





import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				)
				from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  List[Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Dict=13 ,					__lowercase    :  Optional[Any]=7 ,					__lowercase    :  Any=True ,					__lowercase    :  str=True ,					__lowercase    :  Any=True ,					__lowercase    :  Optional[int]=True ,					__lowercase    :  Tuple=99 ,					__lowercase    :  Union[str, Any]=16 ,					__lowercase    :  List[str]=36 ,					__lowercase    :  List[str]=6 ,					__lowercase    :  List[str]=6 ,					__lowercase    :  str=6 ,					__lowercase    :  Tuple=37 ,					__lowercase    :  Optional[int]="gelu" ,					__lowercase    :  Dict=0.1 ,					__lowercase    :  Optional[Any]=0.1 ,					__lowercase    :  Optional[int]=5_12 ,					__lowercase    :  str=16 ,					__lowercase    :  Dict=2 ,					__lowercase    :  Any=0.02 ,					__lowercase    :  List[Any]=3 ,					__lowercase    :  Tuple=4 ,					__lowercase    :  Tuple=None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     seq_length
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_input_mask
						UpperCAmelCase_								=     use_token_type_ids
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     embedding_size
						UpperCAmelCase_								=     hidden_size
						UpperCAmelCase_								=     num_hidden_layers
						UpperCAmelCase_								=     num_hidden_groups
						UpperCAmelCase_								=     num_attention_heads
						UpperCAmelCase_								=     intermediate_size
						UpperCAmelCase_								=     hidden_act
						UpperCAmelCase_								=     hidden_dropout_prob
						UpperCAmelCase_								=     attention_probs_dropout_prob
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     type_vocab_size
						UpperCAmelCase_								=     type_sequence_label_size
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     num_labels
						UpperCAmelCase_								=     num_choices
						UpperCAmelCase_								=     scope





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







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

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

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

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

						UpperCAmelCase_								=     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return AlbertConfig(
						    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 ,					initializer_range=self.initializer_range ,					num_hidden_groups=self.num_hidden_groups ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  Dict ,					__lowercase    :  int ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					__lowercase    :  Tuple ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertModel(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase		)
						UpperCAmelCase_								=     model(__lowercase ,					token_type_ids=__lowercase		)
						UpperCAmelCase_								=     model(__lowercase		)
						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        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Tuple ,					__lowercase    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  int ,					__lowercase    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertForPreTraining(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(
						    __lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase ,					labels=__lowercase ,					sentence_order_label=__lowercase ,					)
						self.parent.assertEqual(result.prediction_logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)		)
						self.parent.assertEqual(result.sop_logits.shape ,					(self.batch_size, config.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[str] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertForMaskedLM(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase ,					labels=__lowercase		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[int] ,					__lowercase    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  Any ,					__lowercase    :  Any ,					__lowercase    :  Any ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertForQuestionAnswering(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(
						    __lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase ,					start_positions=__lowercase ,					end_positions=__lowercase ,					)
						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        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  int ,					__lowercase    :  Optional[int] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any] ,					__lowercase    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.num_labels
						UpperCAmelCase_								=     AlbertForSequenceClassification(__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase ,					labels=__lowercase		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Dict ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Any ,					__lowercase    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.num_labels
						UpperCAmelCase_								=     AlbertForTokenClassification(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase ,					token_type_ids=__lowercase ,					labels=__lowercase		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Dict ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Dict ,					__lowercase    :  int ,					__lowercase    :  str ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[str]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







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







@require_torch
class   _UpperCamelCase  (       A_						,       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Any			    =       (
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCamelCase     :     Dict			    =       (
					    {
					        'feature-extraction': AlbertModel,
					        'fill-mask': AlbertForMaskedLM,
					        'question-answering': AlbertForQuestionAnswering,
					        'text-classification': AlbertForSequenceClassification,
					        'token-classification': AlbertForTokenClassification,
					        'zero-shot': AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCamelCase     :     List[str]			    =       True





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[int]=False		):






						'''simple docstring'''







						UpperCAmelCase_								=     super()._prepare_for_class(__lowercase ,					__lowercase ,					return_labels=__lowercase		)

						if return_labels:
							if model_class in get_values(__lowercase		):
								UpperCAmelCase_								=     torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.seq_length) ,					dtype=torch.long ,					device=__lowercase		)
								UpperCAmelCase_								=     torch.zeros(
								    self.model_tester.batch_size ,					dtype=torch.long ,					device=__lowercase		)
						return inputs_dict





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertModelTester(self		)
						UpperCAmelCase_								=     ConfigTester(self ,					config_class=__lowercase ,					hidden_size=37		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						self.config_tester.run_common_tests()





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase		)





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






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__lowercase		)





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






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							UpperCAmelCase_								=     type
							self.model_tester.create_and_check_model(*__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     AlbertModel.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)







@require_torch
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     AlbertModel.from_pretrained("""albert-base-v2"""		)
						UpperCAmelCase_								=     torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]		)
						UpperCAmelCase_								=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
						with torch.no_grad():
							UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase		)[0]
						UpperCAmelCase_								=     torch.Size((1, 11, 7_68)		)
						self.assertEqual(output.shape ,					__lowercase		)
						UpperCAmelCase_								=     torch.tensor(
						    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,					__lowercase ,					atol=1e-4		)		)









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__   : Union[str, Any]					  = 16
UpperCamelCase__   : Tuple					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

	def tokenize_function(A   ):
		# 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   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_								=     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":
			UpperCAmelCase_								=     16
		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

				UpperCamelCase__   : str					  = mocked_dataloaders  # noqa: F811




def    A_(			A				,      A   ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,      A   ) == "1":
		UpperCAmelCase_								=     2
	# New Code #
	UpperCAmelCase_								=     int(args.gradient_accumulation_steps   )
	# Initialize accelerator
	UpperCAmelCase_								=     Accelerator(
	    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=A   )
	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
	UpperCAmelCase_								=     config["""lr"""]
	UpperCAmelCase_								=     int(config["""num_epochs"""]   )
	UpperCAmelCase_								=     int(config["""seed"""]   )
	UpperCAmelCase_								=     int(config["""batch_size"""]   )

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

	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=100				,      num_training_steps=(len(A   ) * 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.
	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   )
			# 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(A   ):
				UpperCAmelCase_								=     model(**A   )
				UpperCAmelCase_								=     output.loss
				accelerator.backward(A   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		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_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    A_(			):
	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."""				,      )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""				,      type=A				,      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."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(A				,      A   )


if __name__ == "__main__":
				main()


