


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				=			logging.get_logger(__name__)







def 	A (		):
     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       :			Tuple							,     _a       :			List[Any]					)     ->      Tuple:
      # 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       :			List[str]							,     _a       :			List[str]					)     ->      List[str]:
      super().__init__(_a					)
      UpperCAmelCase    :					Dict			=      None
      UpperCAmelCase    :					str			=      []
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      Any:
      if self._dtype is None:
           raise ValueError("trainer_config_process() wasn't called yet to tell dtype"					)
      return self._dtype
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			Optional[int]					)     ->      Dict:
      UpperCAmelCase    :					int			=      self.get_value(_a					)
      if val is None:
           return False
      else:
           return val == "auto"
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			List[Any]							,     _a       :			List[str]							,     _a       :			Any=None							,     _a       :			Optional[Any]=True					)     ->      Union[str, Any]:
      UpperCAmelCase	,			UpperCAmelCase    :					str			=      self.find_config_node(_a					)
      if config is None:
           return

      if config.get(_a					) == "auto":
           UpperCAmelCase    :					int			=      hf_val
           return

      if not must_match:
           return

      UpperCAmelCase    :					Optional[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}'					)

 _UpperCamelCase					:			Union[str, Any]     	=       partialmethod(a_   ,    must_match=a_  )
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Tuple					)     ->      List[str]:
      UpperCAmelCase    :					Optional[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"							,     _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:
           UpperCAmelCase    :					Optional[int]			=      "apex" if args.fpaa_backend == "apex" else "amp"
      else:
           UpperCAmelCase    :					Optional[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    :					Any			=      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    :					Any			=      torch.floataa
      else:
           UpperCAmelCase    :					int			=      torch.floataa

 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			List[Any]							,     _a       :			str							,     _a       :			int					)     ->      List[str]:
      UpperCAmelCase    :					Dict			=      [
          "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(_a					)]

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







def 	A (		A__			):
     # 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)
     UpperCAmelCase    :					List[str]			=      weakref.ref(A__			)







def 	A (		):
     # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase    :					List[str]			=      None







def 	A (		):
     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 	A (		):
     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 	A (		A__		,		A__		,		A__		,		A__		,		A__			):
     from accelerate.utils import DummyOptim, DummyScheduler

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

     UpperCAmelCase    :					Any			=      None
     if "scheduler" in config:
          UpperCAmelCase    :					str			=      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."			)
          UpperCAmelCase    :					Dict			=      trainer.create_scheduler(num_training_steps=A__		,		optimizer=A__			)

     return optimizer, lr_scheduler







def 	A (		A__		,		A__		,		A__=False			):
     from deepspeed.utils import logger as ds_logger

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

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

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

     return optimizer, lr_scheduler







def 	A (		A__		,		A__			):
     # 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

     UpperCAmelCase    :					Union[str, 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
          UpperCAmelCase	,			UpperCAmelCase    :					Tuple			=      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}'			)







def 	A (		A__			):
     UpperCAmelCase    :					Dict			=      9

     while num > 0:
          if num % 1_0 != digit:
               return False
          num //= 1_0_0
          digit -= 1

     return True







def 	A (		):
     UpperCAmelCase    :					Optional[Any]			=      1_3_8_9_0_2_6_6_3

     while not is_square_form(num * num			):
          if num % 1_0 == 3:
               num -= 6  # (3 - 6) % 10 = 7
          else:
               num -= 4  # (7 - 4) % 10 = 3

     return num * 1_0


if __name__ == "__main__":
      print(f'''{solution() = }''')



