
'''simple docstring'''




import random
import sys

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

lowercase			:				Optional[int]    		=			'Usage of script: script_name <size_of_canvas:int>'

lowercase			:				Any    		=			[0] * 1_00 + [1] * 10
random.shuffle(choice)







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   List[str]					=						[[False for i in range(snake_case__						)] for j in range(snake_case__						)]
    return canvas







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    for i, row in enumerate(snake_case__						):
        for j, _ in enumerate(snake_case__						):
            A       :   Tuple					=						bool(random.getrandbits(1						)						)







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   List[str]					=						np.array(snake_case__						)
    A       :   Dict					=						np.array(create_canvas(current_canvas.shape[0]						)						)
    for r, row in enumerate(snake_case__						):
        for c, pt in enumerate(snake_case__						):
            A       :   List[str]					=						__judge_point(
                snake_case__		,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]						)

    A       :   Union[str, Any]					=						next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    A       :   list[list[bool]]					=						current_canvas.tolist()
    return return_canvas







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						0
    A       :   int					=						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       :   Union[str, Any]					=						pt
    if pt:
        if alive < 2:
            A       :   Optional[int]					=						False
        elif alive == 2 or alive == 3:
            A       :   Optional[int]					=						True
        elif alive > 3:
            A       :   str					=						False
    else:
        if alive == 3:
            A       :   Optional[Any]					=						True

    return state


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

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







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




