


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "Wav2Vec2FeatureExtractor"
       A__      		=    "AutoTokenizer"





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             super().__init__(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Any					=						self.feature_extractor
             lowerCamelCase__	:       List[Any]					=						False





       @classmethod
       def 		lowerCAmelCase  (     cls							:    Tuple      ,   __lowerCamelCase							:    Optional[int]      ,   **__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             try:
                   return super().from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase )
             except OSError:
                   warnings.warn(
                       f"Loading a tokenizer inside {cls.__name__} from a config that does not"
                       " include a `tokenizer_class` attribute is deprecated and will be "
                       "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
                       " attribute to either your `config.json` or `tokenizer_config.json` "
                       "file to suppress this warning: "      ,   __lowerCamelCase      ,   )

                   lowerCamelCase__	:       List[Any]					=						WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase )
                   lowerCamelCase__	:       Tuple					=						WavaVecaCTCTokenizer.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase )

                   return cls(feature_extractor=__lowerCamelCase      ,   tokenizer=__lowerCamelCase )





       def __call__(     self							:    Optional[int]      ,   *__lowerCamelCase							:    Union[str, Any]      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



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

             if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
                   lowerCamelCase__	:       Union[str, Any]					=						kwargs.pop("raw_speech" )
             else:
                   lowerCamelCase__	:       List[Any]					=						kwargs.pop("audio"      ,   __lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						kwargs.pop("sampling_rate"      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						kwargs.pop("text"      ,   __lowerCamelCase )
             if len(__lowerCamelCase ) > 0:
                   lowerCamelCase__	:       Any					=						args[0]
                   lowerCamelCase__	:       List[str]					=						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:
                   lowerCamelCase__	:       List[Any]					=						self.feature_extractor(__lowerCamelCase      ,   *__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   **__lowerCamelCase )
             if text is not None:
                   lowerCamelCase__	:       Tuple					=						self.tokenizer(__lowerCamelCase      ,   **__lowerCamelCase )

             if text is None:
                   return inputs
             elif audio is None:
                   return encodings
             else:
                   lowerCamelCase__	:       Any					=						encodings["input_ids"]
                   return inputs





       def 		lowerCAmelCase  (     self							:    List[str]      ,   *__lowerCamelCase							:    str      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             if self._in_target_context_manager:
                   return self.current_processor.pad(*__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Optional[int]					=						kwargs.pop("input_features"      ,   __lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						kwargs.pop("labels"      ,   __lowerCamelCase )
             if len(__lowerCamelCase ) > 0:
                   lowerCamelCase__	:       Dict					=						args[0]
                   lowerCamelCase__	:       List[str]					=						args[1:]

             if input_features is not None:
                   lowerCamelCase__	:       Tuple					=						self.feature_extractor.pad(__lowerCamelCase      ,   *__lowerCamelCase      ,   **__lowerCamelCase )
             if labels is not None:
                   lowerCamelCase__	:       Dict					=						self.tokenizer.pad(__lowerCamelCase      ,   **__lowerCamelCase )

             if labels is None:
                   return input_features
             elif input_features is None:
                   return labels
             else:
                   lowerCamelCase__	:       Any					=						labels["input_ids"]
                   return input_features





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   *__lowerCamelCase							:    Optional[int]      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   *__lowerCamelCase							:    Optional[int]      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



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





       @contextmanager
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''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." )
             lowerCamelCase__	:       Tuple					=						True
             lowerCamelCase__	:       Optional[int]					=						self.tokenizer
             yield
             lowerCamelCase__	:       Tuple					=						self.feature_extractor
             lowerCamelCase__	:       Optional[int]					=						False






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


