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

__snake_case         =     logging.get_logger(__name__)





def 	lowerCAmelCase_			(					)->	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       __snake_case (  lowerCAmelCase__ ):
 def __init__(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





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







class       __snake_case (  lowerCAmelCase__ ):
 def __init__(	self		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       super().__init__(lowercase__    )
       UpperCAmelCase     :			List[Any]     	=None
       UpperCAmelCase     :			Any     	=[]
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       if self._dtype is None:
             raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''    )
       return self._dtype
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.get_value(lowercase__    )
       if val is None:
             return False
       else:
             return val == "auto"
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__=None		, snake_case__=True    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			str     	=self.find_config_node(lowercase__    )
       if config is None:
             return

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

       if not must_match:
             return

       UpperCAmelCase     :			Any     	=config.get(lowercase__    )
       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     : int 	=   partialmethod(lowerCAmelCase__       , must_match=lowerCAmelCase__ )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=[
           '''zero_optimization.reduce_bucket_size''',
           '''zero_optimization.stage3_prefetch_bucket_size''',
           '''zero_optimization.stage3_param_persistence_threshold''',
       ]
       UpperCAmelCase     :			Optional[int]     	=[x for x in hidden_size_based_keys if self.is_auto(lowercase__    )]

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

       if len(self.mismatches    ) > 0:
             UpperCAmelCase     :			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\'.'''    )



__snake_case         =     None





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Optional[int]:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					)->	Union[str, Any]:






      '''simple docstring'''





      global _hf_deepspeed_config_weak_ref
      UpperCAmelCase     :			Tuple     	=None





def 	lowerCAmelCase_			(					)->	str:






      '''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_			(					)->	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		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      from accelerate.utils import DummyOptim, DummyScheduler

      UpperCAmelCase     :			Optional[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)

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

      UpperCAmelCase     :			Any     	=None
      if "scheduler" in config:
            UpperCAmelCase     :			Any     	=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.'''    )
            UpperCAmelCase     :			Dict     	=trainer.create_scheduler(num_training_steps=lowerCAmelCase__		,       optimizer=lowerCAmelCase__    )

      return optimizer, lr_scheduler





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False    )->	Optional[Any]:






      '''simple docstring'''





      from deepspeed.utils import logger as ds_logger

      UpperCAmelCase     :			List[Any]     	=trainer.model
      UpperCAmelCase     :			int     	=trainer.args

      UpperCAmelCase     :			Tuple     	=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'''    )
            UpperCAmelCase	,    UpperCAmelCase     :			Any     	=None, None
            UpperCAmelCase     :			int     	=None
      else:
            UpperCAmelCase     :			List[str]     	=None  # important for when deepspeed_init is used as re-init
            UpperCAmelCase     :			List[str]     	=list(filter(lambda __lowerCAmelCase    : p.requires_grad		,       model.parameters()    )    )
            UpperCAmelCase	,    UpperCAmelCase     :			Tuple     	=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		,       __lowerCAmelCase    )->	int:






      '''simple docstring'''





      import glob

      UpperCAmelCase     :			Dict     	=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
            UpperCAmelCase	,    UpperCAmelCase     :			int     	=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}'''    )








def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		__A    =							[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		__A    =							1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(lowerCAmelCase__					,     n + 1	):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

