



'''simple docstring'''





import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class        UpperCAmelCase_			(			_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						BertTokenizer
    lowercase   =						BertTokenizerFast
    lowercase   =						True
    lowercase   =						True
    lowercase   =						filter_non_english




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          super().setUp()

          snake_case__    :       Tuple       		= [
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """[PAD]""",
              """[MASK]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          snake_case__    :       int       		= os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES["""vocab_file"""]		)
          with open(self.vocab_file					,	"""w"""					,	encoding="""utf-8"""		) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)




    def lowerCamelCase			(       self   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       List[Any]       		= """UNwant\u00E9d,running"""
          snake_case__    :       Tuple       		= """unwanted, running"""
          return input_text, output_text




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Optional[Any]       		= self.tokenizer_class(self.vocab_file		)

          snake_case__    :       Dict       		= tokenizer.tokenize("""UNwant\u00E9d,running"""		)
          self.assertListEqual(snake_case_					,	["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_		)					,	[9, 6, 7, 12, 10, 11]		)




    def lowerCamelCase			(       self   :	Tuple		):
          if not self.test_rust_tokenizer:
                return

          snake_case__    :       int       		= self.get_tokenizer()
          snake_case__    :       Optional[Any]       		= self.get_rust_tokenizer()

          snake_case__    :       str       		= """UNwant\u00E9d,running"""

          snake_case__    :       List[str]       		= tokenizer.tokenize(snake_case_		)
          snake_case__    :       Optional[int]       		= rust_tokenizer.tokenize(snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)

          snake_case__    :       Union[str, Any]       		= tokenizer.encode(snake_case_					,	add_special_tokens=snake_case_		)
          snake_case__    :       Optional[Any]       		= rust_tokenizer.encode(snake_case_					,	add_special_tokens=snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)

          snake_case__    :       str       		= self.get_rust_tokenizer()
          snake_case__    :       str       		= tokenizer.encode(snake_case_		)
          snake_case__    :       Tuple       		= rust_tokenizer.encode(snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)

          # With lower casing
          snake_case__    :       List[Any]       		= self.get_tokenizer(do_lower_case=snake_case_		)
          snake_case__    :       Optional[int]       		= self.get_rust_tokenizer(do_lower_case=snake_case_		)

          snake_case__    :       int       		= """UNwant\u00E9d,running"""

          snake_case__    :       List[Any]       		= tokenizer.tokenize(snake_case_		)
          snake_case__    :       Dict       		= rust_tokenizer.tokenize(snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)

          snake_case__    :       Dict       		= tokenizer.encode(snake_case_					,	add_special_tokens=snake_case_		)
          snake_case__    :       Dict       		= rust_tokenizer.encode(snake_case_					,	add_special_tokens=snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)

          snake_case__    :       List[str]       		= self.get_rust_tokenizer()
          snake_case__    :       str       		= tokenizer.encode(snake_case_		)
          snake_case__    :       Any       		= rust_tokenizer.encode(snake_case_		)
          self.assertListEqual(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       List[str]       		= BasicTokenizer()

          self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""		)					,	["""ah""", """\u535A""", """\u63A8""", """zz"""]		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       List[Any]       		= BasicTokenizer(do_lower_case=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)					,	["""hello""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Tuple       		= BasicTokenizer(do_lower_case=snake_case_					,	strip_accents=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hällo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""h\u00E9llo"""]		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       int       		= BasicTokenizer(do_lower_case=snake_case_					,	strip_accents=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       int       		= BasicTokenizer(do_lower_case=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       List[str]       		= BasicTokenizer(do_lower_case=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)					,	["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       Union[str, Any]       		= BasicTokenizer(do_lower_case=snake_case_					,	strip_accents=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Tuple       		= BasicTokenizer(do_lower_case=snake_case_					,	strip_accents=snake_case_		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Any       		= BasicTokenizer(do_lower_case=snake_case_					,	never_split=["""[UNK]"""]		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""		)					,	["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Tuple       		= BasicTokenizer()
          snake_case__    :       Optional[int]       		= """a\n'll !!to?'d of, can't."""
          snake_case__    :       int       		= ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
          self.assertListEqual(tokenizer.tokenize(snake_case_		)					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Optional[int]       		= ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

          snake_case__    :       Optional[Any]       		= {}
          for i, token in enumerate(snake_case_		):
                snake_case__    :       Optional[int]       		= i
          snake_case__    :       Any       		= WordpieceTokenizer(vocab=snake_case_					,	unk_token="""[UNK]"""		)

          self.assertListEqual(tokenizer.tokenize(""""""		)					,	[]		)

          self.assertListEqual(tokenizer.tokenize("""unwanted running"""		)					,	["""un""", """##want""", """##ed""", """runn""", """##ing"""]		)

          self.assertListEqual(tokenizer.tokenize("""unwantedX running"""		)					,	["""[UNK]""", """runn""", """##ing"""]		)




    def lowerCamelCase			(       self   :	Dict		):
          self.assertTrue(_is_whitespace(""" """		)		)
          self.assertTrue(_is_whitespace("""\t"""		)		)
          self.assertTrue(_is_whitespace("""\r"""		)		)
          self.assertTrue(_is_whitespace("""\n"""		)		)
          self.assertTrue(_is_whitespace("""\u00A0"""		)		)

          self.assertFalse(_is_whitespace("""A"""		)		)
          self.assertFalse(_is_whitespace("""-"""		)		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.assertTrue(_is_control("""\u0005"""		)		)

          self.assertFalse(_is_control("""A"""		)		)
          self.assertFalse(_is_control(""" """		)		)
          self.assertFalse(_is_control("""\t"""		)		)
          self.assertFalse(_is_control("""\r"""		)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          self.assertTrue(_is_punctuation("""-"""		)		)
          self.assertTrue(_is_punctuation("""$"""		)		)
          self.assertTrue(_is_punctuation("""`"""		)		)
          self.assertTrue(_is_punctuation("""."""		)		)

          self.assertFalse(_is_punctuation("""A"""		)		)
          self.assertFalse(_is_punctuation(""" """		)		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       int       		= self.get_tokenizer()
          snake_case__    :       Any       		= self.get_rust_tokenizer()

          # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
          self.assertListEqual([tokenizer.tokenize(snake_case_		) for t in ["""Test""", """\xad""", """test"""]]					,	[["""[UNK]"""], [], ["""[UNK]"""]]		)

          self.assertListEqual(
              [rust_tokenizer.tokenize(snake_case_		) for t in ["""Test""", """\xad""", """test"""]]					,	[["""[UNK]"""], [], ["""[UNK]"""]]		)




    @slow
    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Any       		= self.tokenizer_class.from_pretrained("""bert-base-uncased"""		)

          snake_case__    :       List[str]       		= tokenizer.encode("""sequence builders"""					,	add_special_tokens=snake_case_		)
          snake_case__    :       Tuple       		= tokenizer.encode("""multi-sequence build"""					,	add_special_tokens=snake_case_		)

          snake_case__    :       Any       		= tokenizer.build_inputs_with_special_tokens(snake_case_		)
          snake_case__    :       Any       		= tokenizer.build_inputs_with_special_tokens(snake_case_					,	snake_case_		)

          assert encoded_sentence == [101] + text + [102]
          assert encoded_pair == [101] + text + [102] + text_a + [102]




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                      snake_case__    :       Optional[Any]       		= self.rust_tokenizer_class.from_pretrained(snake_case_					,	**snake_case_		)

                      snake_case__    :       Optional[int]       		= f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                      snake_case__    :       Any       		= tokenizer_r.encode_plus(
                          snake_case_					,	return_attention_mask=snake_case_					,	return_token_type_ids=snake_case_					,	return_offsets_mapping=snake_case_					,	add_special_tokens=snake_case_					,	)

                      snake_case__    :       List[Any]       		= tokenizer_r.do_lower_case if hasattr(snake_case_					,	"""do_lower_case"""		) else False
                      snake_case__    :       Any       		= (
                          [
                              ((0, 0), tokenizer_r.cls_token),
                              ((0, 1), """A"""),
                              ((1, 2), ""","""),
                              ((3, 5), """na"""),
                              ((5, 6), """##ï"""),
                              ((6, 8), """##ve"""),
                              ((9, 15), tokenizer_r.mask_token),
                              ((16, 21), """Allen"""),
                              ((21, 23), """##NL"""),
                              ((23, 24), """##P"""),
                              ((25, 33), """sentence"""),
                              ((33, 34), """."""),
                              ((0, 0), tokenizer_r.sep_token),
                          ]
                          if not do_lower_case
                          else [
                              ((0, 0), tokenizer_r.cls_token),
                              ((0, 1), """a"""),
                              ((1, 2), ""","""),
                              ((3, 8), """naive"""),
                              ((9, 15), tokenizer_r.mask_token),
                              ((16, 21), """allen"""),
                              ((21, 23), """##nl"""),
                              ((23, 24), """##p"""),
                              ((25, 33), """sentence"""),
                              ((33, 34), """."""),
                              ((0, 0), tokenizer_r.sep_token),
                          ]
                      )

                      self.assertEqual(
                          [e[1] for e in expected_results]					,	tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]		)		)
                      self.assertEqual([e[0] for e in expected_results]					,	tokens["""offset_mapping"""]		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       Optional[Any]       		= ["""的""", """人""", """有"""]
          snake_case__    :       int       		= """""".join(snake_case_		)
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                      snake_case__    :       List[Any]       		= True
                      snake_case__    :       List[str]       		= self.tokenizer_class.from_pretrained(snake_case_					,	**snake_case_		)
                      snake_case__    :       Optional[int]       		= self.rust_tokenizer_class.from_pretrained(snake_case_					,	**snake_case_		)

                      snake_case__    :       Optional[int]       		= tokenizer_p.encode(snake_case_					,	add_special_tokens=snake_case_		)
                      snake_case__    :       List[Any]       		= tokenizer_r.encode(snake_case_					,	add_special_tokens=snake_case_		)

                      snake_case__    :       List[Any]       		= tokenizer_r.convert_ids_to_tokens(snake_case_		)
                      snake_case__    :       Any       		= tokenizer_p.convert_ids_to_tokens(snake_case_		)

                      # it is expected that each Chinese character is not preceded by "##"
                      self.assertListEqual(snake_case_					,	snake_case_		)
                      self.assertListEqual(snake_case_					,	snake_case_		)

                      snake_case__    :       Union[str, Any]       		= False
                      snake_case__    :       List[Any]       		= self.rust_tokenizer_class.from_pretrained(snake_case_					,	**snake_case_		)
                      snake_case__    :       Optional[int]       		= self.tokenizer_class.from_pretrained(snake_case_					,	**snake_case_		)

                      snake_case__    :       List[str]       		= tokenizer_r.encode(snake_case_					,	add_special_tokens=snake_case_		)
                      snake_case__    :       Tuple       		= tokenizer_p.encode(snake_case_					,	add_special_tokens=snake_case_		)

                      snake_case__    :       List[Any]       		= tokenizer_r.convert_ids_to_tokens(snake_case_		)
                      snake_case__    :       Dict       		= tokenizer_p.convert_ids_to_tokens(snake_case_		)

                      # it is expected that only the first Chinese character is not preceded by "##".
                      snake_case__    :       Union[str, Any]       		= [
                          f"##{token}" if idx != 0 else token for idx, token in enumerate(snake_case_		)
                      ]
                      self.assertListEqual(snake_case_					,	snake_case_		)
                      self.assertListEqual(snake_case_					,	snake_case_		)





'''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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Optional[Any]:
      snake_case__    :       Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Optional[int]       		= load_dataset("""glue"""	,  """mrpc"""						)

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

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

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

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

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

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

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

      # 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__    :       Tuple       		= model.to(accelerator.device						)

      # Instantiate optimizer
      snake_case__    :       Any       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)

      # Instantiate scheduler
      snake_case__    :       List[Any]       		= get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * 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__    :       List[Any]       		= accelerator.prepare(
          _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

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

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

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





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= argparse.ArgumentParser(description="""Simple example of training script."""						)
      parser.add_argument(
          """--mixed_precision"""	,  type=_lowerCAmelCase	,  default=_lowerCAmelCase	,  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=_lowerCAmelCase	,  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__    :       Tuple       		= parser.parse_args()
      snake_case__    :       Dict       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()
