






"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase_		:			str				   =					'''\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'''


lowerCAmelCase_		:			List[Any]				   =					'''\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> 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])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'''


lowerCAmelCase_		:			int				   =					'''\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'''





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



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__=True       ,						snake_case__=None )	->	Any:



         """simple docstring"""





         return {
             "accuracy": float(
                 accuracy_score(__UpperCAmelCase       ,						__UpperCAmelCase       ,						normalize=__UpperCAmelCase       ,						sample_weight=__UpperCAmelCase ) )
         }


"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
			__A       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A       						=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

				super().__init__(
				    __UpperCAmelCase     ,     tokenizer_file=__UpperCAmelCase     ,     bos_token=__UpperCAmelCase     ,     eos_token=__UpperCAmelCase     ,     sep_token=__UpperCAmelCase     ,     cls_token=__UpperCAmelCase     ,     unk_token=__UpperCAmelCase     ,     pad_token=__UpperCAmelCase     ,     mask_token=__UpperCAmelCase     ,     **__UpperCAmelCase     ,     )

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				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     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_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     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
					    """tokenizer."""  )

				if not os.path.isdir(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

				if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCAmelCase  ):
					copyfile(self.vocab_file     ,     __UpperCAmelCase  )

				return (out_vocab_file,)



