
"""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

_lowercase											=			logging.get_logger(__name__)






def  _snake_case			(						):
	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_			( _lowercase      ):



						'''simple docstring'''


						def __init__(	self       : List[str]					,A_       : Tuple )       ->				Dict:
							# set global weakref object
							set_hf_deepspeed_config(self )
							dep_version_check('accelerate' )
							dep_version_check('deepspeed' )
							super().__init__(A_ )







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						def __init__(	self       : List[Any]					,A_       : Optional[int] )       ->				List[Any]:
							super().__init__(A_ )
							A :    Union[str, Any]  				=				None
							A :    Dict  				=				[]


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


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A :    Optional[int]  				=				self.get_value(A_ )
							if val is None:
								return False
							else:
								return val == "auto"


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : List[Any]					,A_       : Optional[int]					,A_       : Tuple=None					,A_       : Dict=True )       ->				Union[str, Any]:
							A				, A :    str  				=				self.find_config_node(A_ )
							if config is None:
								return

							if config.get(A_ ) == "auto":
								A :    Dict  				=				hf_val
								return

							if not must_match:
								return

							A :    str  				=				config.get(A_ )
							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:      Any =      partialmethod(_lowercase						,   must_match=_lowercase      )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : Optional[int] )       ->				List[str]:
							A :    List[str]  				=				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'					,A_					,'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 :    Union[str, 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 :    Optional[Any]  				=				self.config.get('checkpoint'					,{} )
								A :    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 :    int  				=				torch.bfloataa
							elif self.is_false('fp16.enabled' ):
								A :    List[str]  				=				torch.floataa
							else:
								A :    Optional[int]  				=				torch.floataa




						def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : Dict					,A_       : Dict					,A_       : str )       ->				List[Any]:
							A :    int  				=				[
							    'zero_optimization.reduce_bucket_size',
							    'zero_optimization.stage3_prefetch_bucket_size',
							    'zero_optimization.stage3_param_persistence_threshold',
							]
							A :    str  				=				[x for x in hidden_size_based_keys if self.is_auto(A_ )]

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

							if len(self.mismatches ) > 0:
								A :    Optional[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\'.' )

_lowercase											=			None






def  _snake_case			(						snake_case__				:       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 :    Union[str, Any]  				=				weakref.ref(snake_case__       )






def  _snake_case			(						):
	# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
	global _hf_deepspeed_config_weak_ref
	A :    str  				=				None






def  _snake_case			(						):
	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			(						):
	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			(						snake_case__				:       Optional[int]		, snake_case__				:       List[Any]		, snake_case__				:       Any		, snake_case__				:       List[str]		, snake_case__				:       int       ):
	from accelerate.utils import DummyOptim, DummyScheduler

	A :    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)

	A :    Any  				=				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 :    Optional[int]  				=				DummyOptim(params=snake_case__       )
	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 :    List[Any]  				=				trainer.create_optimizer()
		# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
		A :    List[Any]  				=				True

	A :    int  				=				None
	if "scheduler" in config:
		A :    int  				=				DummyScheduler(snake_case__       )
	else:
		if isinstance(snake_case__		, snake_case__       ):
			raise ValueError(
			    'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
			    'Please configure a scheduler in the DeepSpeed config.'       )
		A :    Any  				=				trainer.create_scheduler(num_training_steps=snake_case__		, optimizer=snake_case__       )

	return optimizer, lr_scheduler






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       int		, snake_case__				:       List[Any]=False       ):
	from deepspeed.utils import logger as ds_logger

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

	A :    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(snake_case__		, snake_case__		, snake_case__       )

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

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

	return optimizer, lr_scheduler






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       List[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 :    Tuple  				=				sorted(glob.glob(F'{checkpoint_path}/global_step*'       )       )

	if len(snake_case__       ) > 0:
		logger.info(F'Attempting to resume from {checkpoint_path}'       )
		# this magically updates self.optimizer and self.lr_scheduler
		A				, A :    int  				=				deepspeed_engine.load_checkpoint(
		    snake_case__		, load_optimizer_states=snake_case__		, load_lr_scheduler_states=snake_case__       )
		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  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record