code: 
  
  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_		:			Dict										=   logging.get_logger(__name__)
  
  
  def       __a       (   )      ->     List[Any]:
  
  
  
  
  
  
      '''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 					A_   (      lowercase__						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      int:
          # set global weakref object
          set_hf_deepspeed_config(self  )
          dep_version_check('''accelerate'''  )
          dep_version_check('''deepspeed'''  )
          super().__init__(_A  )
  
  
  
  
  
  
  class 					A_   (      lowercase__						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      Any:
          super().__init__(_A  )
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			[]
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          if self._dtype is None:
              raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''  )
          return self._dtype
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          __UpperCAmelCase									=			self.get_value(_A  )
          if val is None:
              return False
          else:
              return val == "auto"
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=None     ,      lowercase__=True  )     ->      Union[str, Any]:
          __UpperCAmelCase									=			self.find_config_node(_A  )
          if config is None:
              return
  
          if config.get(_A  ) == "auto":
              __UpperCAmelCase									=			hf_val
              return
  
          if not must_match:
              return
  
          __UpperCAmelCase									=			config.get(_A  )
          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}'''  )
  
      a__     							=       partialmethod(lowercase__ ,     must_match=lowercase__						)
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          __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'''     ,      _A     ,      '''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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      int:
          __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(_A  )]
  
          if len(_A  ) > 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'''     ,      10 * hidden_size  )
  
          # scheduler
          self.fill_match('''scheduler.params.total_num_steps'''     ,      _A     ,      '''num_training_steps (calculated)'''  )
          self.fill_match('''scheduler.params.warmup_num_steps'''     ,      args.get_warmup_steps(_A  )     ,      '''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[Any]										=   None
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Optional[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      __UpperCAmelCase									=			weakref.ref(SCREAMING_SNAKE_CASE      )
  
  
  def       __a       (   )      ->     Optional[int]:
  
  
  
  
  
  
      '''simple docstring'''
      global _hf_deepspeed_config_weak_ref
      __UpperCAmelCase									=			None
  
  
  def       __a       (   )      ->     Tuple:
  
  
  
  
  
  
      '''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       __a       (   )      ->     Optional[Any]:
  
  
  
  
  
  
      '''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       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     List[str]:
  
  
  
  
  
  
      '''simple docstring'''
      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=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.
          __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(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.'''      )
          __UpperCAmelCase									=			trainer.create_scheduler(num_training_steps=SCREAMING_SNAKE_CASE       ,   optimizer=SCREAMING_SNAKE_CASE      )
  
      return optimizer, lr_scheduler
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE=False      )      ->     Optional[int]:
  
  
  
  
  
  
      '''simple docstring'''
      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(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'''      )
          __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									=			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       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     List[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      import glob
  
      __UpperCAmelCase									=			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
          __UpperCAmelCase									=			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}'''      )
  
  
  
  
  
  
style_context: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  A_		:			int										=   logging.get_logger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       "speech-encoder-decoder"
      a__     							=       True
  
  
      def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
          super().__init__(**lowercase__  )
          if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                  F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )
  
          __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
          __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
          __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
          __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )
  
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			True
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
  
          return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
          __UpperCAmelCase									=			self.encoder.to_dict()
          __UpperCAmelCase									=			self.decoder.to_dict()
          __UpperCAmelCase									=			self.__class__.model_type
          return output
  
  
  
  
  