
"""simple docstring"""
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

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

  """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					(       _snake_case):
    def __init__(     self    ,     snake_case_  ):
      # set global weakref object
      set_hf_deepspeed_config(self  )
      dep_version_check("accelerate"  )
      dep_version_check("deepspeed"  )
      super().__init__(snake_case_  )






class   _UpperCAmelCase					(       _snake_case):
    def __init__(     self    ,     snake_case_  ):
      super().__init__(snake_case_  )
      _snake_case  :    Optional[Any]            = None
      _snake_case  :    Optional[int]            = []
    def        lowerCamelCase__						(     self  ):
      if self._dtype is None:
        raise ValueError("trainer_config_process() wasn't called yet to tell dtype"  )
      return self._dtype
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Union[str, Any]            = self.get_value(snake_case_  )
      if val is None:
        return False
      else:
        return val == "auto"
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_=None    ,     snake_case_=True  ):
      _snake_case      ,       _snake_case  :    Tuple            = self.find_config_node(snake_case_  )
      if config is None:
        return

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

      if not must_match:
        return

      _snake_case  :    Tuple            = config.get(snake_case_  )
      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}'  )

    __lowercase				:			Any	=       partialmethod(_snake_case		,       must_match=_snake_case)
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    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"    ,     snake_case_    ,     "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  :    List[Any]            = "apex" if args.fpaa_backend == "apex" else "amp"
      else:
        _snake_case  :    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
        _snake_case  :    List[Any]            = self.config.get("checkpoint"    ,     {}  )
        _snake_case  :    Dict            = 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  :    str            = torch.bfloataa
      elif self.is_false("fp16.enabled"  ):
        _snake_case  :    Union[str, Any]            = torch.floataa
      else:
        _snake_case  :    Tuple            = torch.floataa


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = [
          "zero_optimization.reduce_bucket_size",
          "zero_optimization.stage3_prefetch_bucket_size",
          "zero_optimization.stage3_param_persistence_threshold",
      ]
      _snake_case  :    Dict            = [x for x in hidden_size_based_keys if self.is_auto(snake_case_  )]

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

      if len(self.mismatches  ) > 0:
        _snake_case  :    int            = "\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\'.'  )


_a   :      Any       =				None
def 					a__ ( a :					Union[str, Any]		):

  """simple docstring"""







  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  _snake_case  :    Any            = weakref.ref(a		)
def 					a__ ( ):

  """simple docstring"""







  global _hf_deepspeed_config_weak_ref
  _snake_case  :    int            = None
def 					a__ ( ):

  """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 					a__ ( ):

  """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 					a__ ( a :					List[str]							,  a :					List[str]							,  a :					Tuple							,  a :					int							,  a :					Optional[int]		):

  """simple docstring"""







  from accelerate.utils import DummyOptim, DummyScheduler

  _snake_case  :    Dict            = 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  :    List[str]            = 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  :    str            = 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.
    _snake_case  :    Dict            = trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    _snake_case  :    Any            = True

  _snake_case  :    str            = None
  if "scheduler" in config:
    _snake_case  :    int            = 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."		)
    _snake_case  :    Optional[int]            = trainer.create_scheduler(num_training_steps=a							,  optimizer=a		)

  return optimizer, lr_scheduler
def 					a__ ( a :					str							,  a :					Union[str, Any]							,  a :					Tuple=False		):

  """simple docstring"""







  from deepspeed.utils import logger as ds_logger

  _snake_case  :    List[Any]            = trainer.model
  _snake_case  :    Tuple            = trainer.args

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

  """simple docstring"""







  import glob

  _snake_case  :    Dict            = 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
    _snake_case      ,       _snake_case  :    Any            = 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}'		)



"""simple docstring"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

