevant files
						copyfile(a__				,   os.path.join(a__				,   a__   )   )
						copyfile(a__				,   os.path.join(a__				,   """vocab.json"""   )   )

						A								=				AutoProcessor.from_pretrained(a__   )

					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				int:
					with tempfile.TemporaryDirectory() as tmpdirname:
						A								=				WavaVecaFeatureExtractor()
						A								=				AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""   )

						A								=				WavaVecaProcessor(a__				,   a__   )

						# save in new folder
						processor.save_pretrained(a__   )

						# drop `processor_class` in tokenizer
						with open(os.path.join(a__				,   a__   )				,   """r"""   ) as f:
							A								=				json.load(a__   )
							config_dict.pop("""processor_class"""   )

						with open(os.path.join(a__				,   a__   )				,   """w"""   ) as f:
							f.write(json.dumps(a__   )   )

						A								=				AutoProcessor.from_pretrained(a__   )

					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						A								=				WavaVecaFeatureExtractor()
						A								=				AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""   )

						A								=				WavaVecaProcessor(a__				,   a__   )

						# save in new folder
						processor.save_pretrained(a__   )

						# drop `processor_class` in feature extractor
						with open(os.path.join(a__				,   a__   )				,   """r"""   ) as f:
							A								=				json.load(a__   )
							config_dict.pop("""processor_class"""   )

						with open(os.path.join(a__				,   a__   )				,   """w"""   ) as f:
							f.write(json.dumps(a__   )   )

						A								=				AutoProcessor.from_pretrained(a__   )

					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					with tempfile.TemporaryDirectory() as tmpdirname:
						A								=				WavaVecaConfig(processor_class="""Wav2Vec2Processor"""   )
						model_config.save_pretrained(a__   )
						# copy relevant files
						copyfile(a__				,   os.path.join(a__				,   """vocab.json"""   )   )
						# create emtpy sample processor
						with open(os.path.join(a__				,   a__   )				,   """w"""   ) as f:
							f.write("""{}"""   )

						A								=				AutoProcessor.from_pretrained(a__   )

					self.assertIsInstance(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(a__   ):
						A								=				AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""   )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(a__   ):
						A								=				AutoProcessor.from_pretrained(
						    """hf-internal-testing/test_dynamic_processor"""				,   trust_remote_code=a__   )

					A								=				AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""				,   trust_remote_code=a__   )
					self.assertTrue(processor.special_attribute_present   )
					self.assertEqual(processor.__class__.__name__				,   """NewProcessor"""   )

					A								=				processor.feature_extractor
					self.assertTrue(feature_extractor.special_attribute_present   )
					self.assertEqual(feature_extractor.__class__.__name__				,   """NewFeatureExtractor"""   )

					A								=				processor.tokenizer
					self.assertTrue(tokenizer.special_attribute_present   )
					if is_tokenizers_available():
						self.assertEqual(tokenizer.__class__.__name__				,   """NewTokenizerFast"""   )

						# Test we can also load the slow version
						A								=				AutoProcessor.from_pretrained(
						    """hf-internal-testing/test_dynamic_processor"""				,   trust_remote_code=a__				,   use_fast=a__   )
						A								=				new_processor.tokenizer
						self.assertTrue(new_tokenizer.special_attribute_present   )
						self.assertEqual(new_tokenizer.__class__.__name__				,   """NewTokenizer"""   )
					else:
						self.assertEqual(tokenizer.__class__.__name__				,   """NewTokenizer"""   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					try:
						AutoConfig.register("""custom"""				,   a__   )
						AutoFeatureExtractor.register(a__				,   a__   )
						AutoTokenizer.register(a__				,   slow_tokenizer_class=a__   )
						AutoProcessor.register(a__				,   a__   )
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(a__   ):
							AutoProcessor.register(a__				,   a__   )

						# Now that the config is registered, it can be used as any other config with the auto-API
						A								=				CustomFeatureExtractor.from_pretrained(a__   )

						with tempfile.TemporaryDirectory() as tmp_dir:
							A								=				os.path.join(a__				,   """vocab.txt"""   )
							with open(a__				,   """w"""				,   encoding="""utf-8"""   ) as vocab_writer:
								vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )
							A								=				CustomTokenizer(a__   )

						A								=				CustomProcessor(a__				,   a__   )

						with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(a__   )
							A								=				AutoProcessor.from_pretrained(a__   )
							self.assertIsInstance(a__				,   a__   )

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
							del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
						if CustomConfig in TOKENIZER_MAPPING._extra_content:
							del TOKENIZER_MAPPING._extra_content[CustomConfig]
						if CustomConfig in PROCESSOR_MAPPING._extra_content:
							del PROCESSOR_MAPPING._extra_content[CustomConfig]

				def      _UpperCAmelCase					(						self   )	->				Any:






					class       _UpperCamelCase    (					__snake_case				):

									"""simple docstring"""







									lowerCAmelCase 		= False






					class       _UpperCamelCase    (					__snake_case				):

									"""simple docstring"""







									lowerCAmelCase 		= False






					class       _UpperCamelCase    (					__snake_case				):

									"""simple docstring"""







									lowerCAmelCase 		= 'AutoFeatureExtractor'
									lowerCAmelCase 		= 'AutoTokenizer'
									lowerCAmelCase 		= False

					try:
						AutoConfig.register("""custom"""				,   a__   )
						AutoFeatureExtractor.register(a__				,   a__   )
						AutoTokenizer.register(a__				,   slow_tokenizer_class=a__   )
						AutoProcessor.register(a__				,   a__   )
						# If remote code is not set, the default is to use local classes.
						A								=				AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""   )
						self.assertEqual(processor.__class__.__name__				,   """NewProcessor"""   )
						self.assertFalse(processor.special_attribute_present   )
						self.assertFalse(processor.feature_extractor.special_attribute_present   )
						self.assertFalse(processor.tokenizer.special_attribute_present   )

						# If remote code is disabled, we load the local ones.
						A								=				AutoProcessor.from_pretrained(
						    """hf-internal-testing/test_dynamic_processor"""				,   trust_remote_code=a__   )
						self.assertEqual(processor.__class__.__name__				,   """NewProcessor"""   )
						self.assertFalse(processor.special_attribute_present   )
						self.assertFalse(processor.feature_extractor.special_attribute_present   )
						self.assertFalse(processor.tokenizer.special_attribute_present   )

						# If remote is enabled, we load from the Hub.
						A								=				AutoProcessor.from_pretrained(
						    """hf-internal-testing/test_dynamic_processor"""				,   trust_remote_code=a__   )
						self.assertEqual(processor.__class__.__name__				,   """NewProcessor"""   )
						self.assertTrue(processor.special_attribute_present   )
						self.assertTrue(processor.feature_extractor.special_attribute_present   )
						self.assertTrue(processor.tokenizer.special_attribute_present   )

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
							del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
						if CustomConfig in TOKENIZER_MAPPING._extra_content:
							del TOKENIZER_MAPPING._extra_content[CustomConfig]
						if CustomConfig in PROCESSOR_MAPPING._extra_content:
							del PROCESSOR_MAPPING._extra_content[CustomConfig]

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""   )
					self.assertEqual(processor.__class__.__name__				,   """BertTokenizerFast"""   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""   )
					self.assertEqual(processor.__class__.__name__				,   """ConvNextImageProcessor"""   )






@is_staging_test
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

				@classmethod
				def      _UpperCAmelCase					(						cls   )	->				str:
					A								=				TOKEN
					HfFolder.save_token(a__   )

				@classmethod
				def      _UpperCAmelCase					(						cls   )	->				Optional[int]:
					try:
						delete_repo(token=cls._token				,   repo_id="""test-processor"""   )
					except HTTPError:
						pass

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

					try:
						delete_repo(token=cls._token				,   repo_id="""test-dynamic-processor"""   )
					except HTTPError:
						pass

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				WavaVecaProcessor.from_pretrained(a__   )
					with tempfile.TemporaryDirectory() as tmp_dir:
						processor.save_pretrained(
						    os.path.join(a__				,   """test-processor"""   )				,   push_to_hub=a__				,   use_auth_token=self._token   )

						A								=				WavaVecaProcessor.from_pretrained(f'{USER}/test-processor'   )
						for k, v in processor.feature_extractor.__dict__.items():
							self.assertEqual(a__				,   getattr(new_processor.feature_extractor				,   a__   )   )
						self.assertDictEqual(new_processor.tokenizer.get_vocab()				,   processor.tokenizer.get_vocab()   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				WavaVecaProcessor.from_pretrained(a__   )

					with tempfile.TemporaryDirectory() as tmp_dir:
						processor.save_pretrained(
						    os.path.join(a__				,   """test-processor-org"""   )				,   push_to_hub=a__				,   use_auth_token=self._token				,   organization="""valid_org"""				,   )

						A								=				WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""   )
						for k, v in processor.feature_extractor.__dict__.items():
							self.assertEqual(a__				,   getattr(new_processor.feature_extractor				,   a__   )   )
						self.assertDictEqual(new_processor.tokenizer.get_vocab()				,   processor.tokenizer.get_vocab()   )

				def      _UpperCAmelCase					(						self   )	->				int:
					CustomFeatureExtractor.register_for_auto_class()
					CustomTokenizer.register_for_auto_class()
					CustomProcessor.register_for_auto_class()

					A								=				CustomFeatureExtractor.from_pretrained(a__   )

					with tempfile.TemporaryDirectory() as tmp_dir:
						A								=				os.path.join(a__				,   """vocab.txt"""   )
						with open(a__				,   """w"""				,   encoding="""utf-8"""   ) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]   )   )
						A								=				CustomTokenizer(a__   )

					A								=				CustomProcessor(a__				,   a__   )

					with tempfile.TemporaryDirectory() as tmp_dir:
						create_repo(f'{USER}/test-dynamic-processor'				,   token=self._token   )
						A								=				Repository(a__				,   clone_from=f'{USER}/test-dynamic-processor'				,   token=self._token   )
						processor.save_pretrained(a__   )

						# This has added the proper auto_map field to the feature extractor config
						self.assertDictEqual(
						    processor.feature_extractor.auto_map				,   {
						        """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
						        """AutoProcessor""": """custom_processing.CustomProcessor""",
						    }				,   )

						# This has added the proper auto_map field to the tokenizer config
						with open(os.path.join(a__				,   """tokenizer_config.json"""   )   ) as f:
							A								=				json.load(a__   )
						self.assertDictEqual(
						    tokenizer_config["""auto_map"""]				,   {
						        """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
						        """AutoProcessor""": """custom_processing.CustomProcessor""",
						    }				,   )

						# The code has been copied from fixtures
						self.assertTrue(os.path.isfile(os.path.join(a__				,   """custom_feature_extraction.py"""   )   )   )
						self.assertTrue(os.path.isfile(os.path.join(a__				,   """custom_tokenization.py"""   )   )   )
						self.assertTrue(os.path.isfile(os.path.join(a__				,   """custom_processing.py"""   )   )   )

						repo.push_to_hub()

					A								=				AutoProcessor.from_pretrained(f'{USER}/test-dynamic-processor'				,   trust_remote_code=a__   )
					# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
					self.assertEqual(new_processor.__class__.__name__				,   """CustomProcessor"""   )







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


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

	def tokenize_function(UpperCamelCase__:     List[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				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								=				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								=				tokenized_datasets.rename_column("""label"""							,					"""labels"""						)

	def collate_fn(UpperCamelCase__:     Union[str, Any]						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A								=				1_28 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								=				16
		elif accelerator.mixed_precision != "no":
			A								=				8
		else:
			A								=				None

		return tokenizer.pad(
		    UpperCamelCase__							,					padding="""longest"""							,					max_length=UpperCamelCase__							,					pad_to_multiple_of=UpperCamelCase__							,					return_tensors="""pt"""							,					)

	# Instantiate dataloaders.
	A								=				DataLoader(
	    tokenized_datasets["""train"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=UpperCamelCase__						)
	A								=				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

				_lowercase       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







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

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

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

	set_seed(UpperCamelCase__						)
	A					,			A								=				get_dataloaders(UpperCamelCase__							,					UpperCamelCase__						)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A								=				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								=				model.to(accelerator.device						)

	# Instantiate optimizer
	A								=				AdamW(params=model.parameters()							,					lr=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					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								=				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								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				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								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				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								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				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								=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				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								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



