import random
import sys

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

UpperCAmelCase_								=							'''Usage of script: script_name <size_of_canvas:int>'''

UpperCAmelCase_								=							[0] * 100 + [1] * 10
random.shuffle(choice)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						list[list[bool]]:




       '''simple docstring'''






       _A=	[[False for i in range(lowerCAmelCase_   )] for j in range(lowerCAmelCase_   )]
       return canvas

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						None:




       '''simple docstring'''






       for i, row in enumerate(lowerCAmelCase_   ):
              for j, _ in enumerate(lowerCAmelCase_   ):
                     _A=	bool(random.getrandbits(1   )   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						list[list[bool]]:




       '''simple docstring'''






       _A=	np.array(lowerCAmelCase_   )
       _A=	np.array(create_canvas(current_canvas.shape[0]   )   )
       for r, row in enumerate(lowerCAmelCase_   ):
              for c, pt in enumerate(lowerCAmelCase_   ):
                     _A=	__judge_point(
                         lowerCAmelCase_       ,					current_canvas[r - 1 : r + 2, c - 1 : c + 2]   )

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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						bool:




       '''simple docstring'''






       _A=	0
       _A=	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.
       _A=	pt
       if pt:
              if alive < 2:
                     _A=	False
              elif alive == 2 or alive == 3:
                     _A=	True
              elif alive > 3:
                     _A=	False
       else:
              if alive == 3:
                     _A=	True

       return state


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

      UpperCAmelCase_								=							int(sys.argv[1])
      # main working structure of this module.
      UpperCAmelCase_								=							create_canvas(canvas_size)
      seed(c)
      UpperCAmelCase_		,    UpperCAmelCase_								=							plt.subplots()
      fig.show()
      UpperCAmelCase_								=							ListedColormap(['''w''', '''k'''])
      try:
            while True:
                  UpperCAmelCase_								=							run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""speech_to_text_2"""
     _SCREAMING_SNAKE_CASE     :     str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(					self				,      lowerCAmelCase__=10000				,      lowerCAmelCase__=6				,      lowerCAmelCase__=2048				,      lowerCAmelCase__=4				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=256				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=2				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1024				,      **lowerCAmelCase__				,      ):
            _A=	vocab_size
            _A=	d_model
            _A=	decoder_ffn_dim
            _A=	decoder_layers
            _A=	decoder_attention_heads
            _A=	dropout
            _A=	attention_dropout
            _A=	activation_dropout
            _A=	activation_function
            _A=	init_std
            _A=	decoder_layerdrop
            _A=	use_cache
            _A=	decoder_layers
            _A=	scale_embedding  # scale factor will be sqrt(d_model) if True
            _A=	max_target_positions

            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      decoder_start_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )