





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

a_    :				Tuple					=			logging.get_logger(__name__)




def   _SCREAMING_SNAKE_CASE      (	):
     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_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def __init__(  self	,  A						)		->   Tuple:





       '''simple docstring'''







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



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def __init__(  self	,  A						)		->   Dict:





       '''simple docstring'''







       super().__init__(A						)
       __magic_name__				=       None
       __magic_name__				=       []





  def 			__A      (  self						)		->   Union[str, Any]:





       '''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	,  A						)		->   List[Any]:





       '''simple docstring'''







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





  def 			__A      (  self	,  A	,  A	,  A=None	,  A=True						)		->   int:





       '''simple docstring'''







       __magic_name__		,  __magic_name__				=       self.find_config_node(A						)
       if config is None:
            return

       if config.get(A						) == "auto":
            __magic_name__				=       hf_val
            return

       if not must_match:
            return

       __magic_name__				=       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}'						)

  _a								=							partialmethod(SCREAMING_SNAKE_CASE__   ,							must_match=SCREAMING_SNAKE_CASE__			)





  def 			__A      (  self	,  A						)		->   Any:





       '''simple docstring'''







       __magic_name__				=       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:
            __magic_name__				=       '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
       else:
            __magic_name__				=       None

       if args.save_on_each_node:
            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
            __magic_name__				=       self.config.get('''checkpoint'''	,  {}						)
            __magic_name__				=       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'''						):
            __magic_name__				=       torch.bfloataa
       elif self.is_false('''fp16.enabled'''						):
            __magic_name__				=       torch.floataa
       else:
            __magic_name__				=       torch.floataa







  def 			__A      (  self	,  A	,  A	,  A						)		->   int:





       '''simple docstring'''







       __magic_name__				=       [
           '''zero_optimization.reduce_bucket_size''',
           '''zero_optimization.stage3_prefetch_bucket_size''',
           '''zero_optimization.stage3_param_persistence_threshold''',
       ]
       __magic_name__				=       [x for x in hidden_size_based_keys if self.is_auto(A						)]

       if len(A						) > 0:
            if hasattr(model.config	,  '''hidden_size'''						):
                 __magic_name__				=       model.config.hidden_size
            elif hasattr(model.config	,  '''hidden_sizes'''						):
                 # if there are many hidden sizes pick the largest one
                 __magic_name__				=       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:
            __magic_name__				=       '''\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\'.'						)


a_    :				Tuple					=			None




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     # 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)
     __magic_name__				=       weakref.ref(snake_case_						)




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




def   _SCREAMING_SNAKE_CASE      (	):
     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   _SCREAMING_SNAKE_CASE      (	):
     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   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[Any] ,     snake_case_		:						Tuple ,     snake_case_		:						Optional[int] ,     snake_case_		:						Union[str, Any] ,     snake_case_		:						List[str]						):
     from accelerate.utils import DummyOptim, DummyScheduler

     __magic_name__				=       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)

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

     __magic_name__				=       None
     if "scheduler" in config:
          __magic_name__				=       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.'''						)
          __magic_name__				=       trainer.create_scheduler(num_training_steps=snake_case_ ,     optimizer=snake_case_						)

     return optimizer, lr_scheduler




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int ,     snake_case_		:						Optional[Any] ,     snake_case_		:						List[str]=False						):
     from deepspeed.utils import logger as ds_logger

     __magic_name__				=       trainer.model
     __magic_name__				=       trainer.args

     __magic_name__				=       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'''						)
          __magic_name__		,  __magic_name__				=       None, None
          __magic_name__				=       None
     else:
          __magic_name__				=       None  # important for when deepspeed_init is used as re-init
          __magic_name__				=       list(filter(lambda snake_case_						: p.requires_grad ,     model.parameters()						)						)
          __magic_name__		,  __magic_name__				=       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   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[Any] ,     snake_case_		:						List[str]						):
     # 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

     __magic_name__				=       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
          __magic_name__		,  __magic_name__				=       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}'						)






def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()