




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__    :							int       		=     logging.get_logger(__name__)


def 		_lowerCamelCase					(       )      ->		Union[str, 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   UpperCAmelCase    (      A ):






   def __init__(						self :							Optional[int]    ,       UpperCamelCase_ :							List[Any]  )->      Dict:
     # set global weakref object
     set_hf_deepspeed_config(self  )
     dep_version_check('accelerate'  )
     dep_version_check('deepspeed'  )
     super().__init__(UpperCamelCase_  )

class   UpperCAmelCase    (      A ):






   def __init__(						self :							List[str]    ,       UpperCamelCase_ :							Optional[Any]  )->      str:
     super().__init__(UpperCamelCase_  )
     _lowercase:			Union[str, Any]      =     None
     _lowercase:			Optional[Any]      =     []






   def 			_a    (						self :							Optional[Any]  )->      int:
     if self._dtype is None:
       raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'  )
     return self._dtype






   def 			_a    (						self :							Any    ,       UpperCamelCase_ :							List[Any]  )->      List[Any]:
     _lowercase:			List[Any]      =     self.get_value(UpperCamelCase_  )
     if val is None:
       return False
     else:
       return val == "auto"






   def 			_a    (						self :							Tuple    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							List[str]=None    ,       UpperCamelCase_ :							List[str]=True  )->      int:
     _lowercase  ,       _lowercase:			Dict      =     self.find_config_node(UpperCamelCase_  )
     if config is None:
       return

     if config.get(UpperCamelCase_  ) == "auto":
       _lowercase:			Dict      =     hf_val
       return

     if not must_match:
       return

     _lowercase:			List[Any]      =     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}'  )

   __SCREAMING_SNAKE_CASE		:				str      					=       partialmethod(A ,       must_match=A )






   def 			_a    (						self :							Tuple    ,       UpperCamelCase_ :							Tuple  )->      Dict:
     _lowercase:			Optional[Any]      =     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:
       _lowercase:			Optional[int]      =     'apex' if args.fpaa_backend == 'apex' else 'amp'
     else:
       _lowercase:			Optional[int]      =     None

     if args.save_on_each_node:
       # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
       _lowercase:			List[str]      =     self.config.get('checkpoint'    ,       {}  )
       _lowercase:			Tuple      =     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'  ):
       _lowercase:			List[Any]      =     torch.bfloataa
     elif self.is_false('fp16.enabled'  ):
       _lowercase:			Union[str, Any]      =     torch.floataa
     else:
       _lowercase:			Tuple      =     torch.floataa






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							Union[str, Any]    ,       UpperCamelCase_ :							Union[str, Any]    ,       UpperCamelCase_ :							Union[str, Any]  )->      Union[str, Any]:
     _lowercase:			Tuple      =     [
         'zero_optimization.reduce_bucket_size',
         'zero_optimization.stage3_prefetch_bucket_size',
         'zero_optimization.stage3_param_persistence_threshold',
     ]
     _lowercase:			Dict      =     [x for x in hidden_size_based_keys if self.is_auto(UpperCamelCase_  )]

     if len(UpperCamelCase_  ) > 0:
       if hasattr(model.config    ,       'hidden_size'  ):
         _lowercase:			Optional[Any]      =     model.config.hidden_size
       elif hasattr(model.config    ,       'hidden_sizes'  ):
         # if there are many hidden sizes pick the largest one
         _lowercase:			Optional[int]      =     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:
       _lowercase:			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\'.'  )
UpperCAmelCase__    :							str       		=     None


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		List[str]:





  """simple docstring"""





  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  _lowercase:			int      =     weakref.ref(UpperCamelCase				)


def 		_lowerCamelCase					(       )      ->		int:





  """simple docstring"""





  global _hf_deepspeed_config_weak_ref
  _lowercase:			Tuple      =     None


def 		_lowerCamelCase					(       )      ->		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 		_lowerCamelCase					(       )      ->		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 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  from accelerate.utils import DummyOptim, DummyScheduler

  _lowercase:			Optional[int]      =     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)

  _lowercase:			Tuple      =     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.'				)
    _lowercase:			Optional[Any]      =     DummyOptim(params=UpperCamelCase				)
  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.
    _lowercase:			Union[str, Any]      =     trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    _lowercase:			Dict      =     True

  _lowercase:			Union[str, Any]      =     None
  if "scheduler" in config:
    _lowercase:			Optional[Any]      =     DummyScheduler(UpperCamelCase				)
  else:
    if isinstance(UpperCamelCase       ,  UpperCamelCase				):
      raise ValueError(
          'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
          'Please configure a scheduler in the DeepSpeed config.'				)
    _lowercase:			List[str]      =     trainer.create_scheduler(num_training_steps=UpperCamelCase       ,  optimizer=UpperCamelCase				)

  return optimizer, lr_scheduler


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase=False				)      ->		int:





  """simple docstring"""





  from deepspeed.utils import logger as ds_logger

  _lowercase:			List[str]      =     trainer.model
  _lowercase:			int      =     trainer.args

  _lowercase:			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(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

  # 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'				)
    _lowercase  ,       _lowercase:			List[Any]      =     None, None
    _lowercase:			Dict      =     None
  else:
    _lowercase:			Union[str, Any]      =     None  # important for when deepspeed_init is used as re-init
    _lowercase:			Optional[int]      =     list(filter(lambda UpperCamelCase				: p.requires_grad       ,  model.parameters()				)				)
    _lowercase  ,       _lowercase:			Tuple      =     deepspeed_optim_sched(
        UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

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

  return optimizer, lr_scheduler





def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		Any:





  """simple docstring"""





  import glob

  _lowercase:			Optional[int]      =     sorted(glob.glob(F'{checkpoint_path}/global_step*'				)				)

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






import random
import sys

UpperCAmelCase__    :							str       		=     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Any      =     input('Enter message: '				)
  _lowercase:			List[str]      =     'LFWOAYUISVKMNXPBDCRJTQEGHZ'
  _lowercase:			Tuple      =     input('Encrypt/Decrypt [e/d]: '				)

  check_valid_key(UpperCamelCase				)

  if resp.lower().startswith('e'				):
    _lowercase:			int      =     'encrypt'
    _lowercase:			Optional[int]      =     encrypt_message(UpperCamelCase       ,  UpperCamelCase				)
  elif resp.lower().startswith('d'				):
    _lowercase:			str      =     'decrypt'
    _lowercase:			Dict      =     decrypt_message(UpperCamelCase       ,  UpperCamelCase				)

  print(F'\n{mode.title()}ion: \n{translated}'				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		None:





  """simple docstring"""





  _lowercase:			List[Any]      =     list(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  key_list.sort()
  letters_list.sort()

  if key_list != letters_list:
    sys.exit('Error in the key or symbol set.'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'encrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'decrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     ''
  _lowercase:			Any      =     LETTERS
  _lowercase:			Tuple      =     key

  if mode == "decrypt":
    _lowercase  ,       _lowercase:			Tuple      =     chars_b, chars_a

  for symbol in message:
    if symbol.upper() in chars_a:
      _lowercase:			int      =     chars_a.find(symbol.upper()				)
      if symbol.isupper():
        translated += chars_b[sym_index].upper()
      else:
        translated += chars_b[sym_index].lower()
    else:
      translated += symbol

  return translated


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  random.shuffle(UpperCamelCase				)
  return "".join(UpperCamelCase				)


if __name__ == "__main__":
   main()
