
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE   :		Dict					 =       16
SCREAMING_SNAKE_CASE   :		Tuple					 =       32



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# Initialize accelerator
				if args.with_tracking:
								A_       	=  Accelerator(
								    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with='''all''' ,						project_dir=args.project_dir     )
				else:
								A_       	=  Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision     )

				if hasattr(args.checkpointing_steps ,						'''isdigit'''     ):
								if args.checkpointing_steps == "epoch":
												A_       	=  args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												A_       	=  int(args.checkpointing_steps     )
								else:
												raise ValueError(
												    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
				else:
								A_       	=  None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A_       	=  config['''lr''']
				A_       	=  int(config['''num_epochs''']     )
				A_       	=  int(config['''seed''']     )
				A_       	=  int(config['''batch_size''']     )

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								A_       	=  os.path.split(__UpperCamelCase     )[-1].split('''.'''     )[0]
								accelerator.init_trackers(__UpperCamelCase ,						__UpperCamelCase     )

				A_       	=  AutoTokenizer.from_pretrained('''bert-base-cased'''     )
				A_       	=  load_dataset('''glue''' ,						'''mrpc'''     )
				A_       	=  evaluate.load('''glue''' ,						'''mrpc'''     )

				def tokenize_function(__UpperCamelCase     ):
								# max_length=None => use the model max length (it's actually the default)
								A_       	=  tokenizer(examples['''sentence1'''] ,						examples['''sentence2'''] ,						truncation=__UpperCamelCase ,						max_length=__UpperCamelCase     )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								A_       	=  datasets.map(
								    __UpperCamelCase ,						batched=__UpperCamelCase ,						remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				A_       	=  tokenized_datasets.rename_column('''label''' ,						'''labels'''     )

				# If the batch size is too big we use gradient accumulation
				A_       	=  1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A_       	=  batch_size // MAX_GPU_BATCH_SIZE
								A_       	=  MAX_GPU_BATCH_SIZE

				def collate_fn(__UpperCamelCase     ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A_       	=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A_       	=  16
								elif accelerator.mixed_precision != "no":
												A_       	=  8
								else:
												A_       	=  None

								return tokenizer.pad(
								    __UpperCamelCase ,						padding='''longest''' ,						max_length=__UpperCamelCase ,						pad_to_multiple_of=__UpperCamelCase ,						return_tensors='''pt''' ,						)

				# Instantiate dataloaders.
				A_       	=  DataLoader(
				    tokenized_datasets['''train'''] ,						shuffle=__UpperCamelCase ,						collate_fn=__UpperCamelCase ,						batch_size=__UpperCamelCase     )
				A_       	=  DataLoader(
				    tokenized_datasets['''validation'''] ,						shuffle=__UpperCamelCase ,						collate_fn=__UpperCamelCase ,						batch_size=__UpperCamelCase     )

				set_seed(__UpperCamelCase     )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A_       	=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,						return_dict=__UpperCamelCase     )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				A_       	=  model.to(accelerator.device     )

				# Instantiate optimizer
				A_       	=  AdamW(params=model.parameters() ,						lr=__UpperCamelCase     )

				# Instantiate scheduler
				A_       	=  get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase ,						num_warmup_steps=1_00 ,						num_training_steps=(len(__UpperCamelCase     ) * num_epochs) // gradient_accumulation_steps ,						)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				A_	,					A_	,					A_	,					A_	,					A_       	=  accelerator.prepare(
				    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# We need to keep track of how many total steps we have iterated over
				A_       	=  0
				# We also need to keep track of the stating epoch so files are named properly
				A_       	=  0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
												accelerator.load_state(args.resume_from_checkpoint     )
												A_       	=  os.path.basename(args.resume_from_checkpoint     )
								else:
												# Get the most recent checkpoint
												A_       	=  [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
												dirs.sort(key=os.path.getctime     )
												A_       	=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								A_       	=  os.path.splitext(__UpperCamelCase     )[0]

								if "epoch" in training_difference:
												A_       	=  int(training_difference.replace('''epoch_''' ,						''''''     )     ) + 1
												A_       	=  None
								else:
												A_       	=  int(training_difference.replace('''step_''' ,						''''''     )     )
												A_       	=  resume_step // len(__UpperCamelCase     )
												resume_step -= starting_epoch * len(__UpperCamelCase     )

    # Now we train the model
				for epoch in range(__UpperCamelCase ,						__UpperCamelCase     ):
								model.train()
								if args.with_tracking:
												A_       	=  0
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												A_       	=  accelerator.skip_first_batches(__UpperCamelCase ,						__UpperCamelCase     )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												A_       	=  train_dataloader
								for step, batch in enumerate(__UpperCamelCase     ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device     )
												A_       	=  model(**__UpperCamelCase     )
												A_       	=  outputs.loss
												A_       	=  loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(__UpperCamelCase     )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
																A_       	=  F"step_{overall_step}"
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								A_       	=  os.path.join(args.output_dir ,						__UpperCamelCase     )
																				accelerator.save_state(__UpperCamelCase     )

								model.eval()
								for step, batch in enumerate(__UpperCamelCase     ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device     )
												with torch.no_grad():
																A_       	=  model(**__UpperCamelCase     )
												A_       	=  outputs.logits.argmax(dim=-1     )
												A_	,					A_       	=  accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
												metric.add_batch(
												    predictions=__UpperCamelCase ,						references=__UpperCamelCase ,						)

								A_       	=  metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:" ,						__UpperCamelCase     )
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(__UpperCamelCase     ),
												        '''epoch''': epoch,
												    } ,						step=__UpperCamelCase ,						)

								if checkpointing_steps == "epoch":
												A_       	=  F"epoch_{epoch}"
												if args.output_dir is not None:
																A_       	=  os.path.join(args.output_dir ,						__UpperCamelCase     )
												accelerator.save_state(__UpperCamelCase     )

				if args.with_tracking:
								accelerator.end_training()



def        lowerCamelCase_  (   ):
				A_       	=  argparse.ArgumentParser(description='''Simple example of training script.'''     )
				parser.add_argument(
				    '''--mixed_precision''' ,						type=__UpperCamelCase ,						default=__UpperCamelCase ,						choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,						help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.''' ,						)
				parser.add_argument('''--cpu''' ,						action='''store_true''' ,						help='''If passed, will train on the CPU.'''     )
				parser.add_argument(
				    '''--checkpointing_steps''' ,						type=__UpperCamelCase ,						default=__UpperCamelCase ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' ,						)
				parser.add_argument(
				    '''--resume_from_checkpoint''' ,						type=__UpperCamelCase ,						default=__UpperCamelCase ,						help='''If the training should continue from a checkpoint folder.''' ,						)
				parser.add_argument(
				    '''--with_tracking''' ,						action='''store_true''' ,						help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' ,						)
				parser.add_argument(
				    '''--output_dir''' ,						type=__UpperCamelCase ,						default='''.''' ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,						)
				parser.add_argument(
				    '''--project_dir''' ,						type=__UpperCamelCase ,						default='''logs''' ,						help='''Location on where to store experiment tracking logs` and relevent project information''' ,						)
				A_       	=  parser.parse_args()
				A_       	=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase ,						__UpperCamelCase     )


if __name__ == "__main__":
						main()

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

SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   ):
				return importlib.util.find_spec('''deepspeed'''     ) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							Dict:





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






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  None
									A_       	=  []


					def        lowerCAmelCase_    (	self						)		->							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        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=None       ,       a__=True						)		->							List[str]:





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

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

									if not must_match:
													return

									A_       	=  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}"						)

					__magic_name__						:							str     	=	partialmethod(A							,       must_match=A       )


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Tuple:





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

									if len(a__						) > 0:
													if hasattr(model.config       ,       '''hidden_size'''						):
																	A_       	=  model.config.hidden_size
													elif hasattr(model.config       ,       '''hidden_sizes'''						):
																	# if there are many hidden sizes pick the largest one
																	A_       	=  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'''       ,       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_       	=  '''\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'."						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       None



def        lowerCamelCase_  (   __UpperCamelCase     ):
				# 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_       	=  weakref.ref(__UpperCamelCase     )



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



def        lowerCamelCase_  (   ):
				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        lowerCamelCase_  (   ):
				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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				from accelerate.utils import DummyOptim, DummyScheduler

				A_       	=  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_       	=  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_       	=  DummyOptim(params=__UpperCamelCase     )
				else:
								if hf_deepspeed_config.is_offload():
												logger.info(
												    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
												    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''     )

								# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
								# But trainer uses AdamW by default.
								A_       	=  trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								A_       	=  True

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

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=False     ):
				from deepspeed.utils import logger as ds_logger

				A_       	=  trainer.model
				A_       	=  trainer.args

				A_       	=  trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# set the Deepspeed log level consistent with the Trainer
				ds_logger.setLevel(args.get_process_log_level()     )

				if inference:
								# only Z3 makes sense for the inference
								if not hf_deepspeed_config.is_zeroa():
												raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''     )

								# in case the training config is re-used for inference
								hf_deepspeed_config.del_config_sub_tree('''optimizer'''     )
								hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''     )
								A_	,					A_       	=  None, None
								A_       	=  None
				else:
								A_       	=  None  # important for when deepspeed_init is used as re-init
								A_       	=  list(filter(lambda __UpperCamelCase     : p.requires_grad ,						model.parameters()     )     )
								A_	,					A_       	=  deepspeed_optim_sched(
								    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

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

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# 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_       	=  sorted(glob.glob(F"{checkpoint_path}/global_step*"     )     )

				if len(__UpperCamelCase     ) > 0:
								logger.info(F"Attempting to resume from {checkpoint_path}"     )
								# this magically updates self.optimizer and self.lr_scheduler
								A_	,					A_       	=  deepspeed_engine.load_checkpoint(
								    __UpperCamelCase ,						load_optimizer_states=__UpperCamelCase ,						load_lr_scheduler_states=__UpperCamelCase     )
								if load_path is None:
												raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"     )
				else:
								raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"     )