





"""simple docstring"""




from __future__ import annotations

from queue import Queue




class 					_a		:






				def __init__(					self						:       Union[str, Any]				)      -> None:




										'''simple docstring'''



										_UpperCamelCase     :   dict[int, list[int]]      =       {}






				def 							snake_case      (					self						:       Optional[Any]				)      -> None:




										'''simple docstring'''



										for i in self.vertices:
																print(lowerCAmelCase__,					''' : ''',					''' -> '''.join([str(lowerCAmelCase__				) for j in self.vertices[i]]				)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       int				)      -> None:




										'''simple docstring'''



										if from_vertex in self.vertices:
																self.vertices[from_vertex].append(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   List[Any]      =       [to_vertex]






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       int				)      -> set[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       set()

										# create a first in first out queue to store all the vertices for BFS
										_UpperCamelCase     :   Queue      =       Queue()

										# mark the source node as visited and enqueue it
										visited.add(lowerCAmelCase__				)
										queue.put(lowerCAmelCase__				)

										while not queue.empty():
																_UpperCamelCase     :   Optional[int]      =       queue.get()

																# loop through all adjacent vertex and enqueue it if not yet visited
																for adjacent_vertex in self.vertices[vertex]:
																						if adjacent_vertex not in visited:
																												queue.put(lowerCAmelCase__				)
																												visited.add(lowerCAmelCase__				)
										return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						UpperCamelCase_	 =Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]










"""simple docstring"""




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_attn_layer.in_proj_weight.chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       pt_attn_layer.in_proj_bias.chunk(3			,		dim=0  )

						_UpperCamelCase     :   int      =       pt_attn_layer.out_proj.weight
						_UpperCamelCase     :   List[Any]      =       pt_attn_layer.out_proj.bias

						_UpperCamelCase     :   Any      =       q_proj
						_UpperCamelCase     :   str      =       q_proj_bias

						_UpperCamelCase     :   str      =       k_proj
						_UpperCamelCase     :   List[str]      =       k_proj_bias

						_UpperCamelCase     :   Dict      =       v_proj
						_UpperCamelCase     :   Dict      =       v_proj_bias

						_UpperCamelCase     :   Dict      =       out_proj_weights
						_UpperCamelCase     :   Optional[Any]      =       out_proj_bias





def 			a_							(				_lowercase			,		_lowercase  ):
						copy_linear(hf_mlp.fca			,		pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca			,		pt_mlp.c_proj  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       pt_linear.weight
						_UpperCamelCase     :   List[str]      =       pt_linear.bias





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy layer norms
						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 			a_							(				_lowercase			,		_lowercase  ):
						for hf_layer, pt_layer in zip(_lowercase			,		_lowercase  ):
												copy_layer(_lowercase			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy  embeds
						_UpperCamelCase     :   Tuple      =       pt_model.token_embedding.weight
						_UpperCamelCase     :   List[Any]      =       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 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   str      =       pt_model.text_projection.data.T

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





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   Tuple      =       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
						_UpperCamelCase     :   str      =       pt_model.visual.conva.weight.data
						_UpperCamelCase     :   Dict      =       pt_model.visual.class_embedding
						_UpperCamelCase     :   Union[str, Any]      =       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 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None  ):
						if config_path is not None:
												_UpperCamelCase     :   List[Any]      =       CLIPConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   Optional[int]      =       CLIPConfig(projection_dim=512			,		text_config={}			,		vision_config={}  )

						_UpperCamelCase     :   str      =       CLIPModel(_lowercase  ).eval()

						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       load(_lowercase			,		device='''cpu'''			,		jit=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.eval()

						copy_text_model_and_projection(_lowercase			,		_lowercase  )
						copy_vison_model_and_projection(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   int      =       pt_model.logit_scale

						_UpperCamelCase     :   List[Any]      =       torch.arange(0			,		77  ).unsqueeze(0  )
						_UpperCamelCase     :   Optional[int]      =       torch.randn(1			,		3			,		224			,		224  )

						_UpperCamelCase     :   Tuple      =       hf_model(input_ids=_lowercase			,		pixel_values=_lowercase			,		return_dict=_lowercase  )
						_UpperCamelCase     :   Dict      =       hf_outputs.logits_per_image
						_UpperCamelCase     :   Any      =       hf_outputs.logits_per_text
						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_model(_lowercase			,		_lowercase  )

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

						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =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""")
						UpperCamelCase_	 =parser.parse_args()

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



