





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}
class SCREAMING_SNAKE_CASE_  (   _lowerCamelCase   ):
						__magic_name__:     Optional[Any]	 =    "levit"






						def __init__( self : str						,				_A : int=224						,				_A : int=3						,				_A : Optional[Any]=3						,				_A : Union[str, Any]=2						,				_A : str=1						,				_A : Any=16						,				_A : str=[128, 256, 384]						,				_A : Dict=[4, 8, 12]						,				_A : str=[4, 4, 4]						,				_A : Dict=[16, 16, 16]						,				_A : Optional[Any]=0						,				_A : List[Any]=[2, 2, 2]						,				_A : Any=[2, 2, 2]						,				_A : str=0.0_2						,				**_A : Tuple						,				)							->      Union[str, Any]:







							"""simple docstring"""


							super().__init__(**_A		)
							snake_case_						:     List[Any]     =		image_size
							snake_case_						:     Optional[Any]     =		num_channels
							snake_case_						:     Optional[Any]     =		kernel_size
							snake_case_						:     str     =		stride
							snake_case_						:     int     =		padding
							snake_case_						:     Optional[int]     =		hidden_sizes
							snake_case_						:     List[Any]     =		num_attention_heads
							snake_case_						:     List[str]     =		depths
							snake_case_						:     int     =		key_dim
							snake_case_						:     Dict     =		drop_path_rate
							snake_case_						:     Optional[Any]     =		patch_size
							snake_case_						:     List[Any]     =		attention_ratio
							snake_case_						:     Dict     =		mlp_ratio
							snake_case_						:     List[str]     =		initializer_range
							snake_case_						:     Dict     =		[
							    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
class SCREAMING_SNAKE_CASE_  (   _lowerCamelCase   ):
						__magic_name__:     Optional[Any]	 =    version.parse("1.11"   )






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      Tuple:







							"""simple docstring"""


							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      Tuple:







							"""simple docstring"""


							return 1E-4








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		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:
					snake_case_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

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

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

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

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						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))
												_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						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)

