





"""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

lowerCamelCase_	:		Optional[Any]         =   logging.get_logger(__name__)





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  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    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_    ):

        """simple docstring"""

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


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_    ):

        """simple docstring"""

        super().__init__(snake_case_    )
        A_    :			Union[str, Any]									=    None
        A_    :			str									=    []

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Dict									=    self.get_value(snake_case_    )
        if val is None:
          return False
        else:
          return val == "auto"

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_=None   , snake_case_=True    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.find_config_node(snake_case_    )
        if config is None:
          return

        if config.get(snake_case_    ) == "auto":
          A_    :			Tuple									=    hf_val
          return

        if not must_match:
          return

        A_    :			int									=    config.get(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}"""    )

      lowercase_ :					Optional[int]             =  partialmethod(UpperCAmelCase__     ,  must_match=UpperCAmelCase__						)

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			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'   , 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:
          A_    :			List[str]									=    'apex' if args.fpaa_backend == 'apex' else 'amp'
        else:
          A_    :			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
          A_    :			Optional[Any]									=    self.config.get('checkpoint'   , {}    )
          A_    :			int									=    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'    ):
          A_    :			Union[str, Any]									=    torch.bfloataa
        elif self.is_false('fp16.enabled'    ):
          A_    :			List[str]									=    torch.floataa
        else:
          A_    :			List[str]									=    torch.floataa

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        A_    :			Tuple									=    [x for x in hidden_size_based_keys if self.is_auto(snake_case_    )]

        if len(snake_case_    ) > 0:
          if hasattr(model.config   , 'hidden_size'    ):
            A_    :			Optional[Any]									=    model.config.hidden_size
          elif hasattr(model.config   , 'hidden_sizes'    ):
            # if there are many hidden sizes pick the largest one
            A_    :			Dict									=    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'   , snake_case_   , 'num_training_steps (calculated)'    )
        self.fill_match('scheduler.params.warmup_num_steps'   , args.get_warmup_steps(snake_case_    )   , 'warmup_steps'    )

        if len(self.mismatches    ) > 0:
          A_    :			List[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'."""    )


lowerCamelCase_	:		Union[str, Any]         =   None





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  A_    :			Optional[int]									=    weakref.ref(_UpperCAmelCase   )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  global _hf_deepspeed_config_weak_ref
  A_    :			Dict									=    None





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  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__						(	):






  """simple docstring"""





  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__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  from accelerate.utils import DummyOptim, DummyScheduler

  A_    :			Any									=    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)

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

  A_    :			str									=    None
  if "scheduler" in config:
    A_    :			Optional[int]									=    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.'   )
    A_    :			str									=    trainer.create_scheduler(num_training_steps=_UpperCAmelCase					,   optimizer=_UpperCAmelCase   )

  return optimizer, lr_scheduler





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=False   ):






  """simple docstring"""





  from deepspeed.utils import logger as ds_logger

  A_    :			str									=    trainer.model
  A_    :			int									=    trainer.args

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

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

  return optimizer, lr_scheduler





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  import glob

  A_    :			Union[str, Any]									=    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
    A_		,			A_    :			List[Any]									=    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"""




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , *snake_case_   , **snake_case_    ):

        """simple docstring"""

        warnings.warn(
            'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use DetrImageProcessor instead.'   , snake_case_   , )
        super().__init__(*snake_case_   , **snake_case_    )