



'''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		)






from queue import PriorityQueue
from typing import Any

import numpy as np




def  __magic_name__ (   __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						PriorityQueue		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						float | int		,  )			->   float | int:
				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								__lowerCamelCase    					=	cst_fwd.get(__lowerCAmelCase		,  np.inf							)
								__lowerCamelCase    					=	cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt)							)
												__lowerCamelCase    					=	new_cost_f
												__lowerCamelCase    					=	v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																__lowerCamelCase    					=	cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict							)			->   int:
				__lowerCamelCase    					=	-1

				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	{source: 0}
				__lowerCamelCase    					=	{destination: 0}
				__lowerCamelCase    					=	{source: None}
				__lowerCamelCase    					=	{destination: None}
				__lowerCamelCase    					=	PriorityQueue()
				__lowerCamelCase    					=	PriorityQueue()

				__lowerCamelCase    					=	np.inf

				queue_forward.put((0, source)							)
				queue_backward.put((0, destination)							)

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								__lowerCamelCase					,    __lowerCamelCase    					=	queue_forward.get()
								visited_forward.add(__lowerCAmelCase							)

								__lowerCamelCase					,    __lowerCamelCase    					=	queue_backward.get()
								visited_backward.add(__lowerCAmelCase							)

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								__lowerCamelCase    					=	shortest_distance
				return shortest_path_distance


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()



