from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

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

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )



from __future__ import annotations


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				Union[str, Any]     ,   __magic_name__       :				str     ,   __magic_name__       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     text, pattern
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     len(__magic_name__      ), len(__magic_name__      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if char == self.pattern[i]:
                    return i
        return -1







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if self.pattern[i] != self.text[current_pos + i]:
                    return current_pos + i
        return -1







  def       __A   (							self       :				Tuple      )     ->		list[int]:
        # searches pattern in text and returns index positions
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(self.textLen - self.patLen + 1      ):
              SCREAMING_SNAKE_CASE_      =     self.mismatch_in_text(__magic_name__      )
              if mismatch_index == -1:
                    positions.append(__magic_name__      )
              else:
                    SCREAMING_SNAKE_CASE_      =     self.match_in_pattern(self.text[mismatch_index]      )
                    SCREAMING_SNAKE_CASE_      =     (
                        mismatch_index - match_index
                    )  # shifting index lgtm [py/multiple-definition]
        return positions



A   :     Dict       =				"ABAABA"
A   :     Union[str, Any]       =				"AB"
A   :     str       =				BoyerMooreSearch(text, pattern)
A   :     Tuple       =				bms.bad_character_heuristic()

if len(positions) == 0:
      print("No match found")
else:
      print("Pattern found in following positions: ")
      print(positions)


