



"""simple docstring"""







from __future__ import annotations

import math



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:

		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      __UpperCAmelCase      (    )							->      None:
		lowercase__   :    Any				=			[90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
		lowercase__   :    Optional[Any]				=			math.log(len(__lowerCamelCase	)						,     2	)
		print(f"""Optimal value : {minimax(0						,     0						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)}"""	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

'''simple docstring'''




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    use_conv_shortcut

             if self.norm_type not in ["weight_norm", "time_group_norm"]:
                    raise ValueError(
                        F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""       )

             super().__init__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             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]       ):
             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       )       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

