






"""simple docstring"""


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					filter(lambda __UpperCamelCase					: p.requires_grad						,			model.parameters()					)
  __A		     =					sum([np.prod(p.size()					) for p in model_parameters]					)
  return params


lowercase_								=	logging.getLogger(__name__)


class    snake_case						(				pl.Callback		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					{f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
     pl_module.logger.log_metrics(_lowerCamelCase    )


   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict=True    ):

     '''simple docstring'''
     logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'    )
     __A		     =					trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}    )
     # Log results
     __A		     =					Path(pl_module.hparams.output_dir    )
     if type_path == "test":
       __A		     =					od / '''test_results.txt'''
       __A		     =					od / '''test_generations.txt'''
     else:
       # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
       # If people want this it will be easy enough to add back.
       __A		     =					od / f'{type_path}_results/{trainer.global_step:05d}.txt'
       __A		     =					od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
       results_file.parent.mkdir(exist_ok=_lowerCamelCase    )
       generations_file.parent.mkdir(exist_ok=_lowerCamelCase    )
     with open(_lowerCamelCase,			'''a+'''    ) as writer:
       for key in sorted(_lowerCamelCase    ):
         if key in ["log", "progress_bar", "preds"]:
           continue
         __A		     =					metrics[key]
         if isinstance(_lowerCamelCase,			torch.Tensor    ):
           __A		     =					val.item()
         __A		     =					f'{key}: {val:.6f}\n'
         writer.write(_lowerCamelCase    )

     if not save_generations:
       return

     if "preds" in metrics:
       __A		     =					'''\n'''.join(metrics['''preds''']    )
       generations_file.open('''w+'''    ).write(_lowerCamelCase    )


   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     try:
       __A		     =					pl_module.model.model.num_parameters()
     except AttributeError:
       __A		     =					pl_module.model.num_parameters()

     __A		     =					count_trainable_parameters(_lowerCamelCase    )
     # mp stands for million parameters
     trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}    )


   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule    ):

     '''simple docstring'''
     save_json(pl_module.metrics,			pl_module.metrics_save_path    )
     return self._write_logs(_lowerCamelCase,			_lowerCamelCase,			'''test'''    )






   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	str    ):

     '''simple docstring'''
     save_json(pl_module.metrics,			pl_module.metrics_save_path    )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=1						,			__UpperCamelCase=False					):




  """simple docstring"""







  if metric == "rouge2":
    __A		     =					'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    __A		     =					'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "loss":
    __A		     =					'''{val_avg_loss:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        f'seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'
        ''' this function.'''					)

  __A		     =					ModelCheckpoint(
      dirpath=__UpperCamelCase						,			filename=__UpperCamelCase						,			monitor=f'val_{metric}'						,			mode='''min''' if '''loss''' in metric else '''max'''						,			save_top_k=__UpperCamelCase						,			)
  return checkpoint_callback





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  return EarlyStopping(
      monitor=f'val_{metric}'						,			mode='''min''' if '''loss''' in metric else '''max'''						,			patience=__UpperCamelCase						,			verbose=__UpperCamelCase						,			)






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()
