





'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a   	=    TypeVar("T")







class __a			( Generic[T]      ):





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      bool = True     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							{}  # dictionary of lists
      __SCREAMING_SNAKE_CASE   				=							directed





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      T					,lowerCamelCase					:      T     ):

      '''simple docstring'''

      if not self.directed:  # For undirected graphs
         # if both source vertex and destination vertex are both present in the
         # adjacency list, add destination vertex to source vertex list of adjacent
         # vertices and add source vertex to destination vertex list of adjacent
         # vertices.
         if source_vertex in self.adj_list and destination_vertex in self.adj_list:
            self.adj_list[source_vertex].append(lowerCamelCase     )
            self.adj_list[destination_vertex].append(lowerCamelCase     )
         # if only source vertex is present in adjacency list, add destination vertex
         # to source vertex list of adjacent vertices, then create a new vertex with
         # destination vertex as key and assign a list containing the source vertex
         # as it's first adjacent vertex.
         elif source_vertex in self.adj_list:
            self.adj_list[source_vertex].append(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							[source_vertex]
         # if only destination vertex is present in adjacency list, add source vertex
         # to destination vertex list of adjacent vertices, then create a new vertex
         # with source vertex as key and assign a list containing the source vertex
         # as it's first adjacent vertex.
         elif destination_vertex in self.adj_list:
            self.adj_list[destination_vertex].append(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							[destination_vertex]
         # if both source vertex and destination vertex are not present in adjacency
         # list, create a new vertex with source vertex as key and assign a list
         # containing the destination vertex as it's first adjacent vertex also
         # create a new vertex with destination vertex as key and assign a list
         # containing the source vertex as it's first adjacent vertex.
         else:
            __SCREAMING_SNAKE_CASE   				=							[destination_vertex]
            __SCREAMING_SNAKE_CASE   				=							[source_vertex]
      else:  # For directed graphs
         # if both source vertex and destination vertex are present in adjacency
         # list, add destination vertex to source vertex list of adjacent vertices.
         if source_vertex in self.adj_list and destination_vertex in self.adj_list:
            self.adj_list[source_vertex].append(lowerCamelCase     )
         # if only source vertex is present in adjacency list, add destination
         # vertex to source vertex list of adjacent vertices and create a new vertex
         # with destination vertex as key, which has no adjacent vertex
         elif source_vertex in self.adj_list:
            self.adj_list[source_vertex].append(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							[]
         # if only destination vertex is present in adjacency list, create a new
         # vertex with source vertex as key and assign a list containing destination
         # vertex as first adjacent vertex
         elif destination_vertex in self.adj_list:
            __SCREAMING_SNAKE_CASE   				=							[destination_vertex]
         # if both source vertex and destination vertex are not present in adjacency
         # list, create a new vertex with source vertex as key and a list containing
         # destination vertex as it's first adjacent vertex. Then create a new vertex
         # with destination vertex as key, which has no adjacent vertex
         else:
            __SCREAMING_SNAKE_CASE   				=							[destination_vertex]
            __SCREAMING_SNAKE_CASE   				=							[]

      return self





   def __repr__(       self					:      List[str]     ):

      '''simple docstring'''

      return pformat(self.adj_list     )













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:])




