




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Union[str, Any] ,  __A       :				str						)				->				Dict:
   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3 ,  dim=0						)
   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3 ,  dim=0						)

   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.out_proj.weight"""]
   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.out_proj.bias"""]

   _SCREAMING_SNAKE_CASE =   q_proj
   _SCREAMING_SNAKE_CASE =   q_proj_bias

   _SCREAMING_SNAKE_CASE =   k_proj
   _SCREAMING_SNAKE_CASE =   k_proj_bias

   _SCREAMING_SNAKE_CASE =   v_proj
   _SCREAMING_SNAKE_CASE =   v_proj_bias

   _SCREAMING_SNAKE_CASE =   out_proj_weights
   _SCREAMING_SNAKE_CASE =   out_proj_bias
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Any ,  __A       :				List[str] ,  __A       :				Any						)				->				str:
   copy_linear(hf_mlp.fca ,  lowercase__ ,  f"""{prefix}.c_fc"""						)
   copy_linear(hf_mlp.fca ,  lowercase__ ,  f"""{prefix}.c_proj"""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict ,  __A       :				List[Any]						)				->				Union[str, Any]:
   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.weight"""].data
   _SCREAMING_SNAKE_CASE =   pt_weights[f"""{prefix}.bias"""].data
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict ,  __A       :				List[Any] ,  __A       :				List[Any]						)				->				Any:
   # copy layer norms
   copy_linear(hf_layer.layer_norma ,  lowercase__ ,  f"""{prefix}.ln_1"""						)
   copy_linear(hf_layer.layer_norma ,  lowercase__ ,  f"""{prefix}.ln_2"""						)

   # copy MLP
   copy_mlp(hf_layer.mlp ,  lowercase__ ,  f"""{prefix}.mlp"""						)

   # copy attn
   copy_attn_layer(hf_layer.self_attn ,  lowercase__ ,  f"""{prefix}.attn"""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Optional[int] ,  __A       :				Optional[int]						)				->				str:
   for layer_id, hf_layer in enumerate(lowercase__						):
      copy_layer(lowercase__ ,  lowercase__ ,  f"""{prefix}.{layer_id}"""						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]						)				->				List[str]:
   # copy projection
   _SCREAMING_SNAKE_CASE =   pt_weights["text_projection"].data.T

   # copy text encoder
   for name, param in hf_model.text_model.named_parameters():
      _SCREAMING_SNAKE_CASE =   pt_weights[f"""bert.{name}"""].data
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict ,  __A       :				Optional[Any]						)				->				Tuple:
   # copy projection
   _SCREAMING_SNAKE_CASE =   pt_weights["visual.proj"].data.T

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

   # copy embeddings
   _SCREAMING_SNAKE_CASE =   pt_weights["visual.conv1.weight"].data
   _SCREAMING_SNAKE_CASE =   pt_weights["visual.class_embedding"].data
   _SCREAMING_SNAKE_CASE =   pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers ,  lowercase__ ,  "visual.transformer.resblocks"						)
@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				Any ,  __A       :				Tuple=None						)				->				Dict:
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   _SCREAMING_SNAKE_CASE =   ChineseCLIPConfig.from_pretrained(lowercase__						)

   _SCREAMING_SNAKE_CASE =   ChineseCLIPModel(lowercase__						).eval()

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

   copy_text_model_and_projection(lowercase__ ,  lowercase__						)
   copy_vision_model_and_projection(lowercase__ ,  lowercase__						)
   _SCREAMING_SNAKE_CASE =   pt_weights["logit_scale"].data

   hf_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
 lowerCamelCase_	 =		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.'
 )
 lowerCamelCase_	 =		parser.parse_args()

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







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   len(node.outbound						)

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

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

   print("======= Nodes ======="						)
   for node in nodes:
      print(__A						)

   page_rank(__A						)


if __name__ == "__main__":
 main()

