




"""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
#
########################################################################


_SCREAMING_SNAKE_CASE						 =     1_6
_SCREAMING_SNAKE_CASE						 =     3_2






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Tuple						)    ->       str:





 if args.with_tracking:
  _UpperCamelCase      :	int					      =  Accelerator(
      cpu=args.cpu  ,	mixed_precision=args.mixed_precision  ,	log_with="all"  ,	project_dir=args.project_dir						)
 else:
  _UpperCamelCase      :	List[str]					      =  Accelerator(cpu=args.cpu  ,	mixed_precision=args.mixed_precision						)

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

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _UpperCamelCase      :	List[Any]					      =  os.path.split(lowerCamelCase_						)[-1].split("."						)[0]
  accelerator.init_trackers(lowerCamelCase_  ,	lowerCamelCase_						)

 _UpperCamelCase      :	List[str]					      =  AutoTokenizer.from_pretrained("bert-base-cased"						)
 _UpperCamelCase      :	List[str]					      =  load_dataset("glue"  ,	"mrpc"						)
 _UpperCamelCase      :	int					      =  evaluate.load("glue"  ,	"mrpc"						)

 def tokenize_function(__lowerCAmelCase			:		int						):
  # max_length=None => use the model max length (it's actually the default)
  _UpperCamelCase      :	Union[str, 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():
  _UpperCamelCase      :	int					      =  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
 _UpperCamelCase      :	List[str]					      =  tokenized_datasets.rename_column("label"  ,	"labels"						)

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

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

  return tokenizer.pad(
      lowerCamelCase_  ,	padding="longest"  ,	max_length=lowerCamelCase_  ,	pad_to_multiple_of=lowerCamelCase_  ,	return_tensors="pt"  ,	)

 # Instantiate dataloaders.
 _UpperCamelCase      :	List[Any]					      =  DataLoader(
     tokenized_datasets["train"]  ,	shuffle=lowerCamelCase_  ,	collate_fn=lowerCamelCase_  ,	batch_size=lowerCamelCase_						)
 _UpperCamelCase      :	Optional[int]					      =  DataLoader(
     tokenized_datasets["validation"]  ,	shuffle=lowerCamelCase_  ,	collate_fn=lowerCamelCase_  ,	batch_size=lowerCamelCase_						)

 set_seed(lowerCamelCase_						)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase      :	List[str]					      =  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).
 _UpperCamelCase      :	Dict					      =  model.to(accelerator.device						)

 # Instantiate optimizer
 _UpperCamelCase      :	int					      =  AdamW(params=model.parameters()  ,	lr=lowerCamelCase_						)

 # Instantiate scheduler
 _UpperCamelCase      :	Dict					      =  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.
 _UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      :	Dict					      =  accelerator.prepare(
     lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)

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

  if "epoch" in training_difference:
   _UpperCamelCase      :	Optional[int]					      =  int(training_difference.replace("epoch_"  ,	""						)						) + 1
   _UpperCamelCase      :	Tuple					      =  None
  else:
   _UpperCamelCase      :	Optional[int]					      =  int(training_difference.replace("step_"  ,	""						)						)
   _UpperCamelCase      :	str					      =  resume_step // len(lowerCamelCase_						)
   resume_step -= starting_epoch * len(lowerCamelCase_						)

    # Now we train the model
 for epoch in range(lowerCamelCase_  ,	lowerCamelCase_						):
  model.train()
  if args.with_tracking:
   _UpperCamelCase      :	List[str]					      =  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
   _UpperCamelCase      :	List[str]					      =  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
   _UpperCamelCase      :	Any					      =  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						)
   _UpperCamelCase      :	Tuple					      =  model(**lowerCamelCase_						)
   _UpperCamelCase      :	str					      =  outputs.loss
   _UpperCamelCase      :	Optional[int]					      =  loss / gradient_accumulation_steps
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowerCamelCase_						)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

   if isinstance(lowerCamelCase_  ,	lowerCamelCase_						):
    _UpperCamelCase      :	Optional[Any]					      =  f"step_{overall_step}"
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase      :	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`.
   batch.to(accelerator.device						)
   with torch.no_grad():
    _UpperCamelCase      :	Optional[Any]					      =  model(**lowerCamelCase_						)
   _UpperCamelCase      :	Union[str, Any]					      =  outputs.logits.argmax(dim=-1						)
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  accelerator.gather_for_metrics((predictions, batch["labels"])						)
   metric.add_batch(
       predictions=lowerCamelCase_  ,	references=lowerCamelCase_  ,	)

  _UpperCamelCase      :	Tuple					      =  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:"  ,	lowerCamelCase_						)
  if args.with_tracking:
   accelerator.log(
       {
           "accuracy": eval_metric["accuracy"],
           "f1": eval_metric["f1"],
           "train_loss": total_loss.item() / len(lowerCamelCase_						),
           "epoch": epoch,
       }  ,	step=lowerCamelCase_  ,	)

  if checkpointing_steps == "epoch":
   _UpperCamelCase      :	str					      =  f"epoch_{epoch}"
   if args.output_dir is not None:
    _UpperCamelCase      :	Dict					      =  os.path.join(args.output_dir  ,	lowerCamelCase_						)
   accelerator.save_state(lowerCamelCase_						)

 if args.with_tracking:
  accelerator.end_training()






def 				__lowerCAmelCase					(					)    ->       Union[str, Any]:
 _UpperCamelCase      :	Tuple					      =  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(
     "--resume_from_checkpoint"  ,	type=lowerCamelCase_  ,	default=lowerCamelCase_  ,	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=lowerCamelCase_  ,	default="."  ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."  ,	)
 parser.add_argument(
     "--project_dir"  ,	type=lowerCamelCase_  ,	default="logs"  ,	help="Location on where to store experiment tracking logs` and relevent project information"  ,	)
 _UpperCamelCase      :	List[str]					      =  parser.parse_args()
 _UpperCamelCase      :	Union[str, Any]					      =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(lowerCamelCase_  ,	lowerCamelCase_						)


if __name__ == "__main__":
    main()




def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

