



'''simple docstring'''






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 			snake_case_					( _lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						str  )				->       Any:
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	List[Any]										=		pt_attn_layer.in_proj_weight.chunk(3       ,				dim=0  )
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		pt_attn_layer.in_proj_bias.chunk(3       ,				dim=0  )

							UpperCAmelCase			:	Tuple										=		pt_attn_layer.out_proj.weight
							UpperCAmelCase			:	Optional[Any]										=		pt_attn_layer.out_proj.bias

							UpperCAmelCase			:	Union[str, Any]										=		q_proj
							UpperCAmelCase			:	List[Any]										=		q_proj_bias

							UpperCAmelCase			:	int										=		k_proj
							UpperCAmelCase			:	List[str]										=		k_proj_bias

							UpperCAmelCase			:	Dict										=		v_proj
							UpperCAmelCase			:	List[str]										=		v_proj_bias

							UpperCAmelCase			:	Optional[Any]										=		out_proj_weights
							UpperCAmelCase			:	Optional[int]										=		out_proj_bias


def 			snake_case_					( _lowerCAmelCase  :						Union[str, Any]       ,				_lowerCAmelCase  :						Optional[int]  )				->       str:
							copy_linear(hf_mlp.fca       ,				pt_mlp.c_fc  )
							copy_linear(hf_mlp.fca       ,				pt_mlp.c_proj  )


def 			snake_case_					( _lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						Dict  )				->       Union[str, Any]:
							UpperCAmelCase			:	int										=		pt_linear.weight
							UpperCAmelCase			:	List[str]										=		pt_linear.bias


def 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						Optional[Any]  )				->       Union[str, Any]:
							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 			snake_case_					( _lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						Tuple  )				->       int:

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


def 			snake_case_					( _lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						str  )				->       List[Any]:
							UpperCAmelCase			:	str										=		pt_model.token_embedding.weight
							UpperCAmelCase			:	Union[str, 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 			snake_case_					( _lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						int  )				->       List[str]:
							UpperCAmelCase			:	Any										=		pt_model.text_projection.data.T

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


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						str  )				->       str:
							UpperCAmelCase			:	int										=		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			:	List[str]										=		pt_model.visual.conva.weight.data
							UpperCAmelCase			:	Any										=		pt_model.visual.class_embedding
							UpperCAmelCase			:	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 			snake_case_					( _lowerCAmelCase  :						Union[str, Any]       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Tuple=None  )				->       int:

							if config_path is not None:
														UpperCAmelCase			:	int										=		CLIPConfig.from_pretrained(__lowercase  )
							else:
														UpperCAmelCase			:	Optional[Any]										=		CLIPConfig(projection_dim=512       ,				text_config={}       ,				vision_config={}  )

							UpperCAmelCase			:	Optional[int]										=		CLIPModel(__lowercase  ).eval()

							UpperCAmelCase							,		UpperCAmelCase			:	Dict										=		load(__lowercase       ,				device='''cpu'''       ,				jit=__lowercase  )
							UpperCAmelCase			:	str										=		pt_model.eval()

							copy_text_model_and_projection(__lowercase       ,				__lowercase  )
							copy_vison_model_and_projection(__lowercase       ,				__lowercase  )
							UpperCAmelCase			:	int										=		pt_model.logit_scale

							UpperCAmelCase			:	Dict										=		torch.arange(0       ,				77  ).unsqueeze(0  )
							UpperCAmelCase			:	int										=		torch.randn(1       ,				3       ,				224       ,				224  )

							UpperCAmelCase			:	str										=		hf_model(input_ids=__lowercase       ,				pixel_values=__lowercase       ,				return_dict=__lowercase  )
							UpperCAmelCase			:	Optional[Any]										=		hf_outputs.logits_per_image
							UpperCAmelCase			:	Dict										=		hf_outputs.logits_per_text
							UpperCAmelCase							,		UpperCAmelCase			:	List[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__:       List[str]							     =      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__:       Optional[Any]							     =      parser.parse_args()

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










'''simple docstring'''






UpperCamelCase__:       Any							     =      [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[str]		,      __snake_case      :					int		)      ->		int:
								UpperCAmelCase			:	Any										=		name
								UpperCAmelCase			:	Optional[Any]										=		[]
								UpperCAmelCase			:	Any										=		[]







	def 					A     (						self      :					Dict		,      __snake_case      :					str		)      ->		Tuple:
								self.inbound.append(__snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					str		)      ->		Optional[int]:
								self.outbound.append(__snake_case		)







	def __repr__(						self      :					Optional[int]		)      ->		Optional[int]:
								return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Dict=3       ,				_lowerCAmelCase  :						Tuple=0.8_5  )				->       Optional[Any]:
							UpperCAmelCase			:	Tuple										=		{}
							for node in nodes:
														UpperCAmelCase			:	List[str]										=		1

							UpperCAmelCase			:	List[Any]										=		{}
							for node in nodes:
														UpperCAmelCase			:	str										=		len(node.outbound  )

							for i in range(_lowerCAmelCase  ):
														print(f"""======= Iteration {i + 1} ======="""  )
														for _, node in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase			:	str										=		(1 - d) + d * sum(
																					    ranks[ib] / outbounds[ib] for ib in node.inbound  )
														print(_lowerCAmelCase  )


def 			snake_case_					( )				->       List[str]:
							UpperCAmelCase			:	str										=		list(input('''Enter Names of the Nodes: '''  ).split()  )

							UpperCAmelCase			:	Dict										=		[Node(_lowerCAmelCase  ) for name in names]

							for ri, row in enumerate(_lowerCAmelCase  ):
														for ci, col in enumerate(_lowerCAmelCase  ):
																					if col == 1:
																												nodes[ci].add_inbound(names[ri]  )
																												nodes[ri].add_outbound(names[ci]  )

							print('''======= Nodes ======='''  )
							for node in nodes:
														print(_lowerCAmelCase  )

							page_rank(_lowerCAmelCase  )


if __name__ == "__main__":
						main()





