





'''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    UpperCamelCase__	(		_a  ):
	lowerCAmelCase__       :      List[str]										=    42






class    UpperCamelCase__	(		_a						,_a  ):







	@register_to_config
	def __init__(					self      : Any			,			lowerCamelCase      : str = 6_5_5_3_6			,			lowerCamelCase      : Dict = None			,			lowerCamelCase      : Union[str, Any] = 2			,			lowerCamelCase      : int = 2			,			lowerCamelCase      : List[Any] = 0			,			lowerCamelCase      : List[str] = "fourier"			,			lowerCamelCase      : List[str] = True			,			lowerCamelCase      : Union[str, Any] = False			,			lowerCamelCase      : List[str] = 0.0			,			lowerCamelCase      : str = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,			lowerCamelCase      : str = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,			lowerCamelCase      : str = "UNetMidBlock1D"			,			lowerCamelCase      : Dict = None			,			lowerCamelCase      : int = (3_2, 3_2, 6_4)			,			lowerCamelCase      : Optional[int] = None			,			lowerCamelCase      : Optional[int] = 8			,			lowerCamelCase      : Dict = 1			,			lowerCamelCase      : List[Any] = False			,			):




				'''simple docstring'''
				super().__init__()
				a__	 =			sample_size

				# time
				if time_embedding_type == "fourier":
							a__	 =			GaussianFourierProjection(
							    embedding_size=8			,			set_W_to_weight=lowerCamelCase__			,			log=lowerCamelCase__			,			flip_sin_to_cos=lowerCamelCase__		)
							a__	 =			2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							a__	 =			Timesteps(
							    block_out_channels[0]			,			flip_sin_to_cos=lowerCamelCase__			,			downscale_freq_shift=lowerCamelCase__		)
							a__	 =			block_out_channels[0]

				if use_timestep_embedding:
							a__	 =			block_out_channels[0] * 4
							a__	 =			TimestepEmbedding(
							    in_channels=lowerCamelCase__			,			time_embed_dim=lowerCamelCase__			,			act_fn=lowerCamelCase__			,			out_dim=block_out_channels[0]			,			)

				a__	 =			nn.ModuleList([]		)
				a__	 =			None
				a__	 =			nn.ModuleList([]		)
				a__	 =			None

				# down
				a__	 =			in_channels
				for i, down_block_type in enumerate(lowerCamelCase__		):
							a__	 =			output_channel
							a__	 =			block_out_channels[i]

							if i == 0:
										input_channel += extra_in_channels

							a__	 =			i == len(lowerCamelCase__		) - 1

							a__	 =			get_down_block(
							    lowerCamelCase__			,			num_layers=lowerCamelCase__			,			in_channels=lowerCamelCase__			,			out_channels=lowerCamelCase__			,			temb_channels=block_out_channels[0]			,			add_downsample=not is_final_block or downsample_each_block			,			)
							self.down_blocks.append(lowerCamelCase__		)

				# mid
				a__	 =			get_mid_block(
				    lowerCamelCase__			,			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=lowerCamelCase__			,			add_downsample=lowerCamelCase__			,			)

				# up
				a__	 =			list(reversed(lowerCamelCase__		)		)
				a__	 =			reversed_block_out_channels[0]
				if out_block_type is None:
							a__	 =			out_channels
				else:
							a__	 =			block_out_channels[0]

				for i, up_block_type in enumerate(lowerCamelCase__		):
							a__	 =			output_channel
							a__	 =			(
							    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__		) - 1 else final_upsample_channels
							)

							a__	 =			i == len(lowerCamelCase__		) - 1

							a__	 =			get_up_block(
							    lowerCamelCase__			,			num_layers=lowerCamelCase__			,			in_channels=lowerCamelCase__			,			out_channels=lowerCamelCase__			,			temb_channels=block_out_channels[0]			,			add_upsample=not is_final_block			,			)
							self.up_blocks.append(lowerCamelCase__		)
							a__	 =			output_channel

				# out
				a__	 =			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,			3_2		)
				a__	 =			get_out_block(
				    out_block_type=lowerCamelCase__			,			num_groups_out=lowerCamelCase__			,			embed_dim=block_out_channels[0]			,			out_channels=lowerCamelCase__			,			act_fn=lowerCamelCase__			,			fc_dim=block_out_channels[-1] // 4			,			)







	def     __a (					self      : Dict			,			lowerCamelCase      : Tuple			,			lowerCamelCase      : List[Any]			,			lowerCamelCase      : int = True			,			):




				'''simple docstring'''
				a__	 =			timestep
				if not torch.is_tensor(lowerCamelCase__		):
							a__	 =			torch.tensor([timesteps]			,			dtype=torch.long			,			device=sample.device		)
				elif torch.is_tensor(lowerCamelCase__		) and len(timesteps.shape		) == 0:
							a__	 =			timesteps[None].to(sample.device		)

				a__	 =			self.time_proj(lowerCamelCase__		)
				if self.config.use_timestep_embedding:
							a__	 =			self.time_mlp(lowerCamelCase__		)
				else:
							a__	 =			timestep_embed[..., None]
							a__	 =			timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
							a__	 =			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

				# 2. down
				a__	 =			()
				for downsample_block in self.down_blocks:
							a__	 =			downsample_block(hidden_states=lowerCamelCase__			,			temb=lowerCamelCase__		)
							down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
							a__	 =			self.mid_block(lowerCamelCase__			,			lowerCamelCase__		)

				# 4. up
				for i, upsample_block in enumerate(self.up_blocks		):
							a__	 =			down_block_res_samples[-1:]
							a__	 =			down_block_res_samples[:-1]
							a__	 =			upsample_block(lowerCamelCase__			,			res_hidden_states_tuple=lowerCamelCase__			,			temb=lowerCamelCase__		)

				# 5. post-process
				if self.out_block:
							a__	 =			self.out_block(lowerCamelCase__			,			lowerCamelCase__		)

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=lowerCamelCase__		)






from __future__ import annotations

from typing import Any

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= num_of_nodes
						UpperCAmelCase__:	list[list[int]]									= []
						UpperCAmelCase__:	dict[int, int]									= {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						self.m_edges.append([u_node, v_node, weight]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] == u_node:
							return u_node
						return self.find_component(self.m_component[u_node]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] != u_node:
							for k in self.m_component:
								UpperCAmelCase__:	Optional[Any]									= self.find_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):

						if component_size[u_node] <= component_size[v_node]:
							UpperCAmelCase__:	Tuple									= v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(lowerCamelCase__						)

						elif component_size[u_node] >= component_size[v_node]:
							UpperCAmelCase__:	Dict									= self.find_component(lowerCamelCase__						)
							component_size[u_node] += component_size[v_node]
							self.set_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= []
						UpperCAmelCase__:	int									= 0

						UpperCAmelCase__:	list[Any]									= [-1] * self.m_num_of_nodes

						# A list of components (initialized to all of the nodes)
						for node in range(self.m_num_of_nodes						):
							self.m_component.update({node: node}						)
							component_size.append(1						)

						UpperCAmelCase__:	Union[str, Any]									= self.m_num_of_nodes

						while num_of_components > 1:
							for edge in self.m_edges:
								UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

								UpperCAmelCase__:	str									= self.m_component[u]
								UpperCAmelCase__:	Optional[Any]									= self.m_component[v]

								if u_component != v_component:

									for component in (u_component, v_component):
										if (
										    minimum_weight_edge[component] == -1
										    or minimum_weight_edge[component][2] > w
										):
											UpperCAmelCase__:	List[Any]									= [u, v, w]

							for edge in minimum_weight_edge:
								if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
									UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

									UpperCAmelCase__:	str									= self.m_component[u]
									UpperCAmelCase__:	int									= self.m_component[v]

									if u_component != v_component:
										mst_weight += w
										self.union(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
										print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"						)
										num_of_components -= 1

							UpperCAmelCase__:	Any									= [-1] * self.m_num_of_nodes
						print(F"The total weight of the minimal spanning tree is: {mst_weight}"						)



def 			_A	(      ):
	pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()