





import unittest

from transformers import LayoutLMConfig, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )





class 						_A:


    """simple docstring"""


    def __init__(      self					, _A					, _A=13					, _A=7					, _A=True					, _A=True					, _A=True					, _A=True					, _A=99					, _A=32					, _A=5					, _A=4					, _A=37					, _A="gelu"					, _A=0.1					, _A=0.1					, _A=512					, _A=16					, _A=2					, _A=0.0_2					, _A=3					, _A=4					, _A=None					, _A=1000					, ):
         __A     :						List[Any]          =    parent
         __A     :						Optional[int]          =    batch_size
         __A     :						Optional[int]          =    seq_length
         __A     :						List[Any]          =    is_training
         __A     :						Dict          =    use_input_mask
         __A     :						Union[str, Any]          =    use_token_type_ids
         __A     :						Union[str, Any]          =    use_labels
         __A     :						Dict          =    vocab_size
         __A     :						List[Any]          =    hidden_size
         __A     :						Optional[Any]          =    num_hidden_layers
         __A     :						Any          =    num_attention_heads
         __A     :						Optional[int]          =    intermediate_size
         __A     :						List[str]          =    hidden_act
         __A     :						int          =    hidden_dropout_prob
         __A     :						Union[str, Any]          =    attention_probs_dropout_prob
         __A     :						Optional[Any]          =    max_position_embeddings
         __A     :						Optional[Any]          =    type_vocab_size
         __A     :						Optional[Any]          =    type_sequence_label_size
         __A     :						Tuple          =    initializer_range
         __A     :						int          =    num_labels
         __A     :						int          =    num_choices
         __A     :						Optional[int]          =    scope
         __A     :						List[Any]          =    range_bbox



    def  UpperCAmelCase_		(      self					):
         __A     :						Optional[int]          =    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size					)

         __A     :						Any          =    ids_tensor([self.batch_size, self.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]:
                        __A     :						Union[str, Any]          =    bbox[i, j, 3]
                        __A     :						str          =    bbox[i, j, 1]
                        __A     :						List[Any]          =    t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        __A     :						Optional[Any]          =    bbox[i, j, 2]
                        __A     :						str          =    bbox[i, j, 0]
                        __A     :						Tuple          =    t

         __A     :						Any          =    None
         if self.use_input_mask:
              __A     :						Dict          =    ids_tensor([self.batch_size, self.seq_length]					, vocab_size=2					)

         __A     :						str          =    None
         if self.use_token_type_ids:
              __A     :						Optional[Any]          =    ids_tensor([self.batch_size, self.seq_length]					, self.type_vocab_size					)

         __A     :						List[str]          =    None
         __A     :						List[str]          =    None
         __A     :						Any          =    None
         if self.use_labels:
              __A     :						int          =    ids_tensor([self.batch_size]					, self.type_sequence_label_size					)
              __A     :						Tuple          =    ids_tensor([self.batch_size, self.seq_length]					, self.num_labels					)
              __A     :						Union[str, Any]          =    ids_tensor([self.batch_size]					, self.num_choices					)

         __A     :						Tuple          =    self.get_config()

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



    def  UpperCAmelCase_		(      self					):
         return LayoutLMConfig(
             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					, )



    def  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					, _A					, _A					):
         __A     :						List[str]          =    LayoutLMModel(config=_A					)
         model.to(_A					)
         model.eval()
         __A     :						List[str]          =    model(_A					, _A					, attention_mask=_A					, token_type_ids=_A					)
         __A     :						Dict          =    model(_A					, _A					, token_type_ids=_A					)
         __A     :						str          =    model(_A					, _A					)
         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  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					, _A					, _A					):
         __A     :						Union[str, Any]          =    LayoutLMForMaskedLM(config=_A					)
         model.to(_A					)
         model.eval()
         __A     :						Tuple          =    model(_A					, _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  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					, _A					, _A					):
         __A     :						int          =    self.num_labels
         __A     :						Any          =    LayoutLMForSequenceClassification(_A					)
         model.to(_A					)
         model.eval()
         __A     :						List[str]          =    model(
             _A					, _A					, attention_mask=_A					, token_type_ids=_A					, labels=_A					)
         self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)					)



    def  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					, _A					, _A					):
         __A     :						List[Any]          =    self.num_labels
         __A     :						List[Any]          =    LayoutLMForTokenClassification(config=_A					)
         model.to(_A					)
         model.eval()
         __A     :						Any          =    model(_A					, _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  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					, _A					, _A					):
         __A     :						Union[str, Any]          =    LayoutLMForQuestionAnswering(config=_A					)
         model.to(_A					)
         model.eval()
         __A     :						int          =    model(
             _A					, bbox=_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  UpperCAmelCase_		(      self					):
         __A     :						Any          =    self.prepare_config_and_inputs()
         (
             __A    
         )     :						int          =    config_and_inputs
         __A     :						Any          =    {
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict





@require_torch
class 						_A( snake_case__    ,			snake_case__    ,			unittest.TestCase       ):


    """simple docstring"""


    UpperCamelCase   :  Optional[int]    =     (
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    UpperCamelCase   :  Tuple    =     (
        {
            '''document-question-answering''': LayoutLMForQuestionAnswering,
            '''feature-extraction''': LayoutLMModel,
            '''fill-mask''': LayoutLMForMaskedLM,
            '''text-classification''': LayoutLMForSequenceClassification,
            '''token-classification''': LayoutLMForTokenClassification,
            '''zero-shot''': LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCamelCase   :  List[str]    =     True



    def  UpperCAmelCase_		(      self					):
         __A     :						Union[str, Any]          =    LayoutLMModelTester(self					)
         __A     :						int          =    ConfigTester(self					, config_class=_A					, hidden_size=37					)



    def  UpperCAmelCase_		(      self					):
         self.config_tester.run_common_tests()



    def  UpperCAmelCase_		(      self					):
         __A     :						List[str]          =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						Any          =    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              __A     :						str          =    type
              self.model_tester.create_and_check_model(*_A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						Any          =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						List[Any]          =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						int          =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						Union[str, Any]          =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_A					)





    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
    def  UpperCAmelCase_		(      self					):
         pass







def 			_SCREAMING_SNAKE_CASE    (       )				->				Optional[int]:
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     __A     :						Dict          =    torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]] ,						device=a  )  # noqa: E231
     __A     :						List[str]          =    torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ,						device=a  )  # noqa: E231
     __A     :						List[Any]          =    torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]] ,						device=a  )  # noqa: E231
     __A     :						List[Any]          =    torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ,						device=a  )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     __A     :						int          =    torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]] ,						device=a  )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class 						_A( unittest.TestCase       ):


    """simple docstring"""


    @slow
    def  UpperCAmelCase_		(      self					):
         __A     :						Optional[int]          =    LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'					).to(_A					)

         __A     :						Any          =    prepare_layoutlm_batch_inputs()

         # forward pass
         __A     :						int          =    model(input_ids=_A					, bbox=_A					, attention_mask=_A					, token_type_ids=_A					)

         # test the sequence output on [0, :3, :3]
         __A     :						str          =    torch.tensor(
             [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]					, device=_A					, )

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					, _A					, atol=1e-3					)					)

         # test the pooled output on [1, :3]
         __A     :						Tuple          =    torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]					, device=_A					)

         self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					, _A					, atol=1e-3					)					)



    @slow
    def  UpperCAmelCase_		(      self					):
         # initialize model with randomly initialized sequence classification head
         __A     :						Optional[int]          =    LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'					, num_labels=2					).to(
             _A					)

         __A     :						List[Any]          =    prepare_layoutlm_batch_inputs()

         # forward pass
         __A     :						Union[str, Any]          =    model(
             input_ids=_A					, bbox=_A					, attention_mask=_A					, token_type_ids=_A					, labels=torch.tensor([1, 1]					, device=_A					)					, )

         # test whether we get a loss as a scalar
         __A     :						Any          =    outputs.loss
         __A     :						str          =    torch.Size([]					)
         self.assertEqual(loss.shape					, _A					)

         # test the shape of the logits
         __A     :						Optional[int]          =    outputs.logits
         __A     :						Optional[Any]          =    torch.Size((2, 2)					)
         self.assertEqual(logits.shape					, _A					)



    @slow
    def  UpperCAmelCase_		(      self					):
         # initialize model with randomly initialized token classification head
         __A     :						Optional[Any]          =    LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'					, num_labels=13					).to(
             _A					)

         __A     :						Any          =    prepare_layoutlm_batch_inputs()

         # forward pass
         __A     :						Optional[int]          =    model(
             input_ids=_A					, bbox=_A					, attention_mask=_A					, token_type_ids=_A					, labels=_A					)

         # test the loss calculation to be around 2.65
         # expected_loss = torch.tensor(2.65, device=torch_device)

         # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
         # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

         # test the shape of the logits
         __A     :						List[Any]          =    outputs.logits
         __A     :						Dict          =    torch.Size((2, 25, 13)					)
         self.assertEqual(logits.shape					, _A					)





    @slow
    def  UpperCAmelCase_		(      self					):
         # initialize model with randomly initialized token classification head
         __A     :						int          =    LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'					).to(_A					)

         __A     :						Optional[Any]          =    prepare_layoutlm_batch_inputs()

         # forward pass
         __A     :						Optional[Any]          =    model(input_ids=_A					, bbox=_A					, attention_mask=_A					, token_type_ids=_A					)

         # test the shape of the logits
         __A     :						List[str]          =    torch.Size((2, 25)					)
         self.assertEqual(outputs.start_logits.shape					, _A					)
         self.assertEqual(outputs.end_logits.shape					, _A					)











import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase						:       List[Any]        =					32







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

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						str          =    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():
										__A     :						Optional[int]          =    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
					__A     :						Tuple          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					def collate_fn(a  ):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						Union[str, Any]          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

					# Instantiate dataloaders.
					__A     :						Any          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						List[Any]          =    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						:       Tuple        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
										__A     :						Dict          =    2
					# Initialize accelerator
					__A     :						int          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						Dict          =    config['lr']
					__A     :						Optional[Any]          =    int(config['num_epochs']  )
					__A     :						int          =    int(config['seed']  )
					__A     :						int          =    int(config['batch_size']  )

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    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).
										__A     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						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.
										__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Tuple          =    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  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.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():
																									__A     :						List[Any]          =    model(**a  )
																				__A     :						List[Any]          =    outputs.logits.argmax(dim=-1  )
																				__A   ,    __A     :						Tuple          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a ,						references=a ,						)

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    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.' ,						)
					parser.add_argument('--cpu' ,						action='store_true' ,						help='If passed, will train on the CPU.'  )
					__A     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




