




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
lowercase_      :							Union[str, Any]    							=					'\\n'

lowercase_      :							int    							=					'\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

lowercase_      :							Union[str, Any]    							=					'\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

lowercase_      :							int    							=					['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
lowercase_      :							Tuple    							=					''

try:
				from jiwer import transforms as tr

				lowercase_      :							Union[str, Any]    							=					True
except ImportError:
				lowercase_      :							int    							=					False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):






				class        __lowerCAmelCase					(			tr.AbstractTransform					):







							def __init__(			self       :     int       ,       snake_case__       :     str = " " ):




													"""simple docstring"""


													_UpperCAmelCase						=	sentence_delimiter







							def UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     str ):




													"""simple docstring"""


													return list(snake_case__ )







							def UpperCamelCase     (			self       :     int       ,       snake_case__       :     List[str] ):




													"""simple docstring"""


													_UpperCAmelCase						=	[]
													for sent_idx, sentence in enumerate(snake_case__ ):
																			chars.extend(self.process_string(snake_case__ ) )
																			if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case__ ) - 1:
																									chars.append(self.sentence_delimiter )
													return chars




				lowercase_      :							List[Any]    							=					tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				lowercase_      :							List[str]    							=					tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				lowercase_      :							Optional[Any]    							=					None


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						return float((preds == labels).mean()		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						return {
						    "f1": float(fa_score(y_true=snake_case_      ,       y_pred=snake_case_      ,       average="macro"		)		),
						    "accuracy": simple_accuracy(snake_case_      ,       snake_case_		),
						}


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_="exp"      ,       snake_case_=None      ,       snake_case_=False      ,       snake_case_=False      ,       snake_case_=None      ,       snake_case_=False      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	[[label] for label in labels]
						_UpperCAmelCase						=	list(snake_case_		)
						try:
												import sacrebleu as scb
						except ImportError:
												raise ValueError(
												    "sacrebleu has to be installed in order to apply the bleu metric for covost2."
												    "You can install it via `pip install sacrebleu`."		)

						if version.parse(scb.__version__		) < version.parse("1.4.12"		):
												raise ImportWarning(
												    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
												    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."		)

						_UpperCAmelCase						=	len(labels[0]		)
						if any(len(snake_case_		) != references_per_prediction for refs in labels		):
												raise ValueError("Sacrebleu requires the same number of references for each prediction"		)
						_UpperCAmelCase						=	[[refs[i] for refs in labels] for i in range(snake_case_		)]
						_UpperCAmelCase						=	scb.corpus_bleu(
						    snake_case_      ,       snake_case_      ,       smooth_method=snake_case_      ,       smooth_value=snake_case_      ,       force=snake_case_      ,       lowercase=snake_case_      ,       use_effective_order=snake_case_      ,       **({"tokenize": tokenize} if tokenize else {})      ,       )
						return {"bleu": output.score}


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						try:
												from jiwer import compute_measures
						except ImportError:
												raise ValueError(
												    f"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
												    "You can install it via `pip install jiwer`."		)

						if concatenate_texts:
												_UpperCAmelCase						=	compute_measures(snake_case_      ,       snake_case_		)["wer"]

												_UpperCAmelCase						=	compute_measures(snake_case_      ,       snake_case_      ,       truth_transform=snake_case_      ,       hypothesis_transform=snake_case_		)["wer"]
												return {"wer": wer, "cer": cer}
						else:

												def compute_score(snake_case_      ,       snake_case_      ,       snake_case_="wer"		):
																		_UpperCAmelCase						=	0
																		_UpperCAmelCase						=	0
																		for prediction, reference in zip(snake_case_      ,       snake_case_		):
																								if score_type == "wer":
																														_UpperCAmelCase						=	compute_measures(snake_case_      ,       snake_case_		)
																								elif score_type == "cer":
																														_UpperCAmelCase						=	compute_measures(
																														    snake_case_      ,       snake_case_      ,       truth_transform=snake_case_      ,       hypothesis_transform=snake_case_		)
																								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																		return incorrect / total

												return {"wer": compute_score(snake_case_      ,       snake_case_      ,       "wer"		), "cer": compute_score(snake_case_      ,       snake_case_      ,       "cer"		)}






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







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									if self.config_name not in _CONFIG_NAMES:
															raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}""" )

									_UpperCAmelCase						=	"int64" if self.config_name in ["fleurs-lang_id", "minds14"] else "string"

									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,       citation=_CITATION       ,       inputs_description=_KWARGS_DESCRIPTION       ,       features=datasets.Features(
									        {"predictions": datasets.Value(snake_case__ ), "references": datasets.Value(snake_case__ )} )       ,       codebase_urls=[]       ,       reference_urls=[]       ,       format="numpy"       ,       )







			def UpperCamelCase     (			self       :     Dict       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     str=None       ,       snake_case__       :     List[Any]=None ):




									"""simple docstring"""


									_UpperCAmelCase						=	bleu_kwargs if bleu_kwargs is not None else {}
									_UpperCAmelCase						=	wer_kwargs if wer_kwargs is not None else {}

									if self.config_name == "fleurs-lang_id":
															return {"accuracy": simple_accuracy(snake_case__       ,       snake_case__ )}
									elif self.config_name == "minds14":
															return fa_and_simple_accuracy(snake_case__       ,       snake_case__ )
									elif self.config_name == "covost2":
															_UpperCAmelCase						=	bleu_kwargs.pop("smooth_method"       ,       "exp" )
															_UpperCAmelCase						=	bleu_kwargs.pop("smooth_value"       ,       snake_case__ )
															_UpperCAmelCase						=	bleu_kwargs.pop("force"       ,       snake_case__ )
															_UpperCAmelCase						=	bleu_kwargs.pop("lowercase"       ,       snake_case__ )
															_UpperCAmelCase						=	bleu_kwargs.pop("tokenize"       ,       snake_case__ )
															_UpperCAmelCase						=	bleu_kwargs.pop("use_effective_order"       ,       snake_case__ )
															return bleu(
															    preds=snake_case__       ,       labels=snake_case__       ,       smooth_method=snake_case__       ,       smooth_value=snake_case__       ,       force=snake_case__       ,       lowercase=snake_case__       ,       tokenize=snake_case__       ,       use_effective_order=snake_case__       ,       )
									elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
															_UpperCAmelCase						=	wer_kwargs.pop("concatenate_texts"       ,       snake_case__ )
															return wer_and_cer(snake_case__       ,       snake_case__       ,       snake_case__       ,       self.config_name )
									else:
															raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}""" )










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_      :							int    							=					logging.get_logger(__name__)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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					(			UpperCAmelCase__					):







			def __init__(			self       :     Dict       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									set_hf_deepspeed_config(self )
									dep_version_check("accelerate" )
									dep_version_check("deepspeed" )
									super().__init__(snake_case__ )






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     str       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									super().__init__(snake_case__ )
									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	[]







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     str       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_value(snake_case__ )
									if val is None:
															return False
									else:
															return val == "auto"







			def UpperCamelCase     (			self       :     Union[str, Any]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Dict=None       ,       snake_case__       :     int=True ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	self.find_config_node(snake_case__ )
									if config is None:
															return

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

									if not must_match:
															return

									_UpperCAmelCase						=	config.get(snake_case__ )
									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_ :					Optional[Any]						=	partialmethod(UpperCAmelCase__   ,						must_match=UpperCAmelCase__					)







			def UpperCamelCase     (			self       :     Any       ,       snake_case__       :     Optional[int] ):




									"""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"       ,       snake_case__       ,       "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 UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     Dict       ,       snake_case__       :     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(snake_case__ )]

									if len(snake_case__ ) > 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"       ,       10 * hidden_size )

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




lowercase_      :							Union[str, Any]    							=					None


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
						_UpperCAmelCase						=	weakref.ref(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						_UpperCAmelCase						=	None


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						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=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.
												_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(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."		)
												_UpperCAmelCase						=	trainer.create_scheduler(num_training_steps=snake_case_      ,       optimizer=snake_case_		)

						return optimizer, lr_scheduler


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=False		):

						'''simple docstring'''


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

						'''simple docstring'''


						import glob

						_UpperCAmelCase						=	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
												_UpperCAmelCase				,					_UpperCAmelCase						=	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}"""		)




