


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



def 			_lowerCAmelCase						(							)->Optional[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     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[Any]    ,				_lowerCAmelCase       :       List[str]  )   -> Dict:




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






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[int]  )   -> List[Any]:




     """simple docstring"""
     super().__init__(_lowerCAmelCase  )
     snake_case_					    =					None
     snake_case_					    =					[]



  def 					lowerCAmelCase__  (     self       :       int  )   -> Optional[Any]:




     """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       :       Tuple    ,				_lowerCAmelCase       :       Any  )   -> List[str]:




     """simple docstring"""
     snake_case_					    =					self.get_value(_lowerCAmelCase  )
     if val is None:
        return False
     else:
        return val == "auto"



  def 					lowerCAmelCase__  (     self       :       List[str]    ,				_lowerCAmelCase       :       Tuple    ,				_lowerCAmelCase       :       Optional[int]    ,				_lowerCAmelCase       :       int=None    ,				_lowerCAmelCase       :       List[str]=True  )   -> List[str]:




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

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

     if not must_match:
        return

     snake_case_					    =					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(a ,  must_match=a					)



  def 					lowerCAmelCase__  (     self       :       Any    ,				_lowerCAmelCase       :       Optional[Any]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					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:
        snake_case_					    =					"apex" if args.fpaa_backend == "apex" else "amp"
     else:
        snake_case_					    =					None

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



  def 					lowerCAmelCase__  (     self       :       str    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       List[Any]    ,				_lowerCAmelCase       :       Any  )   -> int:




     """simple docstring"""
     snake_case_					    =					[
         "zero_optimization.reduce_bucket_size",
         "zero_optimization.stage3_prefetch_bucket_size",
         "zero_optimization.stage3_param_persistence_threshold",
     ]
     snake_case_					    =					[x for x in hidden_size_based_keys if self.is_auto(_lowerCAmelCase  )]

     if len(_lowerCAmelCase  ) > 0:
        if hasattr(model.config    ,				"hidden_size"  ):
           snake_case_					    =					model.config.hidden_size
        elif hasattr(model.config    ,				"hidden_sizes"  ):
           # if there are many hidden sizes pick the largest one
           snake_case_					    =					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"    ,				_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:
        snake_case_					    =					"\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	:Optional[int]  		=     None



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Any							)->Optional[int]:
   '''simple docstring'''






   global _hf_deepspeed_config_weak_ref
   # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
   snake_case_					    =					weakref.ref(lowerCAmelCase_							)



def 			_lowerCAmelCase						(							)->List[Any]:
   '''simple docstring'''






   global _hf_deepspeed_config_weak_ref
   snake_case_					    =					None



def 			_lowerCAmelCase						(							)->List[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().is_zeroa()
   else:
      return False



def 			_lowerCAmelCase						(							)->Optional[int]:
   '''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 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[int]		,						lowerCAmelCase_							:List[str]		,						lowerCAmelCase_							:int		,						lowerCAmelCase_							:Any		,						lowerCAmelCase_							:Tuple							)->Any:
   '''simple docstring'''






   from accelerate.utils import DummyOptim, DummyScheduler

   snake_case_					    =					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)

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

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

   return optimizer, lr_scheduler



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Any		,						lowerCAmelCase_							:List[Any]		,						lowerCAmelCase_							:List[Any]=False							)->List[str]:
   '''simple docstring'''






   from deepspeed.utils import logger as ds_logger

   snake_case_					    =					trainer.model
   snake_case_					    =					trainer.args

   snake_case_					    =					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(lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_							)

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

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

   return optimizer, lr_scheduler



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[Any]		,						lowerCAmelCase_							:Optional[int]							)->List[str]:
   '''simple docstring'''






   import glob

   snake_case_					    =					sorted(glob.glob(F'''{checkpoint_path}/global_step*'''							)							)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE	:List[Any]  		=     {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:int  		=     [
       '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Speech2Text2ForCausalLM''',
       '''Speech2Text2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
   from .processing_speech_to_text_a import SpeechaTextaProcessor
   from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_to_text_a import (
          SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechaTextaForCausalLM,
          SpeechaTextaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE	:str  		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




