code: 
  
  
  
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  __lowerCamelCase  				=					{
      """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
      """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
      """tokenizer_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"""},
  }
  
  __lowerCamelCase  				=					{
      """openai-gpt""": 5_1_2,
  }
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			VOCAB_FILES_NAMES
   lowerCamelCase_          =			PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_          =			['''input_ids''', '''attention_mask''']
   lowerCamelCase_          =			OpenAIGPTTokenizer
   def __init__(						self	,    a=None	,    a=None	,    a=None	,    a="<unk>"	,    **a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__(a	,    a	,    tokenizer_file=a	,    unk_token=a	,    **a					)
   @property
   def lowercase_		(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return True
  
  
  
   def lowercase_		(						self	,    a	,    a = None					)	->							Tuple[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self._tokenizer.model.save(a	,    name=a					)
     return tuple(a					)
style_context: 
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  __lowerCamelCase  				=					"""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  __lowerCamelCase  				=					"""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
  class 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return datasets.MetricInfo(
         description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''					),
                 '''references''': datasets.Value('''int32'''					),
             }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }