

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Dict    ,       snake_case_					:     str  )							->		Union[str, Any]:
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     pt_weights[F"{prefix}.in_proj_weight"].chunk(3    ,       dim=0  )
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     pt_weights[F"{prefix}.in_proj_bias"].chunk(3    ,       dim=0  )

  __UpperCAmelCase	:  Any		=     pt_weights[F"{prefix}.out_proj.weight"]
  __UpperCAmelCase	:  int		=     pt_weights[F"{prefix}.out_proj.bias"]

  __UpperCAmelCase	:  Dict		=     q_proj
  __UpperCAmelCase	:  Optional[Any]		=     q_proj_bias

  __UpperCAmelCase	:  List[Any]		=     k_proj
  __UpperCAmelCase	:  str		=     k_proj_bias

  __UpperCAmelCase	:  Dict		=     v_proj
  __UpperCAmelCase	:  Any		=     v_proj_bias

  __UpperCAmelCase	:  Tuple		=     out_proj_weights
  __UpperCAmelCase	:  Optional[int]		=     out_proj_bias





def  _snake_case		(snake_case_					:     Tuple    ,       snake_case_					:     List[str]    ,       snake_case_					:     Any  )							->		str:
  copy_linear(hf_mlp.fca    ,       snake_case_    ,       F"{prefix}.c_fc"  )
  copy_linear(hf_mlp.fca    ,       snake_case_    ,       F"{prefix}.c_proj"  )





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Union[str, Any]    ,       snake_case_					:     int  )							->		Tuple:
  __UpperCAmelCase	:  Optional[Any]		=     pt_weights[F"{prefix}.weight"].data
  __UpperCAmelCase	:  Tuple		=     pt_weights[F"{prefix}.bias"].data





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     List[str]  )							->		Any:
  # copy layer norms
  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  _snake_case		(snake_case_					:     Tuple    ,       snake_case_					:     Dict    ,       snake_case_					:     Tuple  )							->		Optional[Any]:
  for layer_id, hf_layer in enumerate(snake_case_  ):
    copy_layer(snake_case_    ,       snake_case_    ,       F"{prefix}.{layer_id}"  )





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Dict  )							->		str:
  # copy projection
  __UpperCAmelCase	:  str		=     pt_weights['text_projection'].data.T

  # copy text encoder
  for name, param in hf_model.text_model.named_parameters():
    __UpperCAmelCase	:  Dict		=     pt_weights[F"bert.{name}"].data





def  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     int  )							->		int:
  # copy projection
  __UpperCAmelCase	:  Union[str, Any]		=     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
  __UpperCAmelCase	:  Dict		=     pt_weights['visual.conv1.weight'].data
  __UpperCAmelCase	:  int		=     pt_weights['visual.class_embedding'].data
  __UpperCAmelCase	:  Optional[int]		=     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  _snake_case		(snake_case_					:     Dict    ,       snake_case_					:     Union[str, Any]    ,       snake_case_					:     List[str]=None  )							->		Optional[int]:
  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  __UpperCAmelCase	:  str		=     ChineseCLIPConfig.from_pretrained(snake_case_  )

  __UpperCAmelCase	:  List[str]		=     ChineseCLIPModel(snake_case_  ).eval()

  __UpperCAmelCase	:  Optional[int]		=     torch.load(snake_case_    ,       map_location='cpu'  )['state_dict']
  __UpperCAmelCase	:  str		=     {(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_  )
  __UpperCAmelCase	:  int		=     pt_weights['logit_scale'].data

  hf_model.save_pretrained(snake_case_  )


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			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.'''
      )
      _SCREAMING_SNAKE_CASE							 =			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))





