



'''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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    torch.FloatTensor

class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase   ):







							@register_to_config
							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       int = 65536	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       int = 2	,_UpperCAmelCase					:       int = 2	,_UpperCAmelCase					:       int = 0	,_UpperCAmelCase					:       str = "fourier"	,_UpperCAmelCase					:       bool = True	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,_UpperCAmelCase					:       Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,_UpperCAmelCase					:       Tuple[str] = "UNetMidBlock1D"	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       Tuple[int] = (32, 32, 64)	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       int = 8	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       bool = False	,):
									super().__init__()
									_a     :      str        =							sample_size

									# time
									if time_embedding_type == "fourier":
											_a     :      Union[str, Any]        =							GaussianFourierProjection(
											    embedding_size=8	,set_W_to_weight=_UpperCAmelCase	,log=_UpperCAmelCase	,flip_sin_to_cos=_UpperCAmelCase		)
											_a     :      Optional[int]        =							2 * block_out_channels[0]
									elif time_embedding_type == "positional":
											_a     :      Optional[Any]        =							Timesteps(
											    block_out_channels[0]	,flip_sin_to_cos=_UpperCAmelCase	,downscale_freq_shift=_UpperCAmelCase		)
											_a     :      Dict        =							block_out_channels[0]

									if use_timestep_embedding:
											_a     :      Tuple        =							block_out_channels[0] * 4
											_a     :      Any        =							TimestepEmbedding(
											    in_channels=_UpperCAmelCase	,time_embed_dim=_UpperCAmelCase	,act_fn=_UpperCAmelCase	,out_dim=block_out_channels[0]	,)

									_a     :      Dict        =							nn.ModuleList([]		)
									_a     :      Optional[Any]        =							None
									_a     :      Union[str, Any]        =							nn.ModuleList([]		)
									_a     :      Optional[Any]        =							None

									# down
									_a     :      List[Any]        =							in_channels
									for i, down_block_type in enumerate(_UpperCAmelCase		):
											_a     :      Optional[Any]        =							output_channel
											_a     :      Any        =							block_out_channels[i]

											if i == 0:
													input_channel += extra_in_channels

											_a     :      Tuple        =							i == len(_UpperCAmelCase		) - 1

											_a     :      Any        =							get_down_block(
											    _UpperCAmelCase	,num_layers=_UpperCAmelCase	,in_channels=_UpperCAmelCase	,out_channels=_UpperCAmelCase	,temb_channels=block_out_channels[0]	,add_downsample=not is_final_block or downsample_each_block	,)
											self.down_blocks.append(_UpperCAmelCase		)

									# mid
									_a     :      List[Any]        =							get_mid_block(
									    _UpperCAmelCase	,in_channels=block_out_channels[-1]	,mid_channels=block_out_channels[-1]	,out_channels=block_out_channels[-1]	,embed_dim=block_out_channels[0]	,num_layers=_UpperCAmelCase	,add_downsample=_UpperCAmelCase	,)

									# up
									_a     :      List[str]        =							list(reversed(_UpperCAmelCase		)		)
									_a     :      List[Any]        =							reversed_block_out_channels[0]
									if out_block_type is None:
											_a     :      List[Any]        =							out_channels
									else:
											_a     :      List[str]        =							block_out_channels[0]

									for i, up_block_type in enumerate(_UpperCAmelCase		):
											_a     :      str        =							output_channel
											_a     :      List[str]        =							(
											    reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase		) - 1 else final_upsample_channels
											)

											_a     :      str        =							i == len(_UpperCAmelCase		) - 1

											_a     :      Tuple        =							get_up_block(
											    _UpperCAmelCase	,num_layers=_UpperCAmelCase	,in_channels=_UpperCAmelCase	,out_channels=_UpperCAmelCase	,temb_channels=block_out_channels[0]	,add_upsample=not is_final_block	,)
											self.up_blocks.append(_UpperCAmelCase		)
											_a     :      Optional[Any]        =							output_channel

									# out
									_a     :      Dict        =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,32		)
									_a     :      List[str]        =							get_out_block(
									    out_block_type=_UpperCAmelCase	,num_groups_out=_UpperCAmelCase	,embed_dim=block_out_channels[0]	,out_channels=_UpperCAmelCase	,act_fn=_UpperCAmelCase	,fc_dim=block_out_channels[-1] // 4	,)







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       torch.FloatTensor	,_UpperCAmelCase					:       Union[torch.Tensor, float, int]	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Union[str, Any]        =							timestep
									if not torch.is_tensor(_UpperCAmelCase		):
											_a     :      Dict        =							torch.tensor([timesteps]	,dtype=torch.long	,device=sample.device		)
									elif torch.is_tensor(_UpperCAmelCase		) and len(timesteps.shape		) == 0:
											_a     :      Optional[Any]        =							timesteps[None].to(sample.device		)

									_a     :      int        =							self.time_proj(_UpperCAmelCase		)
									if self.config.use_timestep_embedding:
											_a     :      Optional[Any]        =							self.time_mlp(_UpperCAmelCase		)
									else:
											_a     :      Optional[int]        =							timestep_embed[..., None]
											_a     :      Union[str, Any]        =							timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
											_a     :      Any        =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

									# 2. down
									_a     :      Optional[Any]        =							()
									for downsample_block in self.down_blocks:
											_a       ,					_a     :      str        =							downsample_block(hidden_states=_UpperCAmelCase	,temb=_UpperCAmelCase		)
											down_block_res_samples += res_samples

									# 3. mid
									if self.mid_block:
											_a     :      Union[str, Any]        =							self.mid_block(_UpperCAmelCase	,_UpperCAmelCase		)

									# 4. up
									for i, upsample_block in enumerate(self.up_blocks		):
											_a     :      List[Any]        =							down_block_res_samples[-1:]
											_a     :      str        =							down_block_res_samples[:-1]
											_a     :      Union[str, Any]        =							upsample_block(_UpperCAmelCase	,res_hidden_states_tuple=_UpperCAmelCase	,temb=_UpperCAmelCase		)

									# 5. post-process
									if self.out_block:
											_a     :      int        =							self.out_block(_UpperCAmelCase	,_UpperCAmelCase		)

									if not return_dict:
											return (sample,)

									return UNetaDOutput(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




