"""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


__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)







class 				_A		( lowerCAmelCase			):
      snake_case__    :     List[Any]													=			['input_values', 'padding_mask']

      def __init__(		self	, __lowerCAmelCase = 1	, __lowerCAmelCase = 2_4000	, __lowerCAmelCase = 0.0	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(feature_size=__lowerCAmelCase	, sampling_rate=__lowerCAmelCase	, padding_value=__lowerCAmelCase	, **__lowerCAmelCase		)
            lowercase														=			chunk_length_s
            lowercase														=			overlap

      @property
      def        A__ (		self		):







            """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		):







            """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	, ):







            """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														=			True

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

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

            # always return batch
            if not is_batched:
                  lowercase														=			[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														=			None
            lowercase														=			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														=			min(array.shape[0] for array in raw_audio		)
                        lowercase														=			int(np.floor(max_length / self.chunk_stride		)		)
                        lowercase														=			(nb_step - 1) * self.chunk_stride + self.chunk_length
                  elif padding:
                        lowercase														=			max(array.shape[0] for array in raw_audio		)
                        lowercase														=			int(np.ceil(max_length / self.chunk_stride		)		)
                        lowercase														=			(nb_step - 1) * self.chunk_stride + self.chunk_length
                        lowercase														=			"""max_length"""
                  else:
                        lowercase														=			input_values

        # normal padding on batch
            if padded_inputs is None:
                  lowercase														=			self.pad(
                      __lowerCAmelCase	, max_length=__lowerCAmelCase	, truncation=__lowerCAmelCase	, padding=__lowerCAmelCase	, return_attention_mask=__lowerCAmelCase	, )
                  if padding:
                        lowercase														=			padded_inputs.pop("""attention_mask"""		)

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

            lowercase														=			input_values
            if return_tensors is not None:
                  lowercase														=			padded_inputs.convert_to_tensors(__lowerCAmelCase		)

            return padded_inputs




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					bool:
      '''simple docstring'''


      lowercase														=			(1 + 2_4 * n) ** 0.5
      return ((1 + root) / 6) % 1 == 0


def     UpperCAmelCase__ (       lowerCAmelCase__  :int = 5_0_0_0	)				->					int:
      '''simple docstring'''


      lowercase														=			[(i * (3 * i - 1)) // 2 for i in range(1      ,	lowerCAmelCase__	)]
      for i, pentagonal_i in enumerate(lowerCAmelCase__	):
            for j in range(lowerCAmelCase__      ,	len(lowerCAmelCase__	)	):
                  lowercase														=			pentagonal_nums[j]
                  lowercase														=			pentagonal_i + pentagonal_j
                  lowercase														=			pentagonal_j - pentagonal_i
                  if is_pentagonal(lowerCAmelCase__	) and is_pentagonal(lowerCAmelCase__	):
                        return b

      return -1


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



