code: 
  
  '''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
  
  lowercase_			       =				logging.get_logger(__name__)
  
  
  
  
  
  def lowerCamelCase		(  )		->Dict:
      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_  (       A_ ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A )	->     List[str]:
          # set global weakref object
          set_hf_deepspeed_config(self )
          dep_version_check("""accelerate""" )
          dep_version_check("""deepspeed""" )
          super().__init__(A )
  
  
  
  
  class       a_  (       A_ ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A )	->     Optional[int]:
          super().__init__(A )
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    []
  
  
      def     snake_case_(	self )	->     List[str]:
          if self._dtype is None:
              raise ValueError("""trainer_config_process() wasn\'t called yet to tell dtype""" )
          return self._dtype
  
  
      def     snake_case_(	self				,	A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.get_value(A )
          if val is None:
              return False
          else:
              return val == "auto"
  
  
      def     snake_case_(	self				,	A				,	A				,	A=None				,	A=True )	->     Dict:
          _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}' )
  
      UpperCamelCase									=				partialmethod(A_   ,					must_match=A_ )
  
  
      def     snake_case_(	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     snake_case_(	self				,	A				,	A				,	A )	->     List[Any]:
          _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\'.' )
  
  
  
  
  lowercase_			       =				None
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      _SCREAMING_SNAKE_CASE             =    weakref.ref(lowercase__					)
  
  
  
  
  
  def lowerCamelCase		(  )		->Dict:
      global _hf_deepspeed_config_weak_ref
      _SCREAMING_SNAKE_CASE             =    None
  
  
  
  
  
  def lowerCamelCase		(  )		->Optional[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().is_zeroa()
      else:
          return False
  
  
  
  
  
  def lowerCamelCase		(  )		->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 lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Any      , __lowerCamelCase				:					Optional[int]					)		->List[str]:
      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=lowercase__					)
      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(lowercase__					)
      else:
          if isinstance(lowercase__      , lowercase__					):
              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=lowercase__      , optimizer=lowercase__					)
  
      return optimizer, lr_scheduler
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					Optional[int]=False					)		->List[Any]:
      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(lowercase__      , lowercase__      , lowercase__					)
  
      # 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 __lowerCamelCase					: p.requires_grad      , model.parameters()					)					)
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    deepspeed_optim_sched(
              lowercase__      , lowercase__      , lowercase__      , lowercase__      , lowercase__					)
  
      # keep for quick debug:
      # from pprint import pprint; pprint(config)
  
      return optimizer, lr_scheduler
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Dict      , __lowerCamelCase				:					List[Any]					)		->str:
      import glob
  
      _SCREAMING_SNAKE_CASE             =    sorted(glob.glob(F'{checkpoint_path}/global_step*'					)					)
  
      if len(lowercase__					) > 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(
              lowercase__      , load_optimizer_states=lowercase__      , load_lr_scheduler_states=lowercase__					)
          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}'					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from timeit import timeit
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
      while True:
          _SCREAMING_SNAKE_CASE             =    n // 10
          digits += 1
          if n == 0:
              break
      return digits
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return len(str(abs(__lowerCamelCase					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  )		->None:
      from collections.abc import Callable
  
      def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
          _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
          _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
          print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)
  
      for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
          for func in (num_digits, num_digits_fast, num_digits_faster):
              benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
          print()
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
        benchmark()
  
  
  
  