
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


_UpperCamelCase:					Optional[Any]     =logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase:					Dict     ={
    '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
_UpperCamelCase:					List[str]     ={
    '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
}
_UpperCamelCase:					List[str]     =sorted(arg_to_scheduler.keys())
_UpperCamelCase:					Dict     ='{' + ', '.join(arg_to_scheduler_choices) + '}'





class 						__lowercase(     pl.LightningModule   ):


       """simple docstring"""







       def __init__(     self					:			Tuple  ,      _lowerCAmelCase					:			argparse.Namespace  ,      _lowerCAmelCase					:			List[str]=None  ,      _lowerCAmelCase					:			Tuple="base"  ,      _lowerCAmelCase					:			int=None  ,      _lowerCAmelCase					:			List[Any]=None  ,      _lowerCAmelCase					:			Union[str, Any]=None  ,      **_lowerCAmelCase					:			Dict  ,      )					->   Dict:
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(_lowerCAmelCase       )
         _lowerCAmelCase						=		0
         _lowerCAmelCase						=		Path(self.hparams.output_dir       )
         _lowerCAmelCase						=		self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
           _lowerCAmelCase						=		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=_lowerCAmelCase  ,      **_lowerCAmelCase  ,      )
         else:
           _lowerCAmelCase						=		config

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

         if tokenizer is None:
           _lowerCAmelCase						=		AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,      cache_dir=_lowerCAmelCase  ,      )
         else:
           _lowerCAmelCase						=		tokenizer
         _lowerCAmelCase						=		MODEL_MODES[mode]
         if model is None:
           _lowerCAmelCase						=		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=_lowerCAmelCase  ,      )
         else:
           _lowerCAmelCase						=		model



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      *_lowerCAmelCase					:			List[str]  ,      **_lowerCAmelCase					:			Dict       )					->   int:
         _lowerCAmelCase						=		self.model_type.from_pretrained(*_lowerCAmelCase  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Any:
         _lowerCAmelCase						=		arg_to_scheduler[self.hparams.lr_scheduler]
         _lowerCAmelCase						=		get_schedule_func(
             self.opt  ,      num_warmup_steps=self.hparams.warmup_steps  ,      num_training_steps=self.total_steps()       )
         _lowerCAmelCase						=		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
         return scheduler



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.model
         _lowerCAmelCase						=		['bias', 'LayerNorm.weight']
         _lowerCAmelCase						=		[
             {
                 '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:
           _lowerCAmelCase						=		Adafactor(
               _lowerCAmelCase  ,      lr=self.hparams.learning_rate  ,      scale_parameter=_lowerCAmelCase  ,      relative_step=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		self.get_lr_scheduler()

         return [optimizer], [scheduler]



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[int]       )					->   List[Any]:
         return self.validation_step(_lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			Optional[Any]       )					->   Dict:
         return self.validation_end(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   int:
         _lowerCAmelCase						=		max(1  ,      self.hparams.gpus       )  # TODO: consider num_tpu_cores
         _lowerCAmelCase						=		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Any       )					->   Optional[int]:
         if mode == "test":
           _lowerCAmelCase						=		len(self.test_dataloader().dataset       )
         else:
           _lowerCAmelCase						=		self.get_dataloader('train'  ,      self.hparams.train_batch_size  ,      shuffle=_lowerCAmelCase       )
           _lowerCAmelCase						=		len(self.train_dataloader().dataset       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			bool = False       )					->   Any:
         raise NotImplementedError('You must implement this for your task'       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[str]:
         return self.train_loader



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Optional[int]:
         return self.get_dataloader('dev'  ,      self.hparams.eval_batch_size  ,      shuffle=_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Dict:
         return self.get_dataloader('test'  ,      self.hparams.eval_batch_size  ,      shuffle=_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Optional[Any]       )					->   Tuple:
         return os.path.join(
             self.hparams.data_dir  ,      'cached_{}_{}_{}'.format(
                 _lowerCAmelCase  ,      list(filter(_lowerCAmelCase  ,      self.hparams.model_name_or_path.split('/'       )       )       ).pop()  ,      str(self.hparams.max_seq_length       )  ,      )  ,      )



       @pl.utilities.rank_zero_only
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Dict[str, Any]       )					->   None:
         _lowerCAmelCase						=		self.output_dir.joinpath('best_tfmr'       )
         _lowerCAmelCase						=		self.step_count
         self.model.save_pretrained(_lowerCAmelCase       )
         self.tokenizer.save_pretrained(_lowerCAmelCase       )






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





class 						__lowercase(     pl.Callback   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			List[Any]       )					->   str:
         _lowerCAmelCase						=		trainer.lr_schedulers[0]['scheduler']
         _lowerCAmelCase						=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
         pl_module.logger.log_metrics(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			pl.Trainer  ,      _lowerCAmelCase					:			pl.LightningModule       )					->   List[str]:
         rank_zero_info('***** Validation results *****'       )
         _lowerCAmelCase						=		trainer.callback_metrics
         # Log results
         for key in sorted(_lowerCAmelCase       ):
           if key not in ["log", "progress_bar"]:
             rank_zero_info('{} = {}\n'.format(_lowerCAmelCase  ,      str(metrics[key]       )       )       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			pl.Trainer  ,      _lowerCAmelCase					:			pl.LightningModule       )					->   List[Any]:
         rank_zero_info('***** Test results *****'       )
         _lowerCAmelCase						=		trainer.callback_metrics
         # Log and save results to file
         _lowerCAmelCase						=		os.path.join(pl_module.hparams.output_dir  ,      'test_results.txt'       )
         with open(_lowerCAmelCase  ,      'w'       ) as writer:
           for key in sorted(_lowerCAmelCase       ):
             if key not in ["log", "progress_bar"]:
               rank_zero_info('{} = {}\n'.format(_lowerCAmelCase  ,      str(metrics[key]       )       )       )
               writer.write('{} = {}\n'.format(_lowerCAmelCase  ,      str(metrics[key]       )       )       )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  parser.add_argument(
      '--output_dir'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE    )
  parser.add_argument('--max_grad_norm'		,				dest='gradient_clip_val'		,				default=1.0		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=1		,				help='Number of updates steps to accumulate before performing a backward/update pass.'		,				)
  parser.add_argument('--seed'		,				type=__SCREAMING_SNAKE_CASE		,				default=42		,				help='random seed for initialization'    )
  parser.add_argument(
      '--data_dir'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,				)




def 	_a  (					__SCREAMING_SNAKE_CASE					:  BaseTransformer		,				__SCREAMING_SNAKE_CASE					:  argparse.Namespace		,				__SCREAMING_SNAKE_CASE					:  Dict=None		,				__SCREAMING_SNAKE_CASE					:  Dict=True		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]=[]		,				__SCREAMING_SNAKE_CASE					:  Tuple=None		,				__SCREAMING_SNAKE_CASE					:  Optional[int]=None		,				**__SCREAMING_SNAKE_CASE					:  str		,				):






  """simple docstring"""




  pl.seed_everything(args.seed    )

  # init model
  _lowerCAmelCase						=		Path(model.hparams.output_dir    )
  odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE    )

  # add custom checkpoints
  if checkpoint_callback is None:
    _lowerCAmelCase						=		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(__SCREAMING_SNAKE_CASE    )
  if logging_callback is None:
    _lowerCAmelCase						=		LoggingCallback()

  _lowerCAmelCase						=		{}

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

  if args.gpus > 1:
    _lowerCAmelCase						=		'ddp'

  _lowerCAmelCase						=		args.accumulate_grad_batches
  _lowerCAmelCase						=		extra_train_kwargs.get('accelerator'		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		extra_train_kwargs.get('profiler'		,				__SCREAMING_SNAKE_CASE    )

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

  if args.do_train:
    trainer.fit(__SCREAMING_SNAKE_CASE    )

  return trainer


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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_UpperCamelCase:					Dict     =16
_UpperCamelCase:					Optional[int]     =32



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Accelerator		,				__SCREAMING_SNAKE_CASE					:  int = 16    ):






  """simple docstring"""




  _lowerCAmelCase						=		AutoTokenizer.from_pretrained('bert-base-cased'    )
  _lowerCAmelCase						=		load_dataset('glue'		,				'mrpc'    )

  def tokenize_function(__SCREAMING_SNAKE_CASE					:  str    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase						=		tokenizer(examples['sentence1']		,				examples['sentence2']		,				truncation=__SCREAMING_SNAKE_CASE		,				max_length=__SCREAMING_SNAKE_CASE    )
    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():
    _lowerCAmelCase						=		datasets.map(
        __SCREAMING_SNAKE_CASE		,				batched=__SCREAMING_SNAKE_CASE		,				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
  _lowerCAmelCase						=		tokenized_datasets.rename_column('label'		,				'labels'    )

  def collate_fn(__SCREAMING_SNAKE_CASE					:  Any    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowerCAmelCase						=		128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCAmelCase						=		16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase						=		8
    else:
      _lowerCAmelCase						=		None

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

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

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       _UpperCamelCase:					int     =mocked_dataloaders  # noqa: F811



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				__SCREAMING_SNAKE_CASE    ) == "1":
    _lowerCAmelCase						=		2
  # Initialize accelerator
  _lowerCAmelCase						=		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						=		config['lr']
  _lowerCAmelCase						=		int(config['num_epochs']    )
  _lowerCAmelCase						=		int(config['seed']    )
  _lowerCAmelCase						=		int(config['batch_size']    )

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps		,				'isdigit'    ):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase						=		args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						=		int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
  else:
    _lowerCAmelCase						=		None

  set_seed(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase	,       _lowerCAmelCase						=		get_dataloaders(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		evaluate.load('glue'		,				'mrpc'    )

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

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

  # 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).
  _lowerCAmelCase						=		model.to(accelerator.device    )

  # Instantiate optimizer
  _lowerCAmelCase						=		AdamW(params=model.parameters()		,				lr=__SCREAMING_SNAKE_CASE    )

  # Instantiate scheduler
  _lowerCAmelCase						=		get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE		,				num_warmup_steps=100		,				num_training_steps=(len(__SCREAMING_SNAKE_CASE    ) * 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.
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

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

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  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    )
      _lowerCAmelCase						=		os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						=		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      _lowerCAmelCase						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						=		os.path.splitext(__SCREAMING_SNAKE_CASE    )[0]

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

    # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    model.train()
    # New Code #
    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
      _lowerCAmelCase						=		accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						=		train_dataloader
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.loss
      _lowerCAmelCase						=		loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
        _lowerCAmelCase						=		f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
          accelerator.save_state(__SCREAMING_SNAKE_CASE    )

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

    _lowerCAmelCase						=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''		,				__SCREAMING_SNAKE_CASE    )

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						=		f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
      accelerator.save_state(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		,				)
  parser.add_argument(
      '--output_dir'		,				type=__SCREAMING_SNAKE_CASE		,				default='.'		,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,				)
  parser.add_argument(
      '--resume_from_checkpoint'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='If the training should continue from a checkpoint folder.'		,				)
  _lowerCAmelCase						=		parser.parse_args()
  _lowerCAmelCase						=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()
