






"""simple docstring"""






from __future__ import annotations



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None				):
				A__							 =       word_bank or []
				# create a table
				A__							 =       len(lowerCAmelCase__				) + 1

				A__							 =       []
				for _ in range(lowerCAmelCase__				):
								table.append([]				)
				# seed value
				A__							 =       [[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(lowerCAmelCase__				):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(lowerCAmelCase__				)] == word:
																				A__							 =       [
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(lowerCAmelCase__				)] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(lowerCAmelCase__				)]:
								combination.reverse()

				return table[len(lowerCAmelCase__				)]


if __name__ == "__main__":
					print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
					print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
					print(
					    all_construct(
					        '''hexagonosaurus''',
					        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
					    )
					)












"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					"""WhisperFeatureExtractor"""
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""WhisperTokenizer"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(__a  ,					__a						)
					A__							 =       self.feature_extractor
					A__							 =       False


	def       _UpperCAmelCase						(      self  ,					__a=None  ,					__a=None  ,					__a=True						):


					"""simple docstring"""

					return self.tokenizer.get_decoder_prompt_ids(task=__a  ,					language=__a  ,					no_timestamps=__a						)


	def __call__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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

					A__							 =       kwargs.pop('audio'  ,					__a						)
					A__							 =       kwargs.pop('sampling_rate'  ,					__a						)
					A__							 =       kwargs.pop('text'  ,					__a						)
					if len(__a						) > 0:
									A__							 =       args[0]
									A__							 =       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:
									A__							 =       self.feature_extractor(__a  ,					*__a  ,					sampling_rate=__a  ,					**__a						)
					if text is not None:
									A__							 =       self.tokenizer(__a  ,					**__a						)

					if text is None:
									return inputs

					elif audio is None:
									return encodings
					else:
									A__							 =       encodings['input_ids']
									return inputs


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a="np"						):


					"""simple docstring"""

					return self.tokenizer.get_prompt_ids(__a  ,					return_tensors=__a						)


