





"""simple docstring"""




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase_     ="""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

UpperCamelCase_     ="""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


UpperCamelCase_     ="""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					[rgram for rgrams in rgramslist for rgram in rgrams]
						_UpperCamelCase    :      Tuple						     =					Counter(_lowercase     )

						_UpperCamelCase    :      str						     =					Counter(_lowercase     )
						_UpperCamelCase    :      int						     =					Counter()
						for sgram, scount in sgramcounter.items():
												_UpperCamelCase    :      str						     =					scount * numref

						_UpperCamelCase    :      Tuple						     =					Counter(_lowercase     )
						_UpperCamelCase    :      Optional[Any]						     =					Counter()
						for cgram, ccount in cgramcounter.items():
												_UpperCamelCase    :      Tuple						     =					ccount * numref

						# KEEP
						_UpperCamelCase    :      Dict						     =					sgramcounter_rep & cgramcounter_rep
						_UpperCamelCase    :      int						     =					keepgramcounter_rep & rgramcounter
						_UpperCamelCase    :      List[str]						     =					sgramcounter_rep & rgramcounter

						_UpperCamelCase    :      Tuple						     =					0
						_UpperCamelCase    :      Union[str, Any]						     =					0
						for keepgram in keepgramcountergood_rep:
												keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
												# Fix an alleged bug [2] in the keep score computation.
												# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
												keeptmpscorea += keepgramcountergood_rep[keepgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						#      a target exactly.
						_UpperCamelCase    :      List[Any]						     =					1
						_UpperCamelCase    :      str						     =					1
						if len(_lowercase     ) > 0:
												_UpperCamelCase    :      Any						     =					keeptmpscorea / len(_lowercase     )
						if len(_lowercase     ) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												_UpperCamelCase    :      Tuple						     =					keeptmpscorea / sum(keepgramcounterall_rep.values()     )
						_UpperCamelCase    :      Optional[Any]						     =					0
						if keepscore_precision > 0 or keepscore_recall > 0:
												_UpperCamelCase    :      Optional[int]						     =					2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						_UpperCamelCase    :      List[Any]						     =					sgramcounter_rep - cgramcounter_rep
						_UpperCamelCase    :      Optional[Any]						     =					delgramcounter_rep - rgramcounter
						_UpperCamelCase    :      List[Any]						     =					sgramcounter_rep - rgramcounter
						_UpperCamelCase    :      Any						     =					0
						_UpperCamelCase    :      Tuple						     =					0
						for delgram in delgramcountergood_rep:
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						_UpperCamelCase    :      List[str]						     =					1
						if len(_lowercase     ) > 0:
												_UpperCamelCase    :      str						     =					deltmpscorea / len(_lowercase     )

						# ADDITION
						_UpperCamelCase    :      Tuple						     =					set(_lowercase     ) - set(_lowercase     )
						_UpperCamelCase    :      List[str]						     =					set(_lowercase     ) & set(_lowercase     )
						_UpperCamelCase    :      Dict						     =					set(_lowercase     ) - set(_lowercase     )

						_UpperCamelCase    :      str						     =					0
						for addgram in addgramcountergood:
												addtmpscore += 1

						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						_UpperCamelCase    :      List[str]						     =					1
						_UpperCamelCase    :      Dict						     =					1
						if len(_lowercase     ) > 0:
												_UpperCamelCase    :      Optional[int]						     =					addtmpscore / len(_lowercase     )
						if len(_lowercase     ) > 0:
												_UpperCamelCase    :      List[str]						     =					addtmpscore / len(_lowercase     )
						_UpperCamelCase    :      Optional[int]						     =					0
						if addscore_precision > 0 or addscore_recall > 0:
												_UpperCamelCase    :      List[str]						     =					2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)





def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Dict						     =					len(_lowercase     )

						_UpperCamelCase    :      Tuple						     =					ssent.split(''' '''     )
						_UpperCamelCase    :      str						     =					csent.split(''' '''     )
						_UpperCamelCase    :      Dict						     =					[]
						_UpperCamelCase    :      Optional[int]						     =					[]
						_UpperCamelCase    :      str						     =					[]
						_UpperCamelCase    :      str						     =					[]
						_UpperCamelCase    :      str						     =					[]
						_UpperCamelCase    :      Optional[Any]						     =					[]

						_UpperCamelCase    :      List[Any]						     =					[]
						_UpperCamelCase    :      Any						     =					[]
						_UpperCamelCase    :      Union[str, Any]						     =					[]
						_UpperCamelCase    :      List[Any]						     =					[]
						for rsent in rsents:
												_UpperCamelCase    :      List[str]						     =					rsent.split(''' '''     )
												_UpperCamelCase    :      Tuple						     =					[]
												_UpperCamelCase    :      int						     =					[]
												_UpperCamelCase    :      Any						     =					[]
												ragramslist.append(_lowercase     )
												for i in range(0 ,		len(_lowercase     ) - 1     ):
																		if i < len(_lowercase     ) - 1:
																								_UpperCamelCase    :      Dict						     =					ragrams[i] + ''' ''' + ragrams[i + 1]
																								ragrams.append(_lowercase     )
																		if i < len(_lowercase     ) - 2:
																								_UpperCamelCase    :      int						     =					ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																								ragrams.append(_lowercase     )
																		if i < len(_lowercase     ) - 3:
																								_UpperCamelCase    :      Dict						     =					ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																								ragrams.append(_lowercase     )
												ragramslist.append(_lowercase     )
												ragramslist.append(_lowercase     )
												ragramslist.append(_lowercase     )

						for i in range(0 ,		len(_lowercase     ) - 1     ):
												if i < len(_lowercase     ) - 1:
																		_UpperCamelCase    :      Optional[Any]						     =					sagrams[i] + ''' ''' + sagrams[i + 1]
																		sagrams.append(_lowercase     )
												if i < len(_lowercase     ) - 2:
																		_UpperCamelCase    :      int						     =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																		sagrams.append(_lowercase     )
												if i < len(_lowercase     ) - 3:
																		_UpperCamelCase    :      List[str]						     =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																		sagrams.append(_lowercase     )

						for i in range(0 ,		len(_lowercase     ) - 1     ):
												if i < len(_lowercase     ) - 1:
																		_UpperCamelCase    :      Union[str, Any]						     =					cagrams[i] + ''' ''' + cagrams[i + 1]
																		cagrams.append(_lowercase     )
												if i < len(_lowercase     ) - 2:
																		_UpperCamelCase    :      int						     =					cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																		cagrams.append(_lowercase     )
												if i < len(_lowercase     ) - 3:
																		_UpperCamelCase    :      List[Any]						     =					cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																		cagrams.append(_lowercase     )

						((_UpperCamelCase)     ,							(_UpperCamelCase)     ,							(_UpperCamelCase))    :      Dict						     =					SARIngram(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
						((_UpperCamelCase)     ,							(_UpperCamelCase)     ,							(_UpperCamelCase))    :      str						     =					SARIngram(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
						((_UpperCamelCase)     ,							(_UpperCamelCase)     ,							(_UpperCamelCase))    :      Any						     =					SARIngram(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
						((_UpperCamelCase)     ,							(_UpperCamelCase)     ,							(_UpperCamelCase))    :      Tuple						     =					SARIngram(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
						_UpperCamelCase    :      Optional[int]						     =					sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
						_UpperCamelCase    :      Union[str, Any]						     =					sum([delascore, delascore, delascore, delascore]     ) / 4
						_UpperCamelCase    :      int						     =					sum([addascore, addascore, addascore, addascore]     ) / 4
						_UpperCamelCase    :      List[Any]						     =					(avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore





def  a_       (							_lowercase ,		_lowercase = True ,		_lowercase = "13a" ,		_lowercase = True     ):
						# Normalization is requried for the ASSET dataset (one of the primary
						# datasets in sentence simplification) to allow using space
						# to split the sentence. Even though Wiki-Auto and TURK datasets,
						# do not require normalization, we do it for consistency.
						# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
						# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

						if lowercase:
												_UpperCamelCase    :      Optional[int]						     =					sentence.lower()

						if tokenizer in ["13a", "intl"]:
												if version.parse(sacrebleu.__version__     ).major >= 2:
																		_UpperCamelCase    :      Union[str, Any]						     =					sacrebleu.metrics.bleu._get_tokenizer(_lowercase     )()(_lowercase     )
												else:
																		_UpperCamelCase    :      Any						     =					sacrebleu.TOKENIZERS[tokenizer]()(_lowercase     )
						elif tokenizer == "moses":
												_UpperCamelCase    :      List[str]						     =					sacremoses.MosesTokenizer().tokenize(_lowercase ,		return_str=_lowercase ,		escape=_lowercase     )
						elif tokenizer == "penn":
												_UpperCamelCase    :      int						     =					sacremoses.MosesTokenizer().penn_tokenize(_lowercase ,		return_str=_lowercase     )
						else:
												_UpperCamelCase    :      Tuple						     =					sentence

						if not return_str:
												_UpperCamelCase    :      Optional[Any]						     =					normalized_sent.split()

						return normalized_sent






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION       )
class 				_a		(   datasets.Metric       ):






				def        snake_case			(				self	:					List[str]							)			->   List[Any]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION,				citation=_CITATION,				inputs_description=_KWARGS_DESCRIPTION,				features=datasets.Features(
										        {
										            '''sources''': datasets.Value('''string''',				id='''sequence'''							),
										            '''predictions''': datasets.Value('''string''',				id='''sequence'''							),
										            '''references''': datasets.Sequence(datasets.Value('''string''',				id='''sequence'''							),				id='''references'''							),
										        }							),				codebase_urls=[
										        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
										        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
										    ],				reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf'''],				)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int							)			->   Dict:




										'''simple docstring'''



										if not (len(lowerCAmelCase__							) == len(lowerCAmelCase__							) == len(lowerCAmelCase__							)):
																raise ValueError('''Sources length must match predictions and references lengths.'''							)
										_UpperCamelCase    :      Tuple						     =					0
										for src, pred, refs in zip(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							):
																sari_score += SARIsent(normalize(lowerCAmelCase__							),				normalize(lowerCAmelCase__							),				[normalize(lowerCAmelCase__							) for sent in refs]							)
										_UpperCamelCase    :      Dict						     =					sari_score / len(lowerCAmelCase__							)
										return {"sari": 1_0_0 * sari_score}










"""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_     =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 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Optional[Any],				lowerCAmelCase__	:					Tuple							)			->   List[Any]:




										'''simple docstring'''



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




class 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Any,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										super().__init__(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					None
										_UpperCamelCase    :      List[str]						     =					[]






				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



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






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					Any							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.get_value(lowerCAmelCase__							)
										if val is None:
																return False
										else:
																return val == "auto"






				def        snake_case			(				self	:					Optional[Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str]=None,				lowerCAmelCase__	:					Dict=True							)			->   int:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Tuple						     =					self.find_config_node(lowerCAmelCase__							)
										if config is None:
																return

										if config.get(lowerCAmelCase__							) == "auto":
																_UpperCamelCase    :      int						     =					hf_val
																return

										if not must_match:
																return

										_UpperCamelCase    :      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}"""							)

				UpperCamelCase			=					partialmethod(_lowerCAmelCase			,       must_match=_lowerCAmelCase       )






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					List[str]							)			->   int:




										'''simple docstring'''



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






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					[
										    '''zero_optimization.reduce_bucket_size''',
										    '''zero_optimization.stage3_prefetch_bucket_size''',
										    '''zero_optimization.stage3_param_persistence_threshold''',
										]
										_UpperCamelCase    :      Dict						     =					[x for x in hidden_size_based_keys if self.is_auto(lowerCAmelCase__							)]

										if len(lowerCAmelCase__							) > 0:
																if hasattr(model.config,				'''hidden_size'''							):
																						_UpperCamelCase    :      str						     =					model.config.hidden_size
																elif hasattr(model.config,				'''hidden_sizes'''							):
																						# if there are many hidden sizes pick the largest one
																						_UpperCamelCase    :      Union[str, 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''',				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    :      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'."""							)

UpperCamelCase_     =None





def  a_       (							_lowercase     ):
						# 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    :      int						     =					weakref.ref(_lowercase     )





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    :      Optional[int]						     =					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_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						from accelerate.utils import DummyOptim, DummyScheduler

						_UpperCamelCase    :      Tuple						     =					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    :      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.'''     )
												_UpperCamelCase    :      Optional[int]						     =					DummyOptim(params=_lowercase     )
						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    :      Tuple						     =					True

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

						return optimizer, lr_scheduler





def  a_       (							_lowercase ,		_lowercase ,		_lowercase=False     ):
						from deepspeed.utils import logger as ds_logger

						_UpperCamelCase    :      Tuple						     =					trainer.model
						_UpperCamelCase    :      List[str]						     =					trainer.args

						_UpperCamelCase    :      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(_lowercase ,		_lowercase ,		_lowercase     )

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

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

						return optimizer, lr_scheduler






def  a_       (							_lowercase ,		_lowercase     ):
						# 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    :      Tuple						     =					sorted(glob.glob(F"""{checkpoint_path}/global_step*"""     )     )

						if len(_lowercase     ) > 0:
												logger.info(F"""Attempting to resume from {checkpoint_path}"""     )
												# this magically updates self.optimizer and self.lr_scheduler
												_UpperCamelCase     ,							_UpperCamelCase    :      Any						     =					deepspeed_engine.load_checkpoint(
												    _lowercase ,		load_optimizer_states=_lowercase ,		load_lr_scheduler_states=_lowercase     )
												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}"""     )



