





import random
import sys

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

__A							:			int         =			"Usage of script: script_name <size_of_canvas:int>"

__A							:			List[str]         =			[0] * 100 + [1] * 10
random.shuffle(choice)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> list[list[bool]]:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> None:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> list[list[bool]]:




    """simple docstring"""




    _A     =							np.array(__a				)
    _A     =							np.array(create_canvas(current_canvas.shape[0]				)				)
    for r, row in enumerate(__a				):
        for c, pt in enumerate(__a				):
            _A     =							__judge_point(
                __a							,     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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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)

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








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



