


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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						[]
 for i in range(len(SCREAMING_SNAKE_CASE__						)						):
  __lowerCamelCase		:							List[str]		 =						[]
  for j in range(len(cells[i]						)						):
   # Get the number of live neighbours
   __lowerCamelCase		:							int		 =						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(SCREAMING_SNAKE_CASE__						) - 1 and j > 0:
    neighbour_count += cells[i + 1][j - 1]
   if i < len(SCREAMING_SNAKE_CASE__						) - 1:
    neighbour_count += cells[i + 1][j]
   if i < len(SCREAMING_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.
   __lowerCamelCase		:							str		 =						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(SCREAMING_SNAKE_CASE__						)
 return next_generation
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						[]
 for _ in range(SCREAMING_SNAKE_CASE__						):
  # Create output image
  __lowerCamelCase		:							List[Any]		 =						Image.new('RGB'      ,	(len(cells[0]						), len(SCREAMING_SNAKE_CASE__						))						)
  __lowerCamelCase		:							List[Any]		 =						img.load()

  # Save cells to image
  for x in range(len(SCREAMING_SNAKE_CASE__						)						):
   for y in range(len(cells[0]						)						):
    __lowerCamelCase		:							List[Any]		 =						255 - cells[y][x] * 255
    __lowerCamelCase		:							Dict		 =						(colour, colour, colour)

        # Save image
  images.append(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Tuple		 =						new_generation(SCREAMING_SNAKE_CASE__						)
 return images


if __name__ == "__main__":
       lowercase_	   =     generate_images(GLIDER, 1_6)
       images[0].save('out.gif', save_all=True, append_images=images[1:])






from typing import TYPE_CHECKING

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


lowercase_	   =     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['MBartTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['MBartTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MBartForCausalLM',
           'MBartForConditionalGeneration',
           'MBartForQuestionAnswering',
           'MBartForSequenceClassification',
           'MBartModel',
           'MBartPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFMBartForConditionalGeneration',
           'TFMBartModel',
           'TFMBartPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'FlaxMBartForConditionalGeneration',
           'FlaxMBartForQuestionAnswering',
           'FlaxMBartForSequenceClassification',
           'FlaxMBartModel',
           'FlaxMBartPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

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

