


'''simple docstring'''
from sklearn.metrics import roc_auc_score

import datasets


__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		'''\nThis 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.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n'''

__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		'''\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:\n    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- 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`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `\'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.\n    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric("roc_auc")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results[\'roc_auc\']])\n        [0.83, 0.38, 0.94]\n'''

__SCREAMING_SNAKE_CASE				:							int		       =		'''\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={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.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							, _KWARGS_DESCRIPTION	)
class 				__lowerCAmelCase      (      datasets.Metric	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			Optional[Any]  ):
													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 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Tuple="macro"   ,     lowerCAmelCase       :			Optional[int]=None   ,     lowerCAmelCase       :			Union[str, Any]=None   ,     lowerCAmelCase       :			Dict="raise"   ,     lowerCAmelCase       :			Optional[Any]=None   ,     ):
													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_   ,     )
													}









'''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


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							List[str]		       =		{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__SCREAMING_SNAKE_CASE				:							int		       =		{
    '''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'''
        ),
    },
}

__SCREAMING_SNAKE_CASE				:							int		       =		{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}







@lru_cache()
def 			a_							(  ):
							A_ 						=		(
							    list(range(ord("!"   )	,     ord("~"   ) + 1   )   ) + list(range(ord("¡"   )	,     ord("¬"   ) + 1   )   ) + list(range(ord("®"   )	,     ord("ÿ"   ) + 1   )   )
							)
							A_ 						=		bs[:]
							A_ 						=		0
							for b in range(2**8   ):
														if b not in bs:
																					bs.append(UpperCamelCase_   )
																					cs.append(2**8 + n   )
																					n += 1
							A_ 						=		[chr(UpperCamelCase_   ) for n in cs]
							return dict(zip(UpperCamelCase_	,     UpperCamelCase_   )   )







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		set()
							A_ 						=		word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)   )
														A_ 						=		char
							return pairs







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					List[str]      	=VOCAB_FILES_NAMES
						_UpperCAmelCase		:					Optional[Any]      	=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:					List[Any]      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCAmelCase		:					Optional[Any]      	=["input_ids", "attention_mask"]






						def __init__(     self       :			int   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			Optional[int]   ,     lowerCAmelCase       :			Any="replace"   ,     lowerCAmelCase       :			Optional[Any]="<s>"   ,     lowerCAmelCase       :			int="</s>"   ,     lowerCAmelCase       :			Tuple="</s>"   ,     lowerCAmelCase       :			Dict="<s>"   ,     lowerCAmelCase       :			Any="<unk>"   ,     lowerCAmelCase       :			str="<pad>"   ,     lowerCAmelCase       :			Tuple="<mask>"   ,     lowerCAmelCase       :			Tuple=False   ,     **lowerCAmelCase       :			str   ,     ):
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else bos_token
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else eos_token
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else sep_token
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else cls_token
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else unk_token
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A_ 						=		AddedToken(lowerCAmelCase   ,     lstrip=lowerCAmelCase   ,     rstrip=lowerCAmelCase  ) if isinstance(lowerCAmelCase   ,     lowerCAmelCase  ) else mask_token

													super().__init__(
													    errors=lowerCAmelCase   ,     bos_token=lowerCAmelCase   ,     eos_token=lowerCAmelCase   ,     unk_token=lowerCAmelCase   ,     sep_token=lowerCAmelCase   ,     cls_token=lowerCAmelCase   ,     pad_token=lowerCAmelCase   ,     mask_token=lowerCAmelCase   ,     add_prefix_space=lowerCAmelCase   ,     **lowerCAmelCase   ,     )

													with open(lowerCAmelCase   ,     encoding="utf-8"  ) as vocab_handle:
																				A_ 						=		json.load(lowerCAmelCase  )
													A_ 						=		{v: k for k, v in self.encoder.items()}
													A_ 						=		errors  # how to handle errors in decoding
													A_ 						=		bytes_to_unicode()
													A_ 						=		{v: k for k, v in self.byte_encoder.items()}
													with open(lowerCAmelCase   ,     encoding="utf-8"  ) as merges_handle:
																				A_ 						=		merges_handle.read().split("\n"  )[1:-1]
													A_ 						=		[tuple(merge.split()  ) for merge in bpe_merges]
													A_ 						=		dict(zip(lowerCAmelCase   ,     range(len(lowerCAmelCase  )  )  )  )
													A_ 						=		{}
													A_ 						=		add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A_ 						=		re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"  )






						@property
						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													return len(self.encoder  )






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													return dict(self.encoder   ,     **self.added_tokens_encoder  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			List[Any]  ):
													if token in self.cache:
																				return self.cache[token]
													A_ 						=		tuple(lowerCAmelCase  )
													A_ 						=		get_pairs(lowerCAmelCase  )

													if not pairs:
																				return token

													while True:
																				A_ 						=		min(lowerCAmelCase   ,     key=lambda lowerCAmelCase  : self.bpe_ranks.get(lowerCAmelCase   ,     float("inf"  )  )  )
																				if bigram not in self.bpe_ranks:
																											break
																				A_						,				A_ 						=		bigram
																				A_ 						=		[]
																				A_ 						=		0
																				while i < len(lowerCAmelCase  ):
																											try:
																																		A_ 						=		word.index(lowerCAmelCase   ,     lowerCAmelCase  )
																											except ValueError:
																																		new_word.extend(word[i:]  )
																																		break
																											else:
																																		new_word.extend(word[i:j]  )
																																		A_ 						=		j

																											if word[i] == first and i < len(lowerCAmelCase  ) - 1 and word[i + 1] == second:
																																		new_word.append(first + second  )
																																		i += 2
																											else:
																																		new_word.append(word[i]  )
																																		i += 1
																				A_ 						=		tuple(lowerCAmelCase  )
																				A_ 						=		new_word
																				if len(lowerCAmelCase  ) == 1:
																											break
																				else:
																											A_ 						=		get_pairs(lowerCAmelCase  )
													A_ 						=		" ".join(lowerCAmelCase  )
													A_ 						=		word
													return word






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			int  ):
													A_ 						=		[]
													for token in re.findall(self.pat   ,     lowerCAmelCase  ):
																				A_ 						=		"".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(lowerCAmelCase  ).split(" "  )  )
													return bpe_tokens






						def 							_UpperCAmelCase					(     self       :			str   ,     lowerCAmelCase       :			List[str]  ):
													return self.encoder.get(lowerCAmelCase   ,     self.encoder.get(self.unk_token  )  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			Union[str, Any]  ):
													return self.decoder.get(lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			Tuple  ):
													A_ 						=		"".join(lowerCAmelCase  )
													A_ 						=		bytearray([self.byte_decoder[c] for c in text]  ).decode("utf-8"   ,     errors=self.errors  )
													return text






						def 							_UpperCAmelCase					(     self       :			Dict   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			Optional[str] = None  ):
													if not os.path.isdir(lowerCAmelCase  ):
																				logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
																				return
													A_ 						=		os.path.join(
													    lowerCAmelCase   ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
													A_ 						=		os.path.join(
													    lowerCAmelCase   ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

													with open(lowerCAmelCase   ,     "w"   ,     encoding="utf-8"  ) as f:
																				f.write(json.dumps(self.encoder   ,     indent=2   ,     sort_keys=lowerCAmelCase   ,     ensure_ascii=lowerCAmelCase  ) + "\n"  )

													A_ 						=		0
													with open(lowerCAmelCase   ,     "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 lowerCAmelCase  : 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!"  )
																																		A_ 						=		token_index
																											writer.write(" ".join(lowerCAmelCase  ) + "\n"  )
																											index += 1

													return vocab_file, merge_file






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			List[int]   ,     lowerCAmelCase       :			Optional[List[int]] = None  ):
													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													A_ 						=		[self.cls_token_id]
													A_ 						=		[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			List[int]   ,     lowerCAmelCase       :			Optional[List[int]] = None   ,     lowerCAmelCase       :			bool = False  ):
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=lowerCAmelCase   ,     token_ids_a=lowerCAmelCase   ,     already_has_special_tokens=lowerCAmelCase  )

													if token_ids_a is None:
																				return [1] + ([0] * len(lowerCAmelCase  )) + [1]
													return [1] + ([0] * len(lowerCAmelCase  )) + [1, 1] + ([0] * len(lowerCAmelCase  )) + [1]






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			List[int]   ,     lowerCAmelCase       :			Optional[List[int]] = None  ):
													A_ 						=		[self.sep_token_id]
													A_ 						=		[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 							_UpperCAmelCase					(     self       :			Tuple   ,     lowerCAmelCase       :			List[Any]   ,     lowerCAmelCase       :			str=False   ,     **lowerCAmelCase       :			Optional[Any]  ):
													A_ 						=		kwargs.pop("add_prefix_space"   ,     self.add_prefix_space  )
													if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase  ) > 0 and not text[0].isspace()):
																				A_ 						=		" " + text
													return (text, kwargs)





