



"""simple docstring"""





import random
import sys

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

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

lowerCamelCase      =		[0] * 100 + [1] * 10
random.shuffle(choice)
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[[False for i in range(lowerCAmelCase__   )] for j in range(lowerCAmelCase__   )]
      return canvas
def      a__			(   lowerCAmelCase__   ):
      for i, row in enumerate(lowerCAmelCase__   ):
            for j, _ in enumerate(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							bool(random.getrandbits(1   )   )
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							np.array(lowerCAmelCase__   )
      UpperCAmelCase_    				=							np.array(create_canvas(current_canvas.shape[0]   )   )
      for r, row in enumerate(lowerCAmelCase__   ):
            for c, pt in enumerate(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							__judge_point(
                      lowerCAmelCase__			,    current_canvas[r - 1 : r + 2, c - 1 : c + 2]   )

      UpperCAmelCase_    				=							next_gen_canvas
      del next_gen_canvas  # cleaning memory as we move on.
      UpperCAmelCase_    				=							current_canvas.tolist()
      return return_canvas
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							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.
      UpperCAmelCase_    				=							pt
      if pt:
            if alive < 2:
                  UpperCAmelCase_    				=							False
            elif alive == 2 or alive == 3:
                  UpperCAmelCase_    				=							True
            elif alive > 3:
                  UpperCAmelCase_    				=							False
      else:
            if alive == 3:
                  UpperCAmelCase_    				=							True

      return state


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

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









"""simple docstring"""





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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """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    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''mobilenet_v1'''



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						int=3	,       _UpperCAmelCase    :						Union[str, Any]=224	,       _UpperCAmelCase    :						Any=1.0	,       _UpperCAmelCase    :						Any=8	,       _UpperCAmelCase    :						List[Any]="relu6"	,       _UpperCAmelCase    :						List[str]=True	,       _UpperCAmelCase    :						Dict=0.999	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[Any]=0.001	,       **_UpperCAmelCase    :						str	,       )				->		Optional[int]:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

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

             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							depth_multiplier
             UpperCAmelCase_    				=							min_depth
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							tf_padding
             UpperCAmelCase_    				=							classifier_dropout_prob
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict([("pixel_values", {0: "batch"})]					)



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             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    :						Tuple					)				->		float:


             '''simple docstring'''




             return 1e-4




