






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_						:						List[Any]						      =					16
A_						:						int						      =					32

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = 1_6      ,       UpperCAmelCase__ = "bert-base-cased"    )			->							str:
  UpperCamelCase_:   Union[str, Any]        =							AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
  UpperCamelCase_:   Optional[Any]        =							load_dataset('glue'      ,       'mrpc'    )

  def tokenize_function(UpperCAmelCase__    ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCamelCase_:   Any        =							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
  UpperCamelCase_:   Any        =							datasets.map(
      SCREAMING_SNAKE_CASE_      ,       batched=SCREAMING_SNAKE_CASE_      ,       remove_columns=['idx', 'sentence1', 'sentence2']      ,       load_from_cache_file=SCREAMING_SNAKE_CASE_    )

  # 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'    )

  def collate_fn(UpperCAmelCase__    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(SCREAMING_SNAKE_CASE_      ,       padding='max_length'      ,       max_length=1_2_8      ,       return_tensors='pt'    )
    return tokenizer.pad(SCREAMING_SNAKE_CASE_      ,       padding='longest'      ,       return_tensors='pt'    )

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

  return train_dataloader, eval_dataloader

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  model.eval()
  UpperCamelCase_:   Optional[int]        =							0
  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    )
    with torch.no_grad():
      UpperCamelCase_:   Optional[Any]        =							model(**SCREAMING_SNAKE_CASE_    )
    UpperCamelCase_:   List[str]        =							outputs.logits.argmax(dim=-1    )
    # It is slightly faster to call this once, than multiple times
    UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							accelerator.gather(
        (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(SCREAMING_SNAKE_CASE_    ) - 1:
        UpperCamelCase_:   List[str]        =							predictions[: len(eval_dataloader.dataset    ) - samples_seen]
        UpperCamelCase_:   List[str]        =							references[: len(eval_dataloader.dataset    ) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=SCREAMING_SNAKE_CASE_      ,       references=SCREAMING_SNAKE_CASE_      ,       )

  UpperCamelCase_:   List[Any]        =							metric.compute()
  return eval_metric["accuracy"]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   List[Any]        =							Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCamelCase_:   Union[str, Any]        =							config['lr']
  UpperCamelCase_:   int        =							int(config['num_epochs']    )
  UpperCamelCase_:   Optional[Any]        =							int(config['seed']    )
  UpperCamelCase_:   Union[str, Any]        =							int(config['batch_size']    )
  UpperCamelCase_:   List[Any]        =							args.model_name_or_path

  set_seed(SCREAMING_SNAKE_CASE_    )
  UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							get_dataloaders(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCamelCase_:   List[Any]        =							AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_      ,       return_dict=SCREAMING_SNAKE_CASE_    )

  # Instantiate optimizer
  UpperCamelCase_:   Union[str, Any]        =							(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  UpperCamelCase_:   Tuple        =							optimizer_cls(params=model.parameters()      ,       lr=SCREAMING_SNAKE_CASE_    )

  if accelerator.state.deepspeed_plugin is not None:
    UpperCamelCase_:   List[Any]        =							accelerator.state.deepspeed_plugin.deepspeed_config[
        'gradient_accumulation_steps'
    ]
  else:
    UpperCamelCase_:   Optional[int]        =							1
  UpperCamelCase_:   Optional[Any]        =							(len(SCREAMING_SNAKE_CASE_    ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    UpperCamelCase_:   Any        =							get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE_      ,       num_warmup_steps=0      ,       num_training_steps=SCREAMING_SNAKE_CASE_      ,       )
  else:
    UpperCamelCase_:   Union[str, Any]        =							DummyScheduler(SCREAMING_SNAKE_CASE_      ,       total_num_steps=SCREAMING_SNAKE_CASE_      ,       warmup_num_steps=0    )

  # 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_:   Union[str, Any]        =							accelerator.prepare(
      SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )

  # We need to keep track of how many total steps we have iterated over
  UpperCamelCase_:   Dict        =							0
  # We also need to keep track of the stating epoch so files are named properly
  UpperCamelCase_:   int        =							0
  UpperCamelCase_:   Tuple        =							evaluate.load('glue'      ,       'mrpc'    )
  UpperCamelCase_:   List[str]        =							num_epochs

  if args.partial_train_epoch is not None:
    UpperCamelCase_:   Optional[int]        =							args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint    )
    UpperCamelCase_:   List[str]        =							args.resume_from_checkpoint.split('epoch_'    )[1]
    UpperCamelCase_:   int        =							''
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    UpperCamelCase_:   Dict        =							int(SCREAMING_SNAKE_CASE_    ) + 1
    UpperCamelCase_:   Tuple        =							evaluation_loop(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )
    accelerator.print('resumed checkpoint performance:'      ,       SCREAMING_SNAKE_CASE_    )
    accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,       lr_scheduler.get_lr()[0]    )
    accelerator.print('resumed optimizers\'s lr:'      ,       optimizer.param_groups[0]['lr']    )
    with open(os.path.join(args.output_dir      ,       F'''state_{starting_epoch-1}.json'''    )      ,       'r'    ) as f:
      UpperCamelCase_:   Optional[int]        =							json.load(SCREAMING_SNAKE_CASE_    )
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  UpperCamelCase_:   Dict        =							{}
  for epoch in range(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    ):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
      UpperCamelCase_:   Tuple        =							model(**SCREAMING_SNAKE_CASE_    )
      UpperCamelCase_:   List[Any]        =							outputs.loss
      UpperCamelCase_:   Dict        =							loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE_    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    UpperCamelCase_:   Tuple        =							F'''epoch_{epoch}'''
    UpperCamelCase_:   Tuple        =							os.path.join(args.output_dir      ,       SCREAMING_SNAKE_CASE_    )
    accelerator.save_state(SCREAMING_SNAKE_CASE_    )
    UpperCamelCase_:   List[Any]        =							evaluation_loop(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )
    UpperCamelCase_:   List[str]        =							accuracy
    UpperCamelCase_:   Any        =							lr_scheduler.get_lr()[0]
    UpperCamelCase_:   Any        =							optimizer.param_groups[0]['lr']
    UpperCamelCase_:   Dict        =							epoch
    UpperCamelCase_:   str        =							overall_step
    accelerator.print(F'''epoch {epoch}:'''      ,       SCREAMING_SNAKE_CASE_    )

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir      ,       F'''state_{epoch}.json'''    )      ,       'w'    ) as f:
        json.dump(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )

def     snake_case   ()			->							List[str]:
  UpperCamelCase_:   Any        =							argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
  parser.add_argument(
      '--model_name_or_path'      ,       type=SCREAMING_SNAKE_CASE_      ,       default='bert-base-cased'      ,       help='Path to pretrained model or model identifier from huggingface.co/models.'      ,       required=SCREAMING_SNAKE_CASE_      ,       )
  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.'      ,       )
  parser.add_argument(
      '--partial_train_epoch'      ,       type=SCREAMING_SNAKE_CASE_      ,       default=SCREAMING_SNAKE_CASE_      ,       help='If passed, the training will stop after this number of epochs.'      ,       )
  parser.add_argument(
      '--num_epochs'      ,       type=SCREAMING_SNAKE_CASE_      ,       default=2      ,       help='Number of train epochs.'      ,       )
  UpperCamelCase_:   Any        =							parser.parse_args()
  UpperCamelCase_:   Optional[int]        =							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

  training_function(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
   main()


def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())
