
'''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

lowerCAmelCase_			:			Optional[int]			   =     logging.get_logger(__name__)







def __A  (    ):
     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    (  __a				):



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







class 	__lowerCAmelCase    (  __a				):



     def __init__(self						,	lowerCAmelCase__	):
          super().__init__(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    None
          _UpperCAmelCase					:  List[Any]         =    []



     def     snake_case_ (self	):
          if self._dtype is None:
               raise ValueError("""trainer_config_process() wasn't called yet to tell dtype"""	)
          return self._dtype



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  str         =    self.get_value(lowerCAmelCase__	)
          if val is None:
               return False
          else:
               return val == "auto"



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__=None						,	lowerCAmelCase__=True	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Any         =    self.find_config_node(lowerCAmelCase__	)
          if config is None:
               return

          if config.get(lowerCAmelCase__	) == "auto":
               _UpperCAmelCase					:  Any         =    hf_val
               return

          if not must_match:
               return

          _UpperCAmelCase					:  Optional[Any]         =    config.get(lowerCAmelCase__	)
          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   :    List[Any]							      =      partialmethod(__a       ,					must_match=__a				)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  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"""						,	lowerCAmelCase__						,	"""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					:  Dict         =    """apex""" if args.fpaa_backend == """apex""" else """amp"""
          else:
               _UpperCAmelCase					:  Dict         =    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					:  str         =    self.config.get("""checkpoint"""						,	{}	)
               _UpperCAmelCase					:  List[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					:  Tuple         =    torch.bfloataa
          elif self.is_false("""fp16.enabled"""	):
               _UpperCAmelCase					:  int         =    torch.floataa
          else:
               _UpperCAmelCase					:  List[str]         =    torch.floataa



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

          if len(lowerCAmelCase__	) > 0:
               if hasattr(model.config						,	"""hidden_size"""	):
                    _UpperCAmelCase					:  Tuple         =    model.config.hidden_size
               elif hasattr(model.config						,	"""hidden_sizes"""	):
                    # if there are many hidden sizes pick the largest one
                    _UpperCAmelCase					:  Tuple         =    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"""						,	1_0 * hidden_size	)

        # scheduler
          self.fill_match("""scheduler.params.total_num_steps"""						,	lowerCAmelCase__						,	"""num_training_steps (calculated)"""	)
          self.fill_match("""scheduler.params.warmup_num_steps"""						,	args.get_warmup_steps(lowerCAmelCase__	)						,	"""warmup_steps"""	)

          if len(self.mismatches	) > 0:
               _UpperCAmelCase					:  Union[str, 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'."	)




lowerCAmelCase_			:			Dict			   =     None







def __A  (    lowerCAmelCase_       ):
     # this is a special weakref global object to allow us to get to Deepspeed config from APIs
     # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     _UpperCAmelCase					:  Any         =    weakref.ref(lowerCAmelCase_       )







def __A  (    ):
     # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
     global _hf_deepspeed_config_weak_ref
     _UpperCAmelCase					:  List[Any]         =    None







def __A  (    ):
     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  (    ):
     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  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     from accelerate.utils import DummyOptim, DummyScheduler

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

     _UpperCAmelCase					:  Dict         =    None
     if "scheduler" in config:
          _UpperCAmelCase					:  Optional[int]         =    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					:  int         =    trainer.create_scheduler(num_training_steps=lowerCAmelCase_ ,       optimizer=lowerCAmelCase_       )

     return optimizer, lr_scheduler







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_=False       ):
     from deepspeed.utils import logger as ds_logger

     _UpperCAmelCase					:  Optional[Any]         =    trainer.model
     _UpperCAmelCase					:  Optional[int]         =    trainer.args

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

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

     return optimizer, lr_scheduler







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     # it's possible that the user is trying to resume from model_path, which doesn't necessarily
     # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
     # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
     # path contains what looks like a deepspeed checkpoint
     import glob

     _UpperCAmelCase					:  Any         =    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					:  Optional[Any]         =    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}"       )




'''simple docstring'''

from __future__ import annotations







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Optional[int]         =    {1}
     _UpperCAmelCase					:  Union[str, Any]         =    [1]
     for _ in range(2 ,       depth + 1       ):
          _UpperCAmelCase					:  Dict         =    [*previous_coefficients, 0]
          _UpperCAmelCase					:  int         =    [0, *previous_coefficients]
          _UpperCAmelCase					:  List[Any]         =    []
          for x, y in zip(lowerCAmelCase_ ,       lowerCAmelCase_       ):
               coefficients.add(x + y       )
               previous_coefficients.append(x + y       )
     return coefficients







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Dict         =    set()
     for number in unique_coefficients:
          _UpperCAmelCase					:  Tuple         =    2
          _UpperCAmelCase					:  List[str]         =    number
          while divisor**2 <= copy_number:
               _UpperCAmelCase					:  Tuple         =    0
               while copy_number % divisor == 0:
                    copy_number //= divisor
                    multiplicity += 1
               if multiplicity >= 2:
                    non_squarefrees.add(lowerCAmelCase_       )
                    break
               divisor += 1

     return unique_coefficients.difference(lowerCAmelCase_       )







def __A  (    lowerCAmelCase_ = 51       ):
     _UpperCAmelCase					:  List[str]         =    get_pascal_triangle_unique_coefficients(lowerCAmelCase_       )
     _UpperCAmelCase					:  Optional[Any]         =    get_squarefrees(lowerCAmelCase_       )
     return sum(lowerCAmelCase_       )


if __name__ == "__main__":
   print(F"{solution() = }")


