code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _a      :				Any											=					logging.get_logger(__name__)
  
  _a      :				Union[str, Any]											=					{
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  _a      :				List[str]											=					{
      """vocab_file""": {
          """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
          """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
          """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
          """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
          """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
          """roberta-large-openai-detector""": (
              """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
          ),
      },
      """merges_file""": {
          """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
          """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
          """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
          """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
          """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
          """roberta-large-openai-detector""": (
              """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
          ),
      },
  }
  
  _a      :				Tuple											=					{
      """roberta-base""": 5_1_2,
      """roberta-large""": 5_1_2,
      """roberta-large-mnli""": 5_1_2,
      """distilroberta-base""": 5_1_2,
      """roberta-base-openai-detector""": 5_1_2,
      """roberta-large-openai-detector""": 5_1_2,
  }
  
  
  
  
  
  @lru_cache()
  def      _lowerCAmelCase		(		)      ->      str:
  			__lowerCAmelCase      		=       (
  			    list(range(ord("""!"""			)      ,			ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)      ,			ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)      ,			ord("""ÿ"""			) + 1			)			)
  			)
  			__lowerCAmelCase      		=       bs[:]
  			__lowerCAmelCase      		=       0
  			for b in range(2**8			):
  						if b not in bs:
  									bs.append(lowercase			)
  									cs.append(2**8 + n			)
  									n += 1
  			__lowerCAmelCase      		=       [chr(lowercase			) for n in cs]
  			return dict(zip(lowercase      ,			lowercase			)			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      List[str]:
  			__lowerCAmelCase      		=       set()
  			__lowerCAmelCase      		=       word[0]
  			for char in word[1:]:
  						pairs.add((prev_char, char)			)
  						__lowerCAmelCase      		=       char
  			return pairs
  
  
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		a     :     int     						=VOCAB_FILES_NAMES
  		a     :     str     						=PRETRAINED_VOCAB_FILES_MAP
  		a     :     Optional[Any]     						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		a     :     Dict     						=["""input_ids""", """attention_mask"""]
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="replace",__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="<unk>",__SCREAMING_SNAKE_CASE="<pad>",__SCREAMING_SNAKE_CASE="<mask>",__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else bos_token
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else eos_token
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else sep_token
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else cls_token
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else unk_token
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else pad_token
  
  					# Mask token behave like a normal word, i.e. include the space before it
  					__lowerCAmelCase      		=       AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) else mask_token
  
  					super().__init__(
  					    errors=__SCREAMING_SNAKE_CASE,bos_token=__SCREAMING_SNAKE_CASE,eos_token=__SCREAMING_SNAKE_CASE,unk_token=__SCREAMING_SNAKE_CASE,sep_token=__SCREAMING_SNAKE_CASE,cls_token=__SCREAMING_SNAKE_CASE,pad_token=__SCREAMING_SNAKE_CASE,mask_token=__SCREAMING_SNAKE_CASE,add_prefix_space=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
  
  					with open(__SCREAMING_SNAKE_CASE,encoding="""utf-8"""    ) as vocab_handle:
  								__lowerCAmelCase      		=       json.load(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {v: k for k, v in self.encoder.items()}
  					__lowerCAmelCase      		=       errors  # how to handle errors in decoding
  					__lowerCAmelCase      		=       bytes_to_unicode()
  					__lowerCAmelCase      		=       {v: k for k, v in self.byte_encoder.items()}
  					with open(__SCREAMING_SNAKE_CASE,encoding="""utf-8"""    ) as merges_handle:
  								__lowerCAmelCase      		=       merges_handle.read().split("""\n"""    )[1:-1]
  					__lowerCAmelCase      		=       [tuple(merge.split()    ) for merge in bpe_merges]
  					__lowerCAmelCase      		=       dict(zip(__SCREAMING_SNAKE_CASE,range(len(__SCREAMING_SNAKE_CASE    )    )    )    )
  					__lowerCAmelCase      		=       {}
  					__lowerCAmelCase      		=       add_prefix_space
  
  					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  					__lowerCAmelCase      		=       re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""    )
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return len(self.encoder    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return dict(self.encoder,**self.added_tokens_encoder    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if token in self.cache:
  								return self.cache[token]
  					__lowerCAmelCase      		=       tuple(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       get_pairs(__SCREAMING_SNAKE_CASE    )
  
  					if not pairs:
  								return token
  
  					while True:
  								__lowerCAmelCase      		=       min(__SCREAMING_SNAKE_CASE,key=lambda __SCREAMING_SNAKE_CASE    : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE,float("""inf"""    )    )    )
  								if bigram not in self.bpe_ranks:
  											break
  								__lowerCAmelCase		, __lowerCAmelCase      		=       bigram
  								__lowerCAmelCase      		=       []
  								__lowerCAmelCase      		=       0
  								while i < len(__SCREAMING_SNAKE_CASE    ):
  											try:
  														__lowerCAmelCase      		=       word.index(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  											except ValueError:
  														new_word.extend(word[i:]    )
  														break
  											else:
  														new_word.extend(word[i:j]    )
  														__lowerCAmelCase      		=       j
  
  											if word[i] == first and i < len(__SCREAMING_SNAKE_CASE    ) - 1 and word[i + 1] == second:
  														new_word.append(first + second    )
  														i += 2
  											else:
  														new_word.append(word[i]    )
  														i += 1
  								__lowerCAmelCase      		=       tuple(__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       new_word
  								if len(__SCREAMING_SNAKE_CASE    ) == 1:
  											break
  								else:
  											__lowerCAmelCase      		=       get_pairs(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       """ """.join(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       word
  					return word
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					for token in re.findall(self.pat,__SCREAMING_SNAKE_CASE    ):
  								__lowerCAmelCase      		=       """""".join(
  								    self.byte_encoder[b] for b in token.encode("""utf-8"""    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__SCREAMING_SNAKE_CASE    ).split(""" """    )    )
  					return bpe_tokens
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return self.encoder.get(__SCREAMING_SNAKE_CASE,self.encoder.get(self.unk_token    )    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return self.decoder.get(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       """""".join(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       bytearray([self.byte_decoder[c] for c in text]    ).decode("""utf-8""",errors=self.errors    )
  					return text
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None    ):
  
  
  
  					'''simple docstring'''
  
  					if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
  								logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
  								return
  					__lowerCAmelCase      		=       os.path.join(
  					    __SCREAMING_SNAKE_CASE,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
  					__lowerCAmelCase      		=       os.path.join(
  					    __SCREAMING_SNAKE_CASE,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]    )
  
  					with open(__SCREAMING_SNAKE_CASE,"""w""",encoding="""utf-8"""    ) as f:
  								f.write(json.dumps(self.encoder,indent=2,sort_keys=__SCREAMING_SNAKE_CASE,ensure_ascii=__SCREAMING_SNAKE_CASE    ) + """\n"""    )
  
  					__lowerCAmelCase      		=       0
  					with open(__SCREAMING_SNAKE_CASE,"""w""",encoding="""utf-8"""    ) as writer:
  								writer.write("""#version: 0.2\n"""    )
  								for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda __SCREAMING_SNAKE_CASE    : kv[1]    ):
  											if index != token_index:
  														logger.warning(
  														    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  														    """ Please check that the tokenizer is not corrupted!"""    )
  														__lowerCAmelCase      		=       token_index
  											writer.write(""" """.join(__SCREAMING_SNAKE_CASE    ) + """\n"""    )
  											index += 1
  
  					return vocab_file, merge_file
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None    ):
  
  
  
  					'''simple docstring'''
  
  					if token_ids_a is None:
  								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  					__lowerCAmelCase      		=       [self.cls_token_id]
  					__lowerCAmelCase      		=       [self.sep_token_id]
  					return cls + token_ids_a + sep + sep + token_ids_a + sep
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False    ):
  
  
  
  					'''simple docstring'''
  
  					if already_has_special_tokens:
  								return super().get_special_tokens_mask(
  								    token_ids_a=__SCREAMING_SNAKE_CASE,token_ids_a=__SCREAMING_SNAKE_CASE,already_has_special_tokens=__SCREAMING_SNAKE_CASE    )
  
  					if token_ids_a is None:
  								return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
  					return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       [self.sep_token_id]
  					__lowerCAmelCase      		=       [self.cls_token_id]
  
  					if token_ids_a is None:
  								return len(cls + token_ids_a + sep    ) * [0]
  					return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       kwargs.pop("""add_prefix_space""",self.add_prefix_space    )
  					if (is_split_into_words or add_prefix_space) and (len(__SCREAMING_SNAKE_CASE    ) > 0 and not text[0].isspace()):
  								__lowerCAmelCase      		=       """ """ + text
  					return (text, kwargs)
  
  
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,)
  					}
  
  