



"""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

SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)






def 	__UpperCAmelCase					(       )							->	Tuple:






   """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    ):







   def __init__(self       ,	lowerCamelCase				):




      '''simple docstring'''




      set_hf_deepspeed_config(self				)
      dep_version_check("""accelerate"""				)
      dep_version_check("""deepspeed"""				)
      super().__init__(lowerCamelCase				)





class       __lowerCamelCase	(   __lowercase    ):







   def __init__(self       ,	lowerCamelCase				):




      '''simple docstring'''




      super().__init__(lowerCamelCase				)
      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		[]







   def 							A__						(self				):




      '''simple docstring'''




      if self._dtype is None:
         raise ValueError("""trainer_config_process() wasn't called yet to tell dtype"""				)
      return self._dtype







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_value(lowerCamelCase				)
      if val is None:
         return False
      else:
         return val == "auto"







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase=None       ,	lowerCamelCase=True				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		self.find_config_node(lowerCamelCase				)
      if config is None:
         return

      if config.get(lowerCamelCase				) == "auto":
         _lowerCAmelCase					     =		hf_val
         return

      if not must_match:
         return

      _lowerCAmelCase					     =		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}"""				)

   __UpperCamelCase									=   partialmethod(__lowercase   ,							must_match=__lowercase    )







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _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"""       ,	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:
         _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 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      _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(lowerCamelCase				)]

      if len(lowerCamelCase				) > 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"""       ,	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:
         _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'."""				)





SCREAMING_SNAKE_CASE				:     List[str]          =	None






def 	__UpperCAmelCase					(       snake_case_  :  Any   )							->	Any:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       )							->	List[Any]:






   """simple docstring"""


   global _hf_deepspeed_config_weak_ref
   _lowerCAmelCase					     =		None






def 	__UpperCAmelCase					(       )							->	List[str]:






   """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 	__UpperCAmelCase					(       )							->	Optional[int]:






   """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 	__UpperCAmelCase					(       snake_case_  :  Tuple   ,	snake_case_  :  Any   ,	snake_case_  :  int   ,	snake_case_  :  Any   ,	snake_case_  :  Tuple   )							->	Optional[int]:






   """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=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.
      _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(snake_case_   )
   else:
      if isinstance(snake_case_   ,	snake_case_   ):
         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=snake_case_   ,	optimizer=snake_case_   )

   return optimizer, lr_scheduler






def 	__UpperCAmelCase					(       snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str=False   )							->	Optional[Any]:






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

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

   return optimizer, lr_scheduler






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   )							->	List[Any]:






   """simple docstring"""


   import glob

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

   if len(snake_case_   ) > 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(
          snake_case_   ,	load_optimizer_states=snake_case_   ,	load_lr_scheduler_states=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}"""   )




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()