'''simple docstring'''





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_							(   snake_case_			:					dict     )       -> tuple:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					np.ndarray   ,       snake_case_			:					np.ndarray   ,       snake_case_			:					np.ndarray     )       -> np.ndarray:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   )       -> None:


     '''simple docstring'''




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


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()











"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




