


from __future__ import annotations

from statistics import mean


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[0] * no_of_processes
						__SCREAMING_SNAKE_CASE						:     Tuple					=		[0] * no_of_processes

						# Initialize remaining_time to waiting_time.

						for i in range(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		burst_time[i]
						__SCREAMING_SNAKE_CASE						:     list[int]					=		[]

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		0
						__SCREAMING_SNAKE_CASE						:     Any					=		0

						# When processes are not completed,
						# A process whose arrival time has passed \
						# and has remaining execution time is put into the ready_process.
						# The shortest process in the ready_process, target_process is executed.

						while completed != no_of_processes:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
												__SCREAMING_SNAKE_CASE						:     Tuple					=		-1

												for i in range(lowercase__      ):
																		if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																								ready_process.append(lowercase__      )

												if len(lowercase__      ) > 0:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		ready_process[0]
																		for i in ready_process:
																								if remaining_time[i] < remaining_time[target_process]:
																														__SCREAMING_SNAKE_CASE						:     List[str]					=		i
																		total_time += burst_time[target_process]
																		completed += 1
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		0
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		(
																		    total_time - arrival_time[target_process] - burst_time[target_process]
																		)
												else:
																		total_time += 1

						return waiting_time


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		[0] * no_of_processes
						for i in range(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		burst_time[i] + waiting_time[i]
						return turn_around_time


if __name__ == "__main__":
						print('[TEST CASE 01]')

						__lowerCAmelCase	: Union[str, Any]  				=4
						__lowerCAmelCase	: str  				=[2, 5, 3, 7]
						__lowerCAmelCase	: Optional[int]  				=[0, 0, 0, 0]
						__lowerCAmelCase	: int  				=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
						__lowerCAmelCase	: List[Any]  				=calculate_turnaroundtime(
						    burst_time, no_of_processes, waiting_time
						)

						# Printing the Result
						print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
						for i, process_id in enumerate(list(range(1, 5))):
												print(
												    f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
												    f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
												)
						print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
						print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")




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        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		parent



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




def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

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

						__SCREAMING_SNAKE_CASE						:     str					=		'''
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    '''

						return [html_string_a, html_string_a]
@require_bsa
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		MarkupLMFeatureExtractor if is_bsa_available() else None


				def 		__magic_name__(       self			:int	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		MarkupLMFeatureExtractionTester(self	)


				@property
				def 		__magic_name__(       self			:Any	)		->			Optional[Any]:
										return self.feature_extract_tester.prepare_feat_extract_dict()



				def 		__magic_name__(       self			:Optional[int]	)		->			Any:
										# Initialize feature_extractor
										__SCREAMING_SNAKE_CASE						:     int					=		self.feature_extraction_class()

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()[0]
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     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''']]
										__SCREAMING_SNAKE_CASE						:     List[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			,			lowerCAmelCase__	)
										self.assertEqual(encoding.xpaths			,			lowerCAmelCase__	)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     int					=		expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
										__SCREAMING_SNAKE_CASE						:     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__	)
