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 						a__							(       unittest.TestCase		):



			def __init__(						self				,						UpperCAmelCase   )			->   Dict:
								__a		 =       parent







			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								return {}






def 							lowerCAmelCase(     ):
					__a		 =       '<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>'

					__a		 =       '\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 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       List[Any]        =      MarkupLMFeatureExtractor if is_bsa_available() else None



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       MarkupLMFeatureExtractionTester(self   )



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								return self.feature_extract_tester.prepare_feat_extract_dict()







			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								# Initialize feature_extractor
								__a		 =       self.feature_extraction_class()

								# Test not batched input
								__a		 =       get_html_strings()[0]
								__a		 =       feature_extractor(UpperCAmelCase   )

								# fmt: off
								__a		 =       [['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']]
								__a		 =       [['/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				,						UpperCAmelCase   )
								self.assertEqual(encoding.xpaths				,						UpperCAmelCase   )

								# Test batched
								__a		 =       get_html_strings()
								__a		 =       feature_extractor(UpperCAmelCase   )

								# fmt: off
								__a		 =       expected_nodes + [['My First Heading', 'My first paragraph.']]
								__a		 =       expected_xpaths + [['/html/body/h1', '/html/body/p']]

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

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





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(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       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()




