code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from transformers import ChineseCLIPConfig, ChineseCLIPModel
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					__a    : Optional[int]      					=						pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3		,   dim=0     )
  					__a    : List[str]      					=						pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3		,   dim=0     )
  
  					__a    : Union[str, Any]      					=						pt_weights[F"""{prefix}.out_proj.weight"""]
  					__a    : str      					=						pt_weights[F"""{prefix}.out_proj.bias"""]
  
  					__a    : Dict      					=						q_proj
  					__a    : Optional[int]      					=						q_proj_bias
  
  					__a    : List[Any]      					=						k_proj
  					__a    : Union[str, Any]      					=						k_proj_bias
  
  					__a    : Union[str, Any]      					=						v_proj
  					__a    : Optional[Any]      					=						v_proj_bias
  
  					__a    : Optional[int]      					=						out_proj_weights
  					__a    : Tuple      					=						out_proj_bias
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Any     ):
  					copy_linear(hf_mlp.fca		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.c_fc"""     )
  					copy_linear(hf_mlp.fca		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.c_proj"""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Any     ):
  					__a    : Tuple      					=						pt_weights[F"""{prefix}.weight"""].data
  					__a    : int      					=						pt_weights[F"""{prefix}.bias"""].data
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					# copy layer norms
  					copy_linear(hf_layer.layer_norma		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.ln_1"""     )
  					copy_linear(hf_layer.layer_norma		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.ln_2"""     )
  
  					# copy MLP
  					copy_mlp(hf_layer.mlp		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.mlp"""     )
  
  					# copy attn
  					copy_attn_layer(hf_layer.self_attn		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.attn"""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					for layer_id, hf_layer in enumerate(_SCREAMING_SNAKE_CASE     ):
  										copy_layer(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.{layer_id}"""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]     ):
  					# copy projection
  					__a    : Optional[Any]      					=						pt_weights['text_projection'].data.T
  
  					# copy text encoder
  					for name, param in hf_model.text_model.named_parameters():
  										__a    : str      					=						pt_weights[F"""bert.{name}"""].data
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					# copy projection
  					__a    : Any      					=						pt_weights['visual.proj'].data.T
  
  					# copy layer norms
  					copy_linear(hf_model.vision_model.pre_layrnorm		,   _SCREAMING_SNAKE_CASE		,   'visual.ln_pre'     )
  					copy_linear(hf_model.vision_model.post_layernorm		,   _SCREAMING_SNAKE_CASE		,   'visual.ln_post'     )
  
  					# copy embeddings
  					__a    : int      					=						pt_weights['visual.conv1.weight'].data
  					__a    : Dict      					=						pt_weights['visual.class_embedding'].data
  					__a    : List[Any]      					=						pt_weights['visual.positional_embedding'].data
  
  					# copy encoder
  					copy_layers(hf_model.vision_model.encoder.layers		,   _SCREAMING_SNAKE_CASE		,   'visual.transformer.resblocks'     )
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  					__a    : int      					=						ChineseCLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  
  					__a    : int      					=						ChineseCLIPModel(_SCREAMING_SNAKE_CASE     ).eval()
  
  					__a    : Any      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )['state_dict']
  					__a    : Union[str, Any]      					=						{(name[7:] if name.startswith('module.'     ) else name): value for name, value in pt_weights.items()}
  
  					copy_text_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					__a    : Any      					=						pt_weights['logit_scale'].data
  
  					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  		__lowercase			:		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.'
  		)
  		__lowercase			:		Optional[int] =			parser.parse_args()
  
  		convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  		print('The conversion is finished!')
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  