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



						'''simple docstring'''

						if n < 0:
												return 0
						# table[i] represents the number of ways to get to amount i
						__UpperCamelCase  	=      [0] * (n + 1)

						# There is exactly 1 way to get to zero(You pick no coins).
						__UpperCamelCase  	=      1

						# Pick all coins one by one and update table[] values
						# after the index greater than or equal to the value of the
						# picked coin
						for coin_val in s:
												for j in range(snake_case			,   n + 1			):
																		table[j] += table[j - coin_val]

						return table[n]


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,
}



