






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__            =	logging.get_logger(__name__)
def 		_a					(   )							->    List[Any]:
 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 	lowercase_      (			__lowerCamelCase     ):






      '''simple docstring'''




      def __init__(   self   :		str				,      __UpperCAmelCase   :		Union[str, Any]						)					->List[Any]:




       """simple docstring"""


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






class 	lowercase_      (			__lowerCamelCase     ):






      '''simple docstring'''




      def __init__(   self   :		str				,      __UpperCAmelCase   :		List[Any]						)					->Tuple:




       """simple docstring"""


       super().__init__(__UpperCAmelCase						)
       a							=   None
       a							=   []







      def    __lowerCAmelCase      (   self   :		Dict						)					->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   :		List[Any]				,      __UpperCAmelCase   :		str						)					->List[Any]:




       """simple docstring"""


       a							=   self.get_value(__UpperCAmelCase						)
       if val is None:
        return False
       else:
        return val == "auto"







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[str]				,      __UpperCAmelCase   :		str				,      __UpperCAmelCase   :		Any=None				,      __UpperCAmelCase   :		Optional[Any]=True						)					->Any:




       """simple docstring"""


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

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

       if not must_match:
        return

       a							=   config.get(__UpperCAmelCase						)
       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}"""						)

      __snake_case  						=						partialmethod(__lowerCamelCase				,						must_match=__lowerCamelCase     )







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		Any						)					->Optional[int]:




       """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'''				,      __UpperCAmelCase				,      '''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    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Any				,      __UpperCAmelCase   :		str				,      __UpperCAmelCase   :		Any						)					->List[Any]:




       """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(__UpperCAmelCase						)]

       if len(__UpperCAmelCase						) > 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'''				,      __UpperCAmelCase				,      '''num_training_steps (calculated)'''						)
       self.fill_match('''scheduler.params.warmup_num_steps'''				,      args.get_warmup_steps(__UpperCAmelCase						)				,      '''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__            =	None
def 		_a					(   a				:Optional[Any]				)							->    List[Any]:
 global _hf_deepspeed_config_weak_ref
 # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
 a							=   weakref.ref(a				)
def 		_a					(   )							->    Optional[int]:
 global _hf_deepspeed_config_weak_ref
 a							=   None
def 		_a					(   )							->    Dict:


 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					(   )							->    Union[str, Any]:


 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					(   a				:Tuple			,							a				:Optional[Any]			,							a				:int			,							a				:Tuple			,							a				:Union[str, Any]				)							->    int:
 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=a				)
 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(a				)
 else:
  if isinstance(a			,							a				):
   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=a			,							optimizer=a				)

 return optimizer, lr_scheduler
def 		_a					(   a				:Optional[Any]			,							a				:str			,							a				:Optional[int]=False				)							->    Optional[int]:
 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(a			,							a			,							a				)

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

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

 return optimizer, lr_scheduler

def 		_a					(   a				:str			,							a				:Optional[int]				)							->    int:
 import glob

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

 if len(a				) > 0:
  logger.info(F"""Attempting to resume from {checkpoint_path}"""				)
  # this magically updates self.optimizer and self.lr_scheduler
  a						,  a							=   deepspeed_engine.load_checkpoint(
      a			,							load_optimizer_states=a			,							load_lr_scheduler_states=a				)
  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 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''recursive_lucas_number accepts only integer arguments.'''				)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1				) + recursive_lucas_number(
     n_th_number - 2				)
def 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''				)
 a						,  a							=   2, 1
 for _ in range(a				):
  a						,  a							=   b, a + b
 return a


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCAmelCase__            =	int(input("Enter the number of terms in lucas series:\n").strip())
     print("Using recursive function to calculate lucas series:")
     print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
     print("\nUsing dynamic function to calculate lucas series:")
     print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))


