


'''simple docstring'''

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 lowerCamelCase_  				=						filter(lambda UpperCAmelCase_	: p.requires_grad      ,       model.parameters()	)
 lowerCamelCase_  				=						sum([np.prod(p.size()	) for p in model_parameters]	)
 return params


a_      			=				logging.getLogger(__name__)





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		int	):
 if metric == "rouge2":
  lowerCamelCase_  				=						"{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  lowerCamelCase_  				=						"{val_avg_bleu:.4f}-{step_count}"
 elif metric == "em":
  lowerCamelCase_  				=						"{val_avg_em:.4f}-{step_count}"
 else:
  raise NotImplementedError(
      F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
      " function."	)

 lowerCamelCase_  				=						ModelCheckpoint(
     dirpath=UpperCAmelCase_      ,       filename=UpperCAmelCase_      ,       monitor=F'''val_{metric}'''      ,       mode="max"      ,       save_top_k=3      ,       every_n_epochs=1      ,       )
 return checkpoint_callback





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		List[str]	):
 return EarlyStopping(
     monitor=F'''val_{metric}'''      ,       mode="min" if "loss" in metric else "max"      ,       patience=UpperCAmelCase_      ,       verbose=UpperCAmelCase_      ,       )







class   snake_case					(					pl.Callback    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
   pl_module.logger.log_metrics(UpperCamelCase  )






  @rank_zero_only
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=True  ):







   """simple docstring"""
   logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''  )
   lowerCamelCase_  				=						trainer.callback_metrics
   trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}  )
   # Log results
   lowerCamelCase_  				=						Path(pl_module.hparams.output_dir  )
   if type_path == "test":
    lowerCamelCase_  				=						od / "test_results.txt"
    lowerCamelCase_  				=						od / "test_generations.txt"
   else:
    # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
    # If people want this it will be easy enough to add back.
    lowerCamelCase_  				=						od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
    lowerCamelCase_  				=						od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
    results_file.parent.mkdir(exist_ok=UpperCamelCase  )
    generations_file.parent.mkdir(exist_ok=UpperCamelCase  )
   with open(UpperCamelCase    ,						"a+"  ) as writer:
    for key in sorted(UpperCamelCase  ):
     if key in ["log", "progress_bar", "preds"]:
      continue
     lowerCamelCase_  				=						metrics[key]
     if isinstance(UpperCamelCase    ,						torch.Tensor  ):
      lowerCamelCase_  				=						val.item()
     lowerCamelCase_  				=						f'''{key}: {val:.6f}\n'''
     writer.write(UpperCamelCase  )

   if not save_generations:
    return

   if "preds" in metrics:
    lowerCamelCase_  				=						"\n".join(metrics["preds"]  )
    generations_file.open("w+"  ).write(UpperCamelCase  )






  @rank_zero_only
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   try:
    lowerCamelCase_  				=						pl_module.model.model.num_parameters()
   except AttributeError:
    lowerCamelCase_  				=						pl_module.model.num_parameters()

   lowerCamelCase_  				=						count_trainable_parameters(UpperCamelCase  )
   # mp stands for million parameters
   trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}  )






  @rank_zero_only
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   save_json(pl_module.metrics    ,						pl_module.metrics_save_path  )
   return self._write_logs(UpperCamelCase    ,						UpperCamelCase    ,						"test"  )







  @rank_zero_only
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   save_json(pl_module.metrics    ,						pl_module.metrics_save_path  )
   # Uncommenting this will save val generations
   # return self._write_logs(trainer, pl_module, "valid")







'''simple docstring'''

from __future__ import annotations

import pandas as pd





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						[0] * no_of_processes
 lowerCamelCase_  				=						[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i]

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						999999999
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						False

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

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

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

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

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

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

   if waiting_time[short] < 0:
    lowerCamelCase_  				=						0

        # Increment time
  increment_time += 1
 return waiting_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		list[int]	):
 lowerCamelCase_  				=						[0] * no_of_processes
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i] + waiting_time[i]
 return turn_around_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						total_waiting_time + waiting_time[i]
  lowerCamelCase_  				=						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_						:       Dict      			=				int(input())
     a_						:       Any      			=				[0] * no_of_processes
     a_						:       Optional[int]      			=				[0] * no_of_processes
     a_						:       Tuple      			=				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_						:       str      			=				map(int, input().split())

     a_						:       List[Any]      			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     a_						:       int      			=				burst_time
     a_						:       Union[str, Any]      			=				no_of_processes
     a_						:       Optional[int]      			=				waiting_time
     a_						:       Any      			=				calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     a_						:       Optional[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)



