

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__			:  Optional[Any]					     =     logging.get_logger(__name__)






def        __lowercase   (  )  ->    Any:
      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__							( UpperCAmelCase							):







  """simple docstring"""




  def __init__(      self     :			List[Any]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







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







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  def __init__(      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Optional[Any]		)       ->Optional[int]:
        """simple docstring"""







        super().__init__(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = None
        SCREAMING_SNAKE_CASE			:   str	  = []


  def 			_lowercase   (      self     :			int		)       ->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 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.get_value(UpperCAmelCase__		)
        if val is None:
              return False
        else:
              return val == "auto"


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Optional[Any]=None					,					UpperCAmelCase__     :			Dict=True		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   int	  = self.find_config_node(UpperCAmelCase__		)
        if config is None:
              return

        if config.get(UpperCAmelCase__		) == "auto":
              SCREAMING_SNAKE_CASE			:   List[str]	  = hf_val
              return

        if not must_match:
              return

        SCREAMING_SNAKE_CASE			:   int	  = config.get(UpperCAmelCase__		)
        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__   :			Any      							=partialmethod(UpperCAmelCase						,      must_match=UpperCAmelCase							)


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = 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"""					,					UpperCAmelCase__					,					"""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			:   Any	  = """apex""" if args.fpaa_backend == """apex""" else """amp"""
        else:
              SCREAMING_SNAKE_CASE			:   Dict	  = 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			:   List[Any]	  = self.config.get("""checkpoint"""					,					{}		)
              SCREAMING_SNAKE_CASE			:   List[str]	  = 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			:   int	  = torch.bfloataa
        elif self.is_false("""fp16.enabled"""		):
              SCREAMING_SNAKE_CASE			:   List[str]	  = torch.floataa
        else:
              SCREAMING_SNAKE_CASE			:   int	  = torch.floataa




  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Optional[Any]		)       ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = [
            """zero_optimization.reduce_bucket_size""",
            """zero_optimization.stage3_prefetch_bucket_size""",
            """zero_optimization.stage3_param_persistence_threshold""",
        ]
        SCREAMING_SNAKE_CASE			:   List[Any]	  = [x for x in hidden_size_based_keys if self.is_auto(UpperCAmelCase__		)]

        if len(UpperCAmelCase__		) > 0:
              if hasattr(model.config					,					"""hidden_size"""		):
                    SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = model.config.hidden_size
              elif hasattr(model.config					,					"""hidden_sizes"""		):
                    # if there are many hidden sizes pick the largest one
                    SCREAMING_SNAKE_CASE			:   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"""					,					1_0 * hidden_size		)

        # scheduler
        self.fill_match("""scheduler.params.total_num_steps"""					,					UpperCAmelCase__					,					"""num_training_steps (calculated)"""		)
        self.fill_match("""scheduler.params.warmup_num_steps"""					,					args.get_warmup_steps(UpperCAmelCase__		)					,					"""warmup_steps"""		)

        if len(self.mismatches		) > 0:
              SCREAMING_SNAKE_CASE			:   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'."		)


UpperCAmelCase__			:  Dict					     =     None






def        __lowercase   (  _A      )  ->    Union[str, Any]:
      # 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			:   List[Any]	  = weakref.ref(_A      )






def        __lowercase   (  )  ->    Optional[int]:
      # 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			:   Tuple	  = None






def        __lowercase   (  )  ->    int:
      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        __lowercase   (  )  ->    Any:
      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        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A      )  ->    Union[str, Any]:
      from accelerate.utils import DummyOptim, DummyScheduler

      SCREAMING_SNAKE_CASE			:   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)

      SCREAMING_SNAKE_CASE			:   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."""      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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			:   Dict	  = trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            SCREAMING_SNAKE_CASE			:   List[Any]	  = True

      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = None
      if "scheduler" in config:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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			:   Optional[int]	  = trainer.create_scheduler(num_training_steps=_A    ,     optimizer=_A      )

      return optimizer, lr_scheduler






def        __lowercase   (  _A    ,     _A    ,     _A=False      )  ->    Optional[int]:
      from deepspeed.utils import logger as ds_logger

      SCREAMING_SNAKE_CASE			:   List[str]	  = trainer.model
      SCREAMING_SNAKE_CASE			:   Any	  = trainer.args

      SCREAMING_SNAKE_CASE			:   Dict	  = 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			:   List[Any]	  = None, None
            SCREAMING_SNAKE_CASE			:   Any	  = None
      else:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = None  # important for when deepspeed_init is used as re-init
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = list(filter(lambda _A      : p.requires_grad    ,     model.parameters()      )      )
            SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   str	  = deepspeed_optim_sched(
                _A    ,     _A    ,     _A    ,     _A    ,     _A      )

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

      return optimizer, lr_scheduler







def        __lowercase   (  _A    ,     _A      )  ->    Optional[Any]:
      # 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			:   Any	  = 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			:   Optional[Any]	  = 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}"      )







import safs







class 							a__							( safs.SaFileSystem							):







  """simple docstring"""




