code: 
  
  
  """simple docstring"""
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  UpperCAmelCase   =	'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
  
  UpperCAmelCase   =	'\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'
  
  
  UpperCAmelCase   =	'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION							)
  class   lowercase__							(				datasets.Metric							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
      return datasets.MetricInfo(
          description=_DESCRIPTION			,      citation=_CITATION			,      inputs_description=_KWARGS_DESCRIPTION			,      features=datasets.Features(self._get_feature_types())			,      reference_urls=[
              """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
          ]			,      )
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
      if self.config_name == "multilist":
       return {
           "predictions": datasets.Sequence(datasets.Value("""float""")),
           "references": datasets.Sequence(datasets.Value("""float""")),
       }
      else:
       return {
           "predictions": datasets.Value("""float"""),
           "references": datasets.Value("""float"""),
       }
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="uniform_average")     ->     Optional[int]:
      _lowerCamelCase  :				str      =		mean_absolute_error(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      sample_weight=SCREAMING_SNAKE_CASE			,      multioutput=SCREAMING_SNAKE_CASE)
  
      return {"mae": mae_score}
  
  
style_context: 
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  UpperCAmelCase   =	{
      """vocab_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
      },
      """tokenizer_file""": {
          """openai/clip-vit-base-patch32""": (
              """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  UpperCAmelCase   =	{
      """openai/clip-vit-base-patch32""": 77,
  }
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				VOCAB_FILES_NAMES
     __UpperCAmelCase       					=				PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase       					=				['''input_ids''', '''attention_mask''']
     __UpperCAmelCase       					=				CLIPTokenizer
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|startoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      tokenizer_file=SCREAMING_SNAKE_CASE			,      unk_token=SCREAMING_SNAKE_CASE			,      bos_token=SCREAMING_SNAKE_CASE			,      eos_token=SCREAMING_SNAKE_CASE			,      pad_token=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        if not isinstance(self.backend_tokenizer.pre_tokenizer			,      pre_tokenizers.Sequence):
           raise ValueError(
               """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
               """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
               """ to be compatible with this version.The easiest way to do so is"""
               """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
               """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
               """ transformers.""")
  
        self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		self.backend_tokenizer.decode
  
        def new_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		orig_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		text.replace(self.backend_tokenizer.model.end_of_word_suffix			,      """ """).strip()
           return text
  
        _lowerCamelCase  :				List[str]      =		new_decode_method
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				str      =		[self.bos_token_id]
        _lowerCamelCase  :				Union[str, Any]      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				Any      =		[self.bos_token_id]
        _lowerCamelCase  :				int      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Tuple[str]:
        _lowerCamelCase  :				Dict      =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,      name=SCREAMING_SNAKE_CASE)
        return tuple(SCREAMING_SNAKE_CASE)
  
  