
'''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

UpperCamelCase__ :						List[str]    							=					logging.get_logger(__name__)
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """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			(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     int:
          # set global weakref object
          set_hf_deepspeed_config(self )
          dep_version_check("""accelerate""" )
          dep_version_check("""deepspeed""" )
          super().__init__(A__ )






class 				_a			(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Any:
          super().__init__(A__ )
          _SCREAMING_SNAKE_CASE       	=			None
          _SCREAMING_SNAKE_CASE       	=			[]






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          if self._dtype is None:
             raise ValueError("""trainer_config_process() wasn't called yet to tell dtype""" )
          return self._dtype






       def     UpperCamelCase			(							self    ,							A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.get_value(A__ )
          if val is None:
             return False
          else:
             return val == "auto"






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__=None    ,							A__=True )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.find_config_node(A__ )
          if config is None:
             return

          if config.get(A__ ) == "auto":
             _SCREAMING_SNAKE_CASE       	=			hf_val
             return

          if not must_match:
             return

          _SCREAMING_SNAKE_CASE       	=			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}" )

       SCREAMING_SNAKE_CASE =					partialmethod(_lowerCamelCase					,   must_match=_lowerCamelCase)






       def     UpperCamelCase			(							self    ,							A__ )      ->     Optional[Any]:
          _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"""    ,							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:
             _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     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     int:
          _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(A__ )]

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






UpperCamelCase__ :						Any    							=					None
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Union[str, Any]:




   """simple docstring"""
   # 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)
   _SCREAMING_SNAKE_CASE       	=			weakref.ref(SCREAMING_SNAKE_CASE_  )
def 				lowerCAmelCase_		(			)       ->       Tuple:




   """simple docstring"""
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   _SCREAMING_SNAKE_CASE       	=			None
def 				lowerCAmelCase_		(			)       ->       Tuple:




   """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_		(			)       ->       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().config
   else:
      return None
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """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=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.
      _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(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."""  )
      _SCREAMING_SNAKE_CASE       	=			trainer.create_scheduler(num_training_steps=SCREAMING_SNAKE_CASE_      , optimizer=SCREAMING_SNAKE_CASE_  )

   return optimizer, lr_scheduler
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False  )       ->       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(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"""  )
      _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 SCREAMING_SNAKE_CASE_  : p.requires_grad      , model.parameters()  )  )
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			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 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       int:




   """simple docstring"""
   # 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

   _SCREAMING_SNAKE_CASE       	=			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
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			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}"  )








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   return " ".join(sorted(set(sentence.split()  )  )  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






