





from .number_theory.prime_numbers import next_prime


class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :int			,      snake_case       :int | None = None			,      snake_case       :float | None = None			,      ):





        '''simple docstring'''




        A_					:				List[str]          =			size_table
        A_					:				Optional[Any]          =			[None] * self.size_table
        A_					:				Union[str, Any]          =			0.75 if lim_charge is None else lim_charge
        A_					:				Optional[int]          =			1 if charge_factor is None else charge_factor
        A_					:				list          =			[]
        A_					:				dict          =			{}



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        return self._keys



     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




        return sum(1 for slot in self.values if slot is not None  ) / (
            self.size_table * self.charge_factor
        )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Any  ):





        '''simple docstring'''




        return key % self.size_table



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        print(f"step {step_ord}"  )
        print(list(range(len(self.values  )  )  )  )
        print(self.values  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :Dict  ):





        '''simple docstring'''




        A_					:				List[str]          =			1
        A_					:				Optional[int]          =			values
        for value in values:
           self.insert_data(snake_case  )
           self._step_by_step(snake_case  )
           i += 1



     def 						SCREAMING_SNAKE_CASE		(							self       :int			,      snake_case       :Dict			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        A_					:				List[Any]          =			data
        A_					:				List[str]          =			data



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :List[str]			,      snake_case       :List[str]=None  ):





        '''simple docstring'''




        A_					:				int          =			self.hash_function(key + 1  )

        while self.values[new_key] is not None and self.values[new_key] != key:
           if self.values.count(snake_case  ) > 0:
              A_					:				Dict          =			self.hash_function(new_key + 1  )
           else:
              A_					:				Dict          =			None
              break

        return new_key



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				str          =			[value for value in self.values if value is not None]
        A_					:				int          =			next_prime(self.size_table			,      factor=2  )
        self._keys.clear()
        A_					:				Union[str, Any]          =			[None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
           self.insert_data(snake_case  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			self.hash_function(snake_case  )

        if self.values[key] is None:
           self._set_value(snake_case			,      snake_case  )

        elif self.values[key] == data:
           pass

        else:
           A_					:				List[Any]          =			self._collision_resolution(snake_case			,      snake_case  )
           if collision_resolution is not None:
              self._set_value(snake_case			,      snake_case  )
           else:
              self.rehashing()
              self.insert_data(snake_case  )







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!''')
