
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase											=    logging.get_logger(__name__)


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						["input_values", "padding_mask"]


       def __init__(    self						,       __A = 1						,       __A = 24_000						,       __A = 0.0						,       __A = None						,       __A = None						,       **__A						,       )						->    Any:
              super().__init__(feature_size=__A						,       sampling_rate=__A						,       padding_value=__A						,       **__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=chunk_length_s
              SCREAMING_SNAKE_CASE_							:				Any		=overlap


       @property
       def 	_snake_case				(    self				)						->    Optional[int]:
              if self.chunk_length_s is None:
                     return None
              else:
                     return int(self.chunk_length_s * self.sampling_rate				)


       @property
       def 	_snake_case				(    self				)						->    Optional[int]:
              if self.chunk_length_s is None or self.overlap is None:
                     return None
              else:
                     return max(1						,       int((1.0 - self.overlap) * self.chunk_length				)				)





       def __call__(    self						,       __A						,       __A = None						,       __A = False						,       __A = None						,       __A = None						,       __A = None						,       )						->    BatchFeature:
              if sampling_rate is not None:
                     if sampling_rate != self.sampling_rate:
                            raise ValueError(
                                F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                                F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                                F' {self.sampling_rate} and not {sampling_rate}.'				)
              else:
                     logger.warning(
                         '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                         '''Failing to do so can result in silent errors that might be hard to debug.'''				)

              if padding and truncation:
                     raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''				)
              elif padding is None:
                     # by default let's pad the inputs
                     SCREAMING_SNAKE_CASE_							:				int		=True

              SCREAMING_SNAKE_CASE_							:				List[str]		=bool(
                  isinstance(__A						,       (list, tuple)				) and (isinstance(raw_audio[0]						,       (np.ndarray, tuple, list)				))				)

              if is_batched:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[np.asarray(__A						,       dtype=np.floataa				).T for audio in raw_audio]
              elif not is_batched and not isinstance(__A						,       np.ndarray				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=np.asarray(__A						,       dtype=np.floataa				)
              elif isinstance(__A						,       np.ndarray				) and raw_audio.dtype is np.dtype(np.floataa				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=raw_audio.astype(np.floataa				)

              # always return batch
              if not is_batched:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=[np.asarray(__A				).T]

              # verify inputs are valid
              for idx, example in enumerate(__A				):
                     if example.ndim > 2:
                            raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'				)
                     if self.feature_size == 1 and example.ndim != 1:
                            raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'				)
                     if self.feature_size == 2 and example.shape[-1] != 2:
                            raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'				)

              SCREAMING_SNAKE_CASE_							:				str		=None
              SCREAMING_SNAKE_CASE_							:				Any		=BatchFeature({'''input_values''': raw_audio}				)
              if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                     if truncation:
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=min(array.shape[0] for array in raw_audio				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=int(np.floor(max_length / self.chunk_stride				)				)
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(nb_step - 1) * self.chunk_stride + self.chunk_length
                     elif padding:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=max(array.shape[0] for array in raw_audio				)
                            SCREAMING_SNAKE_CASE_							:				str		=int(np.ceil(max_length / self.chunk_stride				)				)
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(nb_step - 1) * self.chunk_stride + self.chunk_length
                            SCREAMING_SNAKE_CASE_							:				List[Any]		='''max_length'''
                     else:
                            SCREAMING_SNAKE_CASE_							:				Any		=input_values

        # normal padding on batch
              if padded_inputs is None:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self.pad(
                         __A						,       max_length=__A						,       truncation=__A						,       padding=__A						,       return_attention_mask=__A						,       )
                     if padding:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=padded_inputs.pop('''attention_mask'''				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=[]
              for example in padded_inputs.pop('''input_values'''				):
                     if self.feature_size == 1:
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=example[..., None]
                     input_values.append(example.T				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=input_values
              if return_tensors is not None:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=padded_inputs.convert_to_tensors(__A				)

              return padded_inputs


import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


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