'''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


lowercase_  		=			logging.getLogger(__name__)

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

lowercase_  		=			{
    '''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
lowercase_  		=			{
    '''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
}
lowercase_  		=			sorted(arg_to_scheduler.keys())
lowercase_  		=			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class 	SCREAMING_SNAKE_CASE      (    pl.LightningModule):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A=None		,    __A="base"		,    __A=None		,    __A=None		,    __A=None		,    **__A		,    )			->  Tuple:
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(__A					)
								_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=__A		,    **__A		,    )
								else:
														_lowerCAmelCase					    =config

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

								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=__A		,    )
								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=__A		,    )
								else:
														_lowerCAmelCase					    =model






		def 		UpperCamelCase__							( self		,    *__A		,    **__A					)			->  Tuple:
								_lowerCAmelCase					    =self.model_type.from_pretrained(*__A		,    **__A					)






		def 		UpperCamelCase__							( self					)			->  int:
								_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 		UpperCamelCase__							( self					)			->  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(
														    __A		,    lr=self.hparams.learning_rate		,    scale_parameter=__A		,    relative_step=__A					)

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

								_lowerCAmelCase					    =self.get_lr_scheduler()

								return [optimizer], [scheduler]






		def 		UpperCamelCase__							( self		,    __A		,    __A					)			->  Union[str, Any]:
								return self.validation_step(__A		,    __A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  int:
								return self.validation_end(__A					)






		def 		UpperCamelCase__							( self					)			->  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 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:




								if stage == "test":
														_lowerCAmelCase					    =len(self.test_dataloader().dataset					)
								else:
														_lowerCAmelCase					    =self.get_dataloader('train'		,    self.hparams.train_batch_size		,    shuffle=__A					)
														_lowerCAmelCase					    =len(self.train_dataloader().dataset					)






		def 		UpperCamelCase__							( self		,    __A		,    __A		,    __A = False					)			->  Optional[Any]:
								raise NotImplementedError('You must implement this for your task'					)






		def 		UpperCamelCase__							( self					)			->  str:
								return self.train_loader






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






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								return self.get_dataloader('test'		,    self.hparams.eval_batch_size		,    shuffle=__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  int:
								return os.path.join(
								    self.hparams.data_dir		,    'cached_{}_{}_{}'.format(
								        __A		,    list(filter(__A		,    self.hparams.model_name_or_path.split('/'					)					)					).pop()		,    str(self.hparams.max_seq_length					)		,    )		,    )






		@pl.utilities.rank_zero_only
		def 		UpperCamelCase__							( self		,    __A					)			->  Any:
								_lowerCAmelCase					    =self.output_dir.joinpath('best_tfmr'					)
								_lowerCAmelCase					    =self.step_count
								self.model.save_pretrained(__A					)
								self.tokenizer.save_pretrained(__A					)






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

class 	SCREAMING_SNAKE_CASE      (    pl.Callback):







		"""simple docstring"""






		def 		UpperCamelCase__							( self		,    __A		,    __A					)			->  Optional[int]:




								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
														pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.

class 	SCREAMING_SNAKE_CASE      (    pl.Callback):







		"""simple docstring"""






		def 		UpperCamelCase__							( self		,    __A		,    __A					)			->  Any:
								_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(__A					)






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






		def 		UpperCamelCase__							( self		,    __A		,    __A					)			->  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(__A		,    'w'					) as writer:
														for key in sorted(__A					):
																				if key not in ["log", "progress_bar"]:
																										rank_zero_info('{} = {}\n'.format(__A		,    str(metrics[key]					)					)					)
																										writer.write('{} = {}\n'.format(__A		,    str(metrics[key]					)					)					)



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						parser.add_argument(
						    '--output_dir'      ,   default=a__      ,   type=a__      ,   required=a__      ,   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=a__      ,   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=a__   )
						parser.add_argument('--max_grad_norm'      ,   dest='gradient_clip_val'      ,   default=1.0      ,   type=a__      ,   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=a__      ,   default=1      ,   help='Number of updates steps to accumulate before performing a backward/update pass.'      ,   )
						parser.add_argument('--seed'      ,   type=a__      ,   default=4_2      ,   help='random seed for initialization'   )
						parser.add_argument(
						    '--data_dir'      ,   default=a__      ,   type=a__      ,   required=a__      ,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'      ,   )



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__=None      ,   a__=True      ,   a__=None      ,   a__=[]      ,   a__=None      ,   a__=None      ,   **a__      ,   ):


						'''simple docstring'''






						pl.seed_everything(args.seed   )

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

						# 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(a__   )
						if logging_callback is None:
												_lowerCAmelCase					    =LoggingCallback()

						_lowerCAmelCase					    ={}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												_lowerCAmelCase					    =1_6
												# train_params["amp_level"] = args.fp16_opt_level

						if args.gpus > 1:
												_lowerCAmelCase					    ='''auto'''  # "ddp"
												_lowerCAmelCase					    ='''ddp'''

						_lowerCAmelCase					    =args.accumulate_grad_batches
						_lowerCAmelCase					    =None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
						_lowerCAmelCase					    ='''auto'''

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

						if args.do_train:
												trainer.fit(a__   )

						return trainer











'''simple docstring'''






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
#
########################################################################

lowercase						:   Optional[int]      						=							16
lowercase						:   str      						=							32




def 		a_(			lowerCamelCase ,					lowerCamelCase = 16   )					->Union[str, Any]:






       '''simple docstring'''




       lowercase	:		List[Any]    =   AutoTokenizer.from_pretrained('''bert-base-cased'''   )
       lowercase	:		List[Any]    =   load_dataset('''glue''' ,					'''mrpc'''   )

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

       def collate_fn(lowerCamelCase   ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase	:		List[str]    =   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":
                     lowercase	:		Any    =   16
              elif accelerator.mixed_precision != "no":
                     lowercase	:		List[str]    =   8
              else:
                     lowercase	:		Optional[int]    =   None

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

       # Instantiate dataloaders.
       lowercase	:		Optional[Any]    =   DataLoader(
           tokenized_datasets['''train'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )
       lowercase	:		Optional[Any]    =   DataLoader(
           tokenized_datasets['''validation'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )

       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

      lowercase						:   str      						=							mocked_dataloaders  # noqa: F811




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






       '''simple docstring'''




       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,					lowerCamelCase   ) == "1":
              lowercase	:		Optional[int]    =   2
       # Initialize accelerator
       lowercase	:		Optional[Any]    =   Accelerator(cpu=args.cpu ,					mixed_precision=args.mixed_precision   )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase	:		int    =   config['''lr''']
       lowercase	:		int    =   int(config['''num_epochs''']   )
       lowercase	:		Tuple    =   int(config['''seed''']   )
       lowercase	:		Union[str, Any]    =   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":
                     lowercase	:		Any    =   args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     lowercase	:		List[Any]    =   int(args.checkpointing_steps   )
              else:
                     raise ValueError(
                         f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
       else:
              lowercase	:		Any    =   None

       set_seed(lowerCamelCase   )

       lowercase,  lowercase	:		List[str]    =   get_dataloaders(lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		List[Any]    =   evaluate.load('''glue''' ,					'''mrpc'''   )

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

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

       # 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).
       lowercase	:		List[Any]    =   model.to(accelerator.device   )

       # Instantiate optimizer
       lowercase	:		List[Any]    =   AdamW(params=model.parameters() ,					lr=lowerCamelCase   )

       # Instantiate scheduler
       lowercase	:		Tuple    =   get_linear_schedule_with_warmup(
           optimizer=lowerCamelCase ,					num_warmup_steps=100 ,					num_training_steps=(len(lowerCamelCase   ) * 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.
       lowercase,  lowercase,  lowercase,  lowercase,  lowercase	:		Union[str, Any]    =   accelerator.prepare(
           lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       lowercase	:		str    =   0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase	:		Optional[int]    =   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   )
                     lowercase	:		Optional[Any]    =   os.path.basename(args.resume_from_checkpoint   )
              else:
                     # Get the most recent checkpoint
                     lowercase	:		Optional[Any]    =   [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime   )
                     lowercase	:		Any    =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowercase	:		Any    =   os.path.splitext(lowerCamelCase   )[0]

              if "epoch" in training_difference:
                     lowercase	:		Union[str, Any]    =   int(training_difference.replace('''epoch_''' ,					''''''   )   ) + 1
                     lowercase	:		List[Any]    =   None
              else:
                     lowercase	:		int    =   int(training_difference.replace('''step_''' ,					''''''   )   )
                     lowercase	:		Optional[int]    =   resume_step // len(lowerCamelCase   )
                     resume_step -= starting_epoch * len(lowerCamelCase   )

    # Now we train the model
       for epoch in range(lowerCamelCase ,					lowerCamelCase   ):
              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
                     lowercase	:		Dict    =   accelerator.skip_first_batches(lowerCamelCase ,					lowerCamelCase   )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     lowercase	:		Tuple    =   train_dataloader
              for step, batch in enumerate(lowerCamelCase   ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device   )
                     lowercase	:		Dict    =   model(**lowerCamelCase   )
                     lowercase	:		Any    =   outputs.loss
                     lowercase	:		Dict    =   loss / gradient_accumulation_steps
                     accelerator.backward(lowerCamelCase   )
                     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(lowerCamelCase ,					lowerCamelCase   ):
                            lowercase	:		str    =   f"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowercase	:		List[str]    =   os.path.join(args.output_dir ,					lowerCamelCase   )
                                   accelerator.save_state(lowerCamelCase   )

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

              lowercase	:		List[Any]    =   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:""" ,					lowerCamelCase   )

              # 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":
                     lowercase	:		List[str]    =   f"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            lowercase	:		Any    =   os.path.join(args.output_dir ,					lowerCamelCase   )
                     accelerator.save_state(lowerCamelCase   )




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






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   argparse.ArgumentParser(description='''Simple example of training script.'''   )
       parser.add_argument(
           '''--mixed_precision''' ,					type=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					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=lowerCamelCase ,					default=lowerCamelCase ,					help='''If the training should continue from a checkpoint folder.''' ,					)
       lowercase	:		List[Any]    =   parser.parse_args()
       lowercase	:		Optional[Any]    =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowerCamelCase ,					lowerCamelCase   )


if __name__ == "__main__":
      main()




