






'''simple docstring'''

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor






class        a_		:

		def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=99    ,	_SCREAMING_SNAKE_CASE=36    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=37    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=512    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=1000    ,	)						->   Tuple:



								"""simple docstring"""






								UpperCamelCase       						=      parent
								UpperCamelCase       						=      batch_size
								UpperCamelCase       						=      num_channels
								UpperCamelCase       						=      image_size
								UpperCamelCase       						=      patch_size
								UpperCamelCase       						=      text_seq_length
								UpperCamelCase       						=      is_training
								UpperCamelCase       						=      use_input_mask
								UpperCamelCase       						=      use_token_type_ids
								UpperCamelCase       						=      use_labels
								UpperCamelCase       						=      vocab_size
								UpperCamelCase       						=      hidden_size
								UpperCamelCase       						=      num_hidden_layers
								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       						=      coordinate_size
								UpperCamelCase       						=      shape_size
								UpperCamelCase       						=      num_labels
								UpperCamelCase       						=      num_choices
								UpperCamelCase       						=      scope
								UpperCamelCase       						=      range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								UpperCamelCase       						=      text_seq_length
								UpperCamelCase       						=      (image_size // patch_size) ** 2 + 1
								UpperCamelCase       						=      self.text_seq_length + self.image_seq_length

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



								"""simple docstring"""






								UpperCamelCase       						=      ids_tensor([self.batch_size, self.text_seq_length]    ,	self.vocab_size   )

								UpperCamelCase       						=      ids_tensor([self.batch_size, self.text_seq_length, 4]    ,	self.range_bbox   )
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]   ):
														for j in range(bbox.shape[1]   ):
																				if bbox[i, j, 3] < bbox[i, j, 1]:
																										UpperCamelCase       						=      bbox[i, j, 3]
																										UpperCamelCase       						=      bbox[i, j, 1]
																										UpperCamelCase       						=      t
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																										UpperCamelCase       						=      bbox[i, j, 2]
																										UpperCamelCase       						=      bbox[i, j, 0]
																										UpperCamelCase       						=      t

								UpperCamelCase       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								UpperCamelCase       						=      None
								if self.use_input_mask:
														UpperCamelCase       						=      random_attention_mask([self.batch_size, self.text_seq_length]   )

								UpperCamelCase       						=      None
								if self.use_token_type_ids:
														UpperCamelCase       						=      ids_tensor([self.batch_size, self.text_seq_length]    ,	self.type_vocab_size   )

								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.text_seq_length]    ,	self.num_labels   )

								UpperCamelCase       						=      LayoutLMvaConfig(
								    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    ,	coordinate_size=self.coordinate_size    ,	shape_size=self.shape_size    ,	input_size=self.image_size    ,	patch_size=self.patch_size    ,	)

								return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



								"""simple docstring"""






								UpperCamelCase       						=      LayoutLMvaModel(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()

								# text + image
								UpperCamelCase       						=      model(UpperCamelCase__    ,	pixel_values=UpperCamelCase__   )
								UpperCamelCase       						=      model(
								    UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__    ,	attention_mask=UpperCamelCase__    ,	token_type_ids=UpperCamelCase__   )
								UpperCamelCase       						=      model(UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__    ,	token_type_ids=UpperCamelCase__   )
								UpperCamelCase       						=      model(UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__   )

								self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)   )

								# text only
								UpperCamelCase       						=      model(UpperCamelCase__   )
								self.parent.assertEqual(
								    result.last_hidden_state.shape    ,	(self.batch_size, self.text_seq_length, self.hidden_size)   )

								# image only
								UpperCamelCase       						=      model(pixel_values=UpperCamelCase__   )
								self.parent.assertEqual(
								    result.last_hidden_state.shape    ,	(self.batch_size, self.image_seq_length, self.hidden_size)   )

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



								"""simple docstring"""






								UpperCamelCase       						=      self.num_labels
								UpperCamelCase       						=      LayoutLMvaForSequenceClassification(UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCamelCase       						=      model(
								    UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__    ,	attention_mask=UpperCamelCase__    ,	token_type_ids=UpperCamelCase__    ,	labels=UpperCamelCase__    ,	)
								self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



								"""simple docstring"""






								UpperCamelCase       						=      self.num_labels
								UpperCamelCase       						=      LayoutLMvaForTokenClassification(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCamelCase       						=      model(
								    UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__    ,	attention_mask=UpperCamelCase__    ,	token_type_ids=UpperCamelCase__    ,	labels=UpperCamelCase__    ,	)
								self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.text_seq_length, self.num_labels)   )

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



								"""simple docstring"""






								UpperCamelCase       						=      LayoutLMvaForQuestionAnswering(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCamelCase       						=      model(
								    UpperCamelCase__    ,	bbox=UpperCamelCase__    ,	pixel_values=UpperCamelCase__    ,	attention_mask=UpperCamelCase__    ,	token_type_ids=UpperCamelCase__    ,	start_positions=UpperCamelCase__    ,	end_positions=UpperCamelCase__    ,	)
								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 						A__		(   self   )						->   Tuple:



								"""simple docstring"""






								UpperCamelCase       						=      self.prepare_config_and_inputs()
								(
								    UpperCamelCase							
								)       						=      config_and_inputs
								UpperCamelCase       						=      {
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''pixel_values''': pixel_values,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict






@require_torch
class        a_		(    SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, unittest.TestCase       ):
		lowercase  	=   False
		lowercase  	=   False
		lowercase  	=   False

		lowercase  	=   (
		    (
		        LayoutLMvaModel,
		        LayoutLMvaForSequenceClassification,
		        LayoutLMvaForTokenClassification,
		        LayoutLMvaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase  	=   (
		    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
		    if is_torch_available()
		    else {}
		)

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



								"""simple docstring"""






								return True

		def 						A__		(   self   )						->   Optional[Any]:



								"""simple docstring"""






								UpperCamelCase       						=      LayoutLMvaModelTester(self   )
								UpperCamelCase       						=      ConfigTester(self    ,	config_class=UpperCamelCase__    ,	hidden_size=37   )

		def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Tuple:



								"""simple docstring"""






								UpperCamelCase       						=      copy.deepcopy(UpperCamelCase__   )
								if model_class in get_values(UpperCamelCase__   ):
														UpperCamelCase       						=      {
														    k: v.unsqueeze(1   ).expand(-1    ,	self.model_tester.num_choices    ,	-1   ).contiguous()
														    if isinstance(UpperCamelCase__    ,	torch.Tensor   ) and v.ndim > 1
														    else v
														    for k, v in inputs_dict.items()
														}
								if return_labels:
														if model_class in get_values(UpperCamelCase__   ):
																				UpperCamelCase       						=      torch.ones(self.model_tester.batch_size    ,	dtype=torch.long    ,	device=UpperCamelCase__   )
														elif model_class in get_values(UpperCamelCase__   ):
																				UpperCamelCase       						=      torch.zeros(
																				    self.model_tester.batch_size    ,	dtype=torch.long    ,	device=UpperCamelCase__   )
																				UpperCamelCase       						=      torch.zeros(
																				    self.model_tester.batch_size    ,	dtype=torch.long    ,	device=UpperCamelCase__   )
														elif model_class in [
														    *get_values(UpperCamelCase__   ),
														]:
																				UpperCamelCase       						=      torch.zeros(
																				    self.model_tester.batch_size    ,	dtype=torch.long    ,	device=UpperCamelCase__   )
														elif model_class in [
														    *get_values(UpperCamelCase__   ),
														]:
																				UpperCamelCase       						=      torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,	dtype=torch.long    ,	device=UpperCamelCase__    ,	)

								return inputs_dict

		def 						A__		(   self   )						->   Optional[Any]:



								"""simple docstring"""






								self.config_tester.run_common_tests()

		def 						A__		(   self   )						->   Optional[int]:



								"""simple docstring"""






								UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__   )

		def 						A__		(   self   )						->   Any:



								"""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(*UpperCamelCase__   )

		def 						A__		(   self   )						->   Any:



								"""simple docstring"""






								UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__   )

		def 						A__		(   self   )						->   List[Any]:



								"""simple docstring"""






								UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__   )

		def 						A__		(   self   )						->   Any:



								"""simple docstring"""






								UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__   )


		@slow
		def 						A__		(   self   )						->   str:



								"""simple docstring"""






								for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase       						=      LayoutLMvaModel.from_pretrained(UpperCamelCase__   )
														self.assertIsNotNone(UpperCamelCase__   )







def 					lowercase__				(   )->				Optional[int]:
						UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
						return image






@require_torch
class        a_		(    unittest.TestCase       ):

		@cached_property
		def 						A__		(   self   )						->   Union[str, Any]:



								"""simple docstring"""






								return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__   ) if is_vision_available() else None


		@slow
		def 						A__		(   self   )						->   Tuple:



								"""simple docstring"""






								UpperCamelCase       						=      LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""   ).to(UpperCamelCase__   )

								UpperCamelCase       						=      self.default_image_processor
								UpperCamelCase       						=      prepare_img()
								UpperCamelCase       						=      image_processor(images=UpperCamelCase__    ,	return_tensors="""pt"""   ).pixel_values.to(UpperCamelCase__   )

								UpperCamelCase       						=      torch.tensor([[1, 2]]   )
								UpperCamelCase       						=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]   ).unsqueeze(0   )

								# forward pass
								UpperCamelCase       						=      model(
								    input_ids=input_ids.to(UpperCamelCase__   )    ,	bbox=bbox.to(UpperCamelCase__   )    ,	pixel_values=pixel_values.to(UpperCamelCase__   )    ,	)

								# verify the logits
								UpperCamelCase       						=      torch.Size((1, 199, 768)   )
								self.assertEqual(outputs.last_hidden_state.shape    ,	UpperCamelCase__   )

								UpperCamelCase       						=      torch.tensor(
								    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]   ).to(UpperCamelCase__   )

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,	UpperCamelCase__    ,	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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      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():
            UpperCamelCase       						=      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
      UpperCamelCase       						=      tokenized_datasets.rename_column("""label"""	,		"""labels"""					)

      def collate_fn(__UpperCamelCase					):
            # 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(
                __UpperCamelCase	,		padding="""longest"""	,		max_length=__UpperCamelCase	,		pad_to_multiple_of=__UpperCamelCase	,		return_tensors="""pt"""	,		)

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

     SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
            UpperCamelCase       						=      2
      # New Code #
      UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
      UpperCamelCase       						=      int(args.local_sgd_steps					)
      # Initialize accelerator
      UpperCamelCase       						=      Accelerator(
          cpu=args.cpu	,		mixed_precision=args.mixed_precision	,		gradient_accumulation_steps=__UpperCamelCase					)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
      # 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(__UpperCamelCase					)
      UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
      # 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=__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).
      UpperCamelCase       						=      model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

      # Instantiate scheduler
      UpperCamelCase       						=      get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase	,		num_warmup_steps=100	,		num_training_steps=(len(__UpperCamelCase					) * 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(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      # Now we train the model
      for epoch in range(__UpperCamelCase					):
            model.train()
            with LocalSGD(
                accelerator=__UpperCamelCase	,		model=__UpperCamelCase	,		local_sgd_steps=__UpperCamelCase	,		enabled=local_sgd_steps is not None					) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase					):
                        # 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(__UpperCamelCase					):
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                              UpperCamelCase       						=      output.loss
                              accelerator.backward(__UpperCamelCase					)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

            model.eval()
            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():
                        UpperCamelCase       						=      model(**__UpperCamelCase					)
                  UpperCamelCase       						=      outputs.logits.argmax(dim=-1					)
                  UpperCamelCase						,UpperCamelCase       						=      accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
                  metric.add_batch(
                      predictions=__UpperCamelCase	,		references=__UpperCamelCase	,		)

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







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      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."""	,		)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,		type=__UpperCamelCase	,		default=1	,		help="""The number of minibatches to be ran before gradients are accumulated."""	,		)
      parser.add_argument(
          """--local_sgd_steps"""	,		type=__UpperCamelCase	,		default=8	,		help="""Number of local SGD steps or None to disable local SGD"""					)
      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(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()
