




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def 							lowercase    (snake_case__      :       Union[str, Any]       )   ->     Tuple:


				'''simple docstring'''






				return EnvironmentCommand()






class 					SCREAMING_SNAKE_CASE__    (    snake_case_    ):


						@staticmethod
						def      __lowercase       (							lowerCAmelCase :	Optional[Any]    ):
										lowerCAmelCase              = parser.add_parser("""env"""    )
										download_parser.set_defaults(func=lowerCAmelCase    )


						def      __lowercase       (							self :	List[Any]    ):
										lowerCAmelCase              = huggingface_hub.__version__

										lowerCAmelCase              = """not installed"""
										lowerCAmelCase              = """NA"""
										if is_torch_available():
														import torch

														lowerCAmelCase              = torch.__version__
														lowerCAmelCase              = torch.cuda.is_available()

										lowerCAmelCase              = """not installed"""
										if is_transformers_available():
														import transformers

														lowerCAmelCase              = transformers.__version__

										lowerCAmelCase              = """not installed"""
										if is_accelerate_available():
														import accelerate

														lowerCAmelCase              = accelerate.__version__

										lowerCAmelCase              = """not installed"""
										if is_xformers_available():
														import xformers

														lowerCAmelCase              = xformers.__version__

										lowerCAmelCase              = {
										    """`diffusers` version""": version,
										    """Platform""": platform.platform(),
										    """Python version""": platform.python_version(),
										    """PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
										    """Huggingface_hub version""": hub_version,
										    """Transformers version""": transformers_version,
										    """Accelerate version""": accelerate_version,
										    """xFormers version""": xformers_version,
										    """Using GPU in script?""": """<fill in>""",
										    """Using distributed or parallel set-up in script?""": """<fill in>""",
										}

										print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""    )
										print(self.format_dict(lowerCAmelCase    )    )

										return info




						@staticmethod
						def      __lowercase       (							lowerCAmelCase :	List[Any]    ):
										return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]    ) + "\n"













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()





