


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__  :List[Any]             =							logging.get_logger(__name__)


if is_vision_available():
		import PIL

class 		__a				(    UpperCAmelCase      ):
		_a     :  str           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'shortest_edge': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   default_to_square=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   default_to_square=_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else OPENAI_CLIP_STD
									_UpperCAmelCase					=      do_convert_rgb
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   default_to_square=_SCREAMING_SNAKE_CASE						)
									if "shortest_edge" not in size:
																raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
									_UpperCAmelCase					=      get_resize_output_image_size(_SCREAMING_SNAKE_CASE      ,   size=size['shortest_edge']      ,   default_to_square=_SCREAMING_SNAKE_CASE						)
									return resize(_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	List[Any]:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='size'      ,   default_to_square=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'      ,   default_to_square=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std
									_UpperCAmelCase					=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																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_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

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

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
																_UpperCAmelCase					=      [convert_to_rgb(_SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

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

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)




from __future__ import annotations

from PIL import Image

# Define glider example
lowerCAmelCase__  :str             =							[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowerCAmelCase__  :Any             =							[[0, 1, 0], [0, 1, 0], [0, 1, 0]]

def 	lowerCAmelCase__			(      a__:		list[list[int]]	)  ->		list[list[int]]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for i in range(len(a__	)	):
														_UpperCAmelCase					=      []
														for j in range(len(cells[i]	)	):
																					# Get the number of live neighbours
																					_UpperCAmelCase					=      0
																					if i > 0 and j > 0:
																												neighbour_count += cells[i - 1][j - 1]
																					if i > 0:
																												neighbour_count += cells[i - 1][j]
																					if i > 0 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i - 1][j + 1]
																					if j > 0:
																												neighbour_count += cells[i][j - 1]
																					if j < len(cells[i]	) - 1:
																												neighbour_count += cells[i][j + 1]
																					if i < len(a__	) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(a__	) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(a__	) - 1 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i + 1][j + 1]

																					# Rules of the game of life (excerpt from Wikipedia):
																					# 1. Any live cell with two or three live neighbours survives.
																					# 2. Any dead cell with three live neighbours becomes a live cell.
																					# 3. All other live cells die in the next generation.
																					#    Similarly, all other dead cells stay dead.
																					_UpperCAmelCase					=      cells[i][j] == 1
																					if (
																					    (alive and 2 <= neighbour_count <= 3)
																					    or not alive
																					    and neighbour_count == 3
																					):
																												next_generation_row.append(1	)
																					else:
																												next_generation_row.append(0	)

														next_generation.append(a__	)
							return next_generation

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[Image.Image]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for _ in range(a__	):
														# Create output image
														_UpperCAmelCase					=      Image.new('RGB'      ,  (len(cells[0]	), len(a__	))	)
														_UpperCAmelCase					=      img.load()

														# Save cells to image
														for x in range(len(a__	)	):
																					for y in range(len(cells[0]	)	):
																												_UpperCAmelCase					=      2_5_5 - cells[y][x] * 2_5_5
																												_UpperCAmelCase					=      (colour, colour, colour)

        # Save image
														images.append(a__	)
														_UpperCAmelCase					=      new_generation(a__	)
							return images


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							generate_images(GLIDER, 1_6)
		images[0].save('''out.gif''', save_all=True, append_images=images[1:])
