
"""simple docstring"""







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 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Optional[int]					 =     ["""input_values""", """padding_mask"""]




 def __init__(			self							:			Optional[Any]  , a_							:			int = 1  , a_							:			int = 2_40_00  , a_							:			float = 0.0  , a_							:			float = None  , a_							:			float = None  , **a_							:			List[str]  , )->       int:



      """simple docstring"""

      super().__init__(feature_size=a_  , sampling_rate=a_  , padding_value=a_  , **a_			)
      UpperCAmelCase_    :     Dict            =	chunk_length_s
      UpperCAmelCase_    :     Any            =	overlap




 @property
 def 	a   (			self							:			List[Any]			)->       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 	a   (			self							:			Union[str, Any]			)->       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							:			Optional[int]  , a_							:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  , a_							:			Optional[Union[bool, str, PaddingStrategy]] = None  , a_							:			Optional[bool] = False  , a_							:			Optional[int] = None  , a_							:			Optional[Union[str, TensorType]] = None  , a_							:			Optional[int] = 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
           UpperCAmelCase_    :     Tuple            =	True

      UpperCAmelCase_    :     Dict            =	bool(
          isinstance(a_  , (list, tuple)			) and (isinstance(raw_audio[0]  , (np.ndarray, tuple, list)			))			)

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

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

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

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

      UpperCAmelCase_    :     str            =	[]
      for example in padded_inputs.pop("""input_values"""			):
           if self.feature_size == 1:
                UpperCAmelCase_    :     Dict            =	example[..., None]
           input_values.append(example.T			)

      UpperCAmelCase_    :     Optional[Any]            =	input_values
      if return_tensors is not None:
           UpperCAmelCase_    :     List[str]            =	padded_inputs.convert_to_tensors(a_			)

      return padded_inputs



"""simple docstring"""







def       A_      (       lowercase = 1000   )  ->				int:


     """simple docstring"""







     return sum(e for e in range(3    ,			lowercase   ) if e % 3 == 0 or e % 5 == 0   )


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

