code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  _a      :				Dict											=					logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  _a      :				int											=					direct_transformers_import(Path(__file__).parent)
  
  
  _a      :				Union[str, Any]											=					{
      """AutoTokenizer""": """PreTrainedTokenizerBase""",
      """AutoFeatureExtractor""": """FeatureExtractionMixin""",
      """AutoImageProcessor""": """ImageProcessingMixin""",
  }
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCamelCase_     ):
  		a     :     Dict     						=["""feature_extractor""", """tokenizer"""]
  		# Names need to be attr_class for attr in attributes
  		a     :     Dict     						=None
  		a     :     Any     						=None
  		a     :     Dict     						=None
  		def __init__(						self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					for key in kwargs:
  								if key not in self.attributes:
  											raise TypeError(f'Unexpected keyword argument {key}.'    )
  					for arg, attribute_name in zip(__SCREAMING_SNAKE_CASE,self.attributes    ):
  								if attribute_name in kwargs:
  											raise TypeError(f'Got multiple values for argument {attribute_name}.'    )
  								else:
  											__lowerCAmelCase      		=       arg
  
  					if len(__SCREAMING_SNAKE_CASE    ) != len(self.attributes    ):
  								raise ValueError(
  								    f'This processor requires {len(self.attributes    )} arguments: {", ".join(self.attributes    )}. Got '
  								    f'{len(__SCREAMING_SNAKE_CASE    )} arguments instead.'    )
  
  					# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  					for attribute_name, arg in kwargs.items():
  								__lowerCAmelCase      		=       getattr(self,f'{attribute_name}_class'    )
  								# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  								__lowerCAmelCase      		=       AUTO_TO_BASE_CLASS_MAPPING.get(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       tuple(getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for n in class_name if n is not None    )
  								else:
  											__lowerCAmelCase      		=       getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  								if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  											raise ValueError(
  											    f'Received a {type(__SCREAMING_SNAKE_CASE    ).__name__} for argument {attribute_name}, but a {class_name} was expected.'    )
  
  								setattr(self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  		def __repr__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       [f'- {name}: {repr(getattr(self,__SCREAMING_SNAKE_CASE    )    )}' for name in self.attributes]
  					__lowerCAmelCase      		=       "\n".join(__SCREAMING_SNAKE_CASE    )
  					return f'{self.__class__.__name__}:\n{attributes_repr}'
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = False,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					os.makedirs(__SCREAMING_SNAKE_CASE,exist_ok=__SCREAMING_SNAKE_CASE    )
  
  					if push_to_hub:
  								__lowerCAmelCase      		=       kwargs.pop("""commit_message""",__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       kwargs.pop("""repo_id""",save_directory.split(os.path.sep    )[-1]    )
  								__lowerCAmelCase      		=       self._create_repo(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       self._get_files_timestamps(__SCREAMING_SNAKE_CASE    )
  					# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  					# loaded from the Hub.
  					if self._auto_class is not None:
  								__lowerCAmelCase      		=       [getattr(self,__SCREAMING_SNAKE_CASE    ) for attribute_name in self.attributes]
  								__lowerCAmelCase      		=       [(a.init_kwargs if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else a) for a in attrs]
  								custom_object_save(self,__SCREAMING_SNAKE_CASE,config=__SCREAMING_SNAKE_CASE    )
  
  					for attribute_name in self.attributes:
  								__lowerCAmelCase      		=       getattr(self,__SCREAMING_SNAKE_CASE    )
  								# Include the processor class in the attribute config so this processor can then be reloaded with the
  								# `AutoProcessor` API.
  								if hasattr(__SCREAMING_SNAKE_CASE,"""_set_processor_class"""    ):
  											attribute._set_processor_class(self.__class__.__name__    )
  								attribute.save_pretrained(__SCREAMING_SNAKE_CASE    )
  
  					if self._auto_class is not None:
  								# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  								for attribute_name in self.attributes:
  											__lowerCAmelCase      		=       getattr(self,__SCREAMING_SNAKE_CASE    )
  											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  														del attribute.init_kwargs["auto_map"]
  
  					if push_to_hub:
  								self._upload_modified_files(
  								    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,commit_message=__SCREAMING_SNAKE_CASE,token=kwargs.get("""use_auth_token"""    ),)
  		@classmethod
  		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "main",**__SCREAMING_SNAKE_CASE,):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       cache_dir
  					__lowerCAmelCase      		=       force_download
  					__lowerCAmelCase      		=       local_files_only
  					__lowerCAmelCase      		=       revision
  
  					__lowerCAmelCase      		=       kwargs.pop("""use_auth_token""",__SCREAMING_SNAKE_CASE    )
  					if use_auth_token is not None:
  								warnings.warn(
  								    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.""",__SCREAMING_SNAKE_CASE    )
  								if token is not None:
  											raise ValueError(
  											    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""    )
  								__lowerCAmelCase      		=       use_auth_token
  
  					if token is not None:
  								# change to `token` in a follow-up PR
  								__lowerCAmelCase      		=       token
  
  					__lowerCAmelCase      		=       cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  					return cls(*__SCREAMING_SNAKE_CASE    )
  		@classmethod
  		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE="AutoProcessor"    ):
  
  
  
  					'''simple docstring'''
  
  					if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								__lowerCAmelCase      		=       auto_class.__name__
  
  					import transformers.models.auto as auto_module
  
  					if not hasattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								raise ValueError(f'{auto_class} is not a valid auto class.'    )
  
  					__lowerCAmelCase      		=       auto_class
  		@classmethod
  		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					for attribute_name in cls.attributes:
  								__lowerCAmelCase      		=       getattr(cls,f'{attribute_name}_class'    )
  								if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       tuple(getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) if n is not None else None for n in class_name    )
  											__lowerCAmelCase      		=       kwargs.get("""use_fast""",__SCREAMING_SNAKE_CASE    )
  											if use_fast and classes[1] is not None:
  														__lowerCAmelCase      		=       classes[1]
  											else:
  														__lowerCAmelCase      		=       classes[0]
  								else:
  											__lowerCAmelCase      		=       getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  								args.append(attribute_class.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )    )
  					return args
  
  
  
  
  
  
  
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       getattr(self,self.attributes[0]    )
  					return getattr(__SCREAMING_SNAKE_CASE,"""model_input_names""",__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  _a      :				List[str]											=					copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  	_a      :				Optional[int]											=					ProcessorMixin.push_to_hub.__doc__.format(
  	    object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
  	)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  _a      :				str											=					"""
  This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.
  
  This metric has three separate use cases:
      - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
      - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
      - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
  """
  
  _a      :				Optional[int]											=					"""
  Args:
  - references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples,)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples, n_classes)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
      - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
      - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
      - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
      - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
      - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
  - sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
  - max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
  - multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
      - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
      - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
  - labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
      `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
      `prediction_scores` is used. Defaults to `None`.
  Returns:
      roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
  Examples:
      Example 1:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
          >>> refs = [1, 0, 1, 1, 0, 0]
          >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
          >>> print(round(results['roc_auc'], 2))
          0.78
  
      Example 2:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
          >>> refs = [1, 0, 1, 2, 2, 0]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
          >>> print(round(results['roc_auc'], 2))
          0.85
  
      Example 3:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
          >>> refs = [[1, 1, 0],
          ...         [1, 1, 0],
          ...         [0, 1, 0],
          ...         [0, 0, 1],
          ...         [0, 1, 1],
          ...         [1, 0, 1]]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
          >>> print([round(res, 2) for res in results['roc_auc']])
          [0.83, 0.38, 0.94]
  """
  
  _a      :				str											=					"""\
  @article{doi:10.1177/0272989X8900900307,
  author = {Donna Katzman McClish},
  title ={Analyzing a Portion of the ROC Curve},
  journal = {Medical Decision Making},
  volume = {9},
  number = {3},
  pages = {190-195},
  year = {1989},
  doi = {10.1177/0272989X8900900307},
      note ={PMID: 2668680},
  URL = {https://doi.org/10.1177/0272989X8900900307},
  eprint = {https://doi.org/10.1177/0272989X8900900307}
  }
  
  
  @article{10.1023/A:1010920819831,
  author = {Hand, David J. and Till, Robert J.},
  title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
  year = {2001},
  issue_date = {November 2001},
  publisher = {Kluwer Academic Publishers},
  address = {USA},
  volume = {45},
  number = {2},
  issn = {0885-6125},
  url = {https://doi.org/10.1023/A:1010920819831},
  doi = {10.1023/A:1010920819831},
  journal = {Mach. Learn.},
  month = {oct},
  pages = {171–186},
  numpages = {16},
  keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
  }
  
  
  @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 					_UpperCAmelCase							(		datasets.Metric     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return datasets.MetricInfo(
  					    description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
  					        {
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					            """references""": datasets.Value("""int32"""    ),
  					        }
  					        if self.config_name == """multiclass"""
  					        else {
  					            """references""": datasets.Sequence(datasets.Value("""int32"""    )    ),
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					        }
  					        if self.config_name == """multilabel"""
  					        else {
  					            """references""": datasets.Value("""int32"""    ),
  					            """prediction_scores""": datasets.Value("""float"""    ),
  					        }    ),reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"""],)
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="macro",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="raise",__SCREAMING_SNAKE_CASE=None,):
  
  
  
  					'''simple docstring'''
  
  					return {
  					    "roc_auc": roc_auc_score(
  					        __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,average=__SCREAMING_SNAKE_CASE,sample_weight=__SCREAMING_SNAKE_CASE,max_fpr=__SCREAMING_SNAKE_CASE,multi_class=__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE,)
  					}
  
  