

from __future__ import annotations

import pandas as pd

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				list[int]:
	A__							:   Optional[int]					       =		[0] * no_of_processes
	A__							:   Dict					       =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(lowercase_ ):
		A__							:   str					       =		burst_time[i]

	A__							:   Dict					       =		0
	A__							:   Union[str, Any]					       =		0
	A__							:   Union[str, Any]					       =		999999999
	A__							:   str					       =		0
	A__							:   Tuple					       =		False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(lowercase_ ):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					A__							:   Optional[Any]					       =		remaining_time[j]
					A__							:   Any					       =		j
					A__							:   Optional[Any]					       =		True

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

		A__							:   List[Any]					       =		remaining_time[short]
		if minm == 0:
			A__							:   Optional[int]					       =		999999999

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

			# Find finish time of current process
			A__							:   Optional[int]					       =		increment_time + 1

			# Calculate waiting time
			A__							:   Dict					       =		finish_time - arrival_time[short]
			A__							:   Dict					       =		finar - burst_time[short]

			if waiting_time[short] < 0:
				A__							:   Optional[Any]					       =		0

        # Increment time
		increment_time += 1
	return waiting_time

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			int   ,				lowercase_:			list[int] )					->				list[int]:
	A__							:   Dict					       =		[0] * no_of_processes
	for i in range(lowercase_ ):
		A__							:   str					       =		burst_time[i] + waiting_time[i]
	return turn_around_time

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				None:
	A__							:   Optional[int]					       =		0
	A__							:   Dict					       =		0
	for i in range(lowercase_ ):
		A__							:   Dict					       =		total_waiting_time + waiting_time[i]
		A__							:   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')
	A_	:			Optional[Any]  	=      int(input())
	A_	:			Union[str, Any]  	=      [0] * no_of_processes
	A_	:			List[Any]  	=      [0] * no_of_processes
	A_	:			Union[str, Any]  	=      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))
		A_	,      A_	:			int  	=      map(int, input().split())

	A_	:			Dict  	=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

	A_	:			int  	=      burst_time
	A_	:			List[str]  	=      no_of_processes
	A_	:			int  	=      waiting_time
	A_	:			Tuple  	=      calculate_turnaroundtime(bt, n, wt)

	calculate_average_times(waiting_time, turn_around_time, no_of_processes)

	A_	:			List[str]  	=      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)






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



