





import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


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




def  A_(      A					,   A ):



	if isinstance(__lowerCamelCase					,   (list, tuple) ):
		return honor_type(__lowerCamelCase					,   (move_to_device(__lowerCamelCase					,   __lowerCamelCase ) for t in state) )
	elif isinstance(__lowerCamelCase					,   __lowerCamelCase ):
		return type(__lowerCamelCase )({k: move_to_device(__lowerCamelCase					,   __lowerCamelCase ) for k, v in state.items()} )
	elif isinstance(__lowerCamelCase					,   torch.Tensor ):
		return state.to(__lowerCamelCase )
	return state







class 	_UpperCamelCase	(							torch.optim.Optimizer	):







					'''simple docstring'''

					def __init__(    self     :	List[str]			,       __lowercase     :	List[Any]			,       __lowercase     :	Any=True			,       __lowercase     :	Dict=None		):






						'''simple docstring'''







						UpperCAmelCase_		    =  optimizer
						UpperCAmelCase_		    =  scaler
						UpperCAmelCase_		    =  AcceleratorState()
						UpperCAmelCase_		    =  GradientState()
						UpperCAmelCase_		    =  device_placement
						UpperCAmelCase_		    =  False
						UpperCAmelCase_		    =  None

						# Handle device placement
						if device_placement:
							UpperCAmelCase_		    =  self.optimizer.state_dict()
							if self.accelerator_state.distributed_type == DistributedType.TPU:
								xm.send_cpu_data_to_device(__lowercase			,       self.accelerator_state.device		)
							else:
								UpperCAmelCase_		    =  move_to_device(__lowercase			,       self.accelerator_state.device		)
							self.optimizer.load_state_dict(__lowercase		)





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						return self.optimizer.state





					@state.setter
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  state





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						return self.optimizer.param_groups





					@param_groups.setter
					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  param_groups





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						return self.optimizer.defaults





					@defaults.setter
					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict			,       __lowercase     :	str		):






						'''simple docstring'''







						UpperCAmelCase_		    =  defaults





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	List[str]		):






						'''simple docstring'''







						self.optimizer.add_param_group(__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	Tuple		):






						'''simple docstring'''







						if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
							xm.send_cpu_data_to_device(__lowercase			,       self.accelerator_state.device		)
						self.optimizer.load_state_dict(__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						return self.optimizer.state_dict()





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	Optional[int]=None		):






						'''simple docstring'''







						if self.gradient_state.sync_gradients:
							UpperCAmelCase_		    =  """set_to_none""" in inspect.signature(self.optimizer.zero_grad		).parameters
							if accept_arg:
								if set_to_none is None:
									UpperCAmelCase_		    =  False
								self.optimizer.zero_grad(set_to_none=__lowercase		)
							else:
								if set_to_none is not None:
									raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""		)
								self.optimizer.zero_grad()





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	Dict=None		):






						'''simple docstring'''







						if self.gradient_state.sync_gradients:
							if self.accelerator_state.distributed_type == DistributedType.TPU:
								UpperCAmelCase_		    =  {"""closure""": closure} if closure is not None else {}
								xm.optimizer_step(self.optimizer			,       optimizer_args=__lowercase		)
							elif self.scaler is not None:
								UpperCAmelCase_		    =  False
								if self._last_scale is None:
									# `get_scale` is an async operation requiring full synchronization
									# on CPU and GPUs before finishing. As a result, we store away
									# the prior one to reduce the call overhead
									UpperCAmelCase_		    =  self.scaler.get_scale()
									UpperCAmelCase_		    =  True
								self.scaler.step(self.optimizer			,       __lowercase		)
								self.scaler.update()
								UpperCAmelCase_		    =  self.scaler.get_scale()
								if not new_scale:
									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
									UpperCAmelCase_		    =  scale_after < self._last_scale
								UpperCAmelCase_		    =  scale_after

							else:
								self.optimizer.step(__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Optional[Any]		):






						'''simple docstring'''







						for param_group in self.optimizer.param_groups:
							UpperCAmelCase_		    =  [parameters_map.get(__lowercase			,       __lowercase		) for p in param_group["""params"""]]





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						warnings.warn(
						    """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
						    """`optimizer.step_was_skipped` instead."""			,       __lowercase			,       )
						return self._is_overflow





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]		):






						'''simple docstring'''







						return self._is_overflow





					def __getstate__(    self     :	Union[str, Any]		):






						'''simple docstring'''







						return self.__dict__.copy()





					def __setstate__(    self     :	List[str]			,       __lowercase     :	Optional[Any]		):






						'''simple docstring'''







						self.__dict__.update(__lowercase		)






from __future__ import annotations

from statistics import mean




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	lowercase_			 = 2
	lowercase_			 = list(__lowerCamelCase  )
	lowercase_			 = [0] * len(__lowerCamelCase  )
	lowercase_			 = 0
	while True:
		lowercase_			 = True
		for i, burst_time in enumerate(__lowerCamelCase  ):
			if rem_burst_times[i] > 0:
				lowercase_			 = False
				if rem_burst_times[i] > quantum:
					t += quantum
					rem_burst_times[i] -= quantum
				else:
					t += rem_burst_times[i]
					lowercase_			 = t - burst_time
					lowercase_			 = 0
		if done is True:
			return waiting_times




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ,			__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	return [burst + waiting for burst, waiting in zip(__lowerCamelCase  ,			__lowerCamelCase  )]


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				[3, 5, 7]
							SCREAMING_SNAKE_CASE__       			=				calculate_waiting_times(burst_times)
							SCREAMING_SNAKE_CASE__       			=				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}""")

