"""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

UpperCamelCase_    :    int											=						logging.get_logger(__name__)


def 					a__ (						)->		Dict:
						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					):

				def __init__(       self    :      int    ,      _lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										# set global weakref object
										set_hf_deepspeed_config(self       )
										dep_version_check("""accelerate"""       )
										dep_version_check("""deepspeed"""       )
										super().__init__(_lowercase       )


class      _lowercase						(				__lowerCamelCase					):

				def __init__(       self    :      Optional[Any]    ,      _lowercase    :      List[Any]       ):


										'''simple docstring'''
										super().__init__(_lowercase       )
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						[]

				def     A	(       self    :      Tuple       ):


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

				def     A	(       self    :      str    ,      _lowercase    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.get_value(_lowercase       )
										if val is None:
																return False
										else:
																return val == "auto"

				def     A	(       self    :      Dict    ,      _lowercase    :      Optional[Any]    ,      _lowercase    :      int    ,      _lowercase    :      str=None    ,      _lowercase    :      int=True       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.find_config_node(_lowercase       )
										if config is None:
																return

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

										if not must_match:
																return

										UpperCAmelCase__   		=						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__									=    partialmethod(__lowerCamelCase , must_match=__lowerCamelCase					)

				def     A	(       self    :      Optional[Any]    ,      _lowercase    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						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__   		=						"""apex""" if args.fpaa_backend == """apex""" else """amp"""
										else:
																UpperCAmelCase__   		=						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__   		=						self.config.get("""checkpoint"""    ,      {}       )
																UpperCAmelCase__   		=						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__   		=						torch.bfloataa
										elif self.is_false("""fp16.enabled"""       ):
																UpperCAmelCase__   		=						torch.floataa
										else:
																UpperCAmelCase__   		=						torch.floataa

				def     A	(       self    :      str    ,      _lowercase    :      int    ,      _lowercase    :      List[Any]    ,      _lowercase    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						[
										    """zero_optimization.reduce_bucket_size""",
										    """zero_optimization.stage3_prefetch_bucket_size""",
										    """zero_optimization.stage3_param_persistence_threshold""",
										]
										UpperCAmelCase__   		=						[x for x in hidden_size_based_keys if self.is_auto(_lowercase       )]

										if len(_lowercase       ) > 0:
																if hasattr(model.config    ,      """hidden_size"""       ):
																						UpperCAmelCase__   		=						model.config.hidden_size
																elif hasattr(model.config    ,      """hidden_sizes"""       ):
																						# if there are many hidden sizes pick the largest one
																						UpperCAmelCase__   		=						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"""    ,      _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__   		=						"""\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_    :    int											=						None


def 					a__ (						UpperCAmelCase__    :     Optional[int]     )->		Optional[Any]:
						# 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__   		=						weakref.ref(UpperCAmelCase__     )


def 					a__ (						)->		List[Any]:
						# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
						global _hf_deepspeed_config_weak_ref
						UpperCAmelCase__   		=						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__ (						)->		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__ (						UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Tuple     )->		List[Any]:
						from accelerate.utils import DummyOptim, DummyScheduler

						UpperCAmelCase__   		=						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__   		=						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__   		=						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.
												UpperCAmelCase__   		=						trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												UpperCAmelCase__   		=						True

						UpperCAmelCase__   		=						None
						if "scheduler" in config:
												UpperCAmelCase__   		=						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."""     )
												UpperCAmelCase__   		=						trainer.create_scheduler(num_training_steps=UpperCAmelCase__						,     optimizer=UpperCAmelCase__     )

						return optimizer, lr_scheduler


def 					a__ (						UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Dict=False     )->		str:
						from deepspeed.utils import logger as ds_logger

						UpperCAmelCase__   		=						trainer.model
						UpperCAmelCase__   		=						trainer.args

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

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

						return optimizer, lr_scheduler







def 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     List[str]     )->		str:
						# 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__   		=						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
												UpperCAmelCase__		, UpperCAmelCase__   		=						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}'     )


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

