code: 
  
  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     :			Union[str, Any]    =logging.get_logger(__name__)
  
  
  
  def  _A  (			):
  	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    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__	):
  						# set global weakref object
  						set_hf_deepspeed_config(self	)
  						dep_version_check("accelerate"	)
  						dep_version_check("deepspeed"	)
  						super().__init__(lowerCamelCase__	)
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__	):
  						super().__init__(lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   None
  						UpperCAmelCase__:  Dict		       =   []
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						if self._dtype is None:
  							raise ValueError("trainer_config_process() wasn't called yet to tell dtype"	)
  						return self._dtype
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  int		       =   self.get_value(lowerCamelCase__	)
  						if val is None:
  							return False
  						else:
  							return val == "auto"
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__=None							,			lowerCamelCase__=True	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.find_config_node(lowerCamelCase__	)
  						if config is None:
  							return
  
  						if config.get(lowerCamelCase__	) == "auto":
  							UpperCAmelCase__:  Optional[Any]		       =   hf_val
  							return
  
  						if not must_match:
  							return
  
  						UpperCAmelCase__:  List[str]		       =   config.get(lowerCamelCase__	)
  						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__     			=      partialmethod(_a				,must_match=_a		)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Any		       =   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"							,			lowerCamelCase__							,			"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__:  Optional[int]		       =   "apex" if args.fpaa_backend == "apex" else "amp"
  						else:
  							UpperCAmelCase__:  Union[str, Any]		       =   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__:  Any		       =   self.config.get("checkpoint"							,			{}	)
  							UpperCAmelCase__:  Dict		       =   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__:  Union[str, Any]		       =   torch.bfloataa
  						elif self.is_false("fp16.enabled"	):
  							UpperCAmelCase__:  Optional[int]		       =   torch.floataa
  						else:
  							UpperCAmelCase__:  Optional[int]		       =   torch.floataa
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[Any]		       =   [
  						    "zero_optimization.reduce_bucket_size",
  						    "zero_optimization.stage3_prefetch_bucket_size",
  						    "zero_optimization.stage3_param_persistence_threshold",
  						]
  						UpperCAmelCase__:  List[Any]		       =   [x for x in hidden_size_based_keys if self.is_auto(lowerCamelCase__	)]
  
  						if len(lowerCamelCase__	) > 0:
  							if hasattr(model.config							,			"hidden_size"	):
  								UpperCAmelCase__:  Dict		       =   model.config.hidden_size
  							elif hasattr(model.config							,			"hidden_sizes"	):
  								# if there are many hidden sizes pick the largest one
  								UpperCAmelCase__:  List[str]		       =   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"							,			lowerCamelCase__							,			"num_training_steps (calculated)"	)
  						self.fill_match("scheduler.params.warmup_num_steps"							,			args.get_warmup_steps(lowerCamelCase__	)							,			"warmup_steps"	)
  
  						if len(self.mismatches	) > 0:
  							UpperCAmelCase__:  Dict		       =   "\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     :			Optional[int]    =None
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	# 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)
  	UpperCAmelCase__:  Tuple		       =   weakref.ref(SCREAMING_SNAKE_CASE			)
  
  
  
  def  _A  (			):
  	# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  	global _hf_deepspeed_config_weak_ref
  	UpperCAmelCase__:  Any		       =   None
  
  
  
  def  _A  (			):
  	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  _A  (			):
  	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  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	from accelerate.utils import DummyOptim, DummyScheduler
  
  	UpperCAmelCase__:  Optional[int]		       =   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__:  Optional[int]		       =   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__:  List[str]		       =   DummyOptim(params=SCREAMING_SNAKE_CASE			)
  	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__:  Optional[int]		       =   trainer.create_optimizer()
  		# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  		UpperCAmelCase__:  Optional[Any]		       =   True
  
  	UpperCAmelCase__:  int		       =   None
  	if "scheduler" in config:
  		UpperCAmelCase__:  Union[str, Any]		       =   DummyScheduler(SCREAMING_SNAKE_CASE			)
  	else:
  		if isinstance(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  			raise ValueError(
  			    "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
  			    "Please configure a scheduler in the DeepSpeed config."			)
  		UpperCAmelCase__:  Union[str, Any]		       =   trainer.create_scheduler(num_training_steps=SCREAMING_SNAKE_CASE  ,optimizer=SCREAMING_SNAKE_CASE			)
  
  	return optimizer, lr_scheduler
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=False			):
  	from deepspeed.utils import logger as ds_logger
  
  	UpperCAmelCase__:  str		       =   trainer.model
  	UpperCAmelCase__:  Dict		       =   trainer.args
  
  	UpperCAmelCase__:  str		       =   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(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  	# 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__:  int		       =   None, None
  		UpperCAmelCase__:  Optional[Any]		       =   None
  	else:
  		UpperCAmelCase__:  str		       =   None  # important for when deepspeed_init is used as re-init
  		UpperCAmelCase__:  str		       =   list(filter(lambda SCREAMING_SNAKE_CASE			: p.requires_grad  ,model.parameters()			)			)
  		UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   deepspeed_optim_sched(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  	# keep for quick debug:
  	# from pprint import pprint; pprint(config)
  
  	return optimizer, lr_scheduler
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	# 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
  
  	UpperCAmelCase__:  Optional[int]		       =   sorted(glob.glob(f"{checkpoint_path}/global_step*"			)			)
  
  	if len(SCREAMING_SNAKE_CASE			) > 0:
  		logger.info(f"Attempting to resume from {checkpoint_path}"			)
  		# this magically updates self.optimizer and self.lr_scheduler
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   deepspeed_engine.load_checkpoint(
  		    SCREAMING_SNAKE_CASE  ,load_optimizer_states=SCREAMING_SNAKE_CASE  ,load_lr_scheduler_states=SCREAMING_SNAKE_CASE			)
  		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}"			)
style_context: 
  
  from __future__ import annotations
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) == 1
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Optional[Any]		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	UpperCAmelCase__:  List[str]		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  int		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	if b < 0:
  		UpperCAmelCase__:  List[str]		       =   (b % n + n) % n
  	return b
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Union[str, Any]		       =   invert_modulo(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Any		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert a >= 0 and b >= 0
  
  	if b == 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   a, 1, 0
  	else:
  		((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Tuple		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,a % b			)
  		UpperCAmelCase__:  Union[str, Any]		       =   q
  		UpperCAmelCase__:  Union[str, Any]		       =   p - q * (a // b)
  
  	assert a % d == 0 and b % d == 0
  	assert d == a * x + b * y
  
  	return (d, x, y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if b == 0:
  		return (1, 0)
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  List[str]		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,a % b			)
  	UpperCAmelCase__:  Optional[Any]		       =   a // b
  	return (y, x - k * y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if a < b:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   b, a
  
  	while a % b != 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   b, a % b
  
  	return b
  
  
  if __name__ == "__main__":
  					from doctest import testmod
  
  					testmod(name="""modular_division""", verbose=True)
  					testmod(name="""modular_division2""", verbose=True)
  					testmod(name="""invert_modulo""", verbose=True)
  					testmod(name="""extended_gcd""", verbose=True)
  					testmod(name="""extended_euclid""", verbose=True)
  					testmod(name="""greatest_common_divisor""", verbose=True)