



import random
import sys

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

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

snake_case    :						str  =						[0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def 	snake_case__       (					__lowercase       )		->		list[list[bool]]:







						"""simple docstring"""

						A__	:							int						    = [[False for i in range(__lowercase       )] for j in range(__lowercase       )]
						return canvas
def 	snake_case__       (					__lowercase       )		->		None:







						"""simple docstring"""

						for i, row in enumerate(__lowercase       ):
												for j, _ in enumerate(__lowercase       ):
																		A__	:							List[Any]						    = bool(random.getrandbits(1       )       )
def 	snake_case__       (					__lowercase       )		->		list[list[bool]]:







						"""simple docstring"""

						A__	:							str						    = np.array(__lowercase       )
						A__	:							Optional[Any]						    = np.array(create_canvas(current_canvas.shape[0]       )       )
						for r, row in enumerate(__lowercase       ):
												for c, pt in enumerate(__lowercase       ):
																		A__	:							Optional[int]						    = __judge_point(
																		    __lowercase							,     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 	snake_case__       (					__lowercase							,     __lowercase       )		->		bool:







						"""simple docstring"""

						A__	:							str						    = 0
						A__	:							Union[str, 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__	:							List[str]						    = pt
						if pt:
												if alive < 2:
																		A__	:							List[Any]						    = False
												elif alive == 2 or alive == 3:
																		A__	:							Optional[Any]						    = True
												elif alive > 3:
																		A__	:							Optional[Any]						    = False
						else:
												if alive == 3:
																		A__	:							List[str]						    = 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    :						str  =						plt.subplots()
		fig.show()
		snake_case    :						str  =						ListedColormap(['w', 'k'])
		try:
				while True:
						snake_case    :						Any  =						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case    :						str  =						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						['AlbertTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						['AlbertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						[
		    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AlbertForMaskedLM',
		    'AlbertForMultipleChoice',
		    'AlbertForPreTraining',
		    'AlbertForQuestionAnswering',
		    'AlbertForSequenceClassification',
		    'AlbertForTokenClassification',
		    'AlbertModel',
		    'AlbertPreTrainedModel',
		    'load_tf_weights_in_albert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAlbertForMaskedLM',
		    'TFAlbertForMultipleChoice',
		    'TFAlbertForPreTraining',
		    'TFAlbertForQuestionAnswering',
		    'TFAlbertForSequenceClassification',
		    'TFAlbertForTokenClassification',
		    'TFAlbertMainLayer',
		    'TFAlbertModel',
		    'TFAlbertPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						[
		    'FlaxAlbertForMaskedLM',
		    'FlaxAlbertForMultipleChoice',
		    'FlaxAlbertForPreTraining',
		    'FlaxAlbertForQuestionAnswering',
		    'FlaxAlbertForSequenceClassification',
		    'FlaxAlbertForTokenClassification',
		    'FlaxAlbertModel',
		    'FlaxAlbertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

		snake_case    :						Any  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)