





from __future__ import annotations

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : list[int]				,				_lowerCAmelCase : list[int]				,				_lowerCAmelCase : int	) ->						int:

   # Base Case
   if counter == 0 or capacity == 0:
      return 0

   # If weight of the nth item is more than Knapsack of capacity,
   #   then this item cannot be included in the optimal solution,
   # else return the maximum of two cases:
   #   (1) nth item included
   #   (2) not included
   if weights[counter - 1] > capacity:
      return knapsack(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				counter - 1	)
   else:
      A_					:				Tuple          =			capacity - weights[counter - 1]
      A_					:				Any          =			values[counter - 1] + knapsack(
          _lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				counter - 1	)
      A_					:				Optional[int]          =			knapsack(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				counter - 1	)
      return max(_lowerCAmelCase				,				_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : Optional[Any]	) ->						List[str]:
   A_      ,						A_      ,						A_					:				Dict          =			pt_weights[f"{prefix}.in_proj_weight"].chunk(3				,				dim=0	)
   A_      ,						A_      ,						A_					:				Union[str, Any]          =			pt_weights[f"{prefix}.in_proj_bias"].chunk(3				,				dim=0	)

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

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

   A_					:				Tuple          =			k_proj
   A_					:				str          =			k_proj_bias

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

   A_					:				int          =			out_proj_weights
   A_					:				List[Any]          =			out_proj_bias

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Dict				,				_lowerCAmelCase : int	) ->						Dict:
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_fc"	)
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_proj"	)

def      __snake_case						(					_lowerCAmelCase : Tuple				,				_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : Optional[int]	) ->						List[str]:
   A_					:				List[str]          =			pt_weights[f"{prefix}.weight"].data
   A_					:				int          =			pt_weights[f"{prefix}.bias"].data

def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[Any]	) ->						str:
   # copy layer norms
   copy_linear(hf_layer.layer_norma				,				_lowerCAmelCase				,				f"{prefix}.ln_1"	)
   copy_linear(hf_layer.layer_norma				,				_lowerCAmelCase				,				f"{prefix}.ln_2"	)

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

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

def      __snake_case						(					_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[Any]	) ->						List[Any]:
   for layer_id, hf_layer in enumerate(_lowerCAmelCase	):
      copy_layer(_lowerCAmelCase				,				_lowerCAmelCase				,				f"{prefix}.{layer_id}"	)

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Any	) ->						Dict:
   # 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_					:				Any          =			pt_weights[f"bert.{name}"].data

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Any	) ->						List[str]:
   # copy projection
   A_					:				Tuple          =			pt_weights["visual.proj"].data.T

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

   # copy embeddings
   A_					:				List[Any]          =			pt_weights["visual.conv1.weight"].data
   A_					:				Union[str, Any]          =			pt_weights["visual.class_embedding"].data
   A_					:				Tuple          =			pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers				,				_lowerCAmelCase				,				"visual.transformer.resblocks"	)

@torch.no_grad()
def      __snake_case						(					_lowerCAmelCase : List[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[int]=None	) ->						str:
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   A_					:				Optional[Any]          =			ChineseCLIPConfig.from_pretrained(_lowerCAmelCase	)

   A_					:				Tuple          =			ChineseCLIPModel(_lowerCAmelCase	).eval()

   A_					:				Any          =			torch.load(_lowerCAmelCase				,				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(_lowerCAmelCase				,				_lowerCAmelCase	)
   copy_vision_model_and_projection(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				List[Any]          =			pt_weights["logit_scale"].data

   hf_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Optional[Any]						=	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							:      Tuple						=	parser.parse_args()

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