






from statistics import mean

import numpy as np
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  0
      # Number of processes finished
      UpperCamelCase					       =  0
      # Displays the finished process.
      # If it is 0, the performance is completed if it is 1, before the performance.
      UpperCamelCase					       =  [0] * no_of_process
      # List to include calculation results
      UpperCamelCase					       =  [0] * no_of_process

      # Sort by arrival time.
      UpperCamelCase					       =  [burst_time[i] for i in np.argsort(__UpperCAmelCase)]
      UpperCamelCase					       =  [process_name[i] for i in np.argsort(__UpperCAmelCase)]
      arrival_time.sort()

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

            UpperCamelCase					       =  0
            # Index showing the location of the process being performed
            UpperCamelCase					       =  0
            # Saves the current response ratio.
            UpperCamelCase					       =  0
            for i in range(0       ,							__UpperCAmelCase):
                  if finished_process[i] == 0 and arrival_time[i] <= current_time:
                        UpperCamelCase					       =  (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                            i
                        ]
                  if response_ratio < temp:
                        UpperCamelCase					       =  temp
                        UpperCamelCase					       =  i

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

      return turn_around_time
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  [0] * no_of_process
      for i in range(0       ,							__UpperCAmelCase):
            UpperCamelCase					       =  turn_around_time[i] - burst_time[i]
      return waiting_time


if __name__ == "__main__":
       __magic_name__   :  List[Any]        =							5
       __magic_name__   :  Optional[int]        =							['A', 'B', 'C', 'D', 'E']
       __magic_name__   :  Union[str, Any]        =							[1, 2, 3, 4, 5]
       __magic_name__   :  Dict        =							[1, 2, 3, 4, 5]

       __magic_name__   :  List[Any]        =							calculate_turn_around_time(
           process_name, arrival_time, burst_time, no_of_process
       )
       __magic_name__   :  int        =							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}')








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Tuple:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  a__												= eval_examples
  a__												= post_process_function

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = "eval"		,				**SCREAMING_SNAKE_CASE		,				) ->			Dict[str, float]:
  a__												= gen_kwargs.copy()
  a__												= (
      gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''      ) is not None else self.args.generation_max_length
  )
  a__												= (
      gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
  )
  a__												= gen_kwargs

  a__												= self.eval_dataset if eval_dataset is None else eval_dataset
  a__												= self.get_eval_dataloader(SCREAMING_SNAKE_CASE      )
  a__												= self.eval_examples if eval_examples is None else eval_examples

  # Temporarily disable metric computation, we will do it in the loop here.
  a__												= self.compute_metrics
  a__												= None
  a__												= time.time()
  a__												= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
  try:
   a__												= eval_loop(
       SCREAMING_SNAKE_CASE		,				description='''Evaluation'''		,				prediction_loss_only=True if compute_metrics is None else None		,				ignore_keys=SCREAMING_SNAKE_CASE		,				metric_key_prefix=SCREAMING_SNAKE_CASE		,				)
  finally:
   a__												= compute_metrics
  a__												= self.args.eval_batch_size * self.args.world_size
  if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
   start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
  output.metrics.update(
      speed_metrics(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				num_samples=output.num_samples		,				num_steps=math.ceil(output.num_samples / total_batch_size      )		,				)      )

  if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
   # Only the main node write the results by default
   a__												= self.post_process_function(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   a__												= self.compute_metrics(SCREAMING_SNAKE_CASE      )

   # Prefix all keys with metric_key_prefix + '_'
   for key in list(metrics.keys()      ):
    if not key.startswith(f"{metric_key_prefix}_"      ):
     a__												= metrics.pop(SCREAMING_SNAKE_CASE      )

   metrics.update(output.metrics      )
  else:
   a__												= output.metrics

  if self.args.should_log:
   # Only the main node log the results by default
   self.log(SCREAMING_SNAKE_CASE      )

  if self.args.tpu_metrics_debug or self.args.debug:
   # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
   xm.master_print(met.metrics_report()      )

  a__												= self.callback_handler.on_evaluate(self.args		,				self.state		,				self.control		,				SCREAMING_SNAKE_CASE      )
  return metrics



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE = "test"		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= gen_kwargs.copy()

  a__												= self.get_test_dataloader(SCREAMING_SNAKE_CASE      )

  # Temporarily disable metric computation, we will do it in the loop here.
  a__												= self.compute_metrics
  a__												= None
  a__												= time.time()
  a__												= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
  try:
   a__												= eval_loop(
       SCREAMING_SNAKE_CASE		,				description='''Prediction'''		,				prediction_loss_only=True if compute_metrics is None else None		,				ignore_keys=SCREAMING_SNAKE_CASE		,				metric_key_prefix=SCREAMING_SNAKE_CASE		,				)
  finally:
   a__												= compute_metrics

  a__												= self.args.eval_batch_size * self.args.world_size
  if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
   start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
  output.metrics.update(
      speed_metrics(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				num_samples=output.num_samples		,				num_steps=math.ceil(output.num_samples / total_batch_size      )		,				)      )
  if self.post_process_function is None or self.compute_metrics is None:
   return output

  a__												= self.post_process_function(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''predict'''      )
  a__												= self.compute_metrics(SCREAMING_SNAKE_CASE      )

  # Prefix all keys with metric_key_prefix + '_'
  for key in list(metrics.keys()      ):
   if not key.startswith(f"{metric_key_prefix}_"      ):
    a__												= metrics.pop(SCREAMING_SNAKE_CASE      )
  metrics.update(output.metrics      )
  return PredictionOutput(predictions=predictions.predictions		,				label_ids=predictions.label_ids		,				metrics=SCREAMING_SNAKE_CASE      )





