


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		OmegaConf.load(lowercase__      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase__      )      )      )
						return config


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=None    ,       lowercase__=None      ):
						if conf_path is None:
												__SCREAMING_SNAKE_CASE						:     int					=		'''./model_checkpoints/vqgan_only.yaml'''
						__SCREAMING_SNAKE_CASE						:     List[str]					=		load_config(lowercase__    ,       display=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		VQModel(**config.model.params      )
						if ckpt_path is None:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''./model_checkpoints/vqgan_only.pt'''
						__SCREAMING_SNAKE_CASE						:     str					=		torch.load(lowercase__    ,       map_location=lowercase__      )
						if ".ckpt" in ckpt_path:
												__SCREAMING_SNAKE_CASE						:     int					=		sd['''state_dict''']
						model.load_state_dict(lowercase__    ,       strict=lowercase__      )
						model.to(lowercase__      )
						del sd
						return model


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model.encode(lowercase__      )
						print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		model.decode(lowercase__      )
						return xrec


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		string.rsplit('''.'''    ,       1      )
						if reload:
												__SCREAMING_SNAKE_CASE						:     int					=		importlib.import_module(lowercase__      )
												importlib.reload(lowercase__      )
						return getattr(importlib.import_module(lowercase__    ,       package=lowercase__      )    ,       cls      )


def        _UpperCamelCase							(      lowercase__      ):
						if "target" not in config:
												raise KeyError('''Expected key `target` to instantiate.'''      )
						return get_obj_from_str(config['''target''']      )(**config.get('''params'''    ,       {}      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=True    ,       lowercase__=True      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		instantiate_from_config(lowercase__      )
						if sd is not None:
												model.load_state_dict(lowercase__      )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						# load the specified checkpoint
						if ckpt:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.load(lowercase__    ,       map_location='''cpu'''      )
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		pl_sd['''global_step''']
												print(F'''loaded model from global step {global_step}.'''      )
						else:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		{'''state_dict''': None}
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		None
						__SCREAMING_SNAKE_CASE						:     str					=		load_model_from_config(config.model    ,       pl_sd['''state_dict''']    ,       gpu=lowercase__    ,       eval_mode=lowercase__      )['''model''']
						return model, global_step








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 						__UpperCAmelCase			(     a_):
							return (data["data"], data["target"])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							xgboost(a_							,     a_							,     a_)
							# Error printing
							print(f'''Mean Absolute Error : {mean_absolute_error(a_							,     a_)}''')
							print(f'''Mean Square Error  : {mean_squared_error(a_							,     a_)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





