






"""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_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       ['''input_values''', '''padding_mask''']





	def __init__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				int = 24_000			,			lowerCAmelCase_      :				float = 0.0			,			lowerCAmelCase_      :				float = None			,			lowerCAmelCase_      :				float = None			,			**lowerCAmelCase_      :				Any			,			)		->    List[Any]:
				super().__init__(feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			**lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      chunk_length_s
				UpperCAmelCase_  :				Tuple      =      overlap





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





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

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

				if is_batched:
							UpperCAmelCase_  :				List[str]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		).T for audio in raw_audio]
				elif not is_batched and not isinstance(lowerCAmelCase_			,			np.ndarray		):
							UpperCAmelCase_  :				Union[str, Any]      =      np.asarray(lowerCAmelCase_			,			dtype=np.floataa		)
				elif isinstance(lowerCAmelCase_			,			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_  :				Dict      =      [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"""		)

				UpperCAmelCase_  :				Dict      =      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_  :				Optional[int]      =      min(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_  :				Optional[Any]      =      int(np.floor(max_length / self.chunk_stride		)		)
										UpperCAmelCase_  :				int      =      (nb_step - 1) * self.chunk_stride + self.chunk_length
							elif padding:
										UpperCAmelCase_  :				Dict      =      max(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_  :				List[str]      =      int(np.ceil(max_length / self.chunk_stride		)		)
										UpperCAmelCase_  :				List[Any]      =      (nb_step - 1) * self.chunk_stride + self.chunk_length
										UpperCAmelCase_  :				int      =      "max_length"
							else:
										UpperCAmelCase_  :				List[Any]      =      input_values

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

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

				UpperCAmelCase_  :				int      =      input_values
				if return_tensors is not None:
							UpperCAmelCase_  :				List[str]      =      padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


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



