


"""simple docstring"""


class 			lowerCamelCase__						:







						def __init__(							self   :    Optional[int]		):
								A					       = ''
								A					       = ''
								A					       = []







						def __a						(							self   :    List[Any]			,			_lowercase   :    int			,			_lowercase   :    int		):
								if m == -1:
										return n + 1
								elif n == -1:
										return m + 1
								elif self.dp[m][n] > -1:
										return self.dp[m][n]
								else:
										if self.worda[m] == self.worda[n]:
												A					       = self.__min_dist_top_down_dp(m - 1			,			n - 1		)
										else:
												A					       = self.__min_dist_top_down_dp(_lowercase			,			n - 1		)
												A					       = self.__min_dist_top_down_dp(m - 1			,			_lowercase		)
												A					       = self.__min_dist_top_down_dp(m - 1			,			n - 1		)
												A					       = 1 + min(_lowercase			,			_lowercase			,			_lowercase		)

										return self.dp[m][n]







						def __a						(							self   :    List[Any]			,			_lowercase   :    str			,			_lowercase   :    str		):
								A					       = worda
								A					       = worda
								A					       = [[-1 for _ in range(len(_lowercase		)		)] for _ in range(len(_lowercase		)		)]

								return self.__min_dist_top_down_dp(len(_lowercase		) - 1			,			len(_lowercase		) - 1		)







						def __a						(							self   :    Optional[int]			,			_lowercase   :    str			,			_lowercase   :    str		):
								A					       = worda
								A					       = worda
								A					       = len(_lowercase		)
								A					       = len(_lowercase		)
								A					       = [[0 for _ in range(n + 1		)] for _ in range(m + 1		)]

								for i in range(m + 1		):
										for j in range(n + 1		):
												if i == 0:  # first string is empty
														A					       = j
												elif j == 0:  # second string is empty
														A					       = i
												elif worda[i - 1] == worda[j - 1]:  # last characters are equal
														A					       = self.dp[i - 1][j - 1]
												else:
														A					       = self.dp[i][j - 1]
														A					       = self.dp[i - 1][j]
														A					       = self.dp[i - 1][j - 1]
														A					       = 1 + min(_lowercase			,			_lowercase			,			_lowercase		)
								return self.dp[m][n]


if __name__ == "__main__":
	UpperCamelCase						:				List[str]             =      EditDistance()

	print("****************** Testing Edit Distance DP Algorithm ******************")
	print()

	UpperCamelCase						:				Optional[Any]             =      input("Enter the first string: ").strip()
	UpperCamelCase						:				int             =      input("Enter the second string: ").strip()

	print()
	print(F"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
	print(F"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
	print()
	print("*************** End of Testing Edit Distance DP Algorithm ***************")









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





