


'''simple docstring'''

import os





def __snake_case   ( ):

 with open(os.path.dirname(UpperCAmelCase_						) + "/p022_names.txt"						) as file:
  lowerCamelCase_						=    str(file.readlines()[0]						)
  lowerCamelCase_						=    names.replace("\""   ,							""						).split(","						)

 names.sort()

 lowerCamelCase_						=    0
 lowerCamelCase_						=    0

 for i, name in enumerate(UpperCAmelCase_						):
  for letter in name:
   name_score += ord(UpperCAmelCase_						) - 64

  total_score += (i + 1) * name_score
  lowerCamelCase_						=    0
 return total_score


if __name__ == "__main__":
     print(solution())










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=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 text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



