
'''simple docstring'''







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					str				):



						"""simple docstring"""





						if not isinstance(lowerCamelCase__ , lowerCamelCase__				) or not all(
						    isinstance(lowerCamelCase__ , lowerCamelCase__				) and item >= 0 for item in citations				):
												raise ValueError('''The citations should be a list of non negative integers.'''				)

						citations.sort()
						lowercase_      :	str						=			len(lowerCamelCase__				)

						for i in range(lowerCamelCase__				):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
							import doctest

							doctest.testmod()








"""simple docstring"""







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


A     =      logging.get_logger(__name__)






class   UpperCAmelCase__		(   UpperCamelCase  ):



		def __init__(			self	:     List[str]   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Tuple:






									'''simple docstring'''

									A    =       language
									A    =       train_language



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     Any					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.language if self.train_language is None else self.train_language
									A    =       self._read_tsv(os.path.join(snake_case   ,				f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       f"""train-{i}"""
																A    =       line[0]
																A    =       line[1]
																A    =       'contradiction' if line[2] == 'contradictory' else line[2]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     int   ,				snake_case	:     int					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self._read_tsv(os.path.join(snake_case   ,				'XNLI-1.0/xnli.test.tsv'					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       line[0]
																if language != self.language:
																							continue
																A    =       f"""test-{i}"""
																A    =       line[6]
																A    =       line[7]
																A    =       line[1]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     Any					)      ->    List[Any]:






									'''simple docstring'''

									return ["contradiction", "entailment", "neutral"]



A     =      {
    'xnli': XnliProcessor,
}

A     =      {
    'xnli': 'classification',
}

A     =      {
    'xnli': 3,
}



