


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


SCREAMING_SNAKE_CASE			: List[Any]				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[Any]       					=     ["""input_values""", """padding_mask"""]
       def __init__(   self, lowerCamelCase = 1, lowerCamelCase = 2_40_00, lowerCamelCase = 0.0, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           super().__init__(feature_size=lowerCamelCase, sampling_rate=lowerCamelCase, padding_value=lowerCamelCase, **lowerCamelCase)
           _lowercase			:				int 						=						chunk_length_s
           _lowercase			:				int 						=						overlap
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

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




           """simple docstring"""

           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, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, )			->  BatchFeature:




           """simple docstring"""

           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
               _lowercase			:				Tuple 						=						True

           _lowercase			:				Tuple 						=						bool(
               isinstance(lowerCamelCase, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list))))

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

           # always return batch
           if not is_batched:
               _lowercase			:				str 						=						[np.asarray(lowerCamelCase).T]

           # verify inputs are valid
           for idx, example in enumerate(lowerCamelCase):
               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''')

           _lowercase			:				str 						=						None
           _lowercase			:				Optional[int] 						=						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:
                   _lowercase			:				str 						=						min(array.shape[0] for array in raw_audio)
                   _lowercase			:				Tuple 						=						int(np.floor(max_length / self.chunk_stride))
                   _lowercase			:				Union[str, Any] 						=						(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                   _lowercase			:				Dict 						=						max(array.shape[0] for array in raw_audio)
                   _lowercase			:				Optional[int] 						=						int(np.ceil(max_length / self.chunk_stride))
                   _lowercase			:				List[Any] 						=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                   _lowercase			:				Tuple 						=						'max_length'
               else:
                   _lowercase			:				int 						=						input_values

        # normal padding on batch
           if padded_inputs is None:
               _lowercase			:				int 						=						self.pad(
                   lowerCamelCase, max_length=lowerCamelCase, truncation=lowerCamelCase, padding=lowerCamelCase, return_attention_mask=lowerCamelCase, )
               if padding:
                   _lowercase			:				Optional[Any] 						=						padded_inputs.pop('attention_mask')

           _lowercase			:				Union[str, Any] 						=						[]
           for example in padded_inputs.pop('input_values'):
               if self.feature_size == 1:
                   _lowercase			:				Tuple 						=						example[..., None]
               input_values.append(example.T)

           _lowercase			:				Union[str, Any] 						=						input_values
           if return_tensors is not None:
               _lowercase			:				str 						=						padded_inputs.convert_to_tensors(lowerCamelCase)

           return padded_inputs






from __future__ import annotations



def 							UpperCamelCase_( lowerCamelCase_					)       ->		float:
    if not nums:
        raise ValueError('List is empty'					)
    return sum(lowerCamelCase_					) / len(lowerCamelCase_					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


