


'''simple docstring'''






from ...processing_utils import ProcessorMixin




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "SpeechT5FeatureExtractor"
							lowercase__    :     Optional[int]					=   "SpeechT5Tokenizer"







							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__(lowerCamelCase_      ,					lowerCamelCase_     )







							def __call__(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			kwargs.pop('''audio'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''text'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''text_target'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''audio_target'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''sampling_rate'''      ,					lowerCamelCase_     )

														if audio is not None and text is not None:
																					raise ValueError(
																					    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''     )
														if audio_target is not None and text_target is not None:
																					raise ValueError(
																					    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''     )
														if audio is None and audio_target is None and text is None and text_target is None:
																					raise ValueError(
																					    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''     )

														if audio is not None:
																					lowerCAmelCase__      =			self.feature_extractor(lowerCamelCase_      ,					*lowerCamelCase_      ,					sampling_rate=lowerCamelCase_      ,					**lowerCamelCase_     )
														elif text is not None:
																					lowerCAmelCase__      =			self.tokenizer(lowerCamelCase_      ,					**lowerCamelCase_     )
														else:
																					lowerCAmelCase__      =			None

														if audio_target is not None:
																					lowerCAmelCase__      =			self.feature_extractor(audio_target=lowerCamelCase_      ,					*lowerCamelCase_      ,					sampling_rate=lowerCamelCase_      ,					**lowerCamelCase_     )
																					lowerCAmelCase__      =			targets['''input_values''']
														elif text_target is not None:
																					lowerCAmelCase__      =			self.tokenizer(lowerCamelCase_      ,					**lowerCamelCase_     )
																					lowerCAmelCase__      =			targets['''input_ids''']
														else:
																					lowerCAmelCase__      =			None

														if inputs is None:
																					return targets

														if targets is not None:
																					lowerCAmelCase__      =			labels

																					lowerCAmelCase__      =			targets.get('''attention_mask'''     )
																					if decoder_attention_mask is not None:
																												lowerCAmelCase__      =			decoder_attention_mask

														return inputs







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			kwargs.pop('''input_values'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''input_ids'''      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			kwargs.pop('''labels'''      ,					lowerCamelCase_     )

														if input_values is not None and input_ids is not None:
																					raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''     )
														if input_values is None and input_ids is None and labels is None:
																					raise ValueError(
																					    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''     )

														if input_values is not None:
																					lowerCAmelCase__      =			self.feature_extractor.pad(lowerCamelCase_      ,					*lowerCamelCase_      ,					**lowerCamelCase_     )
														elif input_ids is not None:
																					lowerCAmelCase__      =			self.tokenizer.pad(lowerCamelCase_      ,					**lowerCamelCase_     )
														else:
																					lowerCAmelCase__      =			None

														if labels is not None:
																					if "input_ids" in labels or (isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and "input_ids" in labels[0]):
																												lowerCAmelCase__      =			self.tokenizer.pad(lowerCamelCase_      ,					**lowerCamelCase_     )
																												lowerCAmelCase__      =			targets['''input_ids''']
																					else:
																												lowerCAmelCase__      =			self.feature_extractor.feature_size
																												lowerCAmelCase__      =			self.feature_extractor.num_mel_bins
																												lowerCAmelCase__      =			self.feature_extractor.pad(lowerCamelCase_      ,					*lowerCamelCase_      ,					**lowerCamelCase_     )
																												lowerCAmelCase__      =			feature_size_hack
																												lowerCAmelCase__      =			targets['''input_values''']
														else:
																					lowerCAmelCase__      =			None

														if inputs is None:
																					return targets

														if targets is not None:
																					lowerCAmelCase__      =			labels

																					lowerCAmelCase__      =			targets.get('''attention_mask'''     )
																					if decoder_attention_mask is not None:
																												lowerCAmelCase__      =			decoder_attention_mask

														return inputs







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Dict:
														return self.tokenizer.batch_decode(*lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Union[str, Any]:
														return self.tokenizer.decode(*lowerCamelCase_      ,					**lowerCamelCase_     )



'''simple docstring'''






def       _snake_case     (  A				,					A )		->					bool:
							lowerCAmelCase__      =			len(A ) + 1
							lowerCAmelCase__      =			len(A ) + 1

							# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
							# length i of input_string matches with prefix string of length j of
							# given pattern.
							# "dp" stands for dynamic programming.
							lowerCAmelCase__      =			[[0 for i in range(A )] for j in range(A )]

							# since string of zero length match pattern of zero length
							lowerCAmelCase__      =			1

							# since pattern of zero length will never match with string of non-zero length
							for i in range(1				,					A ):
														lowerCAmelCase__      =			0

							# since string of zero length will match with pattern where there
							# is at least one * alternatively
							for j in range(1				,					A ):
														lowerCAmelCase__      =			dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

							# now using bottom-up approach to find for all remaining lengths
							for i in range(1				,					A ):
														for j in range(1				,					A ):
																					if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																												lowerCAmelCase__      =			dp[i - 1][j - 1]

																					elif pattern[j - 1] == "*":
																												if dp[i][j - 2] == 1:
																																			lowerCAmelCase__      =			1
																												elif pattern[j - 2] in (input_string[i - 1], "."):
																																			lowerCAmelCase__      =			dp[i - 1][j]
																												else:
																																			lowerCAmelCase__      =			0
																					else:
																												lowerCAmelCase__      =			0

							return bool(dp[-1][-1] )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		__UpperCAmelCase					       =							'''aab'''
		__UpperCAmelCase					       =							'''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(f"""{input_string} matches the given pattern {pattern}""")
		else:
				print(f"""{input_string} does not match with the given pattern {pattern}""")