


"""simple docstring"""




def       a_ (	lowercase__ :Union[str, Any],			lowercase__ :int     ):
			return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2





def       a_ (	lowercase__ :List[Any],			lowercase__ :List[str]=0     ):
			return sorted(lowercase__,			key=lambda lowercase__     : x[column]     )





def       a_ (	lowercase__ :Union[str, Any],			lowercase__ :str,			lowercase__ :Tuple=float("""inf"""     )     ):

			for i in range(points_counts - 1     ):
						for j in range(i + 1,			lowercase__     ):
									__lowerCamelCase          =      euclidean_distance_sqr(points[i],			points[j]     )
									if current_dis < min_dis:
												__lowerCamelCase          =      current_dis
			return min_dis





def       a_ (	lowercase__ :str,			lowercase__ :Optional[int],			lowercase__ :Union[str, Any]=float("""inf"""     )     ):

			for i in range(min(6,			points_counts - 1     ),			lowercase__     ):
						for j in range(max(0,			i - 6     ),			lowercase__     ):
									__lowerCamelCase          =      euclidean_distance_sqr(points[i],			points[j]     )
									if current_dis < min_dis:
												__lowerCamelCase          =      current_dis
			return min_dis





def       a_ (	lowercase__ :List[Any],			lowercase__ :List[str],			lowercase__ :int     ):

			# base case
			if points_counts <= 3:
						return dis_between_closest_pair(lowercase__,			lowercase__     )

			# recursion
			__lowerCamelCase          =      points_counts // 2
			__lowerCamelCase          =      closest_pair_of_points_sqr(
			    lowercase__,			points_sorted_on_y[:mid],			lowercase__     )
			__lowerCamelCase          =      closest_pair_of_points_sqr(
			    lowercase__,			points_sorted_on_y[mid:],			points_counts - mid     )
			__lowerCamelCase          =      min(lowercase__,			lowercase__     )
			__lowerCamelCase          =      []
			for point in points_sorted_on_x:
						if abs(point[0] - points_sorted_on_x[mid][0]     ) < closest_pair_dis:
									cross_strip.append(lowercase__     )

			__lowerCamelCase          =      dis_between_closest_in_strip(
			    lowercase__,			len(lowercase__     ),			lowercase__     )
			return min(lowercase__,			lowercase__     )





def       a_ (	lowercase__ :List[Any],			lowercase__ :int     ):
			__lowerCamelCase          =      column_based_sort(lowercase__,			column=0     )
			__lowerCamelCase          =      column_based_sort(lowercase__,			column=1     )
			return (
			    closest_pair_of_points_sqr(
			        lowercase__,			lowercase__,			lowercase__     )
			) ** 0.5


if __name__ == "__main__":
					__magic_name__						: Union[str, Any]    =				[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
					print('Distance:', closest_pair_of_points(points, len(points)))








"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    '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 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      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:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[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:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




