






"""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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 :    List[Any]      =			16
_UpperCamelCase :    Optional[Any]      =			32




def 					a			(  UpperCamelCase_				:					Union[str, Any]  ,						UpperCamelCase_				:					List[Any]		)	->		Tuple:
 # Initialize accelerator
 if args.with_tracking:
  __A						:							Optional[int]      =						Accelerator(
      cpu=args.cpu  ,						mixed_precision=args.mixed_precision  ,						log_with='all'  ,						project_dir=args.project_dir		)
 else:
  __A						:							Union[str, Any]      =						Accelerator(cpu=args.cpu  ,						mixed_precision=args.mixed_precision		)

 if hasattr(args.checkpointing_steps  ,						'isdigit'		):
  if args.checkpointing_steps == "epoch":
   __A						:							Tuple      =						args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   __A						:							List[str]      =						int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
 else:
  __A						:							str      =						None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __A						:							List[Any]      =						config['lr']
 __A						:							Any      =						int(config['num_epochs']		)
 __A						:							Any      =						int(config['seed']		)
 __A						:							Tuple      =						int(config['batch_size']		)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  __A						:							Union[str, Any]      =						os.path.split(UpperCamelCase_		)[-1].split('.'		)[0]
  accelerator.init_trackers(UpperCamelCase_  ,						UpperCamelCase_		)

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

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

 # If the batch size is too big we use gradient accumulation
 __A						:							Tuple      =						1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  __A						:							str      =						batch_size // MAX_GPU_BATCH_SIZE
  __A						:							Optional[Any]      =						MAX_GPU_BATCH_SIZE

 def collate_fn(UpperCamelCase_				:					List[str]		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __A						:							int      =						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   __A						:							List[Any]      =						1_6
  elif accelerator.mixed_precision != "no":
   __A						:							Tuple      =						8
  else:
   __A						:							List[Any]      =						None

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

 # Instantiate dataloaders.
 __A						:							int      =						DataLoader(
     tokenized_datasets['train']  ,						shuffle=UpperCamelCase_  ,						collate_fn=UpperCamelCase_  ,						batch_size=UpperCamelCase_		)
 __A						:							Dict      =						DataLoader(
     tokenized_datasets['validation']  ,						shuffle=UpperCamelCase_  ,						collate_fn=UpperCamelCase_  ,						batch_size=UpperCamelCase_		)

 set_seed(UpperCamelCase_		)

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

 # 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).
 __A						:							Optional[int]      =						model.to(accelerator.device		)

 # Instantiate optimizer
 __A						:							Tuple      =						AdamW(params=model.parameters()  ,						lr=UpperCamelCase_		)

 # Instantiate scheduler
 __A						:							Dict      =						get_linear_schedule_with_warmup(
     optimizer=UpperCamelCase_  ,						num_warmup_steps=1_0_0  ,						num_training_steps=(len(UpperCamelCase_		) * 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.
 __A     ,       __A     ,       __A     ,       __A     ,       __A						:							Tuple      =						accelerator.prepare(
     UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)

 # We need to keep track of how many total steps we have iterated over
 __A						:							Dict      =						0
 # We also need to keep track of the stating epoch so files are named properly
 __A						:							List[Any]      =						0

 # Potentially load in the weights and states from a previous save
 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		)
   __A						:							Tuple      =						os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   __A						:							str      =						[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   __A						:							Optional[int]      =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  __A						:							str      =						os.path.splitext(UpperCamelCase_		)[0]

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

    # Now we train the model
 for epoch in range(UpperCamelCase_  ,						UpperCamelCase_		):
  model.train()
  if args.with_tracking:
   __A						:							int      =						0
  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
   __A						:							Union[str, Any]      =						accelerator.skip_first_batches(UpperCamelCase_  ,						UpperCamelCase_		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   __A						:							Optional[Any]      =						train_dataloader
  for step, batch in enumerate(UpperCamelCase_		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   __A						:							Dict      =						model(**UpperCamelCase_		)
   __A						:							Union[str, Any]      =						outputs.loss
   __A						:							str      =						loss / gradient_accumulation_steps
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(UpperCamelCase_		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

   if isinstance(UpperCamelCase_  ,						UpperCamelCase_		):
    __A						:							Optional[int]      =						F"step_{overall_step}"
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      __A						:							List[str]      =						os.path.join(args.output_dir  ,						UpperCamelCase_		)
     accelerator.save_state(UpperCamelCase_		)

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

  __A						:							List[str]      =						metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"  ,						UpperCamelCase_		)
  if args.with_tracking:
   accelerator.log(
       {
           'accuracy': eval_metric['accuracy'],
           'f1': eval_metric['f1'],
           'train_loss': total_loss.item() / len(UpperCamelCase_		),
           'epoch': epoch,
       }  ,						step=UpperCamelCase_  ,						)

  if checkpointing_steps == "epoch":
   __A						:							List[str]      =						F"epoch_{epoch}"
   if args.output_dir is not None:
    __A						:							Tuple      =						os.path.join(args.output_dir  ,						UpperCamelCase_		)
   accelerator.save_state(UpperCamelCase_		)

 if args.with_tracking:
  accelerator.end_training()




def 					a			(  )	->		Any:
 __A						:							List[str]      =						argparse.ArgumentParser(description='Simple example of training script.'		)
 parser.add_argument(
     '--mixed_precision'  ,						type=UpperCamelCase_  ,						default=UpperCamelCase_  ,						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=UpperCamelCase_  ,						default=UpperCamelCase_  ,						help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'  ,						)
 parser.add_argument(
     '--resume_from_checkpoint'  ,						type=UpperCamelCase_  ,						default=UpperCamelCase_  ,						help='If the training should continue from a checkpoint folder.'  ,						)
 parser.add_argument(
     '--with_tracking'  ,						action='store_true'  ,						help='Whether to load in all available experiment trackers from the environment and use them for logging.'  ,						)
 parser.add_argument(
     '--output_dir'  ,						type=UpperCamelCase_  ,						default='.'  ,						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,						)
 parser.add_argument(
     '--project_dir'  ,						type=UpperCamelCase_  ,						default='logs'  ,						help='Location on where to store experiment tracking logs` and relevent project information'  ,						)
 __A						:							Optional[int]      =						parser.parse_args()
 __A						:							Optional[Any]      =						{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(UpperCamelCase_  ,						UpperCamelCase_		)


if __name__ == "__main__":
   main()













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





