




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    pt_weights[F"{prefix}.in_proj_weight"].chunk(3       ,  dim=0   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    pt_weights[F"{prefix}.in_proj_bias"].chunk(3       ,  dim=0   )

    __UpperCamelCase			=    pt_weights[F"{prefix}.out_proj.weight"]
    __UpperCamelCase			=    pt_weights[F"{prefix}.out_proj.bias"]

    __UpperCamelCase			=    q_proj
    __UpperCamelCase			=    q_proj_bias

    __UpperCamelCase			=    k_proj
    __UpperCamelCase			=    k_proj_bias

    __UpperCamelCase			=    v_proj
    __UpperCamelCase			=    v_proj_bias

    __UpperCamelCase			=    out_proj_weights
    __UpperCamelCase			=    out_proj_bias




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    copy_linear(hf_mlp.fca       ,  lowercase_       ,  F"{prefix}.c_fc"   )
    copy_linear(hf_mlp.fca       ,  lowercase_       ,  F"{prefix}.c_proj"   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    pt_weights[F"{prefix}.weight"].data
    __UpperCamelCase			=    pt_weights[F"{prefix}.bias"].data




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    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 (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[Any]:

    """simple docstring"""


    for layer_id, hf_layer in enumerate(lowercase_   ):
        copy_layer(lowercase_       ,  lowercase_       ,  F"{prefix}.{layer_id}"   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    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
    __UpperCamelCase			=    pt_weights['''visual.conv1.weight'''].data
    __UpperCamelCase			=    pt_weights['''visual.class_embedding'''].data
    __UpperCamelCase			=    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 (	lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						List[Any]:

    """simple docstring"""


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

    __UpperCamelCase			=    ChineseCLIPModel(lowercase_   ).eval()

    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''state_dict''']
    __UpperCamelCase			=    {(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_   )
    __UpperCamelCase			=    pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  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."
 )
 a_ 		=  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))





