

"""simple docstring"""


import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
			from transformers.trainer import DEFAULT_CALLBACKS

			from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel





class     snake_case	(				__lowerCamelCase  ):

				def __init__(	self							:				List[Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			[]

				def 							lowercase_     (	self							:				List[Any]     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						self.events.append("on_init_end")

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						self.events.append("on_train_begin")

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				int)->   str:



						'''simple docstring'''


						self.events.append("on_train_end")

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				int     ,  **UpperCamelCase__							:				List[Any])->   Tuple:



						'''simple docstring'''


						self.events.append("on_epoch_begin")

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Union[str, Any]     ,  **UpperCamelCase__							:				Any)->   str:



						'''simple docstring'''


						self.events.append("on_epoch_end")

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				int     ,  **UpperCamelCase__							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						self.events.append("on_step_begin")

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Optional[Any]     ,  **UpperCamelCase__							:				List[Any])->   Optional[int]:



						'''simple docstring'''


						self.events.append("on_step_end")

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Optional[Any]     ,  **UpperCamelCase__							:				Optional[int])->   str:



						'''simple docstring'''


						self.events.append("on_evaluate")

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				Union[str, Any])->   str:



						'''simple docstring'''


						self.events.append("on_predict")

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				str)->   str:



						'''simple docstring'''


						self.events.append("on_save")

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				str     ,  **UpperCamelCase__							:				Optional[int])->   Dict:



						'''simple docstring'''


						self.events.append("on_log")




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				str)->   List[str]:



						'''simple docstring'''


						self.events.append("on_prediction_step")





@require_torch
class     snake_case	(				unittest.TestCase  ):

				def 							lowercase_     (	self							:				Optional[int])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			tempfile.mkdtemp()

				def 							lowercase_     (	self							:				List[str])->   List[Any]:



						'''simple docstring'''


						shutil.rmtree(self.output_dir)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Tuple=0     ,  UpperCamelCase__							:				Optional[Any]=0     ,  UpperCamelCase__							:				Union[str, Any]=6_4     ,  UpperCamelCase__							:				Any=6_4     ,  UpperCamelCase__							:				Optional[Any]=None     ,  UpperCamelCase__							:				List[str]=False     ,  **UpperCamelCase__							:				str)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			RegressionDataset(length=UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			RegressionDataset(length=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			RegressionModelConfig(a=UpperCamelCase__     ,  b=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			RegressionPreTrainedModel(UpperCamelCase__)

						__lowerCAmelCase:       int          =			TrainingArguments(self.output_dir     ,  disable_tqdm=UpperCamelCase__     ,  report_to=[]     ,  **UpperCamelCase__)
						return Trainer(
						    UpperCamelCase__     ,  UpperCamelCase__     ,  train_dataset=UpperCamelCase__     ,  eval_dataset=UpperCamelCase__     ,  callbacks=UpperCamelCase__     ,  )

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Any)->   Union[str, Any]:



						'''simple docstring'''


						self.assertEqual(len(UpperCamelCase__)     ,  len(UpperCamelCase__))

						# Order doesn't matter
						__lowerCAmelCase:       Union[str, Any]          =			sorted(UpperCamelCase__     ,  key=lambda UpperCamelCase__: cb.__name__ if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else cb.__class__.__name__)
						__lowerCAmelCase:       int          =			sorted(UpperCamelCase__     ,  key=lambda UpperCamelCase__: cb.__name__ if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else cb.__class__.__name__)

						for cba, cba in zip(UpperCamelCase__     ,  UpperCamelCase__):
								if isinstance(UpperCamelCase__     ,  UpperCamelCase__) and isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__)
								elif isinstance(UpperCamelCase__     ,  UpperCamelCase__) and not isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										self.assertEqual(UpperCamelCase__     ,  cba.__class__)
								elif not isinstance(UpperCamelCase__     ,  UpperCamelCase__) and isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										self.assertEqual(cba.__class__     ,  UpperCamelCase__)
								else:
										self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Union[str, Any])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			["""on_init_end""", """on_train_begin"""]
						__lowerCAmelCase:       List[Any]          =			0
						__lowerCAmelCase:       List[Any]          =			len(trainer.get_eval_dataloader())
						__lowerCAmelCase:       Union[str, Any]          =			["""on_prediction_step"""] * len(trainer.get_eval_dataloader()) + ["""on_log""", """on_evaluate"""]
						for _ in range(trainer.state.num_train_epochs):
								expected_events.append("on_epoch_begin")
								for _ in range(UpperCamelCase__):
										step += 1
										expected_events += ["on_step_begin", "on_step_end"]
										if step % trainer.args.logging_steps == 0:
												expected_events.append("on_log")
										if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
												expected_events += evaluation_events.copy()
										if step % trainer.args.save_steps == 0:
												expected_events.append("on_save")
								expected_events.append("on_epoch_end")
								if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
										expected_events += evaluation_events.copy()
						expected_events += ["on_log", "on_train_end"]
						return expected_events

				def 							lowercase_     (	self							:				Optional[Any])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.get_trainer()
						__lowerCAmelCase:       Tuple          =			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						# Callbacks passed at init are added to the default callbacks
						__lowerCAmelCase:       List[str]          =			self.get_trainer(callbacks=[MyTestTrainerCallback])
						expected_callbacks.append(UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
						__lowerCAmelCase:       Any          =			self.get_trainer(disable_tqdm=UpperCamelCase__)
						__lowerCAmelCase:       str          =			DEFAULT_CALLBACKS.copy() + [PrinterCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						__lowerCAmelCase:       Dict          =			self.get_trainer()

						# We can add, pop, or remove by class name
						trainer.remove_callback(UpperCamelCase__)
						expected_callbacks.remove(UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			self.get_trainer()
						__lowerCAmelCase:       Any          =			trainer.pop_callback(UpperCamelCase__)
						self.assertEqual(cb.__class__     ,  UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						trainer.add_callback(UpperCamelCase__)
						expected_callbacks.insert(0     ,  UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						# We can also add, pop, or remove by instance
						__lowerCAmelCase:       Optional[Any]          =			self.get_trainer()
						__lowerCAmelCase:       List[str]          =			trainer.callback_handler.callbacks[0]
						trainer.remove_callback(UpperCamelCase__)
						expected_callbacks.remove(UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						__lowerCAmelCase:       Optional[Any]          =			self.get_trainer()
						__lowerCAmelCase:       Dict          =			trainer.callback_handler.callbacks[0]
						__lowerCAmelCase:       str          =			trainer.pop_callback(UpperCamelCase__)
						self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)

						trainer.add_callback(UpperCamelCase__)
						expected_callbacks.insert(0     ,  UpperCamelCase__)
						self.check_callbacks_equality(trainer.callback_handler.callbacks     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						import warnings

						# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
						warnings.simplefilter(action="ignore"     ,  category=UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			self.get_trainer(callbacks=[MyTestTrainerCallback])
						trainer.train()
						__lowerCAmelCase:       List[Any]          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						# Independent log/save/eval
						__lowerCAmelCase:       Optional[int]          =			self.get_trainer(callbacks=[MyTestTrainerCallback]     ,  logging_steps=5)
						trainer.train()
						__lowerCAmelCase:       int          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						__lowerCAmelCase:       str          =			self.get_trainer(callbacks=[MyTestTrainerCallback]     ,  save_steps=5)
						trainer.train()
						__lowerCAmelCase:       List[str]          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						__lowerCAmelCase:       Any          =			self.get_trainer(callbacks=[MyTestTrainerCallback]     ,  eval_steps=5     ,  evaluation_strategy="steps")
						trainer.train()
						__lowerCAmelCase:       List[str]          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						__lowerCAmelCase:       Tuple          =			self.get_trainer(callbacks=[MyTestTrainerCallback]     ,  evaluation_strategy="epoch")
						trainer.train()
						__lowerCAmelCase:       List[Any]          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						# A bit of everything
						__lowerCAmelCase:       List[Any]          =			self.get_trainer(
						    callbacks=[MyTestTrainerCallback]     ,  logging_steps=3     ,  save_steps=1_0     ,  eval_steps=5     ,  evaluation_strategy="steps"     ,  )
						trainer.train()
						__lowerCAmelCase:       Dict          =			trainer.callback_handler.callbacks[-2].events
						self.assertEqual(UpperCamelCase__     ,  self.get_expected_events(UpperCamelCase__))

						# warning should be emitted for duplicated callbacks
						with patch("transformers.trainer_callback.logger.warning") as warn_mock:
								__lowerCAmelCase:       List[Any]          =			self.get_trainer(
								    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]     ,  )
								assert str(UpperCamelCase__) in warn_mock.call_args[0][0]









from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    __snake_case:						Tuple   =						0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      __snake_case:						Any   =						arrival_time[i]

    __snake_case:						List[Any]   =						0
    # Index showing the location of the process being performed
    __snake_case:						Union[str, Any]   =						0
    # Saves the current response ratio.
    __snake_case:						Optional[Any]   =						0
    for i in range(0					,					SCREAMING_SNAKE_CASE__):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        __snake_case:						Tuple   =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        __snake_case:						Union[str, Any]   =						temp
        __snake_case:						Optional[int]   =						i

        # Calculate the turn around time
    __snake_case:						Optional[Any]   =						current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    __snake_case:						Optional[int]   =						1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							calculate_waiting_time(
          process_name, turn_around_time, burst_time, no_of_process
      )

      print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
      for i in range(0, no_of_process):
            print(
                f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
                f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
            )

      print(f'average waiting time : {mean(waiting_time):.5f}')
      print(f'average turn around time : {mean(turn_around_time):.5f}')






