

from __future__ import annotations

import pandas as pd
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]			,a__    :   list[int]			,a__    :   int       )					->				list[int]:
	__A :			List[str]		    =			[0] * no_of_processes
	__A :			Dict		    =			[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(a__       ):
		__A :			Tuple		    =			burst_time[i]

	__A :			Dict		    =			0
	__A :			Any		    =			0
	__A :			List[Any]		    =			999999999
	__A :			List[str]		    =			0
	__A :			List[str]		    =			False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(a__       ):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					__A :			List[Any]		    =			remaining_time[j]
					__A :			Union[str, Any]		    =			j
					__A :			int		    =			True

		if not check:
			increment_time += 1
			continue
		remaining_time[short] -= 1

		__A :			int		    =			remaining_time[short]
		if minm == 0:
			__A :			List[str]		    =			999999999

		if remaining_time[short] == 0:
			complete += 1
			__A :			Optional[Any]		    =			False

			# Find finish time of current process
			__A :			Union[str, Any]		    =			increment_time + 1

			# Calculate waiting time
			__A :			Tuple		    =			finish_time - arrival_time[short]
			__A :			Tuple		    =			finar - burst_time[short]

			if waiting_time[short] < 0:
				__A :			str		    =			0

        # Increment time
		increment_time += 1
	return waiting_time
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]			,a__    :   int			,a__    :   list[int]       )					->				list[int]:
	__A :			Any		    =			[0] * no_of_processes
	for i in range(a__       ):
		__A :			Optional[Any]		    =			burst_time[i] + waiting_time[i]
	return turn_around_time
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]			,a__    :   list[int]			,a__    :   int       )					->				None:
	__A :			int		    =			0
	__A :			Any		    =			0
	for i in range(a__       ):
		__A :			Dict		    =			total_waiting_time + waiting_time[i]
		__A :			List[str]		    =			total_turn_around_time + turn_around_time[i]
	print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""       )
	print("""Average turn around time ="""			,total_turn_around_time / no_of_processes       )


if __name__ == "__main__":
			print('''Enter how many process you want to analyze''')
			UpperCAmelCase_							:					Tuple		 =   int(input())
			UpperCAmelCase_							:					Dict		 =   [0] * no_of_processes
			UpperCAmelCase_							:					Union[str, Any]		 =   [0] * no_of_processes
			UpperCAmelCase_							:					Optional[int]		 =   list(range(1, no_of_processes + 1))

			for i in range(no_of_processes):
						print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
						UpperCAmelCase_ ,		UpperCAmelCase_							:					Dict		 =   map(int, input().split())

			UpperCAmelCase_							:					str		 =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

			UpperCAmelCase_							:					Union[str, Any]		 =   burst_time
			UpperCAmelCase_							:					Any		 =   no_of_processes
			UpperCAmelCase_							:					int		 =   waiting_time
			UpperCAmelCase_							:					Optional[Any]		 =   calculate_turnaroundtime(bt, n, wt)

			calculate_average_times(waiting_time, turn_around_time, no_of_processes)

			UpperCAmelCase_							:					Tuple		 =   pd.DataFrame(
			    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
			    columns=[
			        '''Process''',
			        '''BurstTime''',
			        '''ArrivalTime''',
			        '''WaitingTime''',
			        '''TurnAroundTime''',
			    ],
			)

			# Printing the dataFrame
			pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
			print(fcfs)








import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





