






'''simple docstring'''




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase_							      =      logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCamelCase_							      =      {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_							      =      {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_							      =      sorted(arg_to_scheduler.keys())
lowerCamelCase_							      =      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class      _UpperCAmelCase    (  pl.LightningModule		):




   """simple docstring"""

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			argparse.Namespace   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Optional[int]="base"   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Optional[int]=None   ,   __UpperCAmelCase				:			int=None   ,   **__UpperCAmelCase				:			Tuple   ,   ):





         '''simple docstring'''





         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(__UpperCAmelCase	)
         _A      =			0
         _A      =			Path(self.hparams.output_dir	)
         _A      =			self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               _A      =			AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path   ,   **({"num_labels": num_labels} if num_labels is not None else {})   ,   cache_dir=__UpperCAmelCase   ,   **__UpperCAmelCase   ,   )
         else:
               _A      =			config

         _A      =			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
         for p in extra_model_params:
               if getattr(self.hparams   ,   __UpperCAmelCase   ,   __UpperCAmelCase	):
                     assert hasattr(self.config   ,   __UpperCAmelCase	), f'''model config doesn\'t have a `{p}` attribute'''
                     setattr(self.config   ,   __UpperCAmelCase   ,   getattr(self.hparams   ,   __UpperCAmelCase	)	)

         if tokenizer is None:
               _A      =			AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,   cache_dir=__UpperCAmelCase   ,   )
         else:
               _A      =			tokenizer
         _A      =			MODEL_MODES[mode]
         if model is None:
               _A      =			self.model_type.from_pretrained(
                   self.hparams.model_name_or_path   ,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path	)   ,   config=self.config   ,   cache_dir=__UpperCAmelCase   ,   )
         else:
               _A      =			model

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   *__UpperCAmelCase				:			str   ,   **__UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			self.model_type.from_pretrained(*__UpperCAmelCase   ,   **__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			arg_to_scheduler[self.hparams.lr_scheduler]
         _A      =			get_schedule_func(
             self.opt   ,   num_warmup_steps=self.hparams.warmup_steps   ,   num_training_steps=self.total_steps()	)
         _A      =			{"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.model
         _A      =			["bias", "LayerNorm.weight"]
         _A      =			[
             {
                 "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
                 "weight_decay": self.hparams.weight_decay,
             },
             {
                 "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
                 "weight_decay": 0.0,
             },
         ]
         if self.hparams.adafactor:
               _A      =			Adafactor(
                   __UpperCAmelCase   ,   lr=self.hparams.learning_rate   ,   scale_parameter=__UpperCAmelCase   ,   relative_step=__UpperCAmelCase	)

         else:
               _A      =			AdamW(
                   __UpperCAmelCase   ,   lr=self.hparams.learning_rate   ,   eps=self.hparams.adam_epsilon	)
         _A      =			optimizer

         _A      =			self.get_lr_scheduler()

         return [optimizer], [scheduler]

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         return self.validation_step(__UpperCAmelCase   ,   __UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         return self.validation_end(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         _A      =			max(1   ,   self.hparams.gpus	)  # TODO: consider num_tpu_cores
         _A      =			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

   def 		lowerCAmelCase				(	self				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         if mode == "test":
               _A      =			len(self.test_dataloader().dataset	)
         else:
               _A      =			self.get_dataloader("train"   ,   self.hparams.train_batch_size   ,   shuffle=__UpperCAmelCase	)
               _A      =			len(self.train_dataloader().dataset	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			bool = False	):





         '''simple docstring'''





         raise NotImplementedError("You must implement this for your task"	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         return self.train_loader

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         return self.get_dataloader("dev"   ,   self.hparams.eval_batch_size   ,   shuffle=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         return self.get_dataloader("test"   ,   self.hparams.eval_batch_size   ,   shuffle=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         return os.path.join(
             self.hparams.data_dir   ,   "cached_{}_{}_{}".format(
                 __UpperCAmelCase   ,   list(filter(__UpperCAmelCase   ,   self.hparams.model_name_or_path.split("/"	)	)	).pop()   ,   str(self.hparams.max_seq_length	)   ,   )   ,   )

   @pl.utilities.rank_zero_only
   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Dict[str, Any]	):





         '''simple docstring'''





         _A      =			self.output_dir.joinpath("best_tfmr"	)
         _A      =			self.step_count
         self.model.save_pretrained(__UpperCAmelCase	)
         self.tokenizer.save_pretrained(__UpperCAmelCase	)

   @staticmethod
   def 		lowerCAmelCase				(	__UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Union[str, Any]	):





         '''simple docstring'''





         parser.add_argument(
             "--model_name_or_path"   ,   default=__UpperCAmelCase   ,   type=__UpperCAmelCase   ,   required=__UpperCAmelCase   ,   help="Path to pretrained model or model identifier from huggingface.co/models"   ,   )
         parser.add_argument(
             "--config_name"   ,   default=""   ,   type=__UpperCAmelCase   ,   help="Pretrained config name or path if not the same as model_name"	)
         parser.add_argument(
             "--tokenizer_name"   ,   default=__UpperCAmelCase   ,   type=__UpperCAmelCase   ,   help="Pretrained tokenizer name or path if not the same as model_name"   ,   )
         parser.add_argument(
             "--cache_dir"   ,   default=""   ,   type=__UpperCAmelCase   ,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"   ,   )
         parser.add_argument(
             "--encoder_layerdrop"   ,   type=__UpperCAmelCase   ,   help="Encoder layer dropout probability (Optional). Goes into model.config"   ,   )
         parser.add_argument(
             "--decoder_layerdrop"   ,   type=__UpperCAmelCase   ,   help="Decoder layer dropout probability (Optional). Goes into model.config"   ,   )
         parser.add_argument(
             "--dropout"   ,   type=__UpperCAmelCase   ,   help="Dropout probability (Optional). Goes into model.config"   ,   )
         parser.add_argument(
             "--attention_dropout"   ,   type=__UpperCAmelCase   ,   help="Attention dropout probability (Optional). Goes into model.config"   ,   )
         parser.add_argument("--learning_rate"   ,   default=5E-5   ,   type=__UpperCAmelCase   ,   help="The initial learning rate for Adam."	)
         parser.add_argument(
             "--lr_scheduler"   ,   default="linear"   ,   choices=__UpperCAmelCase   ,   metavar=__UpperCAmelCase   ,   type=__UpperCAmelCase   ,   help="Learning rate scheduler"   ,   )
         parser.add_argument("--weight_decay"   ,   default=0.0   ,   type=__UpperCAmelCase   ,   help="Weight decay if we apply some."	)
         parser.add_argument("--adam_epsilon"   ,   default=1E-8   ,   type=__UpperCAmelCase   ,   help="Epsilon for Adam optimizer."	)
         parser.add_argument("--warmup_steps"   ,   default=0   ,   type=__UpperCAmelCase   ,   help="Linear warmup over warmup_steps."	)
         parser.add_argument("--num_workers"   ,   default=4   ,   type=__UpperCAmelCase   ,   help="kwarg passed to DataLoader"	)
         parser.add_argument("--num_train_epochs"   ,   dest="max_epochs"   ,   default=3   ,   type=__UpperCAmelCase	)
         parser.add_argument("--train_batch_size"   ,   default=32   ,   type=__UpperCAmelCase	)
         parser.add_argument("--eval_batch_size"   ,   default=32   ,   type=__UpperCAmelCase	)
         parser.add_argument("--adafactor"   ,   action="store_true"	)





class      _UpperCAmelCase    (  pl.Callback		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			trainer.lr_schedulers[0]["scheduler"]
         _A      =			{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
         pl_module.logger.log_metrics(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			pl.Trainer   ,   __UpperCAmelCase				:			pl.LightningModule	):





         '''simple docstring'''





         rank_zero_info("***** Validation results *****"	)
         _A      =			trainer.callback_metrics
         # Log results
         for key in sorted(__UpperCAmelCase	):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info("{} = {}\n".format(__UpperCAmelCase   ,   str(metrics[key]	)	)	)

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			pl.Trainer   ,   __UpperCAmelCase				:			pl.LightningModule	):





         '''simple docstring'''





         rank_zero_info("***** Test results *****"	)
         _A      =			trainer.callback_metrics
         # Log and save results to file
         _A      =			os.path.join(pl_module.hparams.output_dir   ,   "test_results.txt"	)
         with open(__UpperCAmelCase   ,   "w"	) as writer:
               for key in sorted(__UpperCAmelCase	):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info("{} = {}\n".format(__UpperCAmelCase   ,   str(metrics[key]	)	)	)
                           writer.write("{} = {}\n".format(__UpperCAmelCase   ,   str(metrics[key]	)	)	)





def       __lowercase		(    __lowercase     ,						__lowercase ) ->		None:

      '''simple docstring'''


      parser.add_argument(
          "--output_dir"     ,						default=__lowercase     ,						type=__lowercase     ,						required=__lowercase     ,						help="The output directory where the model predictions and checkpoints will be written."     ,						)
      parser.add_argument(
          "--fp16"     ,						action="store_true"     ,						help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"     ,						)

      parser.add_argument(
          "--fp16_opt_level"     ,						type=__lowercase     ,						default="O2"     ,						help=(
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
              "See details at https://nvidia.github.io/apex/amp.html"
          )     ,						)
      parser.add_argument("--n_tpu_cores"     ,						dest="tpu_cores"     ,						type=__lowercase )
      parser.add_argument("--max_grad_norm"     ,						dest="gradient_clip_val"     ,						default=1.0     ,						type=__lowercase     ,						help="Max gradient norm" )
      parser.add_argument("--do_train"     ,						action="store_true"     ,						help="Whether to run training." )
      parser.add_argument("--do_predict"     ,						action="store_true"     ,						help="Whether to run predictions on the test set." )
      parser.add_argument(
          "--gradient_accumulation_steps"     ,						dest="accumulate_grad_batches"     ,						type=__lowercase     ,						default=1     ,						help="Number of updates steps to accumulate before performing a backward/update pass."     ,						)
      parser.add_argument("--seed"     ,						type=__lowercase     ,						default=42     ,						help="random seed for initialization" )
      parser.add_argument(
          "--data_dir"     ,						default=__lowercase     ,						type=__lowercase     ,						required=__lowercase     ,						help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."     ,						)


def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=None     ,						__lowercase=True     ,						__lowercase=[]     ,						__lowercase=None     ,						__lowercase=None     ,						**__lowercase     ,						) ->		List[str]:

      '''simple docstring'''


      pl.seed_everything(args.seed )

      # init model
      _A      =			Path(model.hparams.output_dir )
      odir.mkdir(exist_ok=__lowercase )

      # add custom checkpoints
      if checkpoint_callback is None:
            _A      =			pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir     ,						prefix="checkpoint"     ,						monitor="val_loss"     ,						mode="min"     ,						save_top_k=1 )
      if early_stopping_callback:
            extra_callbacks.append(__lowercase )
      if logging_callback is None:
            _A      =			LoggingCallback()

      _A      =			{}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            _A      =			16
            _A      =			args.fpaa_opt_level

      if args.gpus > 1:
            _A      =			"ddp"

      _A      =			args.accumulate_grad_batches
      _A      =			extra_train_kwargs.get("accelerator"     ,						__lowercase )
      _A      =			extra_train_kwargs.get("profiler"     ,						__lowercase )

      _A      =			pl.Trainer.from_argparse_args(
          __lowercase     ,						weights_summary=__lowercase     ,						callbacks=[logging_callback] + extra_callbacks     ,						logger=__lowercase     ,						checkpoint_callback=__lowercase     ,						**__lowercase     ,						)

      if args.do_train:
            trainer.fit(__lowercase )

      return trainer











'''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

lowerCamelCase_							      =      logging.get_logger(__name__)

def       __lowercase		(    ) ->		Optional[Any]:

      '''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      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





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





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase	)
         _A      =			None
         _A      =			[]

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''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				:			Dict   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			Union[str, Any]=True	):





         '''simple docstring'''





         _A  ,			_A      =			self.find_config_node(__UpperCAmelCase	)
         if config is None:
               return

         if config.get(__UpperCAmelCase	) == "auto":
               _A      =			hf_val
               return

         if not must_match:
               return

         _A      =			config.get(__UpperCAmelCase	)
         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					       =		partialmethod(snake_case_							,					must_match=snake_case_		)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Any	):





         '''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"   ,   __UpperCAmelCase   ,   "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				:			Optional[int]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			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(__UpperCAmelCase	)]

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

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





lowerCamelCase_							      =      None

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''simple docstring'''


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

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      _A      =			None

def       __lowercase		(    ) ->		Optional[int]:

      '''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       __lowercase		(    ) ->		str:

      '''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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      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=__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.
            _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(__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." )
            _A      =			trainer.create_scheduler(num_training_steps=__lowercase     ,						optimizer=__lowercase )

      return optimizer, lr_scheduler

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=False ) ->		Tuple:

      '''simple docstring'''


      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(__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" )
            _A  ,			_A      =			None, None
            _A      =			None
      else:
            _A      =			None  # important for when deepspeed_init is used as re-init
            _A      =			list(filter(lambda __lowercase : p.requires_grad     ,						model.parameters() ) )
            _A  ,			_A      =			deepspeed_optim_sched(
                __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase )

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

      return optimizer, lr_scheduler


def       __lowercase		(    __lowercase     ,						__lowercase ) ->		Union[str, Any]:

      '''simple docstring'''


      import glob

      _A      =			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
            _A  ,			_A      =			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}''' )





