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				      =					logging.get_logger(__name__)
  
  
  def       a			(   )    ->List[Any]:
  
  
  
  
  			'''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 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:Union[str, Any] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				set_hf_deepspeed_config(self )
  				dep_version_check('''accelerate''' )
  				dep_version_check('''deepspeed''' )
  				super().__init__(lowercase )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:List[Any] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__(lowercase )
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   []
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			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    snake_case__    (    self			:List[Any]      ,    lowercase			:str )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_value(lowercase )
  				if val is None:
  							return False
  				else:
  							return val == "auto"
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:List[str]      ,    lowercase			:str      ,    lowercase			:Any=None      ,    lowercase			:Optional[Any]=True )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   self.find_config_node(lowercase )
  				if config is None:
  							return
  
  				if config.get(lowercase ) == "auto":
  							SCREAMING_SNAKE_CASE    	=   hf_val
  							return
  
  				if not must_match:
  							return
  
  				SCREAMING_SNAKE_CASE    	=   config.get(lowercase )
  				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_     :				Union[str, Any]								=  partialmethod(__lowerCamelCase   ,		must_match=__lowerCamelCase				)
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:Any )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   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'''      ,    lowercase      ,    '''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:
  							SCREAMING_SNAKE_CASE    	=   '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
  				else:
  							SCREAMING_SNAKE_CASE    	=   None
  
  				if args.save_on_each_node:
  							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
  							SCREAMING_SNAKE_CASE    	=   self.config.get('''checkpoint'''      ,    {} )
  							SCREAMING_SNAKE_CASE    	=   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''' ):
  							SCREAMING_SNAKE_CASE    	=   torch.bfloataa
  				elif self.is_false('''fp16.enabled''' ):
  							SCREAMING_SNAKE_CASE    	=   torch.floataa
  				else:
  							SCREAMING_SNAKE_CASE    	=   torch.floataa
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:Any      ,    lowercase			:str      ,    lowercase			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [
  				    '''zero_optimization.reduce_bucket_size''',
  				    '''zero_optimization.stage3_prefetch_bucket_size''',
  				    '''zero_optimization.stage3_param_persistence_threshold''',
  				]
  				SCREAMING_SNAKE_CASE    	=   [x for x in hidden_size_based_keys if self.is_auto(lowercase )]
  
  				if len(lowercase ) > 0:
  							if hasattr(model.config      ,    '''hidden_size''' ):
  										SCREAMING_SNAKE_CASE    	=   model.config.hidden_size
  							elif hasattr(model.config      ,    '''hidden_sizes''' ):
  										# if there are many hidden sizes pick the largest one
  										SCREAMING_SNAKE_CASE    	=   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'''      ,    lowercase      ,    '''num_training_steps (calculated)''' )
  				self.fill_match('''scheduler.params.warmup_num_steps'''      ,    args.get_warmup_steps(lowercase )      ,    '''warmup_steps''' )
  
  				if len(self.mismatches ) > 0:
  							SCREAMING_SNAKE_CASE    	=   '''\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				      =					None
  
  
  def       a			(   a  )    ->List[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			global _hf_deepspeed_config_weak_ref
  			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  			SCREAMING_SNAKE_CASE    	=   weakref.ref(a  )
  
  
  def       a			(   )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			global _hf_deepspeed_config_weak_ref
  			SCREAMING_SNAKE_CASE    	=   None
  
  
  def       a			(   )    ->Dict:
  
  
  
  
  			'''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       a			(   )    ->Union[str, 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       a			(   a		,	a		,	a		,	a		,	a  )    ->int:
  
  
  
  
  			'''simple docstring'''
  
  
  			from accelerate.utils import DummyOptim, DummyScheduler
  
  			SCREAMING_SNAKE_CASE    	=   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)
  
  			SCREAMING_SNAKE_CASE    	=   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.'''  )
  						SCREAMING_SNAKE_CASE    	=   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.
  						SCREAMING_SNAKE_CASE    	=   trainer.create_optimizer()
  						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  						SCREAMING_SNAKE_CASE    	=   True
  
  			SCREAMING_SNAKE_CASE    	=   None
  			if "scheduler" in config:
  						SCREAMING_SNAKE_CASE    	=   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.'''  )
  						SCREAMING_SNAKE_CASE    	=   trainer.create_scheduler(num_training_steps=a		,	optimizer=a  )
  
  			return optimizer, lr_scheduler
  
  
  def       a			(   a		,	a		,	a=False  )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			from deepspeed.utils import logger as ds_logger
  
  			SCREAMING_SNAKE_CASE    	=   trainer.model
  			SCREAMING_SNAKE_CASE    	=   trainer.args
  
  			SCREAMING_SNAKE_CASE    	=   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'''  )
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   None, None
  						SCREAMING_SNAKE_CASE    	=   None
  			else:
  						SCREAMING_SNAKE_CASE    	=   None  # important for when deepspeed_init is used as re-init
  						SCREAMING_SNAKE_CASE    	=   list(filter(lambda a  : p.requires_grad		,	model.parameters()  )  )
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   deepspeed_optim_sched(
  						    a		,	a		,	a		,	a		,	a  )
  
  			# keep for quick debug:
  			# from pprint import pprint; pprint(config)
  
  			return optimizer, lr_scheduler
  
  
  
  
  
  def       a			(   a		,	a  )    ->int:
  
  
  
  
  			'''simple docstring'''
  
  
  			import glob
  
  			SCREAMING_SNAKE_CASE    	=   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
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   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}"""  )
style_context: 
  import json
  
  import requests
  
  from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
  
  
  def       a			(   a  )    ->Tuple:
  
  
  
  
  			'''simple docstring'''
  
  
  			class 						lowerCamelCase			:
  
  
  
  
  
  
  
  				def __init__(    self			:int      ,    lowercase			:List[str] )		->			None:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							assert isinstance(lowercase      ,    (bytes, str) )
  							SCREAMING_SNAKE_CASE    	=   content
  
  
  
  
  
  
  
  				def    snake_case__    (    self			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							return json.loads(self.content )
  
  			def mock_response(*a		,	**a  ):
  						assert args[0] == AUTHENTICATED_USER_ENDPOINT
  						assert "Authorization" in kwargs["headers"]
  						assert kwargs["headers"]["Authorization"].startswith('''token '''  )
  						assert "Accept" in kwargs["headers"]
  						return FakeResponse(B'''{"login":"test","id":1}'''  )
  
  			monkeypatch.setattr(a		,	'''get'''		,	a  )
  			SCREAMING_SNAKE_CASE    	=   fetch_github_info('''token'''  )
  			assert result["login"] == "test"
  			assert result["id"] == 1