
from __future__ import annotations

import pandas as pd

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      [0] * no_of_processes
				lowercase         =      [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(__magic_name__						):
								lowercase         =      burst_time[i]

				lowercase         =      0
				lowercase         =      0
				lowercase         =      999999999
				lowercase         =      0
				lowercase         =      False

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

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

								lowercase         =      remaining_time[short]
								if minm == 0:
												lowercase         =      999999999

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

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

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

												if waiting_time[short] < 0:
																lowercase         =      0

        # Increment time
								increment_time += 1
				return waiting_time

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      [0] * no_of_processes
				for i in range(__magic_name__						):
								lowercase         =      burst_time[i] + waiting_time[i]
				return turn_around_time

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      0
				lowercase         =      0
				for i in range(__magic_name__						):
								lowercase         =      total_waiting_time + waiting_time[i]
								lowercase         =      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")
						_snake_case      :							Optional[int]     	=    int(input())
						_snake_case      :							Any     	=    [0] * no_of_processes
						_snake_case      :							Dict     	=    [0] * no_of_processes
						_snake_case      :							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))
												_snake_case	,       _snake_case      :							Any     	=    map(int, input().split())

						_snake_case      :							int     	=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

						_snake_case      :							Any     	=    burst_time
						_snake_case      :							str     	=    no_of_processes
						_snake_case      :							Dict     	=    waiting_time
						_snake_case      :							Tuple     	=    calculate_turnaroundtime(bt, n, wt)

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_snake_case      :							int     	=    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 os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
						raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

										train_parser.add_argument(
										    "--train_data"		,    type=lowerCAmelCase__		,    required=lowerCAmelCase__		,    help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."		,    )
										train_parser.add_argument(
										    "--column_label"		,    type=lowerCAmelCase__		,    default=0		,    help="Column of the dataset csv file with example labels."     )
										train_parser.add_argument(
										    "--column_text"		,    type=lowerCAmelCase__		,    default=1		,    help="Column of the dataset csv file with example texts."     )
										train_parser.add_argument(
										    "--column_id"		,    type=lowerCAmelCase__		,    default=2		,    help="Column of the dataset csv file with example ids."     )
										train_parser.add_argument(
										    "--skip_first_row"		,    action="store_true"		,    help="Skip the first row of the csv file (headers)."     )

										train_parser.add_argument("--validation_data"		,    type=lowerCAmelCase__		,    default=""		,    help="path to validation dataset."     )
										train_parser.add_argument(
										    "--validation_split"		,    type=lowerCAmelCase__		,    default=0.1		,    help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."		,    )

										train_parser.add_argument("--output"		,    type=lowerCAmelCase__		,    default="./"		,    help="path to saved the trained model."     )

										train_parser.add_argument(
										    "--task"		,    type=lowerCAmelCase__		,    default="text_classification"		,    help="Task to train the model on."     )
										train_parser.add_argument(
										    "--model"		,    type=lowerCAmelCase__		,    default="bert-base-uncased"		,    help="Model's name or path to stored model."     )
										train_parser.add_argument("--train_batch_size"		,    type=lowerCAmelCase__		,    default=32		,    help="Batch size for training."     )
										train_parser.add_argument("--valid_batch_size"		,    type=lowerCAmelCase__		,    default=64		,    help="Batch size for validation."     )
										train_parser.add_argument("--learning_rate"		,    type=lowerCAmelCase__		,    default=3E-5		,    help="Learning rate."     )
										train_parser.add_argument("--adam_epsilon"		,    type=lowerCAmelCase__		,    default=1E-0_8		,    help="Epsilon for Adam optimizer."     )
										train_parser.set_defaults(func=lowerCAmelCase__     )





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

										lowercase         =      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      TextClassificationPipeline.from_pretrained(args.model     )
										elif args.task == "token_classification":
														raise NotImplementedError
										elif args.task == "question_answering":
														raise NotImplementedError

										self.logger.info(F'''Loading dataset from {args.train_data}'''     )
										lowercase         =      Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										lowercase         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      Processor.create_from_csv(
														    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										lowercase         =      args.validation_split
										lowercase         =      args.train_batch_size
										lowercase         =      args.valid_batch_size
										lowercase         =      args.learning_rate
										lowercase         =      args.adam_epsilon





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output     )
