




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_				:    Union[str, Any]										=     {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    Optional[int]										=     ['''XLNetTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    List[str]										=     ['''XLNetTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    Union[str, Any]										=     [
					    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XLNetForMultipleChoice''',
					    '''XLNetForQuestionAnswering''',
					    '''XLNetForQuestionAnsweringSimple''',
					    '''XLNetForSequenceClassification''',
					    '''XLNetForTokenClassification''',
					    '''XLNetLMHeadModel''',
					    '''XLNetModel''',
					    '''XLNetPreTrainedModel''',
					    '''load_tf_weights_in_xlnet''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    str										=     [
					    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXLNetForMultipleChoice''',
					    '''TFXLNetForQuestionAnsweringSimple''',
					    '''TFXLNetForSequenceClassification''',
					    '''TFXLNetForTokenClassification''',
					    '''TFXLNetLMHeadModel''',
					    '''TFXLNetMainLayer''',
					    '''TFXLNetModel''',
					    '''TFXLNetPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet import XLNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet_fast import XLNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlnet import (
										    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLNetForMultipleChoice,
										    XLNetForQuestionAnswering,
										    XLNetForQuestionAnsweringSimple,
										    XLNetForSequenceClassification,
										    XLNetForTokenClassification,
										    XLNetLMHeadModel,
										    XLNetModel,
										    XLNetPreTrainedModel,
										    load_tf_weights_in_xlnet,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlnet import (
										    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLNetForMultipleChoice,
										    TFXLNetForQuestionAnsweringSimple,
										    TFXLNetForSequenceClassification,
										    TFXLNetForTokenClassification,
										    TFXLNetLMHeadModel,
										    TFXLNetMainLayer,
										    TFXLNetModel,
										    TFXLNetPreTrainedModel,
										)

else:
					import sys

					a_				:    str										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				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:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

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

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

						if remaining_time[short] == 0:
									complete += 1
									__lowerCamelCase					:	List[str]	       =				False

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

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

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

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, Any]	       =				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_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    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))
										a_						,							a_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     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)



