





"""simple docstring"""




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_		,			A_		,			A_    :			Union[str, Any]									=    pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3					,   dim=0   )
  A_		,			A_		,			A_    :			Any									=    pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3					,   dim=0   )

  A_    :			Any									=    pt_weights[f"""{prefix}.out_proj.weight"""]
  A_    :			Optional[int]									=    pt_weights[f"""{prefix}.out_proj.bias"""]

  A_    :			Any									=    q_proj
  A_    :			Optional[Any]									=    q_proj_bias

  A_    :			Union[str, Any]									=    k_proj
  A_    :			Optional[Any]									=    k_proj_bias

  A_    :			List[Any]									=    v_proj
  A_    :			Optional[int]									=    v_proj_bias

  A_    :			Dict									=    out_proj_weights
  A_    :			str									=    out_proj_bias





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  copy_linear(hf_mlp.fca					,   _UpperCAmelCase					,   f"""{prefix}.c_fc"""   )
  copy_linear(hf_mlp.fca					,   _UpperCAmelCase					,   f"""{prefix}.c_proj"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Union[str, Any]									=    pt_weights[f"""{prefix}.weight"""].data
  A_    :			str									=    pt_weights[f"""{prefix}.bias"""].data





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  copy_linear(hf_layer.layer_norma					,   _UpperCAmelCase					,   f"""{prefix}.ln_1"""   )
  copy_linear(hf_layer.layer_norma					,   _UpperCAmelCase					,   f"""{prefix}.ln_2"""   )

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

  # copy attn
  copy_attn_layer(hf_layer.self_attn					,   _UpperCAmelCase					,   f"""{prefix}.attn"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  for layer_id, hf_layer in enumerate(_UpperCAmelCase   ):
    copy_layer(_UpperCAmelCase					,   _UpperCAmelCase					,   f"""{prefix}.{layer_id}"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Any									=    pt_weights['text_projection'].data.T

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





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Tuple									=    pt_weights['visual.proj'].data.T

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

  # copy embeddings
  A_    :			Tuple									=    pt_weights['visual.conv1.weight'].data
  A_    :			str									=    pt_weights['visual.class_embedding'].data
  A_    :			int									=    pt_weights['visual.positional_embedding'].data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers					,   _UpperCAmelCase					,   'visual.transformer.resblocks'   )





@torch.no_grad()
def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None   ):






  """simple docstring"""





  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  A_    :			str									=    ChineseCLIPConfig.from_pretrained(_UpperCAmelCase   )

  A_    :			List[Any]									=    ChineseCLIPModel(_UpperCAmelCase   ).eval()

  A_    :			int									=    torch.load(_UpperCAmelCase					,   map_location='cpu'   )['state_dict']
  A_    :			Optional[Any]									=    {(name[7:] if name.startswith('module.'   ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(_UpperCAmelCase					,   _UpperCAmelCase   )
  copy_vision_model_and_projection(_UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Optional[Any]									=    pt_weights['logit_scale'].data

  hf_model.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
   lowerCamelCase_	:		Optional[int]         =   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_	:		Dict         =   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"""




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , *snake_case_   , **snake_case_    ):

        """simple docstring"""

        warnings.warn(
            'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use DetrImageProcessor instead.'   , snake_case_   , )
        super().__init__(*snake_case_   , **snake_case_    )