


"""simple docstring"""





import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
       from transformers import MarkupLMFeatureExtractor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			parent


      def 	_UpperCAmelCase (     self							) ->      str:
             return {}





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

       lowercase__    :	Optional[Any]     					=			'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

       return [html_string_a, html_string_a]






@require_bsa
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Dict										=						MarkupLMFeatureExtractor if is_bsa_available() else None


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	Union[str, Any]     					=			MarkupLMFeatureExtractionTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return self.feature_extract_tester.prepare_feat_extract_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # Initialize feature_extractor
             lowercase__    :	List[Any]     					=			self.feature_extraction_class()

             # Test not batched input
             lowercase__    :	Tuple     					=			get_html_strings()[0]
             lowercase__    :	Any     					=			feature_extractor(a							)

             # fmt: off
             lowercase__    :	Dict     					=			[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
             lowercase__    :	str     					=			[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
             # fmt: on

             self.assertEqual(encoding.nodes	,    a							)
             self.assertEqual(encoding.xpaths	,    a							)

             # Test batched
             lowercase__    :	List[Any]     					=			get_html_strings()
             lowercase__    :	List[Any]     					=			feature_extractor(a							)

             # fmt: off
             lowercase__    :	Union[str, Any]     					=			expected_nodes + [['My First Heading', 'My first paragraph.']]
             lowercase__    :	Dict     					=			expected_xpaths + [['/html/body/h1', '/html/body/p']]

             self.assertEqual(len(encoding.nodes							)	,    2							)
             self.assertEqual(len(encoding.xpaths							)	,    2							)

             self.assertEqual(encoding.nodes	,    a							)
             self.assertEqual(encoding.xpaths	,    a							)


"""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()

