




"""simple docstring"""







from __future__ import annotations

import pandas as pd

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->list[int]:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				[0] * no_of_processes
		__lowercase				:		str												=				[0] * no_of_processes
		# Copy the burst time into remaining_time[]
		for i in range(_lowerCamelCase				):
				__lowercase				:		Optional[Any]												=				burst_time[i]

		__lowercase				:		str												=				0
		__lowercase				:		List[Any]												=				0
		__lowercase				:		List[Any]												=				9_99_99_99_99
		__lowercase				:		int												=				0
		__lowercase				:		List[str]												=				False

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

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

				__lowercase				:		str												=				remaining_time[short]
				if minm == 0:
						__lowercase				:		Optional[int]												=				9_99_99_99_99

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

						# Find finish time of current process
						__lowercase				:		List[Any]												=				increment_time + 1

						# Calculate waiting time
						__lowercase				:		Optional[int]												=				finish_time - arrival_time[short]
						__lowercase				:		Optional[int]												=				finar - burst_time[short]

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

        # Increment time
				increment_time += 1
		return waiting_time

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->list[int]:






		"""simple docstring"""


		__lowercase				:		int												=				[0] * no_of_processes
		for i in range(_lowerCamelCase				):
				__lowercase				:		List[str]												=				burst_time[i] + waiting_time[i]
		return turn_around_time

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->None:






		"""simple docstring"""


		__lowercase				:		Any												=				0
		__lowercase				:		Any												=				0
		for i in range(_lowerCamelCase				):
				__lowercase				:		int												=				total_waiting_time + waiting_time[i]
				__lowercase				:		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')
						__A   :  Optional[int]					=      int(input())
						__A   :  str					=      [0] * no_of_processes
						__A   :  List[str]					=      [0] * no_of_processes
						__A   :  List[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   :  Optional[int]					=      map(int, input().split())

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

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						__A   :  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)






"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :  str					=      logging.get_logger(__name__)

__A   :  str					=      {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Tuple       =      "visual_bert"



				def __init__(     self					:	List[Any]    ,   lowercase__					:	Dict=3_0_5_2_2    ,   lowercase__					:	Any=7_6_8    ,   lowercase__					:	Dict=5_1_2    ,   lowercase__					:	Tuple=1_2    ,   lowercase__					:	Any=1_2    ,   lowercase__					:	Union[str, Any]=3_0_7_2    ,   lowercase__					:	Tuple="gelu"    ,   lowercase__					:	Dict=0.1    ,   lowercase__					:	Tuple=0.1    ,   lowercase__					:	str=5_1_2    ,   lowercase__					:	str=2    ,   lowercase__					:	Any=0.0_2    ,   lowercase__					:	Any=1e-12    ,   lowercase__					:	Any=False    ,   lowercase__					:	List[str]=True    ,   lowercase__					:	Dict=1    ,   lowercase__					:	List[Any]=0    ,   lowercase__					:	Tuple=2    ,   **lowercase__					:	Any    ,   ):
						super().__init__(pad_token_id=lowercase__    ,   bos_token_id=lowercase__    ,   eos_token_id=lowercase__    ,   **lowercase__						)

						__lowercase				:		Optional[int]												=				vocab_size
						__lowercase				:		Any												=				max_position_embeddings
						__lowercase				:		Optional[Any]												=				hidden_size
						__lowercase				:		List[str]												=				visual_embedding_dim
						__lowercase				:		Dict												=				num_hidden_layers
						__lowercase				:		Any												=				num_attention_heads
						__lowercase				:		str												=				intermediate_size
						__lowercase				:		Dict												=				hidden_act
						__lowercase				:		Any												=				hidden_dropout_prob
						__lowercase				:		Optional[int]												=				attention_probs_dropout_prob
						__lowercase				:		Dict												=				initializer_range
						__lowercase				:		str												=				type_vocab_size
						__lowercase				:		List[Any]												=				layer_norm_eps
						__lowercase				:		Tuple												=				bypass_transformer
						__lowercase				:		List[str]												=				special_visual_initialize
