




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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


_lowerCamelCase      :						Tuple							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple[int, int]:



       """simple docstring"""

       def constraint_to_multiple_of(lowercase_					,							lowercase_					,							lowercase_=0					,							lowercase_=None		):
              A__ 					=       round(val / multiple		) * multiple

              if max_val is not None and x > max_val:
                     A__ 					=       math.floor(val / multiple		) * multiple

              if x < min_val:
                     A__ 					=       math.ceil(val / multiple		) * multiple

              return x

       A__ 					=       (output_size, output_size) if isinstance(lowercase_					,							lowercase_		) else output_size

       A__					,				A__ 					=       get_image_size(lowercase_		)
       A__					,				A__ 					=       output_size

       # determine new height and width
       A__ 					=       output_height / input_height
       A__ 					=       output_width / input_width

       if keep_aspect_ratio:
              # scale as little as possible
              if abs(1 - scale_width		) < abs(1 - scale_height		):
                     # fit width
                     A__ 					=       scale_width
              else:
                     # fit height
                     A__ 					=       scale_height

       A__ 					=       constraint_to_multiple_of(scale_height * input_height					,							multiple=lowercase_		)
       A__ 					=       constraint_to_multiple_of(scale_width * input_width					,							multiple=lowercase_		)

       return (new_height, new_width)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = ['''pixel_values''']


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





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__ 					=       size if size is not None else {'''height''': 384, '''width''': 384}
           A__ 					=       get_size_dict(UpperCAmelCase__)
           A__ 					=       do_resize
           A__ 					=       size
           A__ 					=       keep_aspect_ratio
           A__ 					=       ensure_multiple_of
           A__ 					=       resample
           A__ 					=       do_rescale
           A__ 					=       rescale_factor
           A__ 					=       do_normalize
           A__ 					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__ 					=       image_std if image_std is not None else IMAGENET_STANDARD_STD


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      np.ndarray   ,					UpperCAmelCase__ :      Dict[str, int]   ,					UpperCAmelCase__ :      bool = False   ,					UpperCAmelCase__ :      int = 1   ,					UpperCAmelCase__ :      PILImageResampling = PILImageResampling.BICUBIC   ,					UpperCAmelCase__ :      Optional[Union[str, ChannelDimension]] = None   ,					**UpperCAmelCase__ :      Union[str, Any]   ,					)		->np.ndarray:





           '''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()}""")
           A__ 					=       get_resize_output_image_size(
               UpperCAmelCase__   ,					output_size=(size['''height'''], size['''width'''])   ,					keep_aspect_ratio=UpperCAmelCase__   ,					multiple=UpperCAmelCase__   ,					)
           return resize(UpperCAmelCase__   ,					size=UpperCAmelCase__   ,					resample=UpperCAmelCase__   ,					data_format=UpperCAmelCase__   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      np.ndarray   ,					UpperCAmelCase__ :      Union[int, float]   ,					UpperCAmelCase__ :      Optional[Union[str, ChannelDimension]] = None   ,					**UpperCAmelCase__ :      Optional[int]   ,					)		->Optional[int]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      np.ndarray   ,					UpperCAmelCase__ :      Union[float, List[float]]   ,					UpperCAmelCase__ :      Union[float, List[float]]   ,					UpperCAmelCase__ :      Optional[Union[str, ChannelDimension]] = None   ,					**UpperCAmelCase__ :      int   ,					)		->np.ndarray:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      ImageInput   ,					UpperCAmelCase__ :      bool = None   ,					UpperCAmelCase__ :      int = None   ,					UpperCAmelCase__ :      bool = None   ,					UpperCAmelCase__ :      int = None   ,					UpperCAmelCase__ :      PILImageResampling = None   ,					UpperCAmelCase__ :      bool = None   ,					UpperCAmelCase__ :      float = None   ,					UpperCAmelCase__ :      bool = None   ,					UpperCAmelCase__ :      Optional[Union[float, List[float]]] = None   ,					UpperCAmelCase__ :      Optional[Union[float, List[float]]] = None   ,					UpperCAmelCase__ :      Optional[Union[str, TensorType]] = None   ,					UpperCAmelCase__ :      ChannelDimension = ChannelDimension.FIRST   ,					**UpperCAmelCase__ :      List[Any]   ,					)		->PIL.Image.Image:





           '''simple docstring'''

           A__ 					=       do_resize if do_resize is not None else self.do_resize
           A__ 					=       size if size is not None else self.size
           A__ 					=       get_size_dict(UpperCAmelCase__)
           A__ 					=       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
           A__ 					=       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
           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_normalize if do_normalize is not None else self.do_normalize
           A__ 					=       image_mean if image_mean is not None else self.image_mean
           A__ 					=       image_std if image_std is not None else self.image_std

           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 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.''')

           # 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_rescale:
                  A__ 					=       [self.rescale(image=UpperCAmelCase__   ,					scale=UpperCAmelCase__) for image in images]

           if do_normalize:
                  A__ 					=       [self.normalize(image=UpperCAmelCase__   ,					mean=UpperCAmelCase__   ,					std=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 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      List[Tuple] = None)		->Optional[int]:





           '''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










from __future__ import annotations

from PIL import Image

# Define glider example
__a   :							Tuple						= [
    [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
__a   :							Optional[Any]						= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



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





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]      ,					__lowercase :      int     )    ->   list[Image.Image]:





    """simple docstring"""

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

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

        # Save image
        images.append(__lowercase     )
        __A       =						new_generation(__lowercase     )
    return images


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




