






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 						_a    (      a :Optional[Any]	,							a :int	,							a :List[str]	,							a :List[str] )							->			Tuple:
 a		  =      s.rsplit(a	,							a )
 return new.join(a )
def 						_a    (      a :Any )							->			List[Any]:
 # encoder.embeddings are double copied in original FLAVA
 return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      {}

 a		  =      ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    a		  =      key.replace(F"""{group_key}."""	,							F"""{group_key}.group.""" )

  if "res_path" in key:
   a		  =      key.replace('''res_path.'''	,							'''res_path.path.''' )

  if key.endswith('''.w''' ):
   a		  =      rreplace(a	,							'''.w'''	,							'''.weight'''	,							1 )
  if key.endswith('''.b''' ):
   a		  =      rreplace(a	,							'''.b'''	,							'''.bias'''	,							1 )

  a		  =      value.float()

 return upgrade
@torch.no_grad()
def 						_a    (      a :Union[str, Any]	,							a :Optional[Any]	,							a :Optional[int]=None	,							a :str=True )							->			Tuple:
 from dall_e import Encoder

 a		  =      Encoder()
 if os.path.exists(a ):
  a		  =      torch.load(a )
 else:
  a		  =      torch.hub.load_state_dict_from_url(a )

 if isinstance(a	,							a ):
  a		  =      ckpt.state_dict()
 encoder.load_state_dict(a )

 if config_path is not None:
  a		  =      FlavaImageCodebookConfig.from_pretrained(a )
 else:
  a		  =      FlavaImageCodebookConfig()

 a		  =      FlavaImageCodebook(a ).eval()
 a		  =      encoder.state_dict()

 a		  =      upgrade_state_dict(a )
 hf_model.load_state_dict(a )
 a		  =      hf_model.state_dict()
 a		  =      count_parameters(a )
 a		  =      count_parameters(a )

 assert torch.allclose(a	,							a	,							atol=1e-3 )

 if save_checkpoint:
  hf_model.save_pretrained(a )
 else:
  return hf_state_dict


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     UpperCAmelCase__				   =		parser.parse_args()

     convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def      A      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					xgboost(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Error printing
       print(f'''Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )
       print(f'''Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()






