





import numpy as np


class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Tuple			,      snake_case       :str=5			,      snake_case       :int=5  ):





        '''simple docstring'''




        A_					:				List[str]          =			depth
        A_					:				Union[str, Any]          =			0
        A_					:				Any          =			None
        A_					:				Tuple          =			None
        A_					:				str          =			min_leaf_size
        A_					:				Optional[int]          =			None



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





        '''simple docstring'''




        if labels.ndim != 1:
           print("Error: Input labels must be one dimensional"  )

        return np.mean((labels - prediction) ** 2  )



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





        '''simple docstring'''




        if x.ndim != 1:
           print("Error: Input data set must be one dimensional"  )
           return
        if len(a_  ) != len(a_  ):
           print("Error: X and y have different lengths"  )
           return
        if y.ndim != 1:
           print("Error: Data set labels must be one dimensional"  )
           return

        if len(a_  ) < 2 * self.min_leaf_size:
           A_					:				Any          =			np.mean(a_  )
           return

        if self.depth == 1:
           A_					:				List[Any]          =			np.mean(a_  )
           return

        A_					:				str          =			0
        A_					:				List[Any]          =			self.mean_squared_error(a_			,      np.mean(a_  )  ) * 2
        for i in range(len(a_  )  ):
           if len(x[:i]  ) < self.min_leaf_size:
              continue
           elif len(x[i:]  ) < self.min_leaf_size:
              continue
           else:
              A_					:				str          =			self.mean_squared_error(x[:i]			,      np.mean(y[:i]  )  )
              A_					:				Optional[Any]          =			self.mean_squared_error(x[i:]			,      np.mean(y[i:]  )  )
              A_					:				Dict          =			error_left + error_right
              if error < min_error:
                 A_					:				str          =			i
                 A_					:				str          =			error

        if best_split != 0:
           A_					:				Dict          =			x[:best_split]
           A_					:				Union[str, Any]          =			y[:best_split]
           A_					:				Dict          =			x[best_split:]
           A_					:				Union[str, Any]          =			y[best_split:]

           A_					:				List[str]          =			x[best_split]
           A_					:				Union[str, Any]          =			DecisionTree(
               depth=self.depth - 1			,      min_leaf_size=self.min_leaf_size  )
           A_					:				Dict          =			DecisionTree(
               depth=self.depth - 1			,      min_leaf_size=self.min_leaf_size  )
           self.left.train(a_			,      a_  )
           self.right.train(a_			,      a_  )
        else:
           A_					:				Tuple          =			np.mean(a_  )

        return





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





        '''simple docstring'''




        if self.prediction is not None:
           return self.prediction
        elif self.left or self.right is not None:
           if x >= self.decision_boundary:
              return self.right.predict(a_  )
           else:
              return self.left.predict(a_  )
        else:
           print("Error: Decision tree not yet trained"  )
           return None




class 					__magic_name__  :





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[str]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			float(0  )
        for label in labels:
           squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size  )




def      __snake_case						(					) ->						Optional[Any]:

   '''simple docstring'''





   A_					:				int          =			np.arange(-1.0				,				1.0				,				0.0_05	)
   A_					:				Optional[Any]          =			np.sin(UpperCamelCase__	)

   A_					:				Union[str, Any]          =			DecisionTree(depth=10				,				min_leaf_size=10	)
   tree.train(UpperCamelCase__				,				UpperCamelCase__	)

   A_					:				List[str]          =			(np.random.rand(10	) * 2) - 1
   A_					:				Any          =			np.array([tree.predict(UpperCamelCase__	) for x in test_cases]	)
   A_					:				Any          =			np.mean((predictions - test_cases) ** 2	)

   print("Test values: " + str(UpperCamelCase__	)	)
   print("Predictions: " + str(UpperCamelCase__	)	)
   print("Average error: " + str(UpperCamelCase__	)	)


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod(name='''mean_squarred_error''', verbose=True)







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!''')
