






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

UpperCamelCase__						:      Dict								=					logging.get_logger(__name__)





def   SCREAMING_SNAKE_CASE__						(   )   ->  str:


 """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_     (	lowercase__     ):
       def __init__(						self		:					List[str]							,__lowerCamelCase		:					Optional[Any]					):





        '''simple docstring'''



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







class 				lowerCamelCase_     (	lowercase__     ):
       def __init__(						self		:					List[Any]							,__lowerCamelCase		:					List[str]					):





        '''simple docstring'''



        super().__init__(_A					)
        a		=			None
        a		=			[]
       def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





        '''simple docstring'''



        if self._dtype is None:
         raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''					)
        return self._dtype
       def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					Union[str, Any]					):





        '''simple docstring'''



        a		=			self.get_value(_A					)
        if val is None:
         return False
        else:
         return val == "auto"
       def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					str=None							,__lowerCamelCase		:					Any=True					):





        '''simple docstring'''



        a		=			self.find_config_node(_A					)
        if config is None:
         return

        if config.get(_A					) == "auto":
         a		=			hf_val
         return

        if not must_match:
         return

        a		=			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}"""					)

       SCREAMING_SNAKE_CASE_						     =			partialmethod(lowercase__ ,						must_match=lowercase__     )
       def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]							,__lowerCamelCase		:					Optional[Any]					):





        '''simple docstring'''



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




       def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					int							,__lowerCamelCase		:					str					):





        '''simple docstring'''



        a		=			[
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        a		=			[x for x in hidden_size_based_keys if self.is_auto(_A					)]

        if len(_A					) > 0:
         if hasattr(model.config							,'''hidden_size'''					):
          a		=			model.config.hidden_size
         elif hasattr(model.config							,'''hidden_sizes'''					):
          # if there are many hidden sizes pick the largest one
          a		=			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:
         a		=			'\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\'."""					)


UpperCamelCase__						:      Optional[Any]								=					None





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  str:


 """simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   )   ->  Optional[int]:


 """simple docstring"""



 global _hf_deepspeed_config_weak_ref
 a		=			None





def   SCREAMING_SNAKE_CASE__						(   )   ->  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().is_zeroa()
 else:
  return False





def   SCREAMING_SNAKE_CASE__						(   )   ->  Dict:


 """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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  List[Any]:


 """simple docstring"""



 from accelerate.utils import DummyOptim, DummyScheduler

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

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

 return optimizer, lr_scheduler





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_=False  )   ->  Dict:


 """simple docstring"""



 from deepspeed.utils import logger as ds_logger

 a		=			trainer.model
 a		=			trainer.args

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

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

 return optimizer, lr_scheduler





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[Any]:


 """simple docstring"""



 import glob

 a		=			sorted(glob.glob(f"""{checkpoint_path}/global_step*"""  )  )

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						:      int								=					{
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
				from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
				from .tokenization_fsmt import FSMTTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
				import sys

				UpperCamelCase__						:      List[Any]								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





