




'''simple docstring'''
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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		list[list[int]]:
			A_									= []
			for i in range(len(SCREAMING_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(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.
									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(SCREAMING_SNAKE_CASE_     )
			return next_generation





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		list[Image.Image]:
			A_									= []
			for _ in range(SCREAMING_SNAKE_CASE_     ):
						# Create output image
						A_									= Image.new('RGB'       ,(len(cells[0]     ), len(SCREAMING_SNAKE_CASE_     ))     )
						A_									= img.load()

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

        # Save image
						images.append(SCREAMING_SNAKE_CASE_     )
						A_									= new_generation(SCREAMING_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:])










'''simple docstring'''
import warnings

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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




