



'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:list[tuple[int, int, int]]			)		->						list[tuple[int, int, int]]:
				snake_case__      :   int      =    sorted(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: edge[2]			)

				snake_case__      :   List[Any]      =    list(range(UpperCamelCase__			)			)

				def find_parent(UpperCamelCase__				:Tuple			):
								if i != parent[i]:
												snake_case__      :   Tuple      =    find_parent(parent[i]			)
								return parent[i]

				snake_case__      :   Dict      =    0
				snake_case__      :   str      =    []

				for edge in edges:
								snake_case__      :   int      =    find_parent(edge[0]			)
								snake_case__      :   Union[str, Any]      =    find_parent(edge[1]			)
								if parent_a != parent_b:
												minimum_spanning_tree_cost += edge[2]
												minimum_spanning_tree.append(UpperCamelCase__			)
												snake_case__      :   List[Any]      =    parent_b

				return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
				_lowercase		,	_lowercase       :						Optional[Any]						=list(map(int, input().strip().split()))
				_lowercase       :						Any						=[]

				for _ in range(num_edges):
								_lowercase		,	_lowercase		,	_lowercase       :						int						=(int(x) for x in input().strip().split())
								edges.append((nodea, nodea, cost))

				kruskal(num_nodes, edges)








import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_ ,	lowercase_ ,	lowercase_			 = pt_attn_layer.in_proj_weight.chunk(3  ,			dim=0  )
	lowercase_ ,	lowercase_ ,	lowercase_			 = pt_attn_layer.in_proj_bias.chunk(3  ,			dim=0  )

	lowercase_			 = pt_attn_layer.out_proj.weight
	lowercase_			 = pt_attn_layer.out_proj.bias

	lowercase_			 = q_proj
	lowercase_			 = q_proj_bias

	lowercase_			 = k_proj
	lowercase_			 = k_proj_bias

	lowercase_			 = v_proj
	lowercase_			 = v_proj_bias

	lowercase_			 = out_proj_weights
	lowercase_			 = out_proj_bias




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		List[str]  ):







	'''simple docstring'''



	copy_linear(hf_mlp.fca  ,			pt_mlp.c_fc  )
	copy_linear(hf_mlp.fca  ,			pt_mlp.c_proj  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = pt_linear.weight
	lowercase_			 = pt_linear.bias




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	copy_linear(hf_layer.layer_norma  ,			pt_layer.ln_a  )
	copy_linear(hf_layer.layer_norma  ,			pt_layer.ln_a  )

	# copy MLP
	copy_mlp(hf_layer.mlp  ,			pt_layer.mlp  )

	# copy attn
	copy_attn_layer(hf_layer.self_attn  ,			pt_layer.attn  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	for hf_layer, pt_layer in zip(__lowerCamelCase  ,			__lowerCamelCase  ):
		copy_layer(__lowerCamelCase  ,			__lowerCamelCase  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		str  ):







	'''simple docstring'''



	lowercase_			 = pt_model.token_embedding.weight
	lowercase_			 = pt_model.positional_embedding

	# copy layer norm
	copy_linear(hf_encoder.final_layer_norm  ,			pt_model.ln_final  )

	# copy hidden layers
	copy_layers(hf_encoder.encoder.layers  ,			pt_model.transformer.resblocks  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = pt_model.text_projection.data.T

	# copy text encoder
	copy_encoder(hf_model.text_model  ,			__lowerCamelCase  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[Any]  ):







	'''simple docstring'''



	lowercase_			 = pt_model.visual.proj.data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layrnorm  ,			pt_model.visual.ln_pre  )
	copy_linear(hf_model.vision_model.post_layernorm  ,			pt_model.visual.ln_post  )

	# copy embeds
	lowercase_			 = pt_model.visual.conva.weight.data
	lowercase_			 = pt_model.visual.class_embedding
	lowercase_			 = pt_model.visual.positional_embedding.data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers  ,			pt_model.visual.transformer.resblocks  )




@torch.no_grad()
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Dict=None  ):







	'''simple docstring'''



	if config_path is not None:
		lowercase_			 = CLIPConfig.from_pretrained(__lowerCamelCase  )
	else:
		lowercase_			 = CLIPConfig(projection_dim=512  ,			text_config={}  ,			vision_config={}  )

	lowercase_			 = CLIPModel(__lowerCamelCase  ).eval()

	lowercase_ ,	lowercase_			 = load(__lowerCamelCase  ,			device="cpu"  ,			jit=__lowerCamelCase  )
	lowercase_			 = pt_model.eval()

	copy_text_model_and_projection(__lowerCamelCase  ,			__lowerCamelCase  )
	copy_vison_model_and_projection(__lowerCamelCase  ,			__lowerCamelCase  )
	lowercase_			 = pt_model.logit_scale

	lowercase_			 = torch.arange(0  ,			77  ).unsqueeze(0  )
	lowercase_			 = torch.randn(1  ,			3  ,			224  ,			224  )

	lowercase_			 = hf_model(input_ids=__lowerCamelCase  ,			pixel_values=__lowerCamelCase  ,			return_dict=__lowerCamelCase  )
	lowercase_			 = hf_outputs.logits_per_image
	lowercase_			 = hf_outputs.logits_per_text
	lowercase_ ,	lowercase_			 = pt_model(__lowerCamelCase  ,			__lowerCamelCase  )

	assert torch.allclose(__lowerCamelCase  ,			__lowerCamelCase  ,			atol=1E-3  )
	assert torch.allclose(__lowerCamelCase  ,			__lowerCamelCase  ,			atol=1E-3  )

	hf_model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							SCREAMING_SNAKE_CASE__       			=				parser.parse_args()

							convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

