
"""simple docstring"""



from __future__ import annotations

from PIL import Image

# Define glider example
UpperCamelCase__          =	[
    [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
UpperCamelCase__          =	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					[]
    for i in range(len(_snake_case ) ):
        UpperCAmelCase__	:      Any     							=					[]
        for j in range(len(cells[i] ) ):
            # Get the number of live neighbours
            UpperCAmelCase__	:      Dict     							=					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(_snake_case ) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(_snake_case ) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(_snake_case ) - 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__	:      Optional[int]     							=					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(_snake_case )
    return next_generation
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					[]
    for _ in range(_snake_case ):
        # Create output image
        UpperCAmelCase__	:      str     							=					Image.new('RGB'    ,(len(cells[0] ), len(_snake_case )) )
        UpperCAmelCase__	:      List[Any]     							=					img.load()

        # Save cells to image
        for x in range(len(_snake_case ) ):
            for y in range(len(cells[0] ) ):
                UpperCAmelCase__	:      str     							=					255 - cells[y][x] * 255
                UpperCAmelCase__	:      int     							=					(colour, colour, colour)

        # Save image
        images.append(_snake_case )
        UpperCAmelCase__	:      List[str]     							=					new_generation(_snake_case )
    return images


if __name__ == "__main__":
    UpperCamelCase__          =	generate_images(GLIDER, 16)
    images[0].save('out.gif', save_all=True, append_images=images[1:])








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 convert_to_rgb, 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


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""pixel_values"""]





   def __init__(			self       ,      snake_case = True       ,      snake_case = None       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = True       ,      snake_case = 1 / 255       ,      snake_case = True       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      super().__init__(**snake_case   )
      a__						:    List[str]   		=	size if size is not None else {"height": 384, "width": 384}
      a__						:    List[str]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    Any   		=	do_resize
      a__						:    Optional[int]   		=	size
      a__						:    int   		=	resample
      a__						:    Optional[Any]   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Dict   		=	do_normalize
      a__						:    Any   		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a__						:    List[str]   		=	image_std if image_std is not None else OPENAI_CLIP_STD
      a__						:    str   		=	do_convert_rgb





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    Optional[int]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )
      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()}"""   )
      a__						:    Optional[int]   		=	(size["height"], size["width"])
      return resize(snake_case       ,      size=snake_case       ,      resample=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	List[str]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = ChannelDimension.FIRST       ,      **snake_case       ,      )					->	PIL.Image.Image:
      """simple docstring"""





      a__						:    List[str]   		=	do_resize if do_resize is not None else self.do_resize
      a__						:    Optional[Any]   		=	resample if resample is not None else self.resample
      a__						:    int   		=	do_rescale if do_rescale is not None else self.do_rescale
      a__						:    int   		=	rescale_factor if rescale_factor is not None else self.rescale_factor
      a__						:    Union[str, Any]   		=	do_normalize if do_normalize is not None else self.do_normalize
      a__						:    List[Any]   		=	image_mean if image_mean is not None else self.image_mean
      a__						:    int   		=	image_std if image_std is not None else self.image_std
      a__						:    Tuple   		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a__						:    Tuple   		=	size if size is not None else self.size
      a__						:    Any   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    str   		=	make_list_of_images(snake_case   )

      if not valid_images(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 or resample is None:
         raise ValueError("Size and resample 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_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:
         a__						:    List[str]   		=	[convert_to_rgb(snake_case   ) for image in images]

      # All transformations expect numpy arrays.
      a__						:    Dict   		=	[to_numpy_array(snake_case   ) for image in images]

      if do_resize:
         a__						:    Union[str, Any]   		=	[self.resize(image=snake_case       ,      size=snake_case       ,      resample=snake_case   ) for image in images]

      if do_rescale:
         a__						:    Tuple   		=	[self.rescale(image=snake_case       ,      scale=snake_case   ) for image in images]

      if do_normalize:
         a__						:    List[str]   		=	[self.normalize(image=snake_case       ,      mean=snake_case       ,      std=snake_case   ) for image in images]

      a__						:    Union[str, Any]   		=	[to_channel_dimension_format(snake_case       ,      snake_case   ) for image in images]

      a__						:    List[Any]   		=	BatchFeature(data={"pixel_values": images}       ,      tensor_type=snake_case   )

      return encoded_outputs

