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 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 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						int    ,     lowerCamelCase    :						Any					)	->							Any:
        # set global weakref object
        set_hf_deepspeed_config(self					)
        dep_version_check('accelerate'					)
        dep_version_check('deepspeed'					)
        super().__init__(lowerCamelCase					)







class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						str    ,     lowerCamelCase    :						int					)	->							List[Any]:
        super().__init__(lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					None
        _UpperCamelCase       :    List[str]					  =					[]

 def    __snake_case   (     self    :						Tuple					)	->							List[Any]:
        if self._dtype is None:
               raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'					)
        return self._dtype

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						Union[str, Any]					)	->							Optional[int]:
        _UpperCamelCase       :    str					  =					self.get_value(lowerCamelCase					)
        if val is None:
               return False
        else:
               return val == "auto"

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						List[Any]    ,     lowerCamelCase    :						List[Any]=None    ,     lowerCamelCase    :						Optional[int]=True					)	->							Dict:
        _UpperCamelCase  , _UpperCamelCase       :    List[str]					  =					self.find_config_node(lowerCamelCase					)
        if config is None:
               return

        if config.get(lowerCamelCase					) == "auto":
               _UpperCamelCase       :    str					  =					hf_val
               return

        if not must_match:
               return

        _UpperCamelCase       :    int					  =					config.get(lowerCamelCase					)
        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  :		Any			      =	partialmethod(a_,		must_match=a_  )

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						Dict					)	->							int:
        _UpperCamelCase       :    int					  =					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'    ,     lowerCamelCase    ,     '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       :    List[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       :    Optional[int]					  =					self.config.get('checkpoint'    ,     {}					)
               _UpperCamelCase       :    Optional[Any]					  =					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       :    Union[str, Any]					  =					torch.bfloataa
        elif self.is_false('fp16.enabled'					):
               _UpperCamelCase       :    Optional[Any]					  =					torch.floataa
        else:
               _UpperCamelCase       :    Any					  =					torch.floataa

 def    __snake_case   (     self    :						Any    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						Optional[int]					)	->							List[str]:
        _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(lowerCamelCase					)]

        if len(lowerCamelCase					) > 0:
               if hasattr(model.config    ,     'hidden_size'					):
                      _UpperCamelCase       :    Tuple					  =					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'    ,     lowerCamelCase    ,     'num_training_steps (calculated)'					)
        self.fill_match('scheduler.params.warmup_num_steps'    ,     args.get_warmup_steps(lowerCamelCase					)    ,     'warmup_steps'					)

        if len(self.mismatches					) > 0:
               _UpperCamelCase       :    Tuple					  =					'\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 SCREAMING_SNAKE_CASE			(       lowercase_:		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)
       _UpperCamelCase       :    Any					  =					weakref.ref(lowercase_			)


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
       _UpperCamelCase       :    str					  =					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			(       lowercase_:		str						,  lowercase_:		Optional[int]						,  lowercase_:		str						,  lowercase_:		Union[str, Any]						,  lowercase_:		Optional[int]			):
       from accelerate.utils import DummyOptim, DummyScheduler

       _UpperCamelCase       :    Optional[int]					  =					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       :    Union[str, Any]					  =					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       :    Any					  =					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.
              _UpperCamelCase       :    Tuple					  =					trainer.create_optimizer()
              # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
              _UpperCamelCase       :    Optional[Any]					  =					True

       _UpperCamelCase       :    Union[str, Any]					  =					None
       if "scheduler" in config:
              _UpperCamelCase       :    Dict					  =					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.'			)
              _UpperCamelCase       :    Any					  =					trainer.create_scheduler(num_training_steps=lowercase_						,  optimizer=lowercase_			)

       return optimizer, lr_scheduler


def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		str						,  lowercase_:		Tuple=False			):
       from deepspeed.utils import logger as ds_logger

       _UpperCamelCase       :    Optional[int]					  =					trainer.model
       _UpperCamelCase       :    int					  =					trainer.args

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

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

       return optimizer, lr_scheduler





def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[int]						,  lowercase_:		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

       _UpperCamelCase       :    Union[str, Any]					  =					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
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					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}"			)




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



