



'''simple docstring'''



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

lowerCAmelCase						:		int						      = logging.get_logger(__name__)



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	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        UpperCAmelCase__						(		UpperCamelCase__			):



					def __init__(     self      ,					UpperCamelCase  )  ->      Dict:
						# set global weakref object
						set_hf_deepspeed_config(self  )
						dep_version_check("accelerate"  )
						dep_version_check("deepspeed"  )
						super().__init__(UpperCamelCase  )






class        UpperCAmelCase__						(		UpperCamelCase__			):



					def __init__(     self      ,					UpperCamelCase  )  ->      Optional[int]:
						super().__init__(UpperCamelCase  )
						__lowerCAmelCase       				=	None
						__lowerCAmelCase       				=	[]



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						if self._dtype is None:
							raise ValueError("trainer_config_process() wasn't called yet to tell dtype"  )
						return self._dtype



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Dict:
						__lowerCAmelCase       				=	self.get_value(UpperCamelCase  )
						if val is None:
							return False
						else:
							return val == "auto"



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=None      ,					UpperCamelCase=True  )  ->      Dict:
						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.find_config_node(UpperCamelCase  )
						if config is None:
							return

						if config.get(UpperCamelCase  ) == "auto":
							__lowerCAmelCase       				=	hf_val
							return

						if not must_match:
							return

						__lowerCAmelCase       				=	config.get(UpperCamelCase  )
						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}'''  )

					a   :  Optional[Any]   =				partialmethod(UpperCamelCase__				,							must_match=UpperCamelCase__			)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	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"      ,					UpperCamelCase      ,					"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:
							__lowerCAmelCase       				=	"apex" if args.fpaa_backend == "apex" else "amp"
						else:
							__lowerCAmelCase       				=	None

						if args.save_on_each_node:
							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
							__lowerCAmelCase       				=	self.config.get("checkpoint"      ,					{}  )
							__lowerCAmelCase       				=	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"  ):
							__lowerCAmelCase       				=	torch.bfloataa
						elif self.is_false("fp16.enabled"  ):
							__lowerCAmelCase       				=	torch.floataa
						else:
							__lowerCAmelCase       				=	torch.floataa



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      str:
						__lowerCAmelCase       				=	[
						    "zero_optimization.reduce_bucket_size",
						    "zero_optimization.stage3_prefetch_bucket_size",
						    "zero_optimization.stage3_param_persistence_threshold",
						]
						__lowerCAmelCase       				=	[x for x in hidden_size_based_keys if self.is_auto(UpperCamelCase  )]

						if len(UpperCamelCase  ) > 0:
							if hasattr(model.config      ,					"hidden_size"  ):
								__lowerCAmelCase       				=	model.config.hidden_size
							elif hasattr(model.config      ,					"hidden_sizes"  ):
								# if there are many hidden sizes pick the largest one
								__lowerCAmelCase       				=	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"      ,					10 * hidden_size  )

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

						if len(self.mismatches  ) > 0:
							__lowerCAmelCase       				=	"\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\'.'''  )


lowerCAmelCase						:		str						      = None



def 	__lowerCAmelCase       (				lowerCamelCase :    Any       ):






	'''simple docstring'''




	global _hf_deepspeed_config_weak_ref
	# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
	__lowerCAmelCase       				=	weakref.ref(lowerCamelCase       )



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	global _hf_deepspeed_config_weak_ref
	__lowerCAmelCase       				=	None



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	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       (				):






	'''simple docstring'''




	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       (				lowerCamelCase :    List[str]					,		lowerCamelCase :    int					,		lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    List[str]       ):






	'''simple docstring'''




	from accelerate.utils import DummyOptim, DummyScheduler

	__lowerCAmelCase       				=	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)

	__lowerCAmelCase       				=	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."       )
		__lowerCAmelCase       				=	DummyOptim(params=lowerCamelCase       )
	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.
		__lowerCAmelCase       				=	trainer.create_optimizer()
		# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
		__lowerCAmelCase       				=	True

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

	return optimizer, lr_scheduler



def 	__lowerCAmelCase       (				lowerCamelCase :    List[str]					,		lowerCamelCase :    Dict					,		lowerCamelCase :    Any=False       ):






	'''simple docstring'''




	from deepspeed.utils import logger as ds_logger

	__lowerCAmelCase       				=	trainer.model
	__lowerCAmelCase       				=	trainer.args

	__lowerCAmelCase       				=	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(lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )

	# 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"       )
		__lowerCAmelCase				,							__lowerCAmelCase       				=	None, None
		__lowerCAmelCase       				=	None
	else:
		__lowerCAmelCase       				=	None  # important for when deepspeed_init is used as re-init
		__lowerCAmelCase       				=	list(filter(lambda lowerCamelCase       : p.requires_grad					,		model.parameters()       )       )
		__lowerCAmelCase				,							__lowerCAmelCase       				=	deepspeed_optim_sched(
		    lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )

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

	return optimizer, lr_scheduler




def 	__lowerCAmelCase       (				lowerCamelCase :    List[str]					,		lowerCamelCase :    int       ):






	'''simple docstring'''




	import glob

	__lowerCAmelCase       				=	sorted(glob.glob(f'''{checkpoint_path}/global_step*'''       )       )

	if len(lowerCamelCase       ) > 0:
		logger.info(f'''Attempting to resume from {checkpoint_path}'''       )
		# this magically updates self.optimizer and self.lr_scheduler
		__lowerCAmelCase				,							__lowerCAmelCase       				=	deepspeed_engine.load_checkpoint(
		    lowerCamelCase					,		load_optimizer_states=lowerCamelCase					,		load_lr_scheduler_states=lowerCamelCase       )
		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}'''       )




'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()