




from __future__ import annotations

import pandas as pd





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

	a										=							0
	a										=							0
	a										=							9_9999_9999
	a										=							0
	a										=							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:
					a										=							remaining_time[j]
					a										=							j
					a										=							True

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

		a										=							remaining_time[short]
		if minm == 0:
			a										=							9_9999_9999

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

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

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

			if waiting_time[short] < 0:
				a										=							0

        # Increment time
		increment_time += 1
	return waiting_time





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       list[int]:
	a										=							[0] * no_of_processes
	for i in range(__lowerCamelCase    ):
		a										=							burst_time[i] + waiting_time[i]
	return turn_around_time





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       None:
	a										=							0
	a										=							0
	for i in range(__lowerCamelCase    ):
		a										=							total_waiting_time + waiting_time[i]
		a										=							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					:       List[Any]    					=      int(input())
		__UpperCamelCase					:       Optional[int]    					=      [0] * no_of_processes
		__UpperCamelCase					:       str    					=      [0] * no_of_processes
		__UpperCamelCase					:       Dict    					=      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					:       str    					=      map(int, input().split())

		__UpperCamelCase					:       List[str]    					=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		__UpperCamelCase					:       List[Any]    					=      burst_time
		__UpperCamelCase					:       int    					=      no_of_processes
		__UpperCamelCase					:       Optional[int]    					=      waiting_time
		__UpperCamelCase					:       Optional[int]    					=      calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		__UpperCamelCase					:       str    					=      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)







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__lowerCAmelCase	(	pl.LightningModule ):
							def __init__(						self						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								super().__init__()
								a										=							model
								a										=							2
								a										=							nn.Linear(self.model.config.hidden_size		,      self.num_labels				)





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

	a										=							torch.load(__lowerCamelCase							,			map_location=torch.device("""cpu"""    )    )
	lightning_model.load_state_dict(ckpt["""state_dict"""]    )

	# init longformer question answering model
	a										=							LongformerForQuestionAnswering.from_pretrained(__lowerCamelCase    )

	# transfer weights
	longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()    )
	longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()    )
	longformer_for_qa.eval()

	# save model
	longformer_for_qa.save_pretrained(__lowerCamelCase    )

	print(f'Conversion successful. Model saved under {pytorch_dump_folder_path}'    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[Any]    					=      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--longformer_model",
		    default=None,
		    type=str,
		    required=True,
		    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
		)
		parser.add_argument(
		    "--longformer_question_answering_ckpt_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch Lightning Checkpoint.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)

