





'''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


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):
		lowercase__:   Tuple				     =   ['''input_values''', '''padding_mask''']

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


					"""simple docstring"""

					super().__init__(feature_size=__magic_name__  ,      sampling_rate=__magic_name__  ,      padding_value=__magic_name__  ,      **__magic_name__			)
					__snake_case					:      Dict							      =     chunk_length_s
					__snake_case					:      Optional[Any]							      =     overlap

		@property
		def 	lowercase__     (   self					:   Union[str, 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 	lowercase__     (   self					:   Optional[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					:   Union[str, Any]  ,      __magic_name__					:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,      __magic_name__					:   Optional[Union[bool, str, PaddingStrategy]] = None  ,      __magic_name__					:   Optional[bool] = False  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   Optional[Union[str, TensorType]] = None  ,      __magic_name__					:   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
								__snake_case					:      Optional[Any]							      =     True

					__snake_case					:      Any							      =     bool(
					    isinstance(__magic_name__  ,      (list, tuple)			) and (isinstance(raw_audio[0]  ,      (np.ndarray, tuple, list)			))			)

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

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

					# verify inputs are valid
					for idx, example in enumerate(__magic_name__			):
								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					:      Dict							      =     None
					__snake_case					:      Union[str, Any]							      =     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					:      Optional[int]							      =     min(array.shape[0] for array in raw_audio			)
											__snake_case					:      Union[str, Any]							      =     int(np.floor(max_length / self.chunk_stride			)			)
											__snake_case					:      int							      =     (nb_step - 1) * self.chunk_stride + self.chunk_length
								elif padding:
											__snake_case					:      Dict							      =     max(array.shape[0] for array in raw_audio			)
											__snake_case					:      str							      =     int(np.ceil(max_length / self.chunk_stride			)			)
											__snake_case					:      List[str]							      =     (nb_step - 1) * self.chunk_stride + self.chunk_length
											__snake_case					:      Tuple							      =     """max_length"""
								else:
											__snake_case					:      Dict							      =     input_values

        # normal padding on batch
					if padded_inputs is None:
								__snake_case					:      str							      =     self.pad(
								    __magic_name__  ,      max_length=__magic_name__  ,      truncation=__magic_name__  ,      padding=__magic_name__  ,      return_attention_mask=__magic_name__  ,      )
								if padding:
											__snake_case					:      List[Any]							      =     padded_inputs.pop("""attention_mask"""			)

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

					__snake_case					:      List[Any]							      =     input_values
					if return_tensors is not None:
								__snake_case					:      List[Any]							      =     padded_inputs.convert_to_tensors(__magic_name__			)

					return padded_inputs









'''simple docstring'''






from __future__ import annotations

import math







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			if depth < 0:
						raise ValueError("""Depth cannot be less than 0""" )

			if not scores:
						raise ValueError("""Scores cannot be empty""" )

			if depth == height:
						return scores[node_index]

			return (
			    max(
			        minimax(depth + 1    ,	node_index * 2    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	minimax(depth + 1    ,	node_index * 2 + 1    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	)
			    if is_max
			    else min(
			        minimax(depth + 1    ,	node_index * 2    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	minimax(depth + 1    ,	node_index * 2 + 1    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	)
			)







def 					_a						(       ) ->				None:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     [90, 23, 6, 33, 21, 65, 123, 3_4423]
			__snake_case					:      Optional[int]							      =     math.log(len(_lowerCamelCase )    ,	2 )
			print(F'''Optimal value : {minimax(0    ,	0    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )}''' )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


