
'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCamelCase__					:						Any											=     '''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

UpperCamelCase__					:						List[str]											=     logging.getLogger(__name__)

@dataclass
class       _a :





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     field(
           default='roberta-large-mnli'     ,    metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'})
       SCREAMING_SNAKE_CASE					=     field(
           default='This example is {}.'     ,    metadata={
               'help': (
                   'Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'
                   'where class name is inserted.'
               )
           }     ,    )
       SCREAMING_SNAKE_CASE					=     field(
           default=32     ,    metadata={'help': 'Batch size for generating teacher predictions.'})
       SCREAMING_SNAKE_CASE					=     field(
           default=lowerCamelCase_     ,    metadata={
               'help': (
                   'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
                   'multi-class multi-label classification).'
               )
           }     ,    )
       SCREAMING_SNAKE_CASE					=     field(
           default=1.0     ,    metadata={'help': 'Temperature applied to teacher softmax for distillation.'})






@dataclass
class       _a :





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     field(
           default='distilbert-base-uncased'     ,    metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'})






@dataclass
class       _a :





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     field(metadata={'help': 'Text file with one unlabeled instance per line.'})
       SCREAMING_SNAKE_CASE					=     field(metadata={'help': 'Text file with one class name per line.'})
       SCREAMING_SNAKE_CASE					=     field(
           default=lowerCamelCase_     ,    metadata={'help': 'Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'}     ,    )






@dataclass
class       _a (lowerCamelCase_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     field(
           default=lowerCamelCase_     ,    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     ,    )
       SCREAMING_SNAKE_CASE					=     field(
           default=32     ,    metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'})
       SCREAMING_SNAKE_CASE					=     field(
           default=1_28     ,    metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'})
       SCREAMING_SNAKE_CASE					=     field(default=1.0     ,    metadata={'help': 'Total number of training epochs to perform.'})
       SCREAMING_SNAKE_CASE					=     field(default=lowerCamelCase_     ,    metadata={'help': 'Whether to run training of student model.'})
       SCREAMING_SNAKE_CASE					=     field(
           default=lowerCamelCase_     ,    metadata={
               'help': (
                   'Whether to evaluate the agreement of the final student predictions and the teacher predictions'
                   'after training.'
               )
           }     ,    )
       SCREAMING_SNAKE_CASE					=     field(
           default=0     ,    metadata={
               'help': (
                   'Limit the total amount of checkpoints.'
                   'Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'
               )
           }     ,    )






class       _a (lowerCamelCase_):





       """simple docstring"""




       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__=False					)			->	str:
          _SCREAMING_SNAKE_CASE          = inputs['labels']
          _SCREAMING_SNAKE_CASE          = model(inputs["""input_ids"""]				,							attention_mask=inputs["""attention_mask"""]					)
          _SCREAMING_SNAKE_CASE          = outputs[0]

          _SCREAMING_SNAKE_CASE          = -torch.sum(target_p * logits.log_softmax(dim=-1					)				,							axis=-1					).mean()

          if return_outputs:
             return loss, outputs

          return loss






def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   with open(lowerCamelCase_			,					"""r"""  ) as f:
      for line in f:
         _SCREAMING_SNAKE_CASE          = line.strip()
         if len(lowerCamelCase_  ) > 0:
            lines.append(lowerCamelCase_  )
   return lines
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   _SCREAMING_SNAKE_CASE          = []
   for example in examples:
      for name in class_names:
         premises.append(lowerCamelCase_  )
         hypotheses.append(hypothesis_template.format(lowerCamelCase_  )  )
   return premises, hypotheses
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[Any]:




   """simple docstring"""





   for label, ind in config.labelaid.items():
      if label.lower().startswith("""entail"""  ):
         return ind
   logger.warning("""Could not identify entailment dimension from teacher config label2id. Setting to -1."""  )
   return -1
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					)   ->   Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_  )
   _SCREAMING_SNAKE_CASE          = model.config
   if not no_cuda and torch.cuda.is_available():
      _SCREAMING_SNAKE_CASE          = nn.DataParallel(model.cuda()  )
      batch_size *= len(model.device_ids  )
   _SCREAMING_SNAKE_CASE          = AutoTokenizer.from_pretrained(lowerCamelCase_			,					use_fast=lowerCamelCase_  )

   _SCREAMING_SNAKE_CASE          = get_premise_hypothesis_pairs(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_  )
   _SCREAMING_SNAKE_CASE          = []

   for i in tqdm(range(0			,					len(lowerCamelCase_  )			,					lowerCamelCase_  )  ):
      _SCREAMING_SNAKE_CASE          = premises[i : i + batch_size]
      _SCREAMING_SNAKE_CASE          = hypotheses[i : i + batch_size]

      _SCREAMING_SNAKE_CASE          = tokenizer(
          lowerCamelCase_			,					lowerCamelCase_			,					padding=lowerCamelCase_			,					truncation="""only_first"""			,					return_tensors="""pt"""			,					)

      with torch.cuda.amp.autocast(enabled=lowerCamelCase_  ):
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE          = model(**lowerCamelCase_  )
      logits.append(outputs.logits.detach().cpu().float()  )

   _SCREAMING_SNAKE_CASE          = get_entailment_id(lowerCamelCase_  )
   _SCREAMING_SNAKE_CASE          = -1 if entail_id == 0 else 0
   _SCREAMING_SNAKE_CASE          = torch.cat(lowerCamelCase_			,					dim=0  )  # N*K x 3
   _SCREAMING_SNAKE_CASE          = logits.reshape(len(lowerCamelCase_  )			,					len(lowerCamelCase_  )			,					-1  )[..., [contr_id, entail_id]]  # N x K x 2

   if multi_label:
      # softmax over (contr, entail) logits for each class independently
      _SCREAMING_SNAKE_CASE          = (nli_logits / temperature).softmax(-1  )
   else:
      # softmax over entail logits across classes s.t. class probabilities sum to 1.
      _SCREAMING_SNAKE_CASE          = (nli_logits / temperature).softmax(1  )

   return nli_prob[..., 1]  # N x K
def       lowerCAmelCase_		( )   ->   Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = HfArgumentParser(
       (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,					description=lowerCamelCase_			,					)

   if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      _SCREAMING_SNAKE_CASE          = parser.parse_json_file(
          json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      _SCREAMING_SNAKE_CASE          = parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   _SCREAMING_SNAKE_CASE          = None
   if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
      _SCREAMING_SNAKE_CASE          = get_last_checkpoint(training_args.output_dir  )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
         raise ValueError(
             F"Output directory ({training_args.output_dir}) already exists and is not empty. "
             """Use --overwrite_output_dir to overcome."""  )
      elif last_checkpoint is not None:
         logger.info(
             F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,					datefmt="""%m/%d/%Y %H:%M:%S"""			,					handlers=[logging.StreamHandler(sys.stdout  )]			,					)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

   # Log on each process the small summary:
   logger.warning(
       F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
       + F"distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"  )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank  ):
      utils.logging.set_verbosity_info()
      utils.logging.enable_default_handler()
      utils.logging.enable_explicit_format()

   if training_args.local_rank != -1:
      raise ValueError("""Distributed training is not currently supported."""  )
   if training_args.tpu_num_cores is not None:
      raise ValueError("""TPU acceleration is not currently supported."""  )

   logger.info(F"Training/evaluation parameters {training_args}"  )

   # Set seed before initializing model.
   set_seed(training_args.seed  )

   # 1. read in data
   _SCREAMING_SNAKE_CASE          = read_lines(data_args.data_file  )
   _SCREAMING_SNAKE_CASE          = read_lines(data_args.class_names_file  )

   # 2. get teacher predictions and load into dataset
   logger.info("""Generating predictions from zero-shot teacher model"""  )
   _SCREAMING_SNAKE_CASE          = get_teacher_predictions(
       teacher_args.teacher_name_or_path			,					lowerCamelCase_			,					lowerCamelCase_			,					teacher_args.hypothesis_template			,					teacher_args.teacher_batch_size			,					teacher_args.temperature			,					teacher_args.multi_label			,					data_args.use_fast_tokenizer			,					training_args.no_cuda			,					training_args.fpaa			,					)
   _SCREAMING_SNAKE_CASE          = Dataset.from_dict(
       {
           """text""": examples,
           """labels""": teacher_soft_preds,
       }  )

   # 3. create student
   logger.info("""Initializing student model"""  )
   _SCREAMING_SNAKE_CASE          = AutoModelForSequenceClassification.from_pretrained(
       student_args.student_name_or_path			,					num_labels=len(lowerCamelCase_  )  )
   _SCREAMING_SNAKE_CASE          = AutoTokenizer.from_pretrained(student_args.student_name_or_path			,					use_fast=data_args.use_fast_tokenizer  )
   _SCREAMING_SNAKE_CASE          = dict(enumerate(lowerCamelCase_  )  )
   _SCREAMING_SNAKE_CASE          = {label: i for i, label in enumerate(lowerCamelCase_  )}

   # 4. train student on teacher predictions
   _SCREAMING_SNAKE_CASE          = dataset.map(lowerCamelCase_			,					input_columns="""text"""  )
   dataset.set_format("""torch"""  )

   def compute_metrics(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=False  ):
      _SCREAMING_SNAKE_CASE          = p.predictions.argmax(-1  )
      _SCREAMING_SNAKE_CASE          = p.label_ids.argmax(-1  )  # "label_ids" are actually distributions
      return {"agreement": (preds == proxy_labels).mean().item()}

   _SCREAMING_SNAKE_CASE          = DistillationTrainer(
       model=lowerCamelCase_			,					tokenizer=lowerCamelCase_			,					args=lowerCamelCase_			,					train_dataset=lowerCamelCase_			,					compute_metrics=lowerCamelCase_			,					)

   if training_args.do_train:
      logger.info("""Training student model on teacher predictions"""  )
      trainer.train()

   if training_args.do_eval:
      _SCREAMING_SNAKE_CASE          = trainer.evaluate(eval_dataset=lowerCamelCase_  )['eval_agreement']
      logger.info(F"Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%"  )

   trainer.save_model()


if __name__ == "__main__":
     main()







'''simple docstring'''


import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
 import torch

snake_case__     :			Dict      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      ):



     """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      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

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







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       super().__init__(snake_case_						)
       UpperCAmelCase_							:   Tuple   =      None
       UpperCAmelCase_							:   Any   =      []


  def _UpperCamelCase     ( self						):




       '''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      ,		snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=None      ,		snake_case_=True						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      self.find_config_node(snake_case_						)
       if config is None:
            return

       if config.get(snake_case_						) == "auto":
            UpperCAmelCase_							:   int   =      hf_val
            return

       if not must_match:
            return

       UpperCAmelCase_							:   Optional[Any]   =      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}'''						)

  lowerCamelCase_		:Tuple						    =		partialmethod(lowerCamelCase_						,	must_match=lowerCamelCase_       )


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      [
           'zero_optimization.reduce_bucket_size',
           'zero_optimization.stage3_prefetch_bucket_size',
           'zero_optimization.stage3_param_persistence_threshold',
       ]
       UpperCAmelCase_							:   str   =      [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_							:   List[Any]   =      model.config.hidden_size
            elif hasattr(model.config      ,		'hidden_sizes'						):
                 # if there are many hidden sizes pick the largest one
                 UpperCAmelCase_							:   List[str]   =      max(model.config.hidden_sizes						)
            else:
                 raise ValueError(
                     'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
                     'therefore it\'s not possible to automatically fill out the following `auto` entries '
                     F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                     '`auto` values for these keys with an integer value of your choice.'						)

            self.fill_only('zero_optimization.reduce_bucket_size'      ,		hidden_size * hidden_size						)
            if self.is_zeroa():
                 # automatically assign the optimal config values based on model config
                 self.fill_only('zero_optimization.stage3_prefetch_bucket_size'      ,		0.9 * hidden_size * hidden_size						)
                 self.fill_only('zero_optimization.stage3_param_persistence_threshold'      ,		1_0 * 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_							:   List[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\'.'''						)







snake_case__     :			List[str]      			=  None


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     UpperCAmelCase_							:   List[str]   =      weakref.ref(lowerCamelCase_   )


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase_							:   int   =      None


def 					_lowerCamelCase    (      ):



     """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 					_lowerCamelCase    (      ):



     """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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Optional[Any]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     from accelerate.utils import DummyOptim, DummyScheduler

     UpperCAmelCase_							:   Optional[Any]   =      hf_deepspeed_config.config

     # Optimizer + Scheduler
     # Currently supported combos:
     # 1. DS scheduler + DS optimizer: Yes
     # 2. HF scheduler + HF optimizer: Yes
     # 3. DS scheduler + HF optimizer: Yes
     # 4. HF scheduler + DS optimizer: No
     #
     # Unless Offload is enabled in which case it's:
     # 1. DS scheduler + DS optimizer: Yes
     # 2. HF scheduler + HF optimizer: Mostly*
     # 3. DS scheduler + HF optimizer: Mostly*
     # 4. HF scheduler + DS optimizer: No
     #
     # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

     UpperCAmelCase_							:   int   =      None
     if "optimizer" in config:
          if args.adafactor:
               raise ValueError(
                   '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
                   'Only one optimizer can be configured.'   )
          UpperCAmelCase_							:   int   =      DummyOptim(params=lowerCamelCase_   )
     else:
          if hf_deepspeed_config.is_offload():
               logger.info(
                   'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
                   ' custom optimizer has both CPU and GPU implementation (except LAMB)'   )

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

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

     return optimizer, lr_scheduler


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							str=False   ):



     """simple docstring"""





     from deepspeed.utils import logger as ds_logger

     UpperCAmelCase_							:   List[str]   =      trainer.model
     UpperCAmelCase_							:   List[Any]   =      trainer.args

     UpperCAmelCase_							:   Optional[int]   =      trainer.accelerator.state.deepspeed_plugin.hf_ds_config

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

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

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

          # in case the training config is re-used for inference
          hf_deepspeed_config.del_config_sub_tree('optimizer'   )
          hf_deepspeed_config.del_config_sub_tree('lr_scheduler'   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[Any]   =      None, None
          UpperCAmelCase_							:   str   =      None
     else:
          UpperCAmelCase_							:   Union[str, Any]   =      None  # important for when deepspeed_init is used as re-init
          UpperCAmelCase_							:   Tuple   =      list(filter(lambda lowerCamelCase_   : p.requires_grad	,     model.parameters()   )   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      deepspeed_optim_sched(
              lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

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

     return optimizer, lr_scheduler





def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     import glob

     UpperCAmelCase_							:   Dict   =      sorted(glob.glob(F'''{checkpoint_path}/global_step*'''   )   )

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






