




'''simple docstring'''


from __future__ import annotations

from PIL import Image

# Define glider example
UpperCamelCase       			= [
    [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
UpperCamelCase       			= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def  SCREAMING_SNAKE_CASE(					__lowercase     )       ->      List[Any]:
     A:						Union[str, Any]						=						[]
     for i in range(len(__lowercase     )     ):
          A:						Tuple						=						[]
          for j in range(len(cells[i]     )     ):
               # Get the number of live neighbours
               A:						List[Any]						=						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.
               A:						List[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(__lowercase     )
     return next_generation
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase     )       ->      str:
     A:						Dict						=						[]
     for _ in range(__lowercase     ):
          # Create output image
          A:						Optional[Any]						=						Image.new('''RGB'''						,							(len(cells[0]     ), len(__lowercase     ))     )
          A:						Optional[Any]						=						img.load()

          # Save cells to image
          for x in range(len(__lowercase     )     ):
               for y in range(len(cells[0]     )     ):
                    A:						List[Any]						=						2_5_5 - cells[y][x] * 2_5_5
                    A:						str						=						(colour, colour, colour)

        # Save image
          images.append(__lowercase     )
          A:						Optional[int]						=						new_generation(__lowercase     )
     return images


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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)