

"""simple docstring"""





import sys
from collections import defaultdict




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[str]	):
    __lowercase								=						[]


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]	):
    return self.node_position[vertex]


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			str	):
    __lowercase								=						pos


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Any	):
    if start > size // 2 - 1:
       return
    else:
       if 2 * start + 2 >= size:
          __lowercase								=						2 * start + 1
       else:
          if heap[2 * start + 1] < heap[2 * start + 2]:
             __lowercase								=						2 * start + 1
          else:
             __lowercase								=						2 * start + 2
       if heap[smallest_child] < heap[start]:
          __lowercase		,__lowercase								=						heap[smallest_child], positions[smallest_child]
          __lowercase		,__lowercase								=						(
              heap[start],
              positions[start],
          )
          __lowercase		,__lowercase								=						temp, tempa

          __lowercase								=						self.get_position(positions[smallest_child]	)
          self.set_position(
              positions[smallest_child],					self.get_position(positions[start]	)	)
          self.set_position(positions[start],					UpperCAmelCase__	)

          self.top_to_bottom(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						position[index]

    while index != 0:
       __lowercase								=						int((index - 2) / 2	) if index % 2 == 0 else int((index - 1) / 2	)

       if val < heap[parent]:
          __lowercase								=						heap[parent]
          __lowercase								=						position[parent]
          self.set_position(position[parent],					UpperCAmelCase__	)
       else:
          __lowercase								=						val
          __lowercase								=						temp
          self.set_position(UpperCAmelCase__,					UpperCAmelCase__	)
          break
       __lowercase								=						parent
    else:
       __lowercase								=						val
       __lowercase								=						temp
       self.set_position(UpperCAmelCase__,					0	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Any	):
    __lowercase								=						len(UpperCAmelCase__	) // 2 - 1
    for i in range(UpperCAmelCase__,					-1,					-1	):
       self.top_to_bottom(UpperCAmelCase__,					UpperCAmelCase__,					len(UpperCAmelCase__	),					UpperCAmelCase__	)



 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						positions[0]
    __lowercase								=						sys.maxsize
    self.top_to_bottom(UpperCAmelCase__,					0,					len(UpperCAmelCase__	),					UpperCAmelCase__	)
    return temp







def   _A  (  UpperCamelCase_     :       Dict)	->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						Heap()

   __lowercase								=						[0] * len(UpperCamelCase_)
   __lowercase								=						[-1] * len(UpperCamelCase_)  # Neighboring Tree Vertex of selected vertex
   # Minimum Distance of explored vertex with neighboring vertex of partial tree
   # formed in graph
   __lowercase								=						[]  # Heap of Distance of vertices from their neighboring vertex
   __lowercase								=						[]

   for vertex in range(len(UpperCamelCase_)):
      distance_tv.append(sys.maxsize)
      positions.append(UpperCamelCase_)
      heap.node_position.append(UpperCamelCase_)

   __lowercase								=						[]
   __lowercase								=						1
   __lowercase								=						sys.maxsize
   for neighbor, distance in adjacency_list[0]:
      __lowercase								=						0
      __lowercase								=						distance
   heap.heapify(UpperCamelCase_,			UpperCamelCase_)

   for _ in range(1,			len(UpperCamelCase_)):
      __lowercase								=						heap.delete_minimum(UpperCamelCase_,			UpperCamelCase_)
      if visited[vertex] == 0:
         tree_edges.append((nbr_tv[vertex], vertex))
         __lowercase								=						1
         for neighbor, distance in adjacency_list[vertex]:
            if (
                visited[neighbor] == 0
                and distance < distance_tv[heap.get_position(UpperCamelCase_)]
            ):
               __lowercase								=						distance
               heap.bottom_to_top(
                   UpperCamelCase_,			heap.get_position(UpperCamelCase_),			UpperCamelCase_,			UpperCamelCase_)
               __lowercase								=						vertex
   return tree_edges


if __name__ == "__main__":  # pragma: no cover
   # < --------- Prims Algorithm --------- >
   _a       =							int(input('Enter number of edges: ').strip())
   _a       =							defaultdict(list)
   for _ in range(edges_number):
      _a       =							[int(x) for x in input().strip().split()]
      adjacency_list[edge[0]].append([edge[1], edge[2]])
      adjacency_list[edge[1]].append([edge[0], edge[2]])
   print(prisms_algorithm(adjacency_list))











'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
