


'''simple docstring'''




import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

_a   :   Tuple												=   "Usage of script: script_name <size_of_canvas:int>"

_a   :   Union[str, Any]												=   [0] * 100 + [1] * 10
random.shuffle(choice)





def   _a		(lowercase__			:   int						)			->			list[list[bool]]:






     """simple docstring"""
     __snake_case           =						[[False for i in range(lowercase__						)] for j in range(lowercase__						)]
     return canvas





def   _a		(lowercase__			:   list[list[bool]]						)			->			None:






     """simple docstring"""
     for i, row in enumerate(lowercase__						):
          for j, _ in enumerate(lowercase__						):
               __snake_case           =						bool(random.getrandbits(1						)						)





def   _a		(lowercase__			:   list[list[bool]]						)			->			list[list[bool]]:






     """simple docstring"""
     __snake_case           =						np.array(lowercase__						)
     __snake_case           =						np.array(create_canvas(current_canvas.shape[0]						)						)
     for r, row in enumerate(lowercase__						):
          for c, pt in enumerate(lowercase__						):
               __snake_case           =						__judge_point(
                   lowercase__							,   current_canvas[r - 1 : r + 2, c - 1 : c + 2]						)

     __snake_case           =						next_gen_canvas
     del next_gen_canvas  # cleaning memory as we move on.
     __snake_case           =						current_canvas.tolist()
     return return_canvas





def   _a		(lowercase__			:   bool							,   lowercase__			:   list[list[bool]]						)			->			bool:






     """simple docstring"""
     __snake_case           =						0
     __snake_case           =						0
     # finding dead or alive neighbours count.
     for i in neighbours:
          for status in i:
               if status:
                    alive += 1
               else:
                    dead += 1

    # handling duplicate entry for focus pt.
     if pt:
          alive -= 1
     else:
          dead -= 1

     # running the rules of game here.
     __snake_case           =						pt
     if pt:
          if alive < 2:
               __snake_case           =						False
          elif alive == 2 or alive == 3:
               __snake_case           =						True
          elif alive > 3:
               __snake_case           =						False
     else:
          if alive == 3:
               __snake_case           =						True

     return state


if __name__ == "__main__":
       if len(sys.argv) != 2:
              raise Exception(usage_doc)

       _a   :   List[Any]												=   int(sys.argv[1])
       # main working structure of this module.
       _a   :   Optional[Any]												=   create_canvas(canvas_size)
       seed(c)
       _a					,     _a   :   Optional[int]												=   plt.subplots()
       fig.show()
       _a   :   Any												=   ListedColormap(["w", "k"])
       try:
              while True:
                     _a   :   Tuple												=   run(c)
                     ax.matshow(c, cmap=cmap)
                     fig.canvas.draw()
                     ax.cla()
       except KeyboardInterrupt:
              # do nothing.
              pass








"""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)   )




