






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
  import PIL

if is_torch_available():
  import torch


__a      :       Union[str, Any]				  =   logging.get_logger(__name__)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["pixel_values"]




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Dict[str, int] = None ,	UpperCamelCase_       :			PILImageResampling = PILImageResampling.BILINEAR ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Union[int, float] = 1 / 255 ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Dict[str, int] = None ,	UpperCamelCase_       :			bool = True ,	**UpperCamelCase_       :			Optional[Any] ,	):


         """simple docstring"""



         super().__init__(**UpperCamelCase_				)
         __A         =	size if size is not None else {"""shortest_edge""": 224}
         __A         =	get_size_dict(UpperCamelCase_ ,	default_to_square=UpperCamelCase_				)
         __A         =	crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
         __A         =	get_size_dict(UpperCamelCase_ ,	param_name="""crop_size"""				)

         __A         =	do_resize
         __A         =	size
         __A         =	resample
         __A         =	do_rescale
         __A         =	rescale_factor
         __A         =	do_center_crop
         __A         =	crop_size
         __A         =	do_flip_channel_order




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			Dict[str, int] ,	UpperCamelCase_       :			PILImageResampling = PIL.Image.BILINEAR ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None ,	**UpperCamelCase_       :			List[str] ,	):


         """simple docstring"""



         __A         =	get_size_dict(UpperCamelCase_ ,	default_to_square=UpperCamelCase_				)
         if "shortest_edge" not in size:
             raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"				)
         __A         =	get_resize_output_image_size(UpperCamelCase_ ,	size=size["""shortest_edge"""] ,	default_to_square=UpperCamelCase_				)
         return resize(UpperCamelCase_ ,	size=UpperCamelCase_ ,	resample=UpperCamelCase_ ,	data_format=UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			Dict[str, int] ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None ,	**UpperCamelCase_       :			int ,	):


         """simple docstring"""



         __A         =	get_size_dict(UpperCamelCase_				)
         if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"				)
         return center_crop(UpperCamelCase_ ,	size=(size["""height"""], size["""width"""]) ,	data_format=UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			Union[int, float] ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None ,	**UpperCamelCase_       :			List[Any] ,	):


         """simple docstring"""



         return rescale(UpperCamelCase_ ,	scale=UpperCamelCase_ ,	data_format=UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None				):


         """simple docstring"""



         return flip_channel_order(UpperCamelCase_ ,	data_format=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			ImageInput ,	UpperCamelCase_       :			bool = None ,	UpperCamelCase_       :			Dict[str, int] = None ,	UpperCamelCase_       :			PILImageResampling = None ,	UpperCamelCase_       :			bool = None ,	UpperCamelCase_       :			float = None ,	UpperCamelCase_       :			bool = None ,	UpperCamelCase_       :			Dict[str, int] = None ,	UpperCamelCase_       :			bool = None ,	UpperCamelCase_       :			Optional[Union[str, TensorType]] = None ,	UpperCamelCase_       :			ChannelDimension = ChannelDimension.FIRST ,	**UpperCamelCase_       :			Dict ,	):


         """simple docstring"""



         __A         =	do_resize if do_resize is not None else self.do_resize
         __A         =	resample if resample is not None else self.resample
         __A         =	do_rescale if do_rescale is not None else self.do_rescale
         __A         =	rescale_factor if rescale_factor is not None else self.rescale_factor
         __A         =	do_center_crop if do_center_crop is not None else self.do_center_crop
         __A         =	(
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

         __A         =	size if size is not None else self.size
         __A         =	get_size_dict(UpperCamelCase_ ,	default_to_square=UpperCamelCase_				)
         __A         =	crop_size if crop_size is not None else self.crop_size
         __A         =	get_size_dict(UpperCamelCase_ ,	param_name="""crop_size"""				)

         __A         =	make_list_of_images(UpperCamelCase_				)

         if not valid_images(UpperCamelCase_				):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""				)

         if do_resize and size is None:
             raise ValueError("""Size must be specified if do_resize is True."""				)

         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

         if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

         # All transformations expect numpy arrays.
         __A         =	[to_numpy_array(UpperCamelCase_				) for image in images]

         if do_resize:
             __A         =	[self.resize(image=UpperCamelCase_ ,	size=UpperCamelCase_ ,	resample=UpperCamelCase_				) for image in images]

         if do_center_crop:
             __A         =	[self.center_crop(image=UpperCamelCase_ ,	size=UpperCamelCase_				) for image in images]

         if do_rescale:
             __A         =	[self.rescale(image=UpperCamelCase_ ,	scale=UpperCamelCase_				) for image in images]

         # the pretrained checkpoints assume images are BGR, not RGB
         if do_flip_channel_order:
             __A         =	[self.flip_channel_order(image=UpperCamelCase_				) for image in images]

         __A         =	[to_channel_dimension_format(UpperCamelCase_ ,	UpperCamelCase_				) for image in images]

         __A         =	{"""pixel_values""": images}
         return BatchFeature(data=UpperCamelCase_ ,	tensor_type=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			List[Tuple] = None				):


         """simple docstring"""



         __A         =	outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
             if len(UpperCamelCase_				) != len(UpperCamelCase_				):
                 raise ValueError(
                     """Make sure that you pass in as many target sizes as the batch dimension of the logits"""				)

             if is_torch_tensor(UpperCamelCase_				):
                 __A         =	target_sizes.numpy()

             __A         =	[]

             for idx in range(len(UpperCamelCase_				)				):
                 __A         =	torch.nn.functional.interpolate(
                     logits[idx].unsqueeze(dim=0				) ,	size=target_sizes[idx] ,	mode="""bilinear""" ,	align_corners=UpperCamelCase_				)
                 __A         =	resized_logits[0].argmax(dim=0				)
                 semantic_segmentation.append(UpperCamelCase_				)
         else:
             __A         =	logits.argmax(dim=1				)
             __A         =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

         return semantic_segmentation












import random
import sys

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

__a      :       List[Any]				  =   "Usage of script: script_name <size_of_canvas:int>"

__a      :       Dict				  =   [0] * 100 + [1] * 10
random.shuffle(choice)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	[[False for i in range(__lowercase    )] for j in range(__lowercase    )]
    return canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      None:





    """simple docstring"""

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



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





    """simple docstring"""

    __A         =	np.array(__lowercase    )
    __A         =	np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowercase    ):
        for c, pt in enumerate(__lowercase    ):
            __A         =	__judge_point(
                __lowercase			,				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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			bool			,				__lowercase   :			list[list[bool]]    )						->      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      :       str				  =   int(sys.argv[1])
  # main working structure of this module.
  __a      :       List[Any]				  =   create_canvas(canvas_size)
  seed(c)
  __a  ,__a      :       int				  =   plt.subplots()
  fig.show()
  __a      :       Union[str, Any]				  =   ListedColormap(["w", "k"])
  try:
    while True:
      __a      :       Dict				  =   run(c)
      ax.matshow(c, cmap=cmap)
      fig.canvas.draw()
      ax.cla()
  except KeyboardInterrupt:
    # do nothing.
    pass




