
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  __magic_name__ (   __lowerCAmelCase	:						str							)			->   str:
				__lowerCamelCase    					=	filter(lambda __lowerCAmelCase							: p.requires_grad		,  model.parameters()							)
				__lowerCamelCase    					=	sum([np.prod(p.size()							) for p in model_parameters]							)
				return params


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    logging.getLogger(__name__)




def  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Dict							)			->   Tuple:
				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}'''
				elif metric == "loss":
								__lowerCamelCase    					=	'''{val_avg_loss:.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=__lowerCAmelCase		,  filename=__lowerCAmelCase		,  monitor=f'''val_{metric}'''		,  mode='''max'''		,  save_top_k=1		,  every_n_epochs=1		,  )

				return checkpoint_callback




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						List[Any]							)			->   int:
				return EarlyStopping(
				    monitor=f'''val_{metric}'''		,  mode='''min''' if '''loss''' in metric else '''max'''		,  patience=__lowerCAmelCase		,  verbose=__lowerCAmelCase		,  )







class    lowerCAmelCase__     (	pl.Callback			):






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	{f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
						pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__  )






		@rank_zero_only
		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							pl.Trainer							,			SCREAMING_SNAKE_CASE__		:							pl.LightningModule							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							List[str]=True  )     ->							None:
						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=SCREAMING_SNAKE_CASE__  )
										generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )
						with open(SCREAMING_SNAKE_CASE__							,			'''a+'''  ) as writer:
										for key in sorted(SCREAMING_SNAKE_CASE__  ):
														if key in ["log", "progress_bar", "preds"]:
																		continue
														__lowerCamelCase    					=	metrics[key]
														if isinstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  ):
																		__lowerCamelCase    					=	val.item()
														__lowerCamelCase    					=	f'''{key}: {val:.6f}\n'''
														writer.write(SCREAMING_SNAKE_CASE__  )

						if not save_generations:
										return

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






		@rank_zero_only
		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Any:
						try:
										__lowerCamelCase    					=	pl_module.model.model.num_parameters()
						except AttributeError:
										__lowerCamelCase    					=	pl_module.model.num_parameters()

						__lowerCamelCase    					=	count_trainable_parameters(SCREAMING_SNAKE_CASE__  )
						# 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  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							pl.Trainer							,			SCREAMING_SNAKE_CASE__		:							pl.LightningModule  )     ->							List[str]:
						save_json(pl_module.metrics							,			pl_module.metrics_save_path  )
						return self._write_logs(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''test'''  )







		@rank_zero_only
		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							pl.Trainer							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Dict:
						save_json(pl_module.metrics							,			pl_module.metrics_save_path  )
						# Uncommenting this will save val generations
						# return self._write_logs(trainer, pl_module, "valid")





from __future__ import annotations

from statistics import mean




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				__lowerCamelCase    					=	[0] * no_of_processes

				# Initialize remaining_time to waiting_time.

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i]
				__lowerCamelCase    					=	[]

				__lowerCamelCase    					=	0
				__lowerCamelCase    					=	0

				# When processes are not completed,
				# A process whose arrival time has passed \
				# and has remaining execution time is put into the ready_process.
				# The shortest process in the ready_process, target_process is executed.

				while completed != no_of_processes:
								__lowerCamelCase    					=	[]
								__lowerCamelCase    					=	-1

								for i in range(__lowerCAmelCase							):
												if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																ready_process.append(__lowerCAmelCase							)

								if len(__lowerCAmelCase							) > 0:
												__lowerCamelCase    					=	ready_process[0]
												for i in ready_process:
																if remaining_time[i] < remaining_time[target_process]:
																				__lowerCamelCase    					=	i
												total_time += burst_time[target_process]
												completed += 1
												__lowerCamelCase    					=	0
												__lowerCamelCase    					=	(
												    total_time - arrival_time[target_process] - burst_time[target_process]
												)
								else:
												total_time += 1

				return waiting_time




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


if __name__ == "__main__":
					print("[TEST CASE 01]")

					SCREAMING_SNAKE_CASE__   :			Tuple												=    4
					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    [2, 5, 3, 7]
					SCREAMING_SNAKE_CASE__   :			List[str]												=    [0, 0, 0, 0]
					SCREAMING_SNAKE_CASE__   :			str												=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
										    F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
										)
					print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
					print(F'Average turnaround time = {mean(turn_around_time):.5f}')



