code: 
  
  
  '''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   :Dict								=    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 _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		List[str]						,  _A	:		Optional[Any]    )						->				int:
  			# set global weakref object
  			set_hf_deepspeed_config(self    )
  			dep_version_check('accelerate'    )
  			dep_version_check('deepspeed'    )
  			super().__init__(_A    )
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		List[Any]						,  _A	:		List[str]    )						->				Any:
  			super().__init__(_A    )
  			__magic_name__ :					List[str]  			=  None
  			__magic_name__ :					List[Any]  			=  []
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Optional[int]:
  			if self._dtype is None:
  				raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'    )
  			return self._dtype
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Union[str, Any]    )						->				int:
  			__magic_name__ :					Any  			=  self.get_value(_A    )
  			if val is None:
  				return False
  			else:
  				return val == "auto"
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Union[str, Any]						,  _A	:		Optional[int]						,  _A	:		str=None						,  _A	:		Any=True    )						->				Union[str, Any]:
  			__magic_name__			,       __magic_name__ :					Tuple  			=  self.find_config_node(_A    )
  			if config is None:
  				return
  
  			if config.get(_A    ) == "auto":
  				__magic_name__ :					Dict  			=  hf_val
  				return
  
  			if not must_match:
  				return
  
  			__magic_name__ :					Union[str, Any]  			=  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}'    )
  
  		A_			:    Dict   					=       partialmethod(lowercase__	,  must_match=lowercase__							)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Optional[Any]    )						->				int:
  			__magic_name__ :					str  			=  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:
  				__magic_name__ :					Tuple  			=  'apex' if args.fpaa_backend == 'apex' else 'amp'
  			else:
  				__magic_name__ :					int  			=  None
  
  			if args.save_on_each_node:
  				# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
  				__magic_name__ :					Union[str, Any]  			=  self.config.get('checkpoint'						,  {}    )
  				__magic_name__ :					Tuple  			=  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'    ):
  				__magic_name__ :					Optional[int]  			=  torch.bfloataa
  			elif self.is_false('fp16.enabled'    ):
  				__magic_name__ :					Optional[int]  			=  torch.floataa
  			else:
  				__magic_name__ :					Optional[int]  			=  torch.floataa
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		List[str]						,  _A	:		int						,  _A	:		str    )						->				int:
  			__magic_name__ :					Any  			=  [
  			    'zero_optimization.reduce_bucket_size',
  			    'zero_optimization.stage3_prefetch_bucket_size',
  			    'zero_optimization.stage3_param_persistence_threshold',
  			]
  			__magic_name__ :					Dict  			=  [x for x in hidden_size_based_keys if self.is_auto(_A    )]
  
  			if len(_A    ) > 0:
  				if hasattr(model.config						,  'hidden_size'    ):
  					__magic_name__ :					Any  			=  model.config.hidden_size
  				elif hasattr(model.config						,  'hidden_sizes'    ):
  					# if there are many hidden sizes pick the largest one
  					__magic_name__ :					Dict  			=  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'						,  _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:
  				__magic_name__ :					str  			=  '\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[Any]								=    None
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	global _hf_deepspeed_config_weak_ref
  	# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  	__magic_name__ :					int  			=  weakref.ref(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	global _hf_deepspeed_config_weak_ref
  	__magic_name__ :					Optional[Any]  			=  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       :			Optional[int]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	from accelerate.utils import DummyOptim, DummyScheduler
  
  	__magic_name__ :					Dict  			=  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)
  
  	__magic_name__ :					List[str]  			=  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.'	)
  		__magic_name__ :					Optional[int]  			=  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.
  		__magic_name__ :					Union[str, Any]  			=  trainer.create_optimizer()
  		# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  		__magic_name__ :					List[Any]  			=  True
  
  	__magic_name__ :					Optional[int]  			=  None
  	if "scheduler" in config:
  		__magic_name__ :					str  			=  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.'	)
  		__magic_name__ :					Optional[int]  			=  trainer.create_scheduler(num_training_steps=lowerCAmelCase	,	optimizer=lowerCAmelCase	)
  
  	return optimizer, lr_scheduler
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			Dict=False	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	from deepspeed.utils import logger as ds_logger
  
  	__magic_name__ :					Union[str, Any]  			=  trainer.model
  	__magic_name__ :					Dict  			=  trainer.args
  
  	__magic_name__ :					List[Any]  			=  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'	)
  		__magic_name__			,       __magic_name__ :					str  			=  None, None
  		__magic_name__ :					List[Any]  			=  None
  	else:
  		__magic_name__ :					List[Any]  			=  None  # important for when deepspeed_init is used as re-init
  		__magic_name__ :					Tuple  			=  list(filter(lambda lowerCAmelCase	: p.requires_grad	,	model.parameters()	)	)
  		__magic_name__			,       __magic_name__ :					Dict  			=  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[Any]	,	lowerCAmelCase       :			Optional[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	import glob
  
  	__magic_name__ :					List[Any]  			=  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
  		__magic_name__			,       __magic_name__ :					List[Any]  			=  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}'	)
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward