





'''simple docstring'''






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						: Tuple		=   16
__lowerCamelCase						: Optional[int]		=   32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16  ,      __SCREAMING_SNAKE_CASE = "bert-base-cased"   ):


  """simple docstring"""

  _UpperCamelCase       			=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=load_dataset('''glue'''  ,      '''mrpc'''   )

  def tokenize_function(__SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase       			=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       			=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       			=tokenized_datasets.rename_column('''label'''  ,      '''labels'''   )

  def collate_fn(__SCREAMING_SNAKE_CASE   ):
    # 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=128  ,      return_tensors='''pt'''   )
    return tokenizer.pad(__SCREAMING_SNAKE_CASE  ,      padding='''longest'''  ,      return_tensors='''pt'''   )

  # Instantiate dataloaders.
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE   )

  return train_dataloader, eval_dataloader


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  model.eval()
  _UpperCamelCase       			=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       			=model(**__SCREAMING_SNAKE_CASE   )
    _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(__SCREAMING_SNAKE_CASE   ) - 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=__SCREAMING_SNAKE_CASE  ,      references=__SCREAMING_SNAKE_CASE  ,      )

  _UpperCamelCase       			=metric.compute()
  return eval_metric["accuracy"]


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _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(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase       			=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       			=AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,      return_dict=__SCREAMING_SNAKE_CASE   )

  # 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=__SCREAMING_SNAKE_CASE   )

  if accelerator.state.deepspeed_plugin is not None:
    _UpperCamelCase       			=accelerator.state.deepspeed_plugin.deepspeed_config[
        '''gradient_accumulation_steps'''
    ]
  else:
    _UpperCamelCase       			=1
  _UpperCamelCase       			=(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       			=get_linear_schedule_with_warmup(
        optimizer=__SCREAMING_SNAKE_CASE  ,      num_warmup_steps=0  ,      num_training_steps=__SCREAMING_SNAKE_CASE  ,      )
  else:
    _UpperCamelCase       			=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       			=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       			=0
  # We also need to keep track of the stating epoch so files are named properly
  _UpperCamelCase       			=0
  _UpperCamelCase       			=evaluate.load('''glue'''  ,      '''mrpc'''   )
  _UpperCamelCase       			=num_epochs

  if args.partial_train_epoch is not None:
    _UpperCamelCase       			=args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint   )
    _UpperCamelCase       			=args.resume_from_checkpoint.split('''epoch_'''   )[1]
    _UpperCamelCase       			=''''''
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    _UpperCamelCase       			=int(__SCREAMING_SNAKE_CASE   ) + 1
    _UpperCamelCase       			=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       			=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       			={}
  for epoch in range(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):
    model.train()
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE   ):
      _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.loss
      _UpperCamelCase       			=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       			=f'''epoch_{epoch}'''
    _UpperCamelCase       			=os.path.join(args.output_dir  ,      __SCREAMING_SNAKE_CASE   )
    accelerator.save_state(__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=evaluation_loop(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=accuracy
    _UpperCamelCase       			=lr_scheduler.get_lr()[0]
    _UpperCamelCase       			=optimizer.param_groups[0]['''lr''']
    _UpperCamelCase       			=epoch
    _UpperCamelCase       			=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 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=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       			=parser.parse_args()
  _UpperCamelCase       			={'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

  training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1 or len(__SCREAMING_SNAKE_CASE   ) <= key:
    return input_string

  for position, character in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[''''''.join(__SCREAMING_SNAKE_CASE   ) for row in temp_grid]
  _UpperCamelCase       			=''''''.join(__SCREAMING_SNAKE_CASE   )

  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1:
    return input_string

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]  # generates template
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append('''*'''   )

  _UpperCamelCase       			=0
  for row in temp_grid:  # fills in the characters
    _UpperCamelCase       			=input_string[counter : counter + len(__SCREAMING_SNAKE_CASE   )]
    grid.append(list(__SCREAMING_SNAKE_CASE   )   )
    counter += len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=''''''  # reads as zigzag
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    output_string += grid[num][0]
    grid[num].pop(0   )
  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={}
  for key_guess in range(1  ,      len(__SCREAMING_SNAKE_CASE   )   ):  # tries every key
    _UpperCamelCase       			=decrypt(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()






