



'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase     		=   {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   ['ConvBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   [
				    'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvBertForMaskedLM',
				    'ConvBertForMultipleChoice',
				    'ConvBertForQuestionAnswering',
				    'ConvBertForSequenceClassification',
				    'ConvBertForTokenClassification',
				    'ConvBertLayer',
				    'ConvBertModel',
				    'ConvBertPreTrainedModel',
				    'load_tf_weights_in_convbert',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   [
				    'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFConvBertForMaskedLM',
				    'TFConvBertForMultipleChoice',
				    'TFConvBertForQuestionAnswering',
				    'TFConvBertForSequenceClassification',
				    'TFConvBertForTokenClassification',
				    'TFConvBertLayer',
				    'TFConvBertModel',
				    'TFConvBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
				from .tokenization_convbert import ConvBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_convbert_fast import ConvBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convbert import (
								    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvBertForMaskedLM,
								    ConvBertForMultipleChoice,
								    ConvBertForQuestionAnswering,
								    ConvBertForSequenceClassification,
								    ConvBertForTokenClassification,
								    ConvBertLayer,
								    ConvBertModel,
								    ConvBertPreTrainedModel,
								    load_tf_weights_in_convbert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convbert import (
								    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFConvBertForMaskedLM,
								    TFConvBertForMultipleChoice,
								    TFConvBertForQuestionAnswering,
								    TFConvBertForSequenceClassification,
								    TFConvBertForTokenClassification,
								    TFConvBertLayer,
								    TFConvBertModel,
								    TFConvBertPreTrainedModel,
								)


else:
				import sys

				UpperCAmelCase     		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

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

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

		lowerCAmelCase   =     remaining_time[short]
		if minm == 0:
			lowerCAmelCase   =     999_999_999

		if remaining_time[short] == 0:
			complete += 1
			lowerCAmelCase   =     False

			# Find finish time of current process
			lowerCAmelCase   =     increment_time + 1

			# Calculate waiting time
			lowerCAmelCase   =     finish_time - arrival_time[short]
			lowerCAmelCase   =     finar - burst_time[short]

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     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     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   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     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				UpperCAmelCase     		=   burst_time
				UpperCAmelCase     		=   no_of_processes
				UpperCAmelCase     		=   waiting_time
				UpperCAmelCase     		=   calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   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)