
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			int				)				->						List[str]:
   _snake_case						=    pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3 ,      dim=0				)
   _snake_case						=    pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3 ,      dim=0				)

   _snake_case						=    pt_weights[f'''{prefix}.out_proj.weight''']
   _snake_case						=    pt_weights[f'''{prefix}.out_proj.bias''']

   _snake_case						=    q_proj
   _snake_case						=    q_proj_bias

   _snake_case						=    k_proj
   _snake_case						=    k_proj_bias

   _snake_case						=    v_proj
   _snake_case						=    v_proj_bias

   _snake_case						=    out_proj_weights
   _snake_case						=    out_proj_bias






def  a_	( __lowercase     :			Dict ,      __lowercase     :			Optional[Any] ,      __lowercase     :			str				)				->						str:
   copy_linear(hf_mlp.fca ,      __lowercase ,      f'''{prefix}.c_fc'''				)
   copy_linear(hf_mlp.fca ,      __lowercase ,      f'''{prefix}.c_proj'''				)






def  a_	( __lowercase     :			str ,      __lowercase     :			Dict ,      __lowercase     :			Dict				)				->						List[Any]:
   _snake_case						=    pt_weights[f'''{prefix}.weight'''].data
   _snake_case						=    pt_weights[f'''{prefix}.bias'''].data






def  a_	( __lowercase     :			Any ,      __lowercase     :			List[Any] ,      __lowercase     :			Optional[Any]				)				->						Optional[Any]:
   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  a_	( __lowercase     :			int ,      __lowercase     :			str ,      __lowercase     :			Optional[int]				)				->						Dict:



   for layer_id, hf_layer in enumerate(__lowercase				):
      copy_layer(__lowercase ,      __lowercase ,      f'''{prefix}.{layer_id}'''				)






def  a_	( __lowercase     :			List[str] ,      __lowercase     :			Union[str, Any]				)				->						List[str]:
   _snake_case						=    pt_weights["text_projection"].data.T

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






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

   _snake_case						=    ChineseCLIPModel(__lowercase				).eval()

   _snake_case						=    torch.load(__lowercase ,      map_location='cpu'				)["state_dict"]
   _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				)
   _snake_case						=    pt_weights["logit_scale"].data

   hf_model.save_pretrained(__lowercase				)


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 :       Optional[Any]     					=    parser.parse_args()

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

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)