


'''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'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			0

							for character_count in character_freq_dict.values():
														if character_count % 2:
																					odd_char += 1
							if odd_char > 1:
														return False
							return True


def       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)
							print(
							    '''> can_string_be_rearranged_as_palindrome()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


if __name__ == "__main__":
		__UpperCAmelCase					       =							input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__UpperCAmelCase					       =							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")