
"""simple docstring"""






from __future__ import annotations

from PIL import Image

# Define glider example
_lowercase							=				[
    [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
_lowercase							=				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]



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



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->list[Image.Image]:
			__lowercase       =       []
			for _ in range(__magic_name__      ):
						# Create output image
						__lowercase       =       Image.new("RGB"			,   (len(cells[0]      ), len(__magic_name__      ))      )
						__lowercase       =       img.load()

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

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


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







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





