



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 				UpperCAmelCase__		(					)->Union[str, Any]:
      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 		UpperCAmelCase    (    snake_case_					):





   def __init__(						self							,  _lowerCAmelCase      ):
         # set global weakref object
         set_hf_deepspeed_config(self      )
         dep_version_check('''accelerate'''      )
         dep_version_check('''deepspeed'''      )
         super().__init__(_lowerCAmelCase      )






class 		UpperCAmelCase    (    snake_case_					):





   def __init__(						self							,  _lowerCAmelCase      ):
         super().__init__(_lowerCAmelCase      )
         _lowerCAmelCase			      =	None
         _lowerCAmelCase			      =	[]





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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	self.get_value(_lowerCAmelCase      )
         if val is None:
               return False
         else:
               return val == "auto"





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase=None							,  _lowerCAmelCase=True      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.find_config_node(_lowerCAmelCase      )
         if config is None:
               return

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

         if not must_match:
               return

         _lowerCAmelCase			      =	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}'''      )

   SCREAMING_SNAKE_CASE__					      =  partialmethod(snake_case_							,must_match=snake_case_					)





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	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:
               _lowerCAmelCase			      =	'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
         else:
               _lowerCAmelCase			      =	None

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	[
             '''zero_optimization.reduce_bucket_size''',
             '''zero_optimization.stage3_prefetch_bucket_size''',
             '''zero_optimization.stage3_param_persistence_threshold''',
         ]
         _lowerCAmelCase			      =	[x for x in hidden_size_based_keys if self.is_auto(_lowerCAmelCase      )]

         if len(_lowerCAmelCase      ) > 0:
               if hasattr(model.config							,  '''hidden_size'''      ):
                     _lowerCAmelCase			      =	model.config.hidden_size
               elif hasattr(model.config							,  '''hidden_sizes'''      ):
                     # if there are many hidden sizes pick the largest one
                     _lowerCAmelCase			      =	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'''							,  _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:
               _lowerCAmelCase			      =	'''\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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->int:
      # 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)
      _lowerCAmelCase			      =	weakref.ref(_SCREAMING_SNAKE_CASE					)
def 				UpperCAmelCase__		(					)->str:
      # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
      global _hf_deepspeed_config_weak_ref
      _lowerCAmelCase			      =	None
def 				UpperCAmelCase__		(					)->List[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 				UpperCAmelCase__		(					)->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().config
      else:
            return None
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						List[str]  ,					_SCREAMING_SNAKE_CASE :						int  ,					_SCREAMING_SNAKE_CASE :						str  ,					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						List[str]					)->List[Any]:
      from accelerate.utils import DummyOptim, DummyScheduler

      _lowerCAmelCase			      =	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)

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

      _lowerCAmelCase			      =	None
      if "scheduler" in config:
            _lowerCAmelCase			      =	DummyScheduler(_SCREAMING_SNAKE_CASE					)
      else:
            if isinstance(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					):
                  raise ValueError(
                      '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                      '''Please configure a scheduler in the DeepSpeed config.'''					)
            _lowerCAmelCase			      =	trainer.create_scheduler(num_training_steps=_SCREAMING_SNAKE_CASE  ,					optimizer=_SCREAMING_SNAKE_CASE					)

      return optimizer, lr_scheduler
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Dict  ,					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						str=False					)->Dict:
      from deepspeed.utils import logger as ds_logger

      _lowerCAmelCase			      =	trainer.model
      _lowerCAmelCase			      =	trainer.args

      _lowerCAmelCase			      =	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(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_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'''					)
            _lowerCAmelCase      , _lowerCAmelCase			      =	None, None
            _lowerCAmelCase			      =	None
      else:
            _lowerCAmelCase			      =	None  # important for when deepspeed_init is used as re-init
            _lowerCAmelCase			      =	list(filter(lambda _SCREAMING_SNAKE_CASE					: p.requires_grad  ,					model.parameters()					)					)
            _lowerCAmelCase      , _lowerCAmelCase			      =	deepspeed_optim_sched(
                _SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)

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

      return optimizer, lr_scheduler


def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						Optional[Any]					)->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

      _lowerCAmelCase			      =	sorted(glob.glob(f'''{checkpoint_path}/global_step*'''					)					)

      if len(_SCREAMING_SNAKE_CASE					) > 0:
            logger.info(f'''Attempting to resume from {checkpoint_path}'''					)
            # this magically updates self.optimizer and self.lr_scheduler
            _lowerCAmelCase      , _lowerCAmelCase			      =	deepspeed_engine.load_checkpoint(
                _SCREAMING_SNAKE_CASE  ,					load_optimizer_states=_SCREAMING_SNAKE_CASE  ,					load_lr_scheduler_states=_SCREAMING_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}'''					)




import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()