"""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

_SCREAMING_SNAKE_CASE  :		str        =      logging.get_logger(__name__)



def 			A__						(  )     ->       Optional[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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				None
      __lowerCamelCase								=				[]







   def       _A							(  self  ):


      '''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   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				self.find_config_node(_SCREAMING_SNAKE_CASE  )
      if config is None:
         return

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

      if not must_match:
         return

      __lowerCamelCase								=				config.get(_SCREAMING_SNAKE_CASE  )
      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  :       Tuple						=						partialmethod(UpperCAmelCase						,							must_match=UpperCAmelCase							)







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __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'   ,							_SCREAMING_SNAKE_CASE   ,							'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       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __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(_SCREAMING_SNAKE_CASE  )]

      if len(_SCREAMING_SNAKE_CASE  ) > 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'   ,							_SCREAMING_SNAKE_CASE   ,							'num_training_steps (calculated)'  )
      self.fill_match('scheduler.params.warmup_num_steps'   ,							args.get_warmup_steps(_SCREAMING_SNAKE_CASE  )   ,							'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\'.'''  )

_SCREAMING_SNAKE_CASE  :		int        =      None



def 			A__						(  lowercase_    :  Any	)     ->       Union[str, Any]:
   # 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(lowercase_	)



def 			A__						(  )     ->       Dict:
   # 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 			A__						(  )     ->       Union[str, 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 			A__						(  )     ->       Dict:
   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__						(  lowercase_    :  Optional[Any]			,   lowercase_    :  Optional[Any]			,   lowercase_    :  List[Any]			,   lowercase_    :  Optional[Any]			,   lowercase_    :  Dict	)     ->       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=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.
      __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(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.'	)
      __lowerCamelCase								=				trainer.create_scheduler(num_training_steps=lowercase_			,   optimizer=lowercase_	)

   return optimizer, lr_scheduler



def 			A__						(  lowercase_    :  Tuple			,   lowercase_    :  Any			,   lowercase_    :  List[Any]=False	)     ->       List[Any]:
   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(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'	)
      __lowerCamelCase		,__lowerCamelCase								=				None, None
      __lowerCamelCase								=				None
   else:
      __lowerCamelCase								=				None  # important for when deepspeed_init is used as re-init
      __lowerCamelCase								=				list(filter(lambda lowercase_	: p.requires_grad			,   model.parameters()	)	)
      __lowerCamelCase		,__lowerCamelCase								=				deepspeed_optim_sched(
          lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)

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

   return optimizer, lr_scheduler



def 			A__						(  lowercase_    :  int			,   lowercase_    :  Any	)     ->       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(lowercase_	) > 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(
          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}'''	)







"""simple docstring"""







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '''--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'''
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






