





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

__A    :   List[Any]          =       logging.get_logger(__name__)





def 						__SCREAMING_SNAKE_CASE  (    )  ->    str:







			'''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      A_	(_A      ):
	def __init__(   self							, _A		):






				'''simple docstring'''







				set_hf_deepspeed_config(self		)
				dep_version_check('''accelerate'''		)
				dep_version_check('''deepspeed'''		)
				super().__init__(UpperCamelCase__		)

class      A_	(_A      ):
	def __init__(   self							, _A		):






				'''simple docstring'''







				super().__init__(UpperCamelCase__		)
				UpperCAmelCase       		=    None
				UpperCAmelCase       		=    []
	def 					_lowercase    (   self		):






				'''simple docstring'''







				if self._dtype is None:
							raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''		)
				return self._dtype
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_value(UpperCamelCase__		)
				if val is None:
							return False
				else:
							return val == "auto"
	def 					_lowercase    (   self							, _A							, _A							, _A=None							, _A=True		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.find_config_node(UpperCamelCase__		)
				if config is None:
							return

				if config.get(UpperCamelCase__		) == "auto":
							UpperCAmelCase       		=    hf_val
							return

				if not must_match:
							return

				UpperCAmelCase       		=    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}"""		)

	UpperCAmelCase__ =      partialmethod(_A					,							must_match=_A      )
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    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:
							UpperCAmelCase       		=    '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
				else:
							UpperCAmelCase       		=    None

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







	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    [
				    '''zero_optimization.reduce_bucket_size''',
				    '''zero_optimization.stage3_prefetch_bucket_size''',
				    '''zero_optimization.stage3_param_persistence_threshold''',
				]
				UpperCAmelCase       		=    [x for x in hidden_size_based_keys if self.is_auto(UpperCamelCase__		)]

				if len(UpperCamelCase__		) > 0:
							if hasattr(model.config							, '''hidden_size'''		):
										UpperCAmelCase       		=    model.config.hidden_size
							elif hasattr(model.config							, '''hidden_sizes'''		):
										# if there are many hidden sizes pick the largest one
										UpperCAmelCase       		=    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'''							, 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:
							UpperCAmelCase       		=    '''\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\'."""		)
__A    :   Optional[int]          =       None





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Tuple:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    )  ->    List[str]:







			'''simple docstring'''

			global _hf_deepspeed_config_weak_ref
			UpperCAmelCase       		=    None





def 						__SCREAMING_SNAKE_CASE  (    )  ->    List[Any]:







			'''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 						__SCREAMING_SNAKE_CASE  (    )  ->    Any:







			'''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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			from accelerate.utils import DummyOptim, DummyScheduler

			UpperCAmelCase       		=    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)

			UpperCAmelCase       		=    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.'''			)
						UpperCAmelCase       		=    DummyOptim(params=A_			)
			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.
						UpperCAmelCase       		=    trainer.create_optimizer()
						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
						UpperCAmelCase       		=    True

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

			return optimizer, lr_scheduler





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__=False			)  ->    Tuple:







			'''simple docstring'''

			from deepspeed.utils import logger as ds_logger

			UpperCAmelCase       		=    trainer.model
			UpperCAmelCase       		=    trainer.args

			UpperCAmelCase       		=    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(A_      ,      A_      ,      A_			)

			# 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'''			)
						UpperCAmelCase		,      UpperCAmelCase       		=    None, None
						UpperCAmelCase       		=    None
			else:
						UpperCAmelCase       		=    None  # important for when deepspeed_init is used as re-init
						UpperCAmelCase       		=    list(filter(lambda UpperCamelCase__			: p.requires_grad      ,      model.parameters()			)			)
						UpperCAmelCase		,      UpperCAmelCase       		=    deepspeed_optim_sched(
						    A_      ,      A_      ,      A_      ,      A_      ,      A_			)

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

			return optimizer, lr_scheduler






def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			import glob

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

			if len(A_			) > 0:
						logger.info(F"""Attempting to resume from {checkpoint_path}"""			)
						# this magically updates self.optimizer and self.lr_scheduler
						UpperCAmelCase		,      UpperCAmelCase       		=    deepspeed_engine.load_checkpoint(
						    A_      ,      load_optimizer_states=A_      ,      load_lr_scheduler_states=A_			)
						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}"""			)










def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    float:







			'''simple docstring'''

			if mass < 0:
						raise ValueError('''The mass of the body cannot be negative'''			)
			if radius <= 0:
						raise ValueError('''The radius is always a positive non zero integer'''			)
			return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)



