



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase    :		Union[str, Any]		      =    {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase    :		Tuple		      =    ["""RobertaTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase    :		Optional[Any]		      =    [
			    """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RobertaForCausalLM""",
			    """RobertaForMaskedLM""",
			    """RobertaForMultipleChoice""",
			    """RobertaForQuestionAnswering""",
			    """RobertaForSequenceClassification""",
			    """RobertaForTokenClassification""",
			    """RobertaModel""",
			    """RobertaPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase    :		List[Any]		      =    [
			    """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFRobertaForCausalLM""",
			    """TFRobertaForMaskedLM""",
			    """TFRobertaForMultipleChoice""",
			    """TFRobertaForQuestionAnswering""",
			    """TFRobertaForSequenceClassification""",
			    """TFRobertaForTokenClassification""",
			    """TFRobertaMainLayer""",
			    """TFRobertaModel""",
			    """TFRobertaPreTrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase    :		int		      =    [
			    """FlaxRobertaForCausalLM""",
			    """FlaxRobertaForMaskedLM""",
			    """FlaxRobertaForMultipleChoice""",
			    """FlaxRobertaForQuestionAnswering""",
			    """FlaxRobertaForSequenceClassification""",
			    """FlaxRobertaForTokenClassification""",
			    """FlaxRobertaModel""",
			    """FlaxRobertaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
			from .tokenization_roberta import RobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roberta_fast import RobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta import (
						    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaForCausalLM,
						    RobertaForMaskedLM,
						    RobertaForMultipleChoice,
						    RobertaForQuestionAnswering,
						    RobertaForSequenceClassification,
						    RobertaForTokenClassification,
						    RobertaModel,
						    RobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta import (
						    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaForCausalLM,
						    TFRobertaForMaskedLM,
						    TFRobertaForMultipleChoice,
						    TFRobertaForQuestionAnswering,
						    TFRobertaForSequenceClassification,
						    TFRobertaForTokenClassification,
						    TFRobertaMainLayer,
						    TFRobertaModel,
						    TFRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta import (
						    FlaxRobertaForCausalLM,
						    FlaxRobertaForMaskedLM,
						    FlaxRobertaForMultipleChoice,
						    FlaxRobertaForQuestionAnswering,
						    FlaxRobertaForSequenceClassification,
						    FlaxRobertaForTokenClassification,
						    FlaxRobertaModel,
						    FlaxRobertaPreTrainedModel,
						)

else:
			import sys

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




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			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

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

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()