
"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class     lowercase(_lowercase       ):
     __snake_case:	Any						=     'Speech2TextFeatureExtractor'
     __snake_case:	int						=     'Speech2TextTokenizer'



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         super().__init__(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.feature_extractor
         a__		=			False



     def __call__(							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



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

         if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
             a__		=			kwargs.pop('raw_speech'   )
         else:
             a__		=			kwargs.pop('audio'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			kwargs.pop('sampling_rate'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			kwargs.pop('text'     ,				__SCREAMING_SNAKE_CASE   )
         if len(__SCREAMING_SNAKE_CASE   ) > 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(__SCREAMING_SNAKE_CASE     ,				*__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )
         if text is not None:
             a__		=			self.tokenizer(__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )

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



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[int]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



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




     @contextmanager
     def 			lowercase__  (							self   )		->     Optional[Any]:







         """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.'   )
         a__		=			True
         a__		=			self.tokenizer
         yield
         a__		=			self.feature_extractor
         a__		=			False




"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

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

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == 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(UpperCamelCase     )] += new_combinations

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

    return table[len(UpperCamelCase     )]


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'],
        )
    )


