






"""simple docstring"""



from __future__ import annotations

from statistics import mean




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[int]   )		->list[int]:
						A__    :	Any			=	2
						A__    :	Union[str, Any]			=	list(UpperCAmelCase__   )
						A__    :	str			=	[0] * len(UpperCAmelCase__   )
						A__    :	Tuple			=	0
						while True:
												A__    :	Dict			=	True
												for i, burst_time in enumerate(UpperCAmelCase__   ):
																		if rem_burst_times[i] > 0:
																								A__    :	List[str]			=	False
																								if rem_burst_times[i] > quantum:
																														t += quantum
																														rem_burst_times[i] -= quantum
																								else:
																														t += rem_burst_times[i]
																														A__    :	Union[str, Any]			=	t - burst_time
																														A__    :	Union[str, Any]			=	0
												if done is True:
																		return waiting_times




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[int],    UpperCAmelCase__							:       list[int]   )		->list[int]:
						return [burst + waiting for burst, waiting in zip(UpperCAmelCase__,    UpperCAmelCase__   )]


if __name__ == "__main__":
						A_     =				[3, 5, 7]
						A_     =				calculate_waiting_times(burst_times)
						A_     =				calculate_turn_around_times(burst_times, waiting_times)
						print('''Process ID \tBurst Time \tWaiting Time \tTurnaround Time''')
						for i, burst_time in enumerate(burst_times):
												print(
												    F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
												    F'{turn_around_times[i]}'
												)
						print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
						print(F'Average turn around time = {mean(turn_around_times):.5f}')














"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       List[dict],    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       GradScaler = None,    )		->Any:
						# Model states
						for i, state in enumerate(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	int			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Model weights saved in {output_model_file}'   )
						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	str			=	opt.state_dict()
												A__    :	Any			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Optional[int]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Optimizer state saved in {output_optimizer_file}'   )
						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	scheduler.state_dict()
												A__    :	Union[str, Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Tuple			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Scheduler state saved in {output_scheduler_file}'   )
						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	scaler.state_dict()
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'   )
						# Random number generator states
						A__    :	str			=	{}
						A__    :	str			=	f'{RNG_STATE_NAME}_{process_index}.pkl'
						A__    :	Optional[Any]			=	random.getstate()
						A__    :	str			=	np.random.get_state()
						A__    :	List[str]			=	torch.get_rng_state()
						if is_xpu_available():
												A__    :	Optional[Any]			=	torch.xpu.get_rng_state_all()
						else:
												A__    :	str			=	torch.cuda.get_rng_state_all()
						if is_tpu_available():
												A__    :	Tuple			=	xm.get_rng_state()
						A__    :	str			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
						torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
						logger.info(f'Random states saved in {output_states_file}'   )
						return output_dir




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]=None,    UpperCAmelCase__							:       str=None,    **UpperCAmelCase__							:       Optional[int],    )		->List[str]:
						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""   )
						if map_location is None:
												A__    :	Union[str, Any]			=	"""cpu"""
						elif map_location == "on_device":
												A__    :	Any			=	PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase__   ):
												A__    :	Any			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												models[i].load_state_dict(torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   ),    **UpperCAmelCase__   )
						logger.info("""All model weights loaded successfully"""   )

						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Dict			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												A__    :	List[Any]			=	torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   )
												optimizers[i].load_state_dict(UpperCAmelCase__   )
						logger.info("""All optimizer states loaded successfully"""   )

						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	List[Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scheduler.load_state_dict(torch.load(UpperCAmelCase__   )   )
						logger.info("""All scheduler states loaded successfully"""   )

						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scaler.load_state_dict(torch.load(UpperCAmelCase__   )   )
												logger.info("""GradScaler state loaded successfully"""   )

						# Random states
						try:
												A__    :	Any			=	torch.load(os.path.join(UpperCAmelCase__,    f'{RNG_STATE_NAME}_{process_index}.pkl'   )   )
												random.setstate(states["""random_state"""]   )
												np.random.set_state(states["""numpy_random_seed"""]   )
												torch.set_rng_state(states["""torch_manual_seed"""]   )
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]   )
												else:
																		torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]   )
												if is_tpu_available():
																		xm.set_rng_state(states["""xm_seed"""]   )
												logger.info("""All random states loaded successfully"""   )
						except Exception:
												logger.info("""Could not load random states"""   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       int = 0   )		->Dict:
						A__    :	Dict			=	Path(UpperCAmelCase__   ) / f'custom_checkpoint_{index}.pkl'
						logger.info(f'Saving the state of {get_pretty_name(UpperCAmelCase__   )} to {save_location}'   )
						torch.save(obj.state_dict(),    UpperCAmelCase__   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int = 0   )		->str:
						A__    :	Optional[int]			=	f'{path}/custom_checkpoint_{index}.pkl'
						logger.info(f'Loading the state of {get_pretty_name(UpperCAmelCase__   )} from {load_location}'   )
						obj.load_state_dict(torch.load(UpperCAmelCase__,    map_location="""cpu"""   )   )






