


"""simple docstring"""





import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						LayoutLMTokenizer
      lowerCamelCase__	:					Tuple										=						LayoutLMTokenizerFast
      lowerCamelCase__	:					Union[str, Any]										=						True
      lowerCamelCase__	:					Optional[Any]										=						True


      def 	_UpperCAmelCase (     self							) ->      Any:
             super().setUp()

             lowercase__    :	List[str]     					=			[
                 '[UNK]',
                 '[CLS]',
                 '[SEP]',
                 'want',
                 '##want',
                 '##ed',
                 'wa',
                 'un',
                 'runn',
                 '##ing',
                 ',',
                 'low',
                 'lowest',
             ]
             lowercase__    :	Any     					=			os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['vocab_file']							)
             with open(self.vocab_file	,    'w'	,    encoding='utf-8'							) as vocab_writer:
                    vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)


      def 	_UpperCAmelCase (     self	,    **a							) ->      Optional[int]:
             return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,    **UpperCamelCase__							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			'UNwant\u00E9d,running'
             lowercase__    :	List[Any]     					=			'unwanted, running'
             return input_text, output_text


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			self.tokenizer_class(self.vocab_file							)

             lowercase__    :	List[str]     					=			tokenizer.tokenize('UNwant\u00E9d,running'							)
             self.assertListEqual(UpperCamelCase__	,    ['un', '##want', '##ed', ',', 'runn', '##ing']							)
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)	,    [7, 4, 5, 1_0, 8, 9]							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


"""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        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			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()

