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						(	__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				[[False for i in range(__lowercase     )] for j in range(__lowercase     )]
		return canvas




def     UpperCamelCase						(	__lowercase						:			list[list[bool]]     ):
		'''simple docstring'''




		for i, row in enumerate(__lowercase     ):
				for j, _ in enumerate(__lowercase     ):
						A_		:    List[Any]         =				bool(random.getrandbits(1     )     )




def     UpperCamelCase						(	__lowercase						:			list[list[bool]]     ):
		'''simple docstring'''




		A_		:    Tuple         =				np.array(__lowercase     )
		A_		:    str         =				np.array(create_canvas(current_canvas.shape[0]     )     )
		for r, row in enumerate(__lowercase     ):
				for c, pt in enumerate(__lowercase     ):
						A_		:    Any         =				__judge_point(
						    __lowercase						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]     )

		A_		:    List[str]         =				next_gen_canvas
		del next_gen_canvas  # cleaning memory as we move on.
		A_		:    list[list[bool]]         =				current_canvas.tolist()
		return return_canvas




def     UpperCamelCase						(	__lowercase						:			bool						,__lowercase						:			list[list[bool]]     ):
		'''simple docstring'''




		A_		:    Tuple         =				0
		A_		:    List[str]         =				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_		:    List[str]         =				pt
		if pt:
				if alive < 2:
						A_		:    str         =				False
				elif alive == 2 or alive == 3:
						A_		:    Tuple         =				True
				elif alive > 3:
						A_		:    List[str]         =				False
		else:
				if alive == 3:
						A_		:    int         =				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            =		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''lilt'''
				def __init__(						self		,     lowercase=3_0_5_2_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=2		,     lowercase=0.02		,     lowercase=1E-12		,     lowercase=0		,     lowercase="absolute"		,     lowercase=None		,     lowercase=4		,     lowercase=1_0_2_4		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase		,     **lowercase )

						A_		:    str         =				vocab_size
						A_		:    Tuple         =				hidden_size
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    int         =				intermediate_size
						A_		:    int         =				hidden_dropout_prob
						A_		:    Union[str, Any]         =				attention_probs_dropout_prob
						A_		:    List[str]         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Any         =				initializer_range
						A_		:    str         =				layer_norm_eps
						A_		:    Optional[int]         =				position_embedding_type
						A_		:    Union[str, Any]         =				classifier_dropout
						A_		:    Optional[Any]         =				channel_shrink_ratio
						A_		:    int         =				max_ad_position_embeddings






