'''simple docstring'''


def 					_lowerCamelCase    (      lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      str(lowerCamelCase_   ).strip()
     if not oct_string:
          raise ValueError('Empty string was passed to the function'   )
     UpperCAmelCase_							:   Tuple   =      oct_string[0] == '-'
     if is_negative:
          UpperCAmelCase_							:   Tuple   =      oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_   ) <= 7 for char in oct_string   ):
          raise ValueError('Non-octal value was passed to the function'   )
     UpperCAmelCase_							:   str   =      0
     for char in oct_string:
          UpperCAmelCase_							:   List[Any]   =      8 * decimal_number + int(lowerCamelCase_   )
     if is_negative:
          UpperCAmelCase_							:   List[Any]   =      -decimal_number
     return decimal_number


if __name__ == "__main__":
 from doctest import testmod

 testmod()







'''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

snake_case__     :			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      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       set_hf_deepspeed_config(self						)
       dep_version_check('accelerate'						)
       dep_version_check('deepspeed'						)
       super().__init__(snake_case_						)







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       super().__init__(snake_case_						)
       UpperCAmelCase_							:   Tuple   =      None
       UpperCAmelCase_							:   Any   =      []


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.get_value(snake_case_						)
       if val is None:
            return False
       else:
            return val == "auto"


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=None      ,		snake_case_=True						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      self.find_config_node(snake_case_						)
       if config is None:
            return

       if config.get(snake_case_						) == "auto":
            UpperCAmelCase_							:   int   =      hf_val
            return

       if not must_match:
            return

       UpperCAmelCase_							:   Optional[Any]   =      config.get(snake_case_						)
       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}'''						)

  lowerCamelCase_		:Tuple						    =		partialmethod(lowerCamelCase_						,	must_match=lowerCamelCase_       )


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      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'      ,		snake_case_      ,		'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:
            UpperCAmelCase_							:   Any   =      'apex' if args.fpaa_backend == 'apex' else 'amp'
       else:
            UpperCAmelCase_							:   Union[str, Any]   =      None

       if args.save_on_each_node:
            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
            UpperCAmelCase_							:   List[str]   =      self.config.get('checkpoint'      ,		{}						)
            UpperCAmelCase_							:   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'						):
            UpperCAmelCase_							:   Dict   =      torch.bfloataa
       elif self.is_false('fp16.enabled'						):
            UpperCAmelCase_							:   List[Any]   =      torch.floataa
       else:
            UpperCAmelCase_							:   Optional[int]   =      torch.floataa






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      [
           'zero_optimization.reduce_bucket_size',
           'zero_optimization.stage3_prefetch_bucket_size',
           'zero_optimization.stage3_param_persistence_threshold',
       ]
       UpperCAmelCase_							:   str   =      [x for x in hidden_size_based_keys if self.is_auto(snake_case_						)]

       if len(snake_case_						) > 0:
            if hasattr(model.config      ,		'hidden_size'						):
                 UpperCAmelCase_							:   List[Any]   =      model.config.hidden_size
            elif hasattr(model.config      ,		'hidden_sizes'						):
                 # if there are many hidden sizes pick the largest one
                 UpperCAmelCase_							:   List[str]   =      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'      ,		snake_case_      ,		'num_training_steps (calculated)'						)
       self.fill_match('scheduler.params.warmup_num_steps'      ,		args.get_warmup_steps(snake_case_						)      ,		'warmup_steps'						)

       if len(self.mismatches						) > 0:
            UpperCAmelCase_							:   List[Any]   =      '\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\'.'''						)







snake_case__     :			List[str]      			=  None


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     UpperCAmelCase_							:   List[str]   =      weakref.ref(lowerCamelCase_   )


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase_							:   int   =      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_					:							Optional[Any]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     from accelerate.utils import DummyOptim, DummyScheduler

     UpperCAmelCase_							:   Optional[Any]   =      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)

     UpperCAmelCase_							:   int   =      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.'   )
          UpperCAmelCase_							:   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.
          UpperCAmelCase_							:   Tuple   =      trainer.create_optimizer()
          # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
          UpperCAmelCase_							:   Any   =      True

     UpperCAmelCase_							:   int   =      None
     if "scheduler" in config:
          UpperCAmelCase_							:   Union[str, Any]   =      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.'   )
          UpperCAmelCase_							:   List[Any]   =      trainer.create_scheduler(num_training_steps=lowerCamelCase_	,     optimizer=lowerCamelCase_   )

     return optimizer, lr_scheduler


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							str=False   ):



     """simple docstring"""





     from deepspeed.utils import logger as ds_logger

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

     UpperCAmelCase_							:   Optional[int]   =      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'   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[Any]   =      None, None
          UpperCAmelCase_							:   str   =      None
     else:
          UpperCAmelCase_							:   Union[str, Any]   =      None  # important for when deepspeed_init is used as re-init
          UpperCAmelCase_							:   Tuple   =      list(filter(lambda lowerCamelCase_   : p.requires_grad	,     model.parameters()   )   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      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_					:							Tuple	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     import glob

     UpperCAmelCase_							:   Dict   =      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
          UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      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}'''   )






