


"""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    				=      logging.get_logger(__name__)





def     lowercase__	(  )					->     Tuple:
  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  lowercase_  ( lowercase__						):







      def __init__(      self :							List[str]		,  _lowercase :							Optional[Any]    ):
        # set global weakref object
        set_hf_deepspeed_config(self    )
        dep_version_check("accelerate"    )
        dep_version_check("deepspeed"    )
        super().__init__(_A    )

class  lowercase_  ( lowercase__						):







      def __init__(      self :							List[Any]		,  _lowercase :							List[str]    ):
        super().__init__(_A    )
        lowerCAmelCase__    :    List[str]								=					None
        lowerCAmelCase__    :    List[Any]								=					[]







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







      def 				_lowerCAmelCase       (      self :							str		,  _lowercase :							Union[str, Any]    ):
        lowerCAmelCase__    :    Any								=					self.get_value(_A    )
        if val is None:
          return False
        else:
          return val == "auto"







      def 				_lowerCAmelCase       (      self :							List[Any]		,  _lowercase :							Union[str, Any]		,  _lowercase :							Optional[int]		,  _lowercase :							str=None		,  _lowercase :							Any=True    ):
        lowerCAmelCase__    :    Tuple								=					self.find_config_node(_A    )
        if config is None:
          return

        if config.get(_A    ) == "auto":
          lowerCAmelCase__    :    Dict								=					hf_val
          return

        if not must_match:
          return

        lowerCAmelCase__    :    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}"    )

      __magic_name__     :							Dict          =				partialmethod(lowercase__							,							must_match=lowercase__						)







      def 				_lowerCAmelCase       (      self :							Optional[int]		,  _lowercase :							Optional[Any]    ):
        lowerCAmelCase__    :    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:
          lowerCAmelCase__    :    Tuple								=					'apex' if args.fpaa_backend == 'apex' else 'amp'
        else:
          lowerCAmelCase__    :    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
          lowerCAmelCase__    :    Union[str, Any]								=					self.config.get("checkpoint"		,  {}    )
          lowerCAmelCase__    :    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"    ):
          lowerCAmelCase__    :    Optional[int]								=					torch.bfloataa
        elif self.is_false("fp16.enabled"    ):
          lowerCAmelCase__    :    Optional[int]								=					torch.floataa
        else:
          lowerCAmelCase__    :    Optional[int]								=					torch.floataa







      def 				_lowerCAmelCase       (      self :							Any		,  _lowercase :							List[str]		,  _lowercase :							int		,  _lowercase :							str    ):
        lowerCAmelCase__    :    Any								=					[
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        lowerCAmelCase__    :    Dict								=					[x for x in hidden_size_based_keys if self.is_auto(_A    )]

        if len(_A    ) > 0:
          if hasattr(model.config		,  "hidden_size"    ):
            lowerCAmelCase__    :    Any								=					model.config.hidden_size
          elif hasattr(model.config		,  "hidden_sizes"    ):
            # if there are many hidden sizes pick the largest one
            lowerCAmelCase__    :    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"		,  _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:
          lowerCAmelCase__    :    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    				=      None





def     lowercase__	(  lowerCamelCase     :  List[Any]				)					->     Union[str, Any]:
  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  lowerCAmelCase__    :    int								=					weakref.ref(lowerCamelCase				)





def     lowercase__	(  )					->     Dict:
  global _hf_deepspeed_config_weak_ref
  lowerCAmelCase__    :    Optional[Any]								=					None





def     lowercase__	(  )					->     List[str]:







  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__	(  )					->     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().config
  else:
    return None





def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  str				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  str				)					->     Optional[int]:
  from accelerate.utils import DummyOptim, DummyScheduler

  lowerCAmelCase__    :    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)

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

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

  return optimizer, lr_scheduler





def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Optional[Any]				,	lowerCamelCase     :  Dict=False				)					->     Union[str, Any]:
  from deepspeed.utils import logger as ds_logger

  lowerCAmelCase__    :    Union[str, Any]								=					trainer.model
  lowerCAmelCase__    :    Dict								=					trainer.args

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

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

  return optimizer, lr_scheduler







def     lowercase__	(  lowerCamelCase     :  List[Any]				,	lowerCamelCase     :  Optional[Any]				)					->     Optional[int]:
  import glob

  lowerCAmelCase__    :    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
    lowerCAmelCase__    :    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}"				)




"""simple docstring"""





from __future__ import annotations





def     lowercase__	(  lowerCamelCase     :  list				,	lowerCamelCase     :  list				)					->     list:
  lowerCAmelCase__    :    Dict								=					[None] * (len(lowerCamelCase				) + len(lowerCamelCase				))

  lowerCAmelCase__    :    Optional[Any]								=					0  # pointer to current index for left Half
  lowerCAmelCase__    :    Any								=					0  # pointer to current index for the right Half
  lowerCAmelCase__    :    Dict								=					0  # pointer to current index for the sorted array Half

  while pointera < len(lowerCamelCase				) and pointera < len(lowerCamelCase				):
    if left_half[pointera] < right_half[pointera]:
      lowerCAmelCase__    :    Tuple								=					left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCAmelCase__    :    int								=					right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Tuple								=					left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Optional[int]								=					right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array





def     lowercase__	(  lowerCamelCase     :  list				)					->     list:
  if len(lowerCamelCase				) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCAmelCase__    :    Optional[int]								=					0 + (len(lowerCamelCase				) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCAmelCase__    :    Union[str, Any]								=					array[:middle]
  lowerCAmelCase__    :    List[str]								=					array[middle:]

  return merge(merge_sort(lowerCamelCase				)				,	merge_sort(lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
