

'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






class     _SCREAMING_SNAKE_CASE       (       lowerCAmelCase_						,unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE	:Optional[Any]     			=	CanineTokenizer
 __SCREAMING_SNAKE_CASE	:List[Any]     			=	False



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      super().setUp()
      __magic_name__		  =							CanineTokenizer()
      tokenizer.save_pretrained(self.tmpdirname     )



 @cached_property
 def 					snake_case__    (       self						:       List[str]     ):
      return CanineTokenizer.from_pretrained('''google/canine-s'''     )



 def 					snake_case__    (       self						:       Dict					, **a__						:       Optional[Any]     ):
      __magic_name__		  =							self.tokenizer_class.from_pretrained(self.tmpdirname					, **__lowerCamelCase     )
      __magic_name__		  =							1024
      return tokenizer



 @require_torch
 def 					snake_case__    (       self						:       str     ):
      __magic_name__		  =							self.canine_tokenizer
      __magic_name__		  =							['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
      # fmt: off
      __magic_name__		  =							[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
      # fmt: on
      __magic_name__		  =							tokenizer(__lowerCamelCase					, padding=__lowerCamelCase					, return_tensors='''pt'''     )
      self.assertIsInstance(__lowerCamelCase					, __lowerCamelCase     )

      __magic_name__		  =							list(batch.input_ids.numpy()[0]     )

      self.assertListEqual(__lowerCamelCase					, __lowerCamelCase     )

      self.assertEqual((2, 39)					, batch.input_ids.shape     )
      self.assertEqual((2, 39)					, batch.attention_mask.shape     )



 @require_torch
 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							self.canine_tokenizer
      __magic_name__		  =							['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
      __magic_name__		  =							tokenizer(__lowerCamelCase					, padding=__lowerCamelCase					, return_tensors='''pt'''     )
      # check if input_ids, attention_mask and token_type_ids are returned
      self.assertIn('''input_ids'''					, __lowerCamelCase     )
      self.assertIn('''attention_mask'''					, __lowerCamelCase     )
      self.assertIn('''token_type_ids'''					, __lowerCamelCase     )



 @require_torch
 def 					snake_case__    (       self						:       Optional[Any]     ):
      __magic_name__		  =							self.canine_tokenizer
      __magic_name__		  =							[
          'What\'s the weater?',
          'It\'s about 25 degrees.',
      ]
      __magic_name__		  =							tokenizer(
          text_target=__lowerCamelCase					, max_length=32					, padding='''max_length'''					, truncation=__lowerCamelCase					, return_tensors='''pt'''     )
      self.assertEqual(32					, targets['''input_ids'''].shape[1]     )



 def 					snake_case__    (       self						:       Any     ):
      # safety check on max_len default value so we are sure the test works
      __magic_name__		  =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                self.assertNotEqual(tokenizer.model_max_length					, 42     )

        # Now let's start the test
      __magic_name__		  =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                # Isolate this from the other tests because we save additional tokens/etc
                __magic_name__		  =							tempfile.mkdtemp()

                __magic_name__		  =							' He is very happy, UNwant\u00E9d,running'
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                tokenizer.save_pretrained(__lowerCamelCase     )

                __magic_name__		  =							tokenizer.__class__.from_pretrained(__lowerCamelCase     )
                __magic_name__		  =							after_tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                self.assertListEqual(__lowerCamelCase					, __lowerCamelCase     )

                shutil.rmtree(__lowerCamelCase     )

      __magic_name__		  =							self.get_tokenizers(model_max_length=42     )
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                # Isolate this from the other tests because we save additional tokens/etc
                __magic_name__		  =							tempfile.mkdtemp()

                __magic_name__		  =							' He is very happy, UNwant\u00E9d,running'

                __magic_name__		  =							tokenizer.additional_special_tokens

                # We can add a new special token for Canine as follows:
                __magic_name__		  =							chr(0xe007     )
                additional_special_tokens.append(__lowerCamelCase     )
                tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}     )
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                tokenizer.save_pretrained(__lowerCamelCase     )

                __magic_name__		  =							tokenizer.__class__.from_pretrained(__lowerCamelCase     )
                __magic_name__		  =							after_tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                self.assertListEqual(__lowerCamelCase					, __lowerCamelCase     )
                self.assertIn(__lowerCamelCase					, after_tokenizer.additional_special_tokens     )
                self.assertEqual(after_tokenizer.model_max_length					, 42     )

                __magic_name__		  =							tokenizer.__class__.from_pretrained(__lowerCamelCase					, model_max_length=43     )
                self.assertEqual(tokenizer.model_max_length					, 43     )

                shutil.rmtree(__lowerCamelCase     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							self.get_tokenizers(do_lower_case=__lowerCamelCase     )
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                __magic_name__		  =							self.get_clean_sequence(__lowerCamelCase     )

                # a special token for Canine can be defined as follows:
                __magic_name__		  =							0xe005
                __magic_name__		  =							chr(__lowerCamelCase     )

                tokenizer.add_special_tokens({'''cls_token''': special_token}     )
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                self.assertEqual(len(__lowerCamelCase     )					, 1     )

                __magic_name__		  =							tokenizer.decode(ids + encoded_special_token					, clean_up_tokenization_spaces=__lowerCamelCase     )
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )

                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                self.assertEqual(__lowerCamelCase					, input_encoded + special_token_id     )

                __magic_name__		  =							tokenizer.decode(__lowerCamelCase					, skip_special_tokens=__lowerCamelCase     )
                self.assertTrue(special_token not in decoded     )



 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							self.get_tokenizers(do_lower_case=__lowerCamelCase     )
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                __magic_name__		  =							chr(0xe005     )
                __magic_name__		  =							chr(0xe006     )

                # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                tokenizer.add_tokens([SPECIAL_TOKEN_1]					, special_tokens=__lowerCamelCase     )
                # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}     )

                __magic_name__		  =							tokenizer.tokenize(__lowerCamelCase     )
                __magic_name__		  =							tokenizer.tokenize(__lowerCamelCase     )

                self.assertEqual(len(__lowerCamelCase     )					, 1     )
                self.assertEqual(len(__lowerCamelCase     )					, 1     )
                self.assertEqual(token_a[0]					, __lowerCamelCase     )
                self.assertEqual(token_a[0]					, __lowerCamelCase     )



 @require_tokenizers
 def 					snake_case__    (       self						:       Dict     ):
      __magic_name__		  =							self.get_tokenizers(do_lower_case=__lowerCamelCase     )
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                # a special token for Canine can be defined as follows:
                __magic_name__		  =							0xe006
                __magic_name__		  =							chr(__lowerCamelCase     )

                __magic_name__		  =							AddedToken(__lowerCamelCase					, lstrip=__lowerCamelCase     )
                tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}     )

                with tempfile.TemporaryDirectory() as tmp_dir_name:
                     tokenizer.save_pretrained(__lowerCamelCase     )
                     tokenizer.from_pretrained(__lowerCamelCase     )



 def 					snake_case__    (       self						:       List[Any]     ):
      __magic_name__		  =							[]
      if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

      if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

      for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(__lowerCamelCase     )

                with open(os.path.join(__lowerCamelCase					, '''special_tokens_map.json'''     )					, encoding='''utf-8'''     ) as json_file:
                     __magic_name__		  =							json.load(__lowerCamelCase     )

                with open(os.path.join(__lowerCamelCase					, '''tokenizer_config.json'''     )					, encoding='''utf-8'''     ) as json_file:
                     __magic_name__		  =							json.load(__lowerCamelCase     )

                # a special token for Canine can be defined as follows:
                __magic_name__		  =							0xe006
                __magic_name__		  =							chr(__lowerCamelCase     )

                __magic_name__		  =							[new_token_a]
                __magic_name__		  =							[new_token_a]

                with open(os.path.join(__lowerCamelCase					, '''special_tokens_map.json'''     )					, '''w'''					, encoding='''utf-8'''     ) as outfile:
                     json.dump(__lowerCamelCase					, __lowerCamelCase     )
                with open(os.path.join(__lowerCamelCase					, '''tokenizer_config.json'''     )					, '''w'''					, encoding='''utf-8'''     ) as outfile:
                     json.dump(__lowerCamelCase					, __lowerCamelCase     )

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                __magic_name__		  =							tokenizer_class.from_pretrained(__lowerCamelCase					, extra_ids=0     )
                self.assertIn(__lowerCamelCase					, tokenizer_without_change_in_init.additional_special_tokens     )
                # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                self.assertEqual(
                    [new_token_a]					, tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]     )     )					, )

                __magic_name__		  =							0xe007
                __magic_name__		  =							chr(__lowerCamelCase     )
                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                __magic_name__		  =							[AddedToken(__lowerCamelCase					, lstrip=__lowerCamelCase     )]
                __magic_name__		  =							tokenizer_class.from_pretrained(
                    __lowerCamelCase					, additional_special_tokens=__lowerCamelCase					, extra_ids=0     )

                self.assertIn(__lowerCamelCase					, tokenizer.additional_special_tokens     )
                # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                self.assertEqual(
                    [new_token_a]					, tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]     )     )     )



 @require_tokenizers
 def 					snake_case__    (       self						:       int     ):
      __magic_name__		  =							self.get_tokenizers(do_lower_case=__lowerCamelCase     )
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                __magic_name__		  =							'hello world'
                if self.space_between_special_tokens:
                     __magic_name__		  =							'[CLS] hello world [SEP]'
                else:
                     __magic_name__		  =							input
                __magic_name__		  =							tokenizer.encode(__lowerCamelCase					, add_special_tokens=__lowerCamelCase     )
                __magic_name__		  =							tokenizer.decode(__lowerCamelCase					, spaces_between_special_tokens=self.space_between_special_tokens     )
                self.assertIn(__lowerCamelCase					, [output, output.lower()]     )



 def 					snake_case__    (       self						:       List[Any]     ):
      __magic_name__		  =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                __magic_name__		  =							[
                    'bos_token',
                    'eos_token',
                    'unk_token',
                    'sep_token',
                    'pad_token',
                    'cls_token',
                    'mask_token',
                ]

      __magic_name__		  =							'a'
      __magic_name__		  =							ord(__lowerCamelCase     )

      for attr in attributes_list:
           setattr(__lowerCamelCase					, attr + '''_id'''					, __lowerCamelCase     )
           self.assertEqual(getattr(__lowerCamelCase					, __lowerCamelCase     )					, __lowerCamelCase     )
           self.assertEqual(getattr(__lowerCamelCase					, attr + '''_id'''     )					, __lowerCamelCase     )

           setattr(__lowerCamelCase					, attr + '''_id'''					, __lowerCamelCase     )
           self.assertEqual(getattr(__lowerCamelCase					, __lowerCamelCase     )					, __lowerCamelCase     )
           self.assertEqual(getattr(__lowerCamelCase					, attr + '''_id'''     )					, __lowerCamelCase     )

      setattr(__lowerCamelCase					, '''additional_special_tokens_ids'''					, []     )
      self.assertListEqual(getattr(__lowerCamelCase					, '''additional_special_tokens'''     )					, []     )
      self.assertListEqual(getattr(__lowerCamelCase					, '''additional_special_tokens_ids'''     )					, []     )

      __magic_name__		  =							0xe006
      __magic_name__		  =							chr(__lowerCamelCase     )
      setattr(__lowerCamelCase					, '''additional_special_tokens_ids'''					, [additional_special_token_id]     )
      self.assertListEqual(getattr(__lowerCamelCase					, '''additional_special_tokens'''     )					, [additional_special_token]     )
      self.assertListEqual(getattr(__lowerCamelCase					, '''additional_special_tokens_ids'''     )					, [additional_special_token_id]     )



 def 					snake_case__    (       self						:       List[Any]     ):
      pass



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      pass



 def 					snake_case__    (       self						:       Optional[Any]     ):
      pass



 def 					snake_case__    (       self						:       int     ):
      pass



 def 					snake_case__    (       self						:       Any     ):
      pass



 def 					snake_case__    (       self						:       Optional[int]     ):
      pass



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      pass



 def 					snake_case__    (       self						:       Optional[Any]     ):
      pass












'''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
#
########################################################################


A_			      =		16
A_			      =		32






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = 16		)    ->   int:
      snake_case__       :   Tuple	      =    AutoTokenizer.from_pretrained('bert-base-cased'		)
      snake_case__       :   Tuple	      =    load_dataset('glue'				,      'mrpc'		)

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

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

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

      # Instantiate dataloaders.
      snake_case__       :   Any	      =    DataLoader(
          tokenized_datasets['train']				,      shuffle=__SCREAMING_SNAKE_CASE				,      collate_fn=__SCREAMING_SNAKE_CASE				,      batch_size=__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Dict	      =    DataLoader(
          tokenized_datasets['validation']				,      shuffle=__SCREAMING_SNAKE_CASE				,      collate_fn=__SCREAMING_SNAKE_CASE				,      batch_size=__SCREAMING_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

      A_			      =		mocked_dataloaders  # noqa: F811






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,      __SCREAMING_SNAKE_CASE		) == "1":
            snake_case__       :   Optional[int]	      =    2
      # New Code #
      snake_case__       :   List[str]	      =    int(args.gradient_accumulation_steps		)
      # Initialize accelerator
      snake_case__       :   Optional[Any]	      =    Accelerator(
          cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=__SCREAMING_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
      snake_case__       :   Optional[Any]	      =    config['lr']
      snake_case__       :   Dict	      =    int(config['num_epochs']		)
      snake_case__       :   Any	      =    int(config['seed']		)
      snake_case__       :   List[str]	      =    int(config['batch_size']		)

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

      set_seed(__SCREAMING_SNAKE_CASE		)
      snake_case__      ,							snake_case__       :   Any	      =    get_dataloaders(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__       :   Dict	      =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,      return_dict=__SCREAMING_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).
      snake_case__       :   Dict	      =    model.to(accelerator.device		)

      # Instantiate optimizer
      snake_case__       :   int	      =    AdamW(params=model.parameters()				,      lr=__SCREAMING_SNAKE_CASE		)

      # Instantiate scheduler
      snake_case__       :   str	      =    get_linear_schedule_with_warmup(
          optimizer=__SCREAMING_SNAKE_CASE				,      num_warmup_steps=100				,      num_training_steps=(len(__SCREAMING_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.
      snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    accelerator.prepare(
          __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      # Now we train the model
      for epoch in range(__SCREAMING_SNAKE_CASE		):
            model.train()
            for step, batch in enumerate(__SCREAMING_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(__SCREAMING_SNAKE_CASE		):
                        snake_case__       :   Any	      =    model(**__SCREAMING_SNAKE_CASE		)
                        snake_case__       :   Dict	      =    output.loss
                        accelerator.backward(__SCREAMING_SNAKE_CASE		)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            snake_case__       :   int	      =    metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"				,      __SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    argparse.ArgumentParser(description='Simple example of training script.'		)
      parser.add_argument(
          '--mixed_precision'				,      type=__SCREAMING_SNAKE_CASE				,      default=__SCREAMING_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=__SCREAMING_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.'		)
      snake_case__       :   Any	      =    parser.parse_args()
      snake_case__       :   Dict	      =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()
