





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

_UpperCamelCase				=       logging.get_logger(__name__)

_UpperCamelCase				=       {
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class  _lowerCamelCase       (			UpperCAmelCase_			):



      """simple docstring"""






      UpperCAmelCase_  :					Tuple      			="""imagegpt"""
      UpperCAmelCase_  :					int      			=["""past_key_values"""]
      UpperCAmelCase_  :					List[str]      			={
          """hidden_size""": """n_embd""",
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }







      def __init__(       self ,  UpperCAmelCase=512 + 1 ,  UpperCAmelCase=32 * 32 ,  UpperCAmelCase=512 ,  UpperCAmelCase=24 ,  UpperCAmelCase=8 ,  UpperCAmelCase=None ,  UpperCAmelCase="quick_gelu" ,  UpperCAmelCase=0.1 ,  UpperCAmelCase=0.1 ,  UpperCAmelCase=0.1 ,  UpperCAmelCase=1E-5 ,  UpperCAmelCase=0.02 ,  UpperCAmelCase=True ,  UpperCAmelCase=True ,  UpperCAmelCase=False ,  UpperCAmelCase=False ,  UpperCAmelCase=False ,  **UpperCAmelCase ,  )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				vocab_size
        __snake_case      : Any						=				n_positions
        __snake_case      : int						=				n_embd
        __snake_case      : Optional[int]						=				n_layer
        __snake_case      : Optional[int]						=				n_head
        __snake_case      : Optional[Any]						=				n_inner
        __snake_case      : str						=				activation_function
        __snake_case      : Tuple						=				resid_pdrop
        __snake_case      : Dict						=				embd_pdrop
        __snake_case      : Optional[Any]						=				attn_pdrop
        __snake_case      : Optional[Any]						=				layer_norm_epsilon
        __snake_case      : Tuple						=				initializer_range
        __snake_case      : Union[str, Any]						=				scale_attn_weights
        __snake_case      : Tuple						=				use_cache
        __snake_case      : Optional[int]						=				scale_attn_by_inverse_layer_idx
        __snake_case      : Tuple						=				reorder_and_upcast_attn
        __snake_case      : int						=				tie_word_embeddings

        super().__init__(tie_word_embeddings=__lowercase ,  **__lowercase      )




class  _lowerCamelCase       (			UpperCAmelCase_			):



      """simple docstring"""






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






        '''simple docstring'''






        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
            ]      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase = 1 ,  UpperCAmelCase = -1 ,  UpperCAmelCase = False ,  UpperCAmelCase = None ,  UpperCAmelCase = 3 ,  UpperCAmelCase = 32 ,  UpperCAmelCase = 32 ,  )				->      Mapping[str, Any]:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				self._generate_dummy_images(__lowercase ,  __lowercase ,  __lowercase ,  __lowercase      )
        __snake_case      : List[str]						=				dict(preprocessor(images=__lowercase ,  return_tensors=__lowercase      )      )

        return inputs


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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    for i in range(len(SCREAMING_SNAKE_CASE	)	):
        __UpperCamelCase     :Dict			  =		[]
        for j in range(len(cells[i]	)	):
            # Get the number of live neighbours
            __UpperCamelCase     :List[str]			  =		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.
            __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	)
    return next_generation



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        # Create output image
        __UpperCamelCase     :Dict			  =		Image.new('''RGB'''  ,			(len(cells[0]	), len(SCREAMING_SNAKE_CASE	))	)
        __UpperCamelCase     :Any			  =		img.load()

        # Save cells to image
        for x in range(len(SCREAMING_SNAKE_CASE	)	):
            for y in range(len(cells[0]	)	):
                __UpperCamelCase     :Optional[Any]			  =		255 - cells[y][x] * 255
                __UpperCamelCase     :int			  =		(colour, colour, colour)

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


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





