





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      lowerCamelCase_						(				_UpperCamelCase )		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						np.ndarray:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  XGBRegressor(verbosity=0 ,				random_state=42 )
				xgb.fit(_UpperCamelCase ,				_UpperCamelCase )
				# Predict target for test data
				snake_case_						:    Optional[Any]				=  xgb.predict(_UpperCamelCase )
				snake_case_						:    List[str]				=  predictions.reshape(len(_UpperCamelCase ) ,				1 )
				return predictions






def      lowerCamelCase_						(				)		->						None:






				"""simple docstring"""


				snake_case_						:    Any				=  fetch_california_housing()
				snake_case_			,							snake_case_						:    Tuple				=  data_handling(_UpperCamelCase )
				snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  train_test_split(
				    _UpperCamelCase ,				_UpperCamelCase ,				test_size=0.25 ,				random_state=1 )
				snake_case_						:    List[Any]				=  xgboost(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
				# Error printing
				print(f'''Mean Absolute Error : {mean_absolute_error(_UpperCamelCase ,				_UpperCamelCase )}''' )
				print(f'''Mean Square Error  : {mean_squared_error(_UpperCamelCase ,				_UpperCamelCase )}''' )


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
