
'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""AutoFeatureExtractor"""
				lowerCAmelCase_			       =				"""Speech2Text2Tokenizer"""







				def __init__(    self		,							A_		,							A_			)->						Dict:







											'''simple docstring'''






											super().__init__(A_		,							A_			)
											UpperCamelCase					       =			self.feature_extractor
											UpperCamelCase					       =			False







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







											'''simple docstring'''






											if self._in_target_context_manager:
																		return self.current_processor(*A_		,							**A_			)

											if "raw_speech" in kwargs:
																		warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
																		UpperCamelCase					       =			kwargs.pop('raw_speech'			)
											else:
																		UpperCamelCase					       =			kwargs.pop('audio'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('sampling_rate'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('text'		,							A_			)
											if len(A_			) > 0:
																		UpperCamelCase					       =			args[0]
																		UpperCamelCase					       =			args[1:]

											if audio is None and text is None:
																		raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

											if audio is not None:
																		UpperCamelCase					       =			self.feature_extractor(A_		,							*A_		,							sampling_rate=A_		,							**A_			)
											if text is not None:
																		UpperCamelCase					       =			self.tokenizer(A_		,							**A_			)

											if text is None:
																		return inputs
											elif audio is None:
																		return encodings
											else:
																		UpperCamelCase					       =			encodings['input_ids']
																		return inputs







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







											'''simple docstring'''






											return self.tokenizer.batch_decode(*A_		,							**A_			)







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







											'''simple docstring'''






											return self.tokenizer.decode(*A_		,							**A_			)







				@contextmanager
				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'			)
											UpperCamelCase					       =			True
											UpperCamelCase					       =			self.tokenizer
											yield
											UpperCamelCase					       =			self.feature_extractor
											UpperCamelCase					       =			False












def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)
						lowerCAmelCase__			  =  [0] * length
						lowerCAmelCase__			  =  [[False for i in range(UpperCamelCase_						)] for j in range(UpperCamelCase_						)]
						for i, c in enumerate(UpperCamelCase_						):
												lowerCAmelCase__			  =  i
												for j in range(i + 1						):
																		if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																								lowerCAmelCase__			  =  True
																								lowerCAmelCase__			  =  min(UpperCamelCase_			,				0 if j == 0 else (cut[j - 1] + 1)						)
												lowerCAmelCase__			  =  mincut
						return cut[length - 1]


if __name__ == "__main__":
					__snake_case			:			Optional[Any]			   =   input("""Enter the string: """).strip()
					__snake_case			:			List[Any]			   =   find_minimum_partitions(s)
					print(f'Minimum number of partitions required for the \'{s}\' is {ans}')





