


def        UpperCamelCase_(   lowerCamelCase_				)					->		list:
    _lowercase :							List[str]   	=	False
    while is_sorted is False:  # Until all the indices are traversed keep looping
        _lowercase :							Optional[Any]   	=	True
        for i in range(0							,				len(lowerCamelCase_				) - 1							,				2				):  # iterating over all even indices
            if input_list[i] > input_list[i + 1]:
                _lowercase	,       _lowercase :							Optional[int]   	=	input_list[i + 1], input_list[i]
                # swapping if elements not in order
                _lowercase :							List[Any]   	=	False

        for i in range(1							,				len(lowerCamelCase_				) - 1							,				2				):  # iterating over all odd indices
            if input_list[i] > input_list[i + 1]:
                _lowercase	,       _lowercase :							Tuple   	=	input_list[i + 1], input_list[i]
                # swapping if elements not in order
                _lowercase :							int   	=	False
    return input_list


if __name__ == "__main__":
 print("Enter list to be sorted")
 SCREAMING_SNAKE_CASE :   List[str]							      =				[int(x) for x in input().split()]
 # inputing elements of the list in one line
 SCREAMING_SNAKE_CASE :   List[str]							      =				odd_even_sort(input_list)
 print("The sorted list is")
 print(sorted_list)






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 _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	parent




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return {}



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							int   	=	'<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 :							List[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 _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Tuple									=    MarkupLMFeatureExtractor if is_bsa_available() else None
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	MarkupLMFeatureExtractionTester(self)
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           return self.feature_extract_tester.prepare_feat_extract_dict()




       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extraction_class()

           # Test not batched input
           _lowercase :							List[Any]   	=	get_html_strings()[0]
           _lowercase :							Optional[Any]   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							str   	=	[['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 :							Dict   	=	[['/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,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)

           # Test batched
           _lowercase :							Optional[int]   	=	get_html_strings()
           _lowercase :							Tuple   	=	feature_extractor(lowerCamelCase)

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

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

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


