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__				:   Any       ):


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





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			XGBRegressor(verbosity=0    ,					random_state=4_2       )
	xgb.fit(a__    ,					a__       )
	# Predict target for test data
	__UpperCAmelCase							=			xgb.predict(a__       )
	__UpperCAmelCase							=			predictions.reshape(len(a__       )    ,					1       )
	return predictions





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			fetch_california_housing()
	__UpperCAmelCase				,			__UpperCAmelCase							=			data_handling(a__       )
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			train_test_split(
	    a__    ,					a__    ,					test_size=0.25    ,					random_state=1       )
	__UpperCAmelCase							=			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()

'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "LlamaForCausalLM",
					    "LlamaModel",
					    "LlamaPreTrainedModel",
					    "LlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
