


from __future__ import annotations

import pandas as pd


def  _snake_case       (					A			,   A			,   A	)						->   list[int]:
							lowerCAmelCase__	       =     [0] * no_of_processes
							lowerCAmelCase__	       =     [0] * no_of_processes
							# Copy the burst time into remaining_time[]
							for i in range(A	):
														lowerCAmelCase__	       =     burst_time[i]

							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     999999999
							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     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__	       =     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__	       =     999999999

														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       (					A			,   A			,   A	)						->   list[int]:
							lowerCAmelCase__	       =     [0] * no_of_processes
							for i in range(A	):
														lowerCAmelCase__	       =     burst_time[i] + waiting_time[i]
							return turn_around_time


def  _snake_case       (					A			,   A			,   A	)						->   None:
							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     0
							for i in range(A	):
														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)



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"distilbert"
							lowercase__      :       Tuple     						=				{
							    "hidden_size": "dim",
							    "num_attention_heads": "n_heads",
							    "num_hidden_layers": "n_layers",
							}







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=False ,   lowerCamelCase_=6 ,   lowerCamelCase_=12 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=4 * 7_68 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.2 ,   lowerCamelCase_=0 ,   **lowerCamelCase_ ,   )	->							Any:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     sinusoidal_pos_embds
														lowerCAmelCase__	       =     n_layers
														lowerCAmelCase__	       =     n_heads
														lowerCAmelCase__	       =     dim
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     qa_dropout
														lowerCAmelCase__	       =     seq_classif_dropout
														super().__init__(**lowerCamelCase_ ,   pad_token_id=lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ]       )