

"""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	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      torch.FloatTensor





class     snake_case	(				__snake_case,							__snake_case  ):

				@register_to_config
				def __init__(	self							:				Dict     ,  UpperCamelCase__							:				int = 3     ,  UpperCamelCase__							:				int = 3     ,  UpperCamelCase__							:				Tuple[str] = ("DownEncoderBlock2D",)     ,  UpperCamelCase__							:				Tuple[str] = ("UpDecoderBlock2D",)     ,  UpperCamelCase__							:				Tuple[int] = (6_4,)     ,  UpperCamelCase__							:				int = 1     ,  UpperCamelCase__							:				str = "silu"     ,  UpperCamelCase__							:				int = 3     ,  UpperCamelCase__							:				int = 3_2     ,  UpperCamelCase__							:				int = 2_5_6     ,  UpperCamelCase__							:				int = 3_2     ,  UpperCamelCase__							:				Optional[int] = None     ,  UpperCamelCase__							:				float = 0.18215     ,  UpperCamelCase__							:				str = "group"     ,  )->   str:



						'''simple docstring'''


						super().__init__()

						# pass init params to Encoder
						__lowerCAmelCase:       Union[str, Any]          =			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__     ,  )

						__lowerCAmelCase:       Dict          =			vq_embed_dim if vq_embed_dim is not None else latent_channels

						__lowerCAmelCase:       List[Any]          =			nn.Convad(UpperCamelCase__     ,  UpperCamelCase__     ,  1)
						__lowerCAmelCase:       Optional[int]          =			VectorQuantizer(UpperCamelCase__     ,  UpperCamelCase__     ,  beta=0.25     ,  remap=UpperCamelCase__     ,  sane_index_shape=UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			nn.Convad(UpperCamelCase__     ,  UpperCamelCase__     ,  1)

						# pass init params to Decoder
						__lowerCAmelCase:       Optional[int]          =			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 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				torch.FloatTensor     ,  UpperCamelCase__							:				bool = True)->   VQEncoderOutput:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.encoder(UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			self.quant_conv(UpperCamelCase__)

						if not return_dict:
								return (h,)

						return VQEncoderOutput(latents=UpperCamelCase__)

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



						'''simple docstring'''


						if not force_not_quantize:
								__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Dict          =			self.quantize(UpperCamelCase__)
						else:
								__lowerCAmelCase:       Dict          =			h
						__lowerCAmelCase:       Union[str, Any]          =			self.post_quant_conv(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.decoder(UpperCamelCase__     ,  quant if self.config.norm_type == "spatial" else None)

						if not return_dict:
								return (dec,)

						return DecoderOutput(sample=UpperCamelCase__)




				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				torch.FloatTensor     ,  UpperCamelCase__							:				bool = True)->   Union[DecoderOutput, torch.FloatTensor]:



						'''simple docstring'''


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

						if not return_dict:
								return (dec,)

						return DecoderOutput(sample=UpperCamelCase__)





"""simple docstring"""


__A						   =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

    # in case there's no path between the 2 nodes
		return []






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			dist[node] + 1
		return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4


