from __future__ import annotations

from PIL import Image

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






def  snake_case  (       snake_case__			:list[list[int]])						->   list[list[int]]:
       _A        =  []
       for i in range(len(snake_case__)):
              _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(snake_case__) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(snake_case__) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(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.
                     _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(snake_case__)
       return next_generation






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

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

        # Save image
              images.append(snake_case__)
              _A        =  new_generation(snake_case__)
       return images


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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''biogpt'''



 def __init__(					self		,				lowerCAmelCase_=4_23_84		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=16		,				lowerCAmelCase_=40_96		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-12		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					Tuple:
        _A        =  vocab_size
        _A        =  max_position_embeddings
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  initializer_range
        _A        =  layer_norm_eps
        _A        =  scale_embedding
        _A        =  use_cache
        _A        =  layerdrop
        _A        =  activation_dropout
        super().__init__(pad_token_id=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_		,				**lowerCAmelCase_							)




