




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)

class 		__lowerCAmelCase	:
							def __init__(						self						:List[str]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:str=13		,      __magic_name__						:int=7		,      __magic_name__						:Tuple=True		,      __magic_name__						:Union[str, Any]=True		,      __magic_name__						:Union[str, Any]=True		,      __magic_name__						:Any=True		,      __magic_name__						:str=99		,      __magic_name__						:List[str]=64		,      __magic_name__						:Optional[int]=32		,      __magic_name__						:int=5		,      __magic_name__						:Optional[Any]=4		,      __magic_name__						:Dict=37		,      __magic_name__						:Union[str, Any]="gelu"		,      __magic_name__						:int=0.1		,      __magic_name__						:str=0.1		,      __magic_name__						:Tuple=512		,      __magic_name__						:List[str]=16		,      __magic_name__						:Union[str, Any]=2		,      __magic_name__						:Dict=0.02		,      __magic_name__						:Optional[int]=3		,      __magic_name__						:Optional[int]=4		,      __magic_name__						:Dict=None		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							seq_length
								a										=							is_training
								a										=							use_input_mask
								a										=							use_token_type_ids
								a										=							use_labels
								a										=							vocab_size
								a										=							hidden_size
								a										=							embedding_size
								a										=							num_hidden_layers
								a										=							num_attention_heads
								a										=							intermediate_size
								a										=							hidden_act
								a										=							hidden_dropout_prob
								a										=							attention_probs_dropout_prob
								a										=							max_position_embeddings
								a										=							type_vocab_size
								a										=							type_sequence_label_size
								a										=							initializer_range
								a										=							num_labels
								a										=							num_choices
								a										=							scope
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size				)

								a										=							None
								if self.use_input_mask:
									a										=							random_attention_mask([self.batch_size, self.seq_length]				)

								a										=							None
								if self.use_token_type_ids:
									a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size				)

								a										=							None
								a										=							None
								a										=							None
								if self.use_labels:
									a										=							ids_tensor([self.batch_size]		,      self.type_sequence_label_size				)
									a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels				)
									a										=							ids_tensor([self.batch_size]		,      self.num_choices				)

								a										=							self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								return MobileBertConfig(
								    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		,      embedding_size=self.embedding_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=__magic_name__		,      initializer_range=self.initializer_range		,      )
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:str		,      __magic_name__						:List[Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:Tuple		,      __magic_name__						:List[Any]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							MobileBertModel(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__				)
								a										=							model(__magic_name__		,      token_type_ids=__magic_name__				)
								a										=							model(__magic_name__				)

								self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)				)
								self.parent.assertEqual(result.pooler_output.shape		,      (self.batch_size, self.hidden_size)				)
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:List[Any]		,      __magic_name__						:List[Any]		,      __magic_name__						:Any		,      __magic_name__						:Optional[int]		,      __magic_name__						:str		,      __magic_name__						:str		,      __magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								a										=							MobileBertForMaskedLM(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__				)
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)				)
							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:str		,      __magic_name__						:Optional[Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							MobileBertForNextSentencePrediction(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(
								    __magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__		,      )
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, 2)				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:List[Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[str]		,      __magic_name__						:List[str]		,      __magic_name__						:Dict		,      __magic_name__						:List[Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								a										=							MobileBertForPreTraining(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(
								    __magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__		,      next_sentence_label=__magic_name__		,      )
								self.parent.assertEqual(result.prediction_logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)				)
								self.parent.assertEqual(result.seq_relationship_logits.shape		,      (self.batch_size, 2)				)
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:Optional[int]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Any		,      __magic_name__						:int		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Tuple		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								a										=							MobileBertForQuestionAnswering(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(
								    __magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      start_positions=__magic_name__		,      end_positions=__magic_name__		,      )
								self.parent.assertEqual(result.start_logits.shape		,      (self.batch_size, self.seq_length)				)
								self.parent.assertEqual(result.end_logits.shape		,      (self.batch_size, self.seq_length)				)
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:Dict		,      __magic_name__						:Tuple		,      __magic_name__						:Tuple		,      __magic_name__						:Tuple		,      __magic_name__						:str		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								a										=							self.num_labels
								a										=							MobileBertForSequenceClassification(__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__				)
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)				)
							def       lowerCamelCase__							(						self						:Any		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Any		,      __magic_name__						:Optional[int]		,      __magic_name__						:Any		,      __magic_name__						:List[str]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							self.num_labels
								a										=							MobileBertForTokenClassification(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__				)
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels)				)
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:int		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:str		,      __magic_name__						:int		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							self.num_choices
								a										=							MobileBertForMultipleChoice(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							input_ids.unsqueeze(1				).expand(-1		,      self.num_choices		,      -1				).contiguous()
								a										=							token_type_ids.unsqueeze(1				).expand(-1		,      self.num_choices		,      -1				).contiguous()
								a										=							input_mask.unsqueeze(1				).expand(-1		,      self.num_choices		,      -1				).contiguous()
								a										=							model(
								    __magic_name__		,      attention_mask=__magic_name__		,      token_type_ids=__magic_name__		,      labels=__magic_name__		,      )
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)				)





							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							self.prepare_config_and_inputs()
								(
								    (
								    a
								)				, (
								    a
								)				, (
								    a
								)				, (
								    a
								)				, (
								    a
								)				, (
								    a
								)				, (
								    a
								)				, 
								)										=							config_and_inputs
								a										=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict







@require_torch
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  (
							    (
							        MobileBertModel,
							        MobileBertForMaskedLM,
							        MobileBertForMultipleChoice,
							        MobileBertForNextSentencePrediction,
							        MobileBertForPreTraining,
							        MobileBertForQuestionAnswering,
							        MobileBertForSequenceClassification,
							        MobileBertForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__					     =  (
							    {
							        '''feature-extraction''': MobileBertModel,
							        '''fill-mask''': MobileBertForMaskedLM,
							        '''question-answering''': MobileBertForQuestionAnswering,
							        '''text-classification''': MobileBertForSequenceClassification,
							        '''token-classification''': MobileBertForTokenClassification,
							        '''zero-shot''': MobileBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__					     =  True
							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:List[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:str=False				):







								'''simple docstring'''







								a										=							super()._prepare_for_class(__magic_name__		,      __magic_name__		,      return_labels=__magic_name__				)

								if return_labels:
									if model_class in get_values(__magic_name__				):
										a										=							torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)		,      dtype=torch.long		,      device=__magic_name__				)
										a										=							torch.zeros(
										    self.model_tester.batch_size		,      dtype=torch.long		,      device=__magic_name__				)
								return inputs_dict
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							MobileBertModelTester(self				)
								a										=							ConfigTester(self		,      config_class=__magic_name__		,      hidden_size=37				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*__magic_name__				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__magic_name__				)





							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*__magic_name__				)







def 		__A		(	__lowerCamelCase    )      ->       Dict:
	return torch.tensor(
	    __lowerCamelCase							,			dtype=torch.long							,			device=__lowerCamelCase							,			)


__UpperCamelCase					:       int    					=      1E-3



@require_torch
@require_sentencepiece
@require_tokenizers
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@slow
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							MobileBertModel.from_pretrained("""google/mobilebert-uncased"""				).to(__magic_name__				)
								a										=							_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]				)
								with torch.no_grad():
									a										=							model(__magic_name__				)[0]
								a										=							torch.Size((1, 9, 512)				)
								self.assertEqual(output.shape		,      __magic_name__				)
								a										=							torch.tensor(
								    [
								        [
								            [-2.4_7_3_6_5_2_6E0_7, 8.2_6_9_1_6_5_6E0_4, 1.6_5_2_1_8_3_8E0_5],
								            [-5.7_5_4_1_7_0_4E-0_1, 3.9_0_5_6_0_2_2E0_0, 4.4_0_1_1_5_0_7E0_0],
								            [2.6_0_4_7_3_5_9E0_0, 1.5_6_7_7_6_5_2E0_0, -1.7_3_2_4_1_8_8E-0_1],
								        ]
								    ]		,      device=__magic_name__		,      )

								# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
								# ~1 difference, it's therefore not a good idea to measure using addition.
								# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
								# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
								a										=							torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE				)
								a										=							torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE				)

								self.assertTrue(lower_bound and upper_bound				)







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
# and perform 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
#
########################################################################


__UpperCamelCase					:       Any    					=      16
__UpperCamelCase					:       Union[str, Any]    					=      32





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16    )      ->       List[str]:
	a										=							AutoTokenizer.from_pretrained("""bert-base-cased"""    )
	a										=							load_dataset("""glue"""							,			"""mrpc"""    )

	def tokenize_function(__lowerCamelCase    ):
		# max_length=None => use the model max length (it's actually the default)
		a										=							tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=__lowerCamelCase							,			max_length=__lowerCamelCase    )
		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(
		    __lowerCamelCase							,			batched=__lowerCamelCase							,			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(__lowerCamelCase    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		a										=							128 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(
		    __lowerCamelCase							,			padding="""longest"""							,			max_length=__lowerCamelCase							,			pad_to_multiple_of=__lowerCamelCase							,			return_tensors="""pt"""							,			)

	# Instantiate dataloaders.
	a										=							DataLoader(
	    tokenized_datasets["""train"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )
	a										=							DataLoader(
	    tokenized_datasets["""validation"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )

	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

		__UpperCamelCase					:       Any    					=      mocked_dataloaders  # noqa: F811





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       List[str]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			__lowerCamelCase    ) == "1":
		a										=							2
	# New Code #
	a										=							int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	a										=							Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=__lowerCamelCase    )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
	# 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"""    )

	set_seed(__lowerCamelCase    )
	a				, a										=							get_dataloaders(__lowerCamelCase							,			__lowerCamelCase    )
	# 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=__lowerCamelCase    )

	# 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=__lowerCamelCase    )

	# Instantiate scheduler
	a										=							get_linear_schedule_with_warmup(
	    optimizer=__lowerCamelCase							,			num_warmup_steps=100							,			num_training_steps=(len(__lowerCamelCase    ) * 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										=							accelerator.prepare(
	    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )

	# Now we train the model
	for epoch in range(__lowerCamelCase    ):
		model.train()
		for step, batch in enumerate(__lowerCamelCase    ):
			# 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(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							output.loss
				accelerator.backward(__lowerCamelCase    )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(__lowerCamelCase    ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device    )
			with torch.no_grad():
				a										=							model(**__lowerCamelCase    )
			a										=							outputs.logits.argmax(dim=-1    )
			a				, a										=							accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
			metric.add_batch(
			    predictions=__lowerCamelCase							,			references=__lowerCamelCase							,			)

		a										=							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,			__lowerCamelCase    )





def 		__A		(	)      ->       int:
	a										=							argparse.ArgumentParser(description="""Simple example of training script."""    )
	parser.add_argument(
	    """--mixed_precision"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			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=__lowerCamelCase							,			default=1							,			help="""The number of minibatches to be ran before gradients are accumulated."""							,			)
	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(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()

