

"""simple docstring"""




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import (
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
          MraModel,
      )
      from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST







class   __lowercase      :

  def __init__(	self		:      Optional[Any]       ,A		:      Any       ,A		:      List[str]=2       ,A		:      Optional[Any]=8       ,A		:      List[Any]=True       ,A		:      str=True       ,A		:      Dict=True       ,A		:      Dict=True       ,A		:      Any=99       ,A		:      Dict=16       ,A		:      Optional[int]=5       ,A		:      Tuple=2       ,A		:      str=36       ,A		:      List[Any]="gelu"       ,A		:      str=0.0       ,A		:      str=0.0       ,A		:      List[str]=512       ,A		:      Dict=16       ,A		:      int=2       ,A		:      int=0.0_2       ,A		:      List[Any]=3       ,A		:      Dict=4       ,A		:      int=None       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       parent
   UpperCAmelCase__       :	Tuple        =       batch_size
   UpperCAmelCase__       :	Tuple        =       seq_length
   UpperCAmelCase__       :	List[Any]        =       is_training
   UpperCAmelCase__       :	str        =       use_input_mask
   UpperCAmelCase__       :	str        =       use_token_type_ids
   UpperCAmelCase__       :	str        =       use_labels
   UpperCAmelCase__       :	List[Any]        =       vocab_size
   UpperCAmelCase__       :	Tuple        =       hidden_size
   UpperCAmelCase__       :	Optional[Any]        =       num_hidden_layers
   UpperCAmelCase__       :	Optional[int]        =       num_attention_heads
   UpperCAmelCase__       :	Union[str, Any]        =       intermediate_size
   UpperCAmelCase__       :	List[str]        =       hidden_act
   UpperCAmelCase__       :	Optional[Any]        =       hidden_dropout_prob
   UpperCAmelCase__       :	int        =       attention_probs_dropout_prob
   UpperCAmelCase__       :	List[str]        =       max_position_embeddings
   UpperCAmelCase__       :	Optional[Any]        =       type_vocab_size
   UpperCAmelCase__       :	Optional[Any]        =       type_sequence_label_size
   UpperCAmelCase__       :	Tuple        =       initializer_range
   UpperCAmelCase__       :	Optional[int]        =       num_labels
   UpperCAmelCase__       :	Tuple        =       num_choices
   UpperCAmelCase__       :	List[Any]        =       scope

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size     )

   UpperCAmelCase__       :	List[Any]        =       None
   if self.use_input_mask:
    UpperCAmelCase__       :	Any        =       random_attention_mask([self.batch_size, self.seq_length]     )

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

   UpperCAmelCase__       :	Optional[Any]        =       None
   UpperCAmelCase__       :	Tuple        =       None
   UpperCAmelCase__       :	Optional[Any]        =       None
   if self.use_labels:
    UpperCAmelCase__       :	Optional[Any]        =       ids_tensor([self.batch_size]       ,self.type_sequence_label_size     )
    UpperCAmelCase__       :	Tuple        =       ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels     )
    UpperCAmelCase__       :	List[Any]        =       ids_tensor([self.batch_size]       ,self.num_choices     )

   UpperCAmelCase__       :	Any        =       self.get_config()

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

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   return MraConfig(
       vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,is_decoder=A       ,initializer_range=self.initializer_range       ,)

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       self.get_config()
   UpperCAmelCase__       :	Dict        =       300
   return config

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   (
       (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   
   )       :	List[str]        =       self.prepare_config_and_inputs()

   UpperCAmelCase__       :	Dict        =       True
   UpperCAmelCase__       :	Tuple        =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   UpperCAmelCase__       :	List[str]        =       ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2     )

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )

  def  __lowercase							(	self		:      List[Any]       ,A		:      List[Any]       ,A		:      Any       ,A		:      List[Any]       ,A		:      List[str]       ,A		:      int       ,A		:      Dict       ,A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       MraModel(config=A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	str        =       model(A       ,attention_mask=A       ,token_type_ids=A     )
   UpperCAmelCase__       :	Union[str, Any]        =       model(A       ,token_type_ids=A     )
   UpperCAmelCase__       :	Any        =       model(A     )
   self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       True
   UpperCAmelCase__       :	List[Any]        =       MraModel(A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	Optional[int]        =       model(
       A       ,attention_mask=A       ,token_type_ids=A       ,encoder_hidden_states=A       ,encoder_attention_mask=A       ,)
   UpperCAmelCase__       :	Optional[int]        =       model(
       A       ,attention_mask=A       ,token_type_ids=A       ,encoder_hidden_states=A       ,)
   UpperCAmelCase__       :	int        =       model(A       ,attention_mask=A       ,token_type_ids=A     )
   self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       MraForMaskedLM(config=A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	int        =       model(A       ,attention_mask=A       ,token_type_ids=A       ,labels=A     )
   self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       MraForQuestionAnswering(config=A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	Dict        =       model(
       A       ,attention_mask=A       ,token_type_ids=A       ,start_positions=A       ,end_positions=A       ,)
   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  __lowercase							(	self		:      List[Any]       ,A		:      List[Any]       ,A		:      int       ,A		:      str       ,A		:      Any       ,A		:      int       ,A		:      str       ,A		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       self.num_labels
   UpperCAmelCase__       :	Any        =       MraForSequenceClassification(A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	Union[str, Any]        =       model(A       ,attention_mask=A       ,token_type_ids=A       ,labels=A     )
   self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.num_labels
   UpperCAmelCase__       :	Any        =       MraForTokenClassification(config=A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	List[str]        =       model(A       ,attention_mask=A       ,token_type_ids=A       ,labels=A     )
   self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       self.num_choices
   UpperCAmelCase__       :	List[str]        =       MraForMultipleChoice(config=A     )
   model.to(A     )
   model.eval()
   UpperCAmelCase__       :	str        =       input_ids.unsqueeze(1     ).expand(-1       ,self.num_choices       ,-1     ).contiguous()
   UpperCAmelCase__       :	List[Any]        =       token_type_ids.unsqueeze(1     ).expand(-1       ,self.num_choices       ,-1     ).contiguous()
   UpperCAmelCase__       :	Any        =       input_mask.unsqueeze(1     ).expand(-1       ,self.num_choices       ,-1     ).contiguous()
   UpperCAmelCase__       :	str        =       model(
       A       ,attention_mask=A       ,token_type_ids=A       ,labels=A       ,)
   self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices)     )




  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       self.prepare_config_and_inputs()
   (
       (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   (
       UpperCAmelCase__
   ) ,   
   )       :	List[Any]        =       config_and_inputs
   UpperCAmelCase__       :	Any        =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							(
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  snake_case_												=							False
  snake_case_												=							False
  snake_case_												=							False
  snake_case_												=							False

  snake_case_												=							()

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       MraModelTester(self     )
   UpperCAmelCase__       :	Tuple        =       ConfigTester(self       ,config_class=A       ,hidden_size=37     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    UpperCAmelCase__       :	List[str]        =       type
    self.model_tester.create_and_check_model(*A     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*A     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*A     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*A     )

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*A     )

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*A     )

  @slow
  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__       :	str        =       MraModel.from_pretrained(A     )
    self.assertIsNotNone(A     )




  @unittest.skip(reason="""MRA does not output attentions"""     )
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return







@require_torch
class   __lowercase      (     unittest.TestCase       ):

  @slow
  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       MraModel.from_pretrained("""uw-madison/mra-base-512-4"""     )
   UpperCAmelCase__       :	str        =       torch.arange(256     ).unsqueeze(0     )

   with torch.no_grad():
    UpperCAmelCase__       :	Dict        =       model(A     )[0]

   UpperCAmelCase__       :	Tuple        =       torch.Size((1, 256, 768)     )
   self.assertEqual(output.shape       ,A     )

   UpperCAmelCase__       :	Optional[int]        =       torch.tensor(
       [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]     )

   self.assertTrue(torch.allclose(output[:, :3, :3]       ,A       ,atol=1e-4     )     )

  @slow
  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""     )
   UpperCAmelCase__       :	Tuple        =       torch.arange(256     ).unsqueeze(0     )

   with torch.no_grad():
    UpperCAmelCase__       :	Dict        =       model(A     )[0]

   UpperCAmelCase__       :	Union[str, Any]        =       50_265

   UpperCAmelCase__       :	Optional[Any]        =       torch.Size((1, 256, vocab_size)     )
   self.assertEqual(output.shape       ,A     )

   UpperCAmelCase__       :	List[str]        =       torch.tensor(
       [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]     )

   self.assertTrue(torch.allclose(output[:, :3, :3]       ,A       ,atol=1e-4     )     )




  @slow
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""     )
   UpperCAmelCase__       :	Optional[int]        =       torch.arange(4_096     ).unsqueeze(0     )

   with torch.no_grad():
    UpperCAmelCase__       :	Any        =       model(A     )[0]

   UpperCAmelCase__       :	Tuple        =       50_265

   UpperCAmelCase__       :	Tuple        =       torch.Size((1, 4_096, vocab_size)     )
   self.assertEqual(output.shape       ,A     )

   UpperCAmelCase__       :	Tuple        =       torch.tensor(
       [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]     )

   self.assertTrue(torch.allclose(output[:, :3, :3]       ,A       ,atol=1e-4     )     )













"""simple docstring"""
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# 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
#
########################################################################


_lowerCAmelCase       =				16
_lowerCAmelCase       =				32






def       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[str]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[Any]						     = load_dataset("""glue""" , """mrpc"""       )

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

	def collate_fn(snake_case__       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A_		:    Optional[Any]						     = 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_		:    Tuple						     = 16
		elif accelerator.mixed_precision != "no":
			A_		:    List[Any]						     = 8
		else:
			A_		:    List[Any]						     = None

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

	# Instantiate dataloaders.
	A_		:    Optional[Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    List[str]						     = DataLoader(
	    tokenized_datasets["""validation"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )

	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       =				mocked_dataloaders  # noqa: F811






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[Any]						     = 2
	# New Code #
	A_		:    Tuple						     = int(args.gradient_accumulation_steps       )
	# Initialize accelerator
	A_		:    Any						     = Accelerator(
	    cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__       )
	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_		:    Union[str, Any]						     = config["""lr"""]
	A_		:    Tuple						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    Dict						     = int(config["""batch_size"""]       )

	A_		:    Tuple						     = evaluate.load("""glue""" , """mrpc"""       )

	set_seed(snake_case__       )
	A_     ,  A_		:    List[Any]						     = get_dataloaders(snake_case__ , snake_case__       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_		:    Dict						     = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=snake_case__       )

	# 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_		:    Union[str, Any]						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[int]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    Union[str, Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * 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_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		for step, batch in enumerate(snake_case__       ):
			# 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(snake_case__       ):
				A_		:    Optional[int]						     = model(**snake_case__       )
				A_		:    Tuple						     = output.loss
				accelerator.backward(snake_case__       )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		A_		:    str						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument(
	    """--mixed_precision""" , type=snake_case__ , default=snake_case__ , 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=snake_case__ , 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_		:    int						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()

