
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			Optional[int]  ,	UpperCamelCase__				:			int = 3  ,	UpperCamelCase__				:			int = 3  ,	UpperCamelCase__				:			Tuple[str] = ("DownEncoderBlock2D",)  ,	UpperCamelCase__				:			Tuple[str] = ("UpDecoderBlock2D",)  ,	UpperCamelCase__				:			Tuple[int] = (64,)  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			str = "silu"  ,	UpperCamelCase__				:			int = 3  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			int = 256  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			float = 0.18_215  ,	UpperCamelCase__				:			str = "group"  ,	)						->			Any:






				"""simple docstring"""







				super().__init__()

				# pass init params to Encoder
				snake_case  :       int     			=						Encoder(
				    in_channels=UpperCamelCase__  ,	out_channels=UpperCamelCase__  ,	down_block_types=UpperCamelCase__  ,	block_out_channels=UpperCamelCase__  ,	layers_per_block=UpperCamelCase__  ,	act_fn=UpperCamelCase__  ,	norm_num_groups=UpperCamelCase__  ,	double_z=UpperCamelCase__  ,	)

				snake_case  :       Tuple     			=						vq_embed_dim if vq_embed_dim is not None else latent_channels

				snake_case  :       Any     			=						nn.Convad(UpperCamelCase__  ,	UpperCamelCase__  ,	1      )
				snake_case  :       List[Any]     			=						VectorQuantizer(UpperCamelCase__  ,	UpperCamelCase__  ,	beta=0.25  ,	remap=UpperCamelCase__  ,	sane_index_shape=UpperCamelCase__      )
				snake_case  :       Dict     			=						nn.Convad(UpperCamelCase__  ,	UpperCamelCase__  ,	1      )

				# pass init params to Decoder
				snake_case  :       Tuple     			=						Decoder(
				    in_channels=UpperCamelCase__  ,	out_channels=UpperCamelCase__  ,	up_block_types=UpperCamelCase__  ,	block_out_channels=UpperCamelCase__  ,	layers_per_block=UpperCamelCase__  ,	act_fn=UpperCamelCase__  ,	norm_num_groups=UpperCamelCase__  ,	norm_type=UpperCamelCase__  ,	)



		@apply_forward_hook
		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			bool = True      )						->			VQEncoderOutput:






				"""simple docstring"""







				snake_case  :       int     			=						self.encoder(UpperCamelCase__      )
				snake_case  :       Dict     			=						self.quant_conv(UpperCamelCase__      )

				if not return_dict:
						return (h,)

				return VQEncoderOutput(latents=UpperCamelCase__      )



		@apply_forward_hook
		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			bool = True      )						->			Union[DecoderOutput, torch.FloatTensor]:






				"""simple docstring"""







				if not force_not_quantize:
						snake_case			,snake_case			,snake_case  :       List[str]     			=						self.quantize(UpperCamelCase__      )
				else:
						snake_case  :       Optional[Any]     			=						h
				snake_case  :       int     			=						self.post_quant_conv(UpperCamelCase__      )
				snake_case  :       Any     			=						self.decoder(UpperCamelCase__  ,	quant if self.config.norm_type == '''spatial''' else None      )

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			bool = True      )						->			Union[DecoderOutput, torch.FloatTensor]:






				"""simple docstring"""







				snake_case  :       Any     			=						sample
				snake_case  :       int     			=						self.encode(UpperCamelCase__      ).latents
				snake_case  :       List[str]     			=						self.decode(UpperCamelCase__      ).sample

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=UpperCamelCase__      )




'''simple docstring'''
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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							list[list[int]]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						[]
		for i in range(len(SCREAMING_SNAKE_CASE__			)			):
				snake_case  :       str     			=						[]
				for j in range(len(cells[i]			)			):
						# Get the number of live neighbours
						snake_case  :       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(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.
						snake_case  :       List[Any]     			=						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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							list[Image.Image]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						[]
		for _ in range(SCREAMING_SNAKE_CASE__			):
				# Create output image
				snake_case  :       List[Any]     			=						Image.new('''RGB''' ,       (len(cells[0]			), len(SCREAMING_SNAKE_CASE__			))			)
				snake_case  :       Optional[Any]     			=						img.load()

				# Save cells to image
				for x in range(len(SCREAMING_SNAKE_CASE__			)			):
						for y in range(len(cells[0]			)			):
								snake_case  :       str     			=						255 - cells[y][x] * 255
								snake_case  :       List[str]     			=						(colour, colour, colour)

        # Save image
				images.append(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Dict     			=						new_generation(SCREAMING_SNAKE_CASE__			)
		return images


if __name__ == "__main__":
						lowercase__						       =      generate_images(GLIDER, 1_6)
						images[0].save("out.gif", save_all=True, append_images=images[1:])


