


from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertModel,
    )


class     lowercase__  :






  def __init__(					self      :					Dict		,						_lowercase      :					List[str]		,						_lowercase      :					Union[str, Any]=13		,						_lowercase      :					List[str]=7		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					int=True		,						_lowercase      :					Tuple=99		,						_lowercase      :					Dict=32		,						_lowercase      :					Optional[int]=2		,						_lowercase      :					Dict=4		,						_lowercase      :					Union[str, Any]=37		,						_lowercase      :					Optional[int]="gelu"		,						_lowercase      :					Tuple=0.1		,						_lowercase      :					Tuple=0.1		,						_lowercase      :					List[str]=5_12		,						_lowercase      :					int=16		,						_lowercase      :					Optional[int]=2		,						_lowercase      :					List[str]=0.0_2		,						_lowercase      :					Union[str, Any]=3		,						_lowercase      :					List[str]=4		,						_lowercase      :					Union[str, Any]=None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     13
         UpperCAmelCase__										=     7
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     99
         UpperCAmelCase__										=     3_84
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     4
         UpperCAmelCase__										=     37
         UpperCAmelCase__										=     "gelu"
         UpperCAmelCase__										=     0.1
         UpperCAmelCase__										=     0.1
         UpperCAmelCase__										=     5_12
         UpperCAmelCase__										=     16
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     0.0_2
         UpperCAmelCase__										=     3
         UpperCAmelCase__										=     4
         UpperCAmelCase__										=     1_28
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     9
         UpperCAmelCase__										=     1
         UpperCAmelCase__										=     None






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """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__										=     ConvBertConfig(
             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		,						return_dict=_lowercase		,						)

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






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					List[str]		,						_lowercase      :					List[str]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertModel(config=_lowercase )
         UpperCAmelCase__										=     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

         UpperCAmelCase__										=     [input_ids, input_mask]
         UpperCAmelCase__										=     model(_lowercase )

         UpperCAmelCase__										=     model(_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					List[str]		,						_lowercase      :					str		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertForMaskedLM(config=_lowercase )
         UpperCAmelCase__										=     {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size) )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					str		,						_lowercase      :					Dict		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					Dict		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     TFConvBertForSequenceClassification(config=_lowercase )
         UpperCAmelCase__										=     {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels) )






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					str		,						_lowercase      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					str		,						_lowercase      :					Any		,						_lowercase      :					List[Any]		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_choices
         UpperCAmelCase__										=     TFConvBertForMultipleChoice(config=_lowercase )
         UpperCAmelCase__										=     tf.tile(tf.expand_dims(_lowercase		,						1 )		,						(1, self.num_choices, 1) )
         UpperCAmelCase__										=     tf.tile(tf.expand_dims(_lowercase		,						1 )		,						(1, self.num_choices, 1) )
         UpperCAmelCase__										=     tf.tile(tf.expand_dims(_lowercase		,						1 )		,						(1, self.num_choices, 1) )
         UpperCAmelCase__										=     {
             "input_ids": multiple_choice_inputs_ids,
             "attention_mask": multiple_choice_input_mask,
             "token_type_ids": multiple_choice_token_type_ids,
         }
         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices) )






  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					List[str]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Any		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[str]		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     TFConvBertForTokenClassification(config=_lowercase )
         UpperCAmelCase__										=     {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels) )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertForQuestionAnswering(config=_lowercase )
         UpperCAmelCase__										=     {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         UpperCAmelCase__										=     model(_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 			_UpperCAmelCase  (					self      :					List[str] ):




         """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_tf
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (
      (
          TFConvBertModel,
          TFConvBertForMaskedLM,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  A__=  (
      {
          'feature-extraction': TFConvBertModel,
          'fill-mask': TFConvBertForMaskedLM,
          'question-answering': TFConvBertForQuestionAnswering,
          'text-classification': TFConvBertForSequenceClassification,
          'token-classification': TFConvBertForTokenClassification,
          'zero-shot': TFConvBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertModelTester(self )
         UpperCAmelCase__										=     ConfigTester(self		,						config_class=_lowercase		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         self.config_tester.run_common_tests()






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




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowercase )






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




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     True

         if hasattr(_lowercase		,						"use_cache" ):
                UpperCAmelCase__										=     True

         UpperCAmelCase__										=     getattr(self.model_tester		,						"encoder_seq_length"		,						self.model_tester.seq_length )
         UpperCAmelCase__										=     getattr(self.model_tester		,						"key_length"		,						_lowercase )

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     self._prepare_for_class(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     model_class(_lowercase )
                UpperCAmelCase__										=     len(model(_lowercase ) )

                with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(_lowercase		,						saved_model=_lowercase )
                       UpperCAmelCase__										=     os.path.join(_lowercase		,						"saved_model"		,						"1" )
                       UpperCAmelCase__										=     tf.keras.models.load_model(_lowercase )
                       UpperCAmelCase__										=     model(_lowercase )

                       if self.is_encoder_decoder:
                              UpperCAmelCase__										=     outputs["encoder_hidden_states"]
                              UpperCAmelCase__										=     outputs["encoder_attentions"]
                       else:
                              UpperCAmelCase__										=     outputs["hidden_states"]
                              UpperCAmelCase__										=     outputs["attentions"]

                       self.assertEqual(len(_lowercase )		,						_lowercase )

                       UpperCAmelCase__										=     getattr(
                           self.model_tester		,						"expected_num_hidden_layers"		,						self.model_tester.num_hidden_layers + 1 )

                       self.assertEqual(len(_lowercase )		,						_lowercase )
                       self.assertListEqual(
                           list(output_hidden_states[0].shape[-2:] )		,						[self.model_tester.seq_length, self.model_tester.hidden_size]		,						)

                       self.assertEqual(len(_lowercase )		,						self.model_tester.num_hidden_layers )
                       self.assertListEqual(
                           list(output_attentions[0].shape[-3:] )		,						[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		,						)






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
         self.assertIsNotNone(_lowercase )






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




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     getattr(self.model_tester		,						"decoder_seq_length"		,						self.model_tester.seq_length )
         UpperCAmelCase__										=     getattr(self.model_tester		,						"encoder_seq_length"		,						self.model_tester.seq_length )
         UpperCAmelCase__										=     getattr(self.model_tester		,						"key_length"		,						_lowercase )
         UpperCAmelCase__										=     getattr(self.model_tester		,						"key_length"		,						_lowercase )

         def check_decoder_attentions_output(_lowercase      :					Union[str, Any] ):
                UpperCAmelCase__										=     len(_lowercase )
                self.assertEqual(out_len % 2		,						0 )
                UpperCAmelCase__										=     outputs.decoder_attentions
                self.assertEqual(len(_lowercase )		,						self.model_tester.num_hidden_layers )
                self.assertListEqual(
                    list(decoder_attentions[0].shape[-3:] )		,						[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]		,						)

         def check_encoder_attentions_output(_lowercase      :					Union[str, Any] ):
                UpperCAmelCase__										=     [
                    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
                ]
                self.assertEqual(len(_lowercase )		,						self.model_tester.num_hidden_layers )
                self.assertListEqual(
                    list(attentions[0].shape[-3:] )		,						[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		,						)

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     True
                UpperCAmelCase__										=     False
                UpperCAmelCase__										=     model_class(_lowercase )
                UpperCAmelCase__										=     model(self._prepare_for_class(_lowercase		,						_lowercase ) )
                UpperCAmelCase__										=     len(_lowercase )
                self.assertEqual(config.output_hidden_states		,						_lowercase )
                check_encoder_attentions_output(_lowercase )

                if self.is_encoder_decoder:
                       UpperCAmelCase__										=     model_class(_lowercase )
                       UpperCAmelCase__										=     model(self._prepare_for_class(_lowercase		,						_lowercase ) )
                       self.assertEqual(config.output_hidden_states		,						_lowercase )
                       check_decoder_attentions_output(_lowercase )

                # Check that output attentions can also be changed via the config
                del inputs_dict["output_attentions"]
                UpperCAmelCase__										=     True
                UpperCAmelCase__										=     model_class(_lowercase )
                UpperCAmelCase__										=     model(self._prepare_for_class(_lowercase		,						_lowercase ) )
                self.assertEqual(config.output_hidden_states		,						_lowercase )
                check_encoder_attentions_output(_lowercase )

                # Check attention is always last and order is fine
                UpperCAmelCase__										=     True
                UpperCAmelCase__										=     True
                UpperCAmelCase__										=     model_class(_lowercase )
                UpperCAmelCase__										=     model(self._prepare_for_class(_lowercase		,						_lowercase ) )

                self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,						len(_lowercase ) )
                self.assertEqual(model.config.output_hidden_states		,						_lowercase )
                check_encoder_attentions_output(_lowercase )




@require_tf
class     lowercase__  (					unittest.TestCase		):






  @slow
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
         UpperCAmelCase__										=     tf.constant([[0, 1, 2, 3, 4, 5]] )
         UpperCAmelCase__										=     model(_lowercase )[0]

         UpperCAmelCase__										=     [1, 6, 7_68]
         self.assertEqual(output.shape		,						_lowercase )

         UpperCAmelCase__										=     tf.constant(
             [
                 [
                     [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                     [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                     [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
                 ]
             ] )
         tf.debugging.assert_near(output[:, :3, :3]		,						_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
#
########################################################################


A      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A = 1_6			)						->					Tuple:



       '''simple docstring'''





       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__										=     1_2_8 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__										=     1_6
              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

    A      = mocked_dataloaders  # noqa: F811





def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       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=1_0_0 ,						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 __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       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": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()





