

'''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

snake_case__          =						logging.get_logger(__name__)




def 						snake_case__    ( )   ->   List[str]:
					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__     ):







						"""simple docstring"""


						def __init__(     self      :       Dict							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







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






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						def __init__(     self      :       str							, _lowerCamelCase      :       Any    ):





											"""simple docstring"""







											super().__init__(_lowerCamelCase    )
											A_ :     Tuple =							None
											A_ :     str =							[]







						def       _a    (     self      :       Union[str, Any]    ):





											"""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      :       Optional[int]							, _lowerCamelCase      :       Any    ):





											"""simple docstring"""







											A_ :     Tuple =							self.get_value(_lowerCamelCase    )
											if val is None:
																return False
											else:
																return val == "auto"







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Any=None							, _lowerCamelCase      :       List[Any]=True    ):





											"""simple docstring"""







											A_ :     int =							self.find_config_node(_lowerCamelCase    )
											if config is None:
																return

											if config.get(_lowerCamelCase    ) == "auto":
																A_ :     Tuple =							hf_val
																return

											if not must_match:
																return

											A_ :     Optional[int] =							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}'    )

						_lowerCAmelCase    						=					partialmethod(a__,      must_match=a__     )







						def       _a    (     self      :       Optional[Any]							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     Optional[int] =							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:
																A_ :     Dict =							'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
											else:
																A_ :     Optional[Any] =							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_ :     str =							self.config.get('''checkpoint'''							, {}    )
																A_ :     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'''    ):
																A_ :     List[Any] =							torch.bfloataa
											elif self.is_false('''fp16.enabled'''    ):
																A_ :     int =							torch.floataa
											else:
																A_ :     Union[str, Any] =							torch.floataa







						def       _a    (     self      :       int							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ :     Dict =							[
											    '''zero_optimization.reduce_bucket_size''',
											    '''zero_optimization.stage3_prefetch_bucket_size''',
											    '''zero_optimization.stage3_param_persistence_threshold''',
											]
											A_ :     List[Any] =							[x for x in hidden_size_based_keys if self.is_auto(_lowerCamelCase    )]

											if len(_lowerCamelCase    ) > 0:
																if hasattr(model.config							, '''hidden_size'''    ):
																					A_ :     List[Any] =							model.config.hidden_size
																elif hasattr(model.config							, '''hidden_sizes'''    ):
																					# if there are many hidden sizes pick the largest one
																					A_ :     List[str] =							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'''							, _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:
																A_ :     Tuple =							'''\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\'.'    )






snake_case__          =						None




def 						snake_case__    ( lowerCamelCase__				:   List[str]			)   ->   List[str]:
					# 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)
					A_ :     Optional[int] =							weakref.ref(lowerCamelCase__			)




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




def 						snake_case__    ( )   ->   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().is_zeroa()
					else:
										return False




def 						snake_case__    ( )   ->   int:
					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 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   Optional[int]			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Tuple			)   ->   int:
					from accelerate.utils import DummyOptim, DummyScheduler

					A_ :     Any =							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_ :     Optional[int] =							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_ :     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.
										A_ :     Optional[int] =							trainer.create_optimizer()
										# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
										A_ :     str =							True

					A_ :     str =							None
					if "scheduler" in config:
										A_ :     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.'''			)
										A_ :     Optional[Any] =							trainer.create_scheduler(num_training_steps=lowerCamelCase__			,		optimizer=lowerCamelCase__			)

					return optimizer, lr_scheduler




def 						snake_case__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   List[Any]=False			)   ->   Union[str, Any]:
					from deepspeed.utils import logger as ds_logger

					A_ :     Tuple =							trainer.model
					A_ :     Union[str, Any] =							trainer.args

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

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

					return optimizer, lr_scheduler





def 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   int			)   ->   Any:
					# 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

					A_ :     Union[str, 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
										A_ :     Dict =							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'''




def 						snake_case__    ( lowerCamelCase__				:   float			)   ->   float:
					return -num if num < 0 else num




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_min() arg is an empty sequence'''			)
					A_ :     Any =							x[0]
					for i in x:
										if abs_val(lowerCamelCase__			) < abs_val(lowerCamelCase__			):
															A_ :     Tuple =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max() arg is an empty sequence'''			)
					A_ :     List[str] =							x[0]
					for i in x:
										if abs(lowerCamelCase__			) > abs(lowerCamelCase__			):
															A_ :     Any =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max_sort() arg is an empty sequence'''			)
					return sorted(lowerCamelCase__			,		key=lowerCamelCase__			)[-1]




def 						snake_case__    ( )   ->   Optional[Any]:
					assert abs_val(0			) == 0
					assert abs_val(3_4			) == 3_4
					assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0			) == 1_0_0_0_0_0_0_0_0_0_0_0

					A_ :     Dict =							[-3, -1, 2, -1_1]
					assert abs_max(lowerCamelCase__			) == -1_1
					assert abs_max_sort(lowerCamelCase__			) == -1_1
					assert abs_min(lowerCamelCase__			) == -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							test_abs_val()
							print(abs_val(-34))  # --> 34



