


"""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__      				=   16
lowerCAmelCase__      				=   32
def 					a__			(				SCREAMING_SNAKE_CASE    :    Accelerator				,      SCREAMING_SNAKE_CASE    :    int = 1_6				,      SCREAMING_SNAKE_CASE    :    str = "bert-base-cased"				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							AutoTokenizer.from_pretrained(__UpperCamelCase				)
  lowerCAmelCase     :     Dict								=							load_dataset("glue"				,      "mrpc"				)

  def tokenize_function(SCREAMING_SNAKE_CASE    :    Optional[Any]				):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase     :     Union[str, 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
  lowerCAmelCase     :     List[Any]								=							datasets.map(
      __UpperCamelCase				,      batched=__UpperCamelCase				,      remove_columns=["idx", "sentence1", "sentence2"]				,      load_from_cache_file=__UpperCamelCase				)

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

  def collate_fn(SCREAMING_SNAKE_CASE    :    List[Any]				):
    # 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(__UpperCamelCase				,      padding="max_length"				,      max_length=1_2_8				,      return_tensors="pt"				)
    return tokenizer.pad(__UpperCamelCase				,      padding="longest"				,      return_tensors="pt"				)

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

  return train_dataloader, eval_dataloader
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  model.eval()
  lowerCAmelCase     :     Optional[int]								=							0
  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():
      lowerCAmelCase     :     Any								=							model(**__UpperCamelCase				)
    lowerCAmelCase     :     Optional[Any]								=							outputs.logits.argmax(dim=-1				)
    # It is slightly faster to call this once, than multiple times
    lowerCAmelCase						,  lowerCAmelCase     :     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(__UpperCamelCase				) - 1:
        lowerCAmelCase     :     Optional[int]								=							predictions[: len(eval_dataloader.dataset				) - samples_seen]
        lowerCAmelCase     :     Tuple								=							references[: len(eval_dataloader.dataset				) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase				,      references=__UpperCamelCase				,      )

  lowerCAmelCase     :     Any								=							metric.compute()
  return eval_metric["accuracy"]
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							Accelerator()

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

  set_seed(__UpperCamelCase				)
  lowerCAmelCase						,  lowerCAmelCase     :     List[Any]								=							get_dataloaders(__UpperCamelCase				,      __UpperCamelCase				,      __UpperCamelCase				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase     :     Dict								=							AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase				,      return_dict=__UpperCamelCase				)

  # Instantiate optimizer
  lowerCAmelCase     :     Optional[Any]								=							(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  lowerCAmelCase     :     Any								=							optimizer_cls(params=model.parameters()				,      lr=__UpperCamelCase				)

  if accelerator.state.deepspeed_plugin is not None:
    lowerCAmelCase     :     str								=							accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    lowerCAmelCase     :     Any								=							1
  lowerCAmelCase     :     Optional[int]								=							(len(__UpperCamelCase				) * num_epochs) // gradient_accumulation_steps

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

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

  if args.partial_train_epoch is not None:
    lowerCAmelCase     :     Dict								=							args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint				)
    lowerCAmelCase     :     Dict								=							args.resume_from_checkpoint.split("epoch_"				)[1]
    lowerCAmelCase     :     List[str]								=							""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    lowerCAmelCase     :     str								=							int(__UpperCamelCase				) + 1
    lowerCAmelCase     :     Tuple								=							evaluation_loop(__UpperCamelCase				,      __UpperCamelCase				,      __UpperCamelCase				,      __UpperCamelCase				)
    accelerator.print("resumed checkpoint performance:"				,      __UpperCamelCase				)
    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:
      lowerCAmelCase     :     int								=							json.load(__UpperCamelCase				)
      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
  lowerCAmelCase     :     List[str]								=							{}
  for epoch in range(__UpperCamelCase				,      __UpperCamelCase				):
    model.train()
    for step, batch in enumerate(__UpperCamelCase				):
      lowerCAmelCase     :     Optional[int]								=							model(**__UpperCamelCase				)
      lowerCAmelCase     :     str								=							outputs.loss
      lowerCAmelCase     :     List[str]								=							loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    lowerCAmelCase     :     str								=							f"""epoch_{epoch}"""
    lowerCAmelCase     :     int								=							os.path.join(args.output_dir				,      __UpperCamelCase				)
    accelerator.save_state(__UpperCamelCase				)
    lowerCAmelCase     :     Any								=							evaluation_loop(__UpperCamelCase				,      __UpperCamelCase				,      __UpperCamelCase				,      __UpperCamelCase				)
    lowerCAmelCase     :     Optional[int]								=							accuracy
    lowerCAmelCase     :     Union[str, Any]								=							lr_scheduler.get_lr()[0]
    lowerCAmelCase     :     Tuple								=							optimizer.param_groups[0]["lr"]
    lowerCAmelCase     :     List[str]								=							epoch
    lowerCAmelCase     :     str								=							overall_step
    accelerator.print(f"""epoch {epoch}:"""				,      __UpperCamelCase				)

    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(__UpperCamelCase				,      __UpperCamelCase				)
def 					a__			(				):


  '''simple docstring'''







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

  training_function(__UpperCamelCase				,      __UpperCamelCase				)


if __name__ == "__main__":
     main()






from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
