
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


UpperCAmelCase      =   logging.get_logger(__name__)







class lowerCAmelCase_						(			UpperCamelCase__	):

 '''simple docstring'''

 __snake_case												=  ["""input_values""", """padding_mask"""]



 def __init__(     self		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = 2_40_00		,				_UpperCAmelCase = 0.0		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       super().__init__(feature_size=__a		,				sampling_rate=__a		,				padding_value=__a		,				**__a )
       snake_case_	=				chunk_length_s
       snake_case_	=				overlap



 @property
 def 						UpperCamelCase__					(     self ):
       if self.chunk_length_s is None:
             return None
       else:
             return int(self.chunk_length_s * self.sampling_rate )



 @property
 def 						UpperCamelCase__					(     self ):
       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		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = False		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				):
       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
             snake_case_	=				True

       snake_case_	=				bool(
           isinstance(__a		,				(list, tuple) ) and (isinstance(raw_audio[0]		,				(np.ndarray, tuple, list) )) )

       if is_batched:
             snake_case_	=				[np.asarray(__a		,				dtype=np.floataa ).T for audio in raw_audio]
       elif not is_batched and not isinstance(__a		,				np.ndarray ):
             snake_case_	=				np.asarray(__a		,				dtype=np.floataa )
       elif isinstance(__a		,				np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
             snake_case_	=				raw_audio.astype(np.floataa )

       # always return batch
       if not is_batched:
             snake_case_	=				[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''' )

       snake_case_	=				None
       snake_case_	=				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:
                   snake_case_	=				min(array.shape[0] for array in raw_audio )
                   snake_case_	=				int(np.floor(max_length / self.chunk_stride ) )
                   snake_case_	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                   snake_case_	=				max(array.shape[0] for array in raw_audio )
                   snake_case_	=				int(np.ceil(max_length / self.chunk_stride ) )
                   snake_case_	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                   snake_case_	=				'''max_length'''
             else:
                   snake_case_	=				input_values

        # normal padding on batch
       if padded_inputs is None:
             snake_case_	=				self.pad(
                 __a		,				max_length=__a		,				truncation=__a		,				padding=__a		,				return_attention_mask=__a		,				)
             if padding:
                   snake_case_	=				padded_inputs.pop('''attention_mask''' )

       snake_case_	=				[]
       for example in padded_inputs.pop('''input_values''' ):
             if self.feature_size == 1:
                   snake_case_	=				example[..., None]
             input_values.append(example.T )

       snake_case_	=				input_values
       if return_tensors is not None:
             snake_case_	=				padded_inputs.convert_to_tensors(__a )

       return padded_inputs



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


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