



'''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 :	List[str]     ,_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					:				List[str]				=							sample_size

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

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

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

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

											if i == 0:
													input_channel += extra_in_channels

											_a					:				Optional[Any]				=							i == len(_UpperCAmelCase		) - 1

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

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

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

											_a					:				Optional[Any]				=							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					:				Tuple				=							output_channel

									# out
									_a					:				List[str]				=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,32		)
									_a					:				Union[str, Any]				=							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 :	List[Any]     ,_UpperCAmelCase :	torch.FloatTensor     ,_UpperCAmelCase :	Union[torch.Tensor, float, int]     ,_UpperCAmelCase :	bool = True     ,):
									_a					:				Dict				=							timestep
									if not torch.is_tensor(_UpperCAmelCase		):
											_a					:				int				=							torch.tensor([timesteps]     ,dtype=torch.long     ,device=sample.device		)
									elif torch.is_tensor(_UpperCAmelCase		) and len(timesteps.shape		) == 0:
											_a					:				Union[str, Any]				=							timesteps[None].to(sample.device		)

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

									# 2. down
									_a					:				str				=							()
									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					:				Optional[Any]				=							self.mid_block(_UpperCAmelCase     ,_UpperCAmelCase		)

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

									# 5. post-process
									if self.out_block:
											_a					:				Optional[Any]				=							self.out_block(_UpperCAmelCase     ,_UpperCAmelCase		)

									if not return_dict:
											return (sample,)

									return UNetaDOutput(sample=_UpperCAmelCase		)









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




