


from __future__ import annotations

from PIL import Image

# Define glider example
_lowerCAmelCase:						Dict   				=       [
    [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
_lowerCAmelCase:						List[Any]   				=       [[0, 1, 0], [0, 1, 0], [0, 1, 0]]






def   _lowercase(					__a				:						list[list[int]]     ):
							a__      =[]
							for i in range(len(__a     )     ):
														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(__a     ) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(__a     ) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(__a     ) - 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(__a     )
							return next_generation






def   _lowercase(					__a				:						list[list[int]]    ,     __a				:						int     ):
							a__      =[]
							for _ in range(__a     ):
														# Create output image
														a__      =Image.new('RGB'    ,     (len(cells[0]     ), len(__a     ))     )
														a__      =img.load()

														# Save cells to image
														for x in range(len(__a     )     ):
																					for y in range(len(cells[0]     )     ):
																												a__      =255 - cells[y][x] * 255
																												a__      =(colour, colour, colour)

        # Save image
														images.append(__a     )
														a__      =new_generation(__a     )
							return images


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

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



