



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def        a( A			:       Any					,	A			:       Dict					,	A			:       str			)						->			Union[str, Any]:
					"""simple docstring"""





					a   	=  pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3					,	dim=0			)
					a   	=  pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3					,	dim=0			)

					a   	=  pt_weights[f'''{prefix}.out_proj.weight''']
					a   	=  pt_weights[f'''{prefix}.out_proj.bias''']

					a   	=  q_proj
					a   	=  q_proj_bias

					a   	=  k_proj
					a   	=  k_proj_bias

					a   	=  v_proj
					a   	=  v_proj_bias

					a   	=  out_proj_weights
					a   	=  out_proj_bias






def        a( A			:       Tuple					,	A			:       List[str]					,	A			:       Any			)						->			str:
					"""simple docstring"""





					copy_linear(hf_mlp.fca					,	snake_case_					,	f'''{prefix}.c_fc'''			)
					copy_linear(hf_mlp.fca					,	snake_case_					,	f'''{prefix}.c_proj'''			)






def        a( A			:       Any					,	A			:       Union[str, Any]					,	A			:       int			)						->			Tuple:
					"""simple docstring"""





					a   	=  pt_weights[f'''{prefix}.weight'''].data
					a   	=  pt_weights[f'''{prefix}.bias'''].data






def        a( A			:       List[Any]					,	A			:       Dict					,	A			:       List[str]			)						->			Any:
					"""simple docstring"""





					copy_linear(hf_layer.layer_norma					,	snake_case_					,	f'''{prefix}.ln_1'''			)
					copy_linear(hf_layer.layer_norma					,	snake_case_					,	f'''{prefix}.ln_2'''			)

					# copy MLP
					copy_mlp(hf_layer.mlp					,	snake_case_					,	f'''{prefix}.mlp'''			)

					# copy attn
					copy_attn_layer(hf_layer.self_attn					,	snake_case_					,	f'''{prefix}.attn'''			)






def        a( A			:       Tuple					,	A			:       Dict					,	A			:       Tuple			)						->			Optional[Any]:
					"""simple docstring"""





					for layer_id, hf_layer in enumerate(snake_case_			):
										copy_layer(snake_case_					,	snake_case_					,	f'''{prefix}.{layer_id}'''			)






def        a( A			:       List[Any]					,	A			:       Dict			)						->			str:
					"""simple docstring"""





					a   	=  pt_weights['text_projection'].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										a   	=  pt_weights[f'''bert.{name}'''].data






def        a( A			:       Optional[Any]					,	A			:       int			)						->			int:
					"""simple docstring"""





					a   	=  pt_weights['visual.proj'].data.T

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

					# copy embeddings
					a   	=  pt_weights['visual.conv1.weight'].data
					a   	=  pt_weights['visual.class_embedding'].data
					a   	=  pt_weights['visual.positional_embedding'].data

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






@torch.no_grad()
def        a( A			:       Dict					,	A			:       Union[str, Any]					,	A			:       List[str]=None			)						->			Optional[int]:
					"""simple docstring"""





					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					a   	=  ChineseCLIPConfig.from_pretrained(snake_case_			)

					a   	=  ChineseCLIPModel(snake_case_			).eval()

					a   	=  torch.load(snake_case_					,	map_location="cpu"			)['state_dict']
					a   	=  {(name[7:] if name.startswith("module."			) else name): value for name, value in pt_weights.items()}

					copy_text_model_and_projection(snake_case_					,	snake_case_			)
					copy_vision_model_and_projection(snake_case_					,	snake_case_			)
					a   	=  pt_weights['logit_scale'].data

					hf_model.save_pretrained(snake_case_			)


if __name__ == "__main__":
				_lowercase:					Any      =						argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    help="Path to the output folder storing converted hf PyTorch model.",
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
				)
				parser.add_argument(
				    "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
				)
				_lowercase:					Union[str, Any]      =						parser.parse_args()

				convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
				print("The conversion is finished!")



def  _snake_case		(snake_case_					:     float  )							->		int:
  if num < 0:
    return -1
  return 1 if num else 0





def  _snake_case		()							->		None:
  assert signum(5  ) == 1
  assert signum(-5  ) == -1
  assert signum(0  ) == 0


if __name__ == "__main__":
      print(signum(1_2))
      print(signum(-1_2))
      print(signum(0))





