

'''simple docstring'''



import random
import sys

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

__snake_case					:      Dict 		=     'Usage of script: script_name <size_of_canvas:int>'

__snake_case					:      Optional[int] 		=     [0] * 100 + [1] * 10
random.shuffle(choice)
def 		__lowerCamelCase			(  __snake_case	:						int			)			->				int:






						"""simple docstring"""





						A__	:  Any           =[[False for i in range(__a			)] for j in range(__a			)]
						return canvas
def 		__lowerCamelCase			(  __snake_case	:						list[list[bool]]			)			->				Any:






						"""simple docstring"""





						for i, row in enumerate(__a			):
												for j, _ in enumerate(__a			):
																		A__	:  Union[str, Any]           =bool(random.getrandbits(1			)			)
def 		__lowerCamelCase			(  __snake_case	:						list[list[bool]]			)			->				Any:






						"""simple docstring"""





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

						A__	:  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	:						bool,						__snake_case	:						list[list[bool]]			)			->				int:






						"""simple docstring"""





						A__	:  int           =0
						A__	:  List[Any]           =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__	:  Optional[Any]           =pt
						if pt:
												if alive < 2:
																		A__	:  Optional[int]           =False
												elif alive == 2 or alive == 3:
																		A__	:  str           =True
												elif alive > 3:
																		A__	:  Tuple           =False
						else:
												if alive == 3:
																		A__	:  int           =True

						return state


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

		__snake_case					:      int 		=     int(sys.argv[1])
		# main working structure of this module.
		__snake_case					:      Union[str, Any] 		=     create_canvas(canvas_size)
		seed(c)
		__snake_case		,  __snake_case					:      Tuple 		=     plt.subplots()
		fig.show()
		__snake_case					:      Optional[int] 		=     ListedColormap(['w', 'k'])
		try:
				while True:
						__snake_case					:      Union[str, Any] 		=     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





