
from __future__ import annotations

from statistics import mean



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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				return [burst + waiting for burst, waiting in zip(__UpperCamelCase ,						__UpperCamelCase     )]


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       [3, 5, 7]
						SCREAMING_SNAKE_CASE   :		Dict					 =       calculate_waiting_times(burst_times)
						SCREAMING_SNAKE_CASE   :		str					 =       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}''')

import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
						import torch

SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   ):
				return importlib.util.find_spec('''deepspeed'''     ) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							Dict:





									'''simple docstring'''
									# set global weakref object
									set_hf_deepspeed_config(self						)
									dep_version_check('''accelerate'''						)
									dep_version_check('''deepspeed'''						)
									super().__init__(a__						)






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  None
									A_       	=  []


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									if self._dtype is None:
													raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''						)
									return self._dtype


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  self.get_value(a__						)
									if val is None:
													return False
									else:
													return val == "auto"


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=None       ,       a__=True						)		->							List[str]:





									'''simple docstring'''
									A_	,					A_       	=  self.find_config_node(a__						)
									if config is None:
													return

									if config.get(a__						) == "auto":
													A_       	=  hf_val
													return

									if not must_match:
													return

									A_       	=  config.get(a__						)
									if ds_val is not None and ds_val != hf_val:
													self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"						)

					__magic_name__						:							str     	=	partialmethod(A							,       must_match=A       )


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    '''train_micro_batch_size_per_gpu'''       ,       args.per_device_train_batch_size       ,       '''per_device_train_batch_size'''						)
									self.fill_match('''gradient_accumulation_steps'''       ,       args.gradient_accumulation_steps       ,       '''gradient_accumulation_steps'''						)
									self.fill_match('''train_batch_size'''       ,       a__       ,       '''train_batch_size (calculated)'''						)
									self.fill_match('''gradient_clipping'''       ,       args.max_grad_norm       ,       '''max_grad_norm'''						)

									self.fill_match('''optimizer.params.lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									self.fill_match('''optimizer.params.betas'''       ,       [args.adam_betaa, args.adam_betaa]       ,       '''adam_beta1+adam_beta2'''						)
									self.fill_match('''optimizer.params.eps'''       ,       args.adam_epsilon       ,       '''adam_epsilon'''						)
									self.fill_match('''optimizer.params.weight_decay'''       ,       args.weight_decay       ,       '''weight_decay'''						)

									self.fill_only('''scheduler.params.warmup_min_lr'''       ,       0						)  # not a trainer arg
									self.fill_match('''scheduler.params.warmup_max_lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
													A_       	=  '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
									else:
													A_       	=  None

									if args.save_on_each_node:
													# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
													A_       	=  self.config.get('''checkpoint'''       ,       {}						)
													A_       	=  args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    '''fp16.enabled'''       ,       ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')       ,       '''fp16|fp16_full_eval+fp16_backend(amp)'''       ,       )

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match('''amp.enabled'''       ,       fpaa_backend == '''apex'''       ,       '''fp16+fp16_backend(apex)'''						)
									self.fill_match('''amp.opt_level'''       ,       args.fpaa_opt_level       ,       '''fp16_opt_level'''						)

									self.fill_match('''bf16.enabled'''       ,       (args.bfaa or args.bfaa_full_eval)       ,       '''bf16|bf16_full_eval'''						)

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true('''bf16.enabled'''						):
													A_       	=  torch.bfloataa
									elif self.is_false('''fp16.enabled'''						):
													A_       	=  torch.floataa
									else:
													A_       	=  torch.floataa


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Tuple:





									'''simple docstring'''
									A_       	=  [
									    '''zero_optimization.reduce_bucket_size''',
									    '''zero_optimization.stage3_prefetch_bucket_size''',
									    '''zero_optimization.stage3_param_persistence_threshold''',
									]
									A_       	=  [x for x in hidden_size_based_keys if self.is_auto(a__						)]

									if len(a__						) > 0:
													if hasattr(model.config       ,       '''hidden_size'''						):
																	A_       	=  model.config.hidden_size
													elif hasattr(model.config       ,       '''hidden_sizes'''						):
																	# if there are many hidden sizes pick the largest one
																	A_       	=  max(model.config.hidden_sizes						)
													else:
																	raise ValueError(
																	    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
																	    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
																	    F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
																	    '''`auto` values for these keys with an integer value of your choice.'''						)

													self.fill_only('''zero_optimization.reduce_bucket_size'''       ,       hidden_size * hidden_size						)
													if self.is_zeroa():
																	# automatically assign the optimal config values based on model config
																	self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''       ,       0.9 * hidden_size * hidden_size						)
																	self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''       ,       1_0 * hidden_size						)

        # scheduler
									self.fill_match('''scheduler.params.total_num_steps'''       ,       a__       ,       '''num_training_steps (calculated)'''						)
									self.fill_match('''scheduler.params.warmup_num_steps'''       ,       args.get_warmup_steps(a__						)       ,       '''warmup_steps'''						)

									if len(self.mismatches						) > 0:
													A_       	=  '''\n'''.join(self.mismatches						)
													raise ValueError(
													    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
													    F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       None



def        lowerCamelCase_  (   __UpperCamelCase     ):
				# this is a special weakref global object to allow us to get to Deepspeed config from APIs
				# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
				global _hf_deepspeed_config_weak_ref
				# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
				A_       	=  weakref.ref(__UpperCamelCase     )



def        lowerCamelCase_  (   ):
				# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
				global _hf_deepspeed_config_weak_ref
				A_       	=  None



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().is_zeroa()
				else:
								return False



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().config
				else:
								return None



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				from accelerate.utils import DummyOptim, DummyScheduler

				A_       	=  hf_deepspeed_config.config

				# Optimizer + Scheduler
				# Currently supported combos:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Yes
				# 3. DS scheduler + HF optimizer: Yes
				# 4. HF scheduler + DS optimizer: No
				#
				# Unless Offload is enabled in which case it's:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Mostly*
				# 3. DS scheduler + HF optimizer: Mostly*
				# 4. HF scheduler + DS optimizer: No
				#
				# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

				A_       	=  None
				if "optimizer" in config:
								if args.adafactor:
												raise ValueError(
												    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
												    '''Only one optimizer can be configured.'''     )
								A_       	=  DummyOptim(params=__UpperCamelCase     )
				else:
								if hf_deepspeed_config.is_offload():
												logger.info(
												    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
												    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''     )

								# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
								# But trainer uses AdamW by default.
								A_       	=  trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								A_       	=  True

				A_       	=  None
				if "scheduler" in config:
								A_       	=  DummyScheduler(__UpperCamelCase     )
				else:
								if isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
												raise ValueError(
												    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
												    '''Please configure a scheduler in the DeepSpeed config.'''     )
								A_       	=  trainer.create_scheduler(num_training_steps=__UpperCamelCase ,						optimizer=__UpperCamelCase     )

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=False     ):
				from deepspeed.utils import logger as ds_logger

				A_       	=  trainer.model
				A_       	=  trainer.args

				A_       	=  trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# set the Deepspeed log level consistent with the Trainer
				ds_logger.setLevel(args.get_process_log_level()     )

				if inference:
								# only Z3 makes sense for the inference
								if not hf_deepspeed_config.is_zeroa():
												raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''     )

								# in case the training config is re-used for inference
								hf_deepspeed_config.del_config_sub_tree('''optimizer'''     )
								hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''     )
								A_	,					A_       	=  None, None
								A_       	=  None
				else:
								A_       	=  None  # important for when deepspeed_init is used as re-init
								A_       	=  list(filter(lambda __UpperCamelCase     : p.requires_grad ,						model.parameters()     )     )
								A_	,					A_       	=  deepspeed_optim_sched(
								    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# keep for quick debug:
				# from pprint import pprint; pprint(config)

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# it's possible that the user is trying to resume from model_path, which doesn't necessarily
				# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
				# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
				# path contains what looks like a deepspeed checkpoint
				import glob

				A_       	=  sorted(glob.glob(F"{checkpoint_path}/global_step*"     )     )

				if len(__UpperCamelCase     ) > 0:
								logger.info(F"Attempting to resume from {checkpoint_path}"     )
								# this magically updates self.optimizer and self.lr_scheduler
								A_	,					A_       	=  deepspeed_engine.load_checkpoint(
								    __UpperCamelCase ,						load_optimizer_states=__UpperCamelCase ,						load_lr_scheduler_states=__UpperCamelCase     )
								if load_path is None:
												raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"     )
				else:
								raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"     )