'''simple docstring'''
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def 			_UpperCamelCase			(  _a						:						List[str]					,  _a						:						str					,  _a						:						List[Any]      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3					,  dim=0      )
    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3					,  dim=0      )

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

    __UpperCamelCase						:						List[Any]		     =	q_proj
    __UpperCamelCase						:						Dict		     =	q_proj_bias

    __UpperCamelCase						:						List[Any]		     =	k_proj
    __UpperCamelCase						:						Tuple		     =	k_proj_bias

    __UpperCamelCase						:						int		     =	v_proj
    __UpperCamelCase						:						Any		     =	v_proj_bias

    __UpperCamelCase						:						Any		     =	out_proj_weights
    __UpperCamelCase						:						List[str]		     =	out_proj_bias







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Tuple					,  _a						:						List[str]      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						Any					,  _a						:						str					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Any		     =	pt_weights[f"""{prefix}.weight"""].data
    __UpperCamelCase						:						int		     =	pt_weights[f"""{prefix}.bias"""].data







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						Optional[int]					,  _a						:						Tuple      ):




    """simple docstring"""



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

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

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







def 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Tuple					,  _a						:						Dict      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						str      ):




    """simple docstring"""



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

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







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						List[Any]      ):




    """simple docstring"""



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

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

    # copy embeddings
    __UpperCamelCase						:						List[str]		     =	pt_weights['visual.conv1.weight'].data
    __UpperCamelCase						:						Any		     =	pt_weights['visual.class_embedding'].data
    __UpperCamelCase						:						List[Any]		     =	pt_weights['visual.positional_embedding'].data

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







@torch.no_grad()
def 			_UpperCamelCase			(  _a						:						str					,  _a						:						Union[str, Any]					,  _a						:						Any=None      ):




    """simple docstring"""



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

    __UpperCamelCase						:						str		     =	ChineseCLIPModel(_A      ).eval()

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

    copy_text_model_and_projection(_A					,  _A      )
    copy_vision_model_and_projection(_A					,  _A      )
    __UpperCamelCase						:						str		     =	pt_weights['logit_scale'].data

    hf_model.save_pretrained(_A      )


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!''')






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A	:   Optional[int]	=					{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:   Tuple	=					[
	    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MobileViTV2ForImageClassification',
	    'MobileViTV2ForSemanticSegmentation',
	    'MobileViTV2Model',
	    'MobileViTV2PreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mobilevitva import (
	    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MobileViTVaConfig,
	    MobileViTVaOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MobileViTVaForImageClassification,
		    MobileViTVaForSemanticSegmentation,
		    MobileViTVaModel,
		    MobileViTVaPreTrainedModel,
		)

else:
	import sys

	__A	:   List[str]	=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
