

'''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

_a :       str       							=  logging.get_logger(__name__)


def 					lowerCamelCase__						(  ):
  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 				lowercase_		(						_UpperCamelCase       ):

  '''simple docstring'''





  def __init__(   self      ,       a_   )							-> Dict:


    """simple docstring"""
    # set global weakref object
    set_hf_deepspeed_config(self   )
    dep_version_check('accelerate'   )
    dep_version_check('deepspeed'   )
    super().__init__(_SCREAMING_SNAKE_CASE   )




class 				lowercase_		(						_UpperCamelCase       ):

  '''simple docstring'''





  def __init__(   self      ,       a_   )							-> Optional[int]:


    """simple docstring"""
    super().__init__(_SCREAMING_SNAKE_CASE   )
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		[]
  def   snake_case_	(   self   )							-> int:


    """simple docstring"""
    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_   )							-> Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.get_value(_SCREAMING_SNAKE_CASE   )
    if val is None:
      return False
    else:
      return val == "auto"
  def   snake_case_	(   self      ,       a_      ,       a_      ,       a_=None      ,       a_=True   )							-> int:


    """simple docstring"""
    UpperCAmelCase							,					UpperCAmelCase  							=		self.find_config_node(_SCREAMING_SNAKE_CASE   )
    if config is None:
      return

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

    if not must_match:
      return

    UpperCAmelCase  							=		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}'''   )

  __lowerCAmelCase    :       Dict			     =      partialmethod(_UpperCamelCase       ,				must_match=_UpperCamelCase       )
  def   snake_case_	(   self      ,       a_   )							-> Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		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:
      UpperCAmelCase  							=		'apex' if args.fpaa_backend == 'apex' else 'amp'
    else:
      UpperCAmelCase  							=		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  							=		self.config.get('checkpoint'      ,       {}   )
      UpperCAmelCase  							=		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  							=		torch.bfloataa
    elif self.is_false('fp16.enabled'   ):
      UpperCAmelCase  							=		torch.floataa
    else:
      UpperCAmelCase  							=		torch.floataa





  def   snake_case_	(   self      ,       a_      ,       a_      ,       a_   )							-> Any:


    """simple docstring"""
    UpperCAmelCase  							=		[
        'zero_optimization.reduce_bucket_size',
        'zero_optimization.stage3_prefetch_bucket_size',
        'zero_optimization.stage3_param_persistence_threshold',
    ]
    UpperCAmelCase  							=		[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'   ):
        UpperCAmelCase  							=		model.config.hidden_size
      elif hasattr(model.config      ,       'hidden_sizes'   ):
        # if there are many hidden sizes pick the largest one
        UpperCAmelCase  							=		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'      ,       _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:
      UpperCAmelCase  							=		'\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\'.'''   )




_a :       Optional[int]       							=  None


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      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)
  UpperCAmelCase  							=		weakref.ref(_UpperCamelCase  )


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


def 					lowerCamelCase__						(  ):
  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__						(  ):
  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__						(  SCREAMING_SNAKE_CASE					:      Union[str, Any]				,      SCREAMING_SNAKE_CASE					:      List[str]				,      SCREAMING_SNAKE_CASE					:      Optional[Any]				,      SCREAMING_SNAKE_CASE					:      Optional[int]				,      SCREAMING_SNAKE_CASE					:      int  ):
  from accelerate.utils import DummyOptim, DummyScheduler

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

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

  return optimizer, lr_scheduler


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

  UpperCAmelCase  							=		trainer.model
  UpperCAmelCase  							=		trainer.args

  UpperCAmelCase  							=		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(_UpperCamelCase				,      _UpperCamelCase				,      _UpperCamelCase  )

  # 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  							=		None, None
    UpperCAmelCase  							=		None
  else:
    UpperCAmelCase  							=		None  # important for when deepspeed_init is used as re-init
    UpperCAmelCase  							=		list(filter(lambda SCREAMING_SNAKE_CASE  : p.requires_grad				,      model.parameters()  )  )
    UpperCAmelCase							,					UpperCAmelCase  							=		deepspeed_optim_sched(
        _UpperCamelCase				,      _UpperCamelCase				,      _UpperCamelCase				,      _UpperCamelCase				,      _UpperCamelCase  )

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

  return optimizer, lr_scheduler



def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      int				,      SCREAMING_SNAKE_CASE					:      Dict  ):
  # 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  							=		sorted(glob.glob(f'''{checkpoint_path}/global_step*'''  )  )

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__snake_case   :      Optional[int]						    =  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Optional[Any]						    =  ['MBart50Tokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Dict						    =  ['MBart50TokenizerFast']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  __snake_case   :      Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






