

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


lowerCAmelCase__       = 16
lowerCAmelCase__       = 32
def      _lowerCAmelCase(	__A   ,       __A = 16   ,       __A = "bert-base-cased"			):
  UpperCAmelCase					      =  AutoTokenizer.from_pretrained(__A			)
  UpperCAmelCase					      =  load_dataset("glue"   ,       "mrpc"			)

  def tokenize_function(__A			):
    # max_length=None => use the model max length (it's actually the default)
    UpperCAmelCase					      =  tokenizer(examples["sentence1"]   ,       examples["sentence2"]   ,       truncation=__A   ,       max_length=__A			)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  UpperCAmelCase					      =  datasets.map(
      __A   ,       batched=__A   ,       remove_columns=["idx", "sentence1", "sentence2"]   ,       load_from_cache_file=__A			)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  UpperCAmelCase					      =  tokenized_datasets.rename_column("label"   ,       "labels"			)

  def collate_fn(__A			):
    # 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(__A   ,       padding="max_length"   ,       max_length=128   ,       return_tensors="pt"			)
    return tokenizer.pad(__A   ,       padding="longest"   ,       return_tensors="pt"			)

  # Instantiate dataloaders.
  UpperCAmelCase					      =  DataLoader(
      tokenized_datasets["train"]   ,       shuffle=__A   ,       collate_fn=__A   ,       batch_size=__A			)
  UpperCAmelCase					      =  DataLoader(
      tokenized_datasets["validation"]   ,       shuffle=__A   ,       collate_fn=__A   ,       batch_size=__A			)

  return train_dataloader, eval_dataloader
def      _lowerCAmelCase(	__A   ,       __A			):
  # Initialize accelerator
  UpperCAmelCase					      =  Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase					      =  config["lr"]
  UpperCAmelCase					      =  int(config["num_epochs"]			)
  UpperCAmelCase					      =  int(config["seed"]			)
  UpperCAmelCase					      =  int(config["batch_size"]			)
  UpperCAmelCase					      =  args.model_name_or_path

  set_seed(__A			)
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dataloaders(__A   ,       __A   ,       __A			)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase					      =  AutoModelForSequenceClassification.from_pretrained(__A   ,       return_dict=__A			)

  # Instantiate optimizer
  UpperCAmelCase					      =  (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  UpperCAmelCase					      =  optimizer_cls(params=model.parameters()   ,       lr=__A			)

  if accelerator.state.deepspeed_plugin is not None:
    UpperCAmelCase					      =  accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    UpperCAmelCase					      =  1
  UpperCAmelCase					      =  (len(__A			) * 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					      =  get_linear_schedule_with_warmup(
        optimizer=__A   ,       num_warmup_steps=0   ,       num_training_steps=__A   ,       )
  else:
    UpperCAmelCase					      =  DummyScheduler(__A   ,       total_num_steps=__A   ,       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					      =  accelerator.prepare(
      __A   ,       __A   ,       __A   ,       __A   ,       __A			)

  # We need to keep track of how many total steps we have iterated over
  UpperCAmelCase					      =  0
  # We also need to keep track of the stating epoch so files are named properly
  UpperCAmelCase					      =  0

  # Now we train the model
  UpperCAmelCase					      =  evaluate.load("glue"   ,       "mrpc"			)
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  {}
  for epoch in range(__A   ,       __A			):
    model.train()
    for step, batch in enumerate(__A			):
      UpperCAmelCase					      =  model(**__A			)
      UpperCAmelCase					      =  outputs.loss
      UpperCAmelCase					      =  loss / gradient_accumulation_steps
      accelerator.backward(__A			)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    UpperCAmelCase					      =  0
    for step, batch in enumerate(__A			):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device			)
      with torch.no_grad():
        UpperCAmelCase					      =  model(**__A			)
      UpperCAmelCase					      =  outputs.logits.argmax(dim=-1			)
      # It is slightly faster to call this once, than multiple times
      UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.gather(
          (predictions, batch["labels"])			)  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(__A			) - 1:
          UpperCAmelCase					      =  predictions[: len(eval_dataloader.dataset			) - samples_seen]
          UpperCAmelCase					      =  references[: len(eval_dataloader.dataset			) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=__A   ,       references=__A   ,       )

    UpperCAmelCase					      =  metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"epoch {epoch}:"   ,       __A			)
    UpperCAmelCase					      =  eval_metric["accuracy"]

    if best_performance < eval_metric["accuracy"]:
      UpperCAmelCase					      =  eval_metric["accuracy"]

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir   ,       "all_results.json"			)   ,       "w"			) as f:
      json.dump(__A   ,       __A			)
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."			)
  parser.add_argument(
      "--model_name_or_path"   ,       type=__A   ,       default="bert-base-cased"   ,       help="Path to pretrained model or model identifier from huggingface.co/models."   ,       required=__A   ,       )
  parser.add_argument(
      "--output_dir"   ,       type=__A   ,       default="."   ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,       )
  parser.add_argument(
      "--performance_lower_bound"   ,       type=__A   ,       default=__A   ,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,       )
  parser.add_argument(
      "--num_epochs"   ,       type=__A   ,       default=3   ,       help="Number of train epochs."   ,       )
  UpperCAmelCase					      =  parser.parse_args()
  UpperCAmelCase					      =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(__A   ,       __A			)


if __name__ == "__main__":
      main()






def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum
def      _lowerCAmelCase(	__A = 100			):
  UpperCAmelCase					      =  1
  UpperCAmelCase					      =  2

  for i in range(2   ,       max_n + 1			):
    UpperCAmelCase					      =  pre_numerator
    UpperCAmelCase					      =  2 * i // 3 if i % 3 == 0 else 1
    UpperCAmelCase					      =  cur_numerator
    UpperCAmelCase					      =  e_cont * pre_numerator + temp

  return sum_digits(__A			)


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



