



"""simple docstring"""
from __future__ import annotations

from PIL import Image

# Define glider example
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      [
    [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
__SCREAMING_SNAKE_CASE  :						Dict    							=      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def        lowerCAmelCase_(   lowercase_     :							list[list[int]]							)	->       list[list[int]]:
				_lowerCamelCase        =				[]
				for i in range(len(lowercase_							)							):
								_lowerCamelCase        =				[]
								for j in range(len(cells[i]							)							):
												# Get the number of live neighbours
												_lowerCamelCase        =				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.
												_lowerCamelCase        =				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        lowerCAmelCase_(   lowercase_     :							list[list[int]]						,   lowercase_     :							int							)	->       list[Image.Image]:
				_lowerCamelCase        =				[]
				for _ in range(lowercase_							):
								# Create output image
								_lowerCamelCase        =				Image.new('''RGB'''						,   (len(cells[0]							), len(lowercase_							))							)
								_lowerCamelCase        =				img.load()

								# Save cells to image
								for x in range(len(lowercase_							)							):
												for y in range(len(cells[0]							)							):
																_lowerCamelCase        =				2_55 - cells[y][x] * 2_55
																_lowerCamelCase        =				(colour, colour, colour)

        # Save image
								images.append(lowercase_							)
								_lowerCamelCase        =				new_generation(lowercase_							)
				return images


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      generate_images(GLIDER, 1_6)
			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_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
