


'''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 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	torch.FloatTensor




class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase		):





     @register_to_config
     def __init__( self       :      Any    ,      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 = 0.18_215    ,      UpperCAmelCase__       :      str = "group"    ,      )				->  int:
        super().__init__()

        # pass init params to Encoder
        lowerCAmelCase     =			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     =			vq_embed_dim if vq_embed_dim is not None else latent_channels

        lowerCAmelCase     =			nn.Convad(UpperCAmelCase__    ,      UpperCAmelCase__    ,      1    )
        lowerCAmelCase     =			VectorQuantizer(UpperCAmelCase__    ,      UpperCAmelCase__    ,      beta=0.25    ,      remap=UpperCAmelCase__    ,      sane_index_shape=UpperCAmelCase__    )
        lowerCAmelCase     =			nn.Convad(UpperCAmelCase__    ,      UpperCAmelCase__    ,      1    )

        # pass init params to Decoder
        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      bool = True    )				->  VQEncoderOutput:
        lowerCAmelCase     =			self.encoder(UpperCAmelCase__    )
        lowerCAmelCase     =			self.quant_conv(UpperCAmelCase__    )

        if not return_dict:
           return (h,)

        return VQEncoderOutput(latents=UpperCAmelCase__    )





     @apply_forward_hook
     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      bool = True    )				->  Union[DecoderOutput, torch.FloatTensor]:
        # also go through quantization layer
        if not force_not_quantize:
           lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self.quantize(UpperCAmelCase__    )
        else:
           lowerCAmelCase     =			h
        lowerCAmelCase     =			self.post_quant_conv(UpperCAmelCase__    )
        lowerCAmelCase     =			self.decoder(UpperCAmelCase__    ,      quant if self.config.norm_type == 'spatial' else None    )

        if not return_dict:
           return (dec,)

        return DecoderOutput(sample=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      bool = True    )				->  Union[DecoderOutput, torch.FloatTensor]:
        lowerCAmelCase     =			sample
        lowerCAmelCase     =			self.encode(UpperCAmelCase__    ).latents
        lowerCAmelCase     =			self.decode(UpperCAmelCase__    ).sample

        if not return_dict:
           return (dec,)

        return DecoderOutput(sample=UpperCAmelCase__    )










'''simple docstring'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							set()
		# keep track of all the paths to be checked
		_a     :      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
				_a     :      Tuple        =							queue.pop(0  )
				# get the last node from the path
				_a     :      Tuple        =							path[-1]
				if node not in explored:
						_a     :      Optional[Any]        =							graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								_a     :      Any        =							list(lowerCAmelCase_  )
								new_path.append(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(lowerCAmelCase_  )

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



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




