



"""simple docstring"""





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


lowerCamelCase       	=						16
lowerCamelCase       	=						32
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 16				):
      UpperCAmelCase_      			=							AutoTokenizer.from_pretrained("bert-base-cased"				)
      UpperCAmelCase_      			=							DatasetDict(
          {
              "train": dataset["train"].select(lowerCAmelCase__				),
              "validation": dataset["train"].select(lowerCAmelCase__				),
              "test": dataset["validation"],
          }				)

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

      def collate_fn(lowerCAmelCase__				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase_      			=							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_      			=							16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_      			=							8
            else:
                  UpperCAmelCase_      			=							None

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

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

      UpperCAmelCase_      			=							DataLoader(
          tokenized_datasets["test"]     ,      shuffle=lowerCAmelCase__     ,      collate_fn=lowerCAmelCase__     ,      batch_size=lowerCAmelCase__				)

      return train_dataloader, eval_dataloader, test_dataloader
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      # New Code #
      UpperCAmelCase_      			=							[]
      # Download the dataset
      UpperCAmelCase_      			=							load_dataset("glue"     ,      "mrpc"				)
      # Create our splits
      UpperCAmelCase_      			=							StratifiedKFold(n_splits=int(args.num_folds				)				)
      # Initialize accelerator
      UpperCAmelCase_      			=							Accelerator(cpu=args.cpu     ,      mixed_precision=args.mixed_precision				)
      # 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_      			=							evaluate.load("glue"     ,      "mrpc"				)

      # If the batch size is too big we use gradient accumulation
      UpperCAmelCase_      			=							1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            UpperCAmelCase_      			=							batch_size // MAX_GPU_BATCH_SIZE
            UpperCAmelCase_      			=							MAX_GPU_BATCH_SIZE

      set_seed(lowerCAmelCase__				)

      # New Code #
      # Create our folds:
      UpperCAmelCase_      			=							kfold.split(np.zeros(datasets["train"].num_rows				)     ,      datasets["train"]["label"]				)
      UpperCAmelCase_      			=							[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase__				):
            UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							get_fold_dataloaders(
                lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            UpperCAmelCase_      			=							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_      			=							model.to(accelerator.device				)

            # Instantiate optimizer
            UpperCAmelCase_      			=							AdamW(params=model.parameters()     ,      lr=lowerCAmelCase__				)

            # Instantiate scheduler
            UpperCAmelCase_      			=							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_      			=							accelerator.prepare(
                lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

            # Now we train the model
            for epoch in range(lowerCAmelCase__				):
                  model.train()
                  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_      			=							model(**lowerCAmelCase__				)
                        UpperCAmelCase_      			=							outputs.loss
                        UpperCAmelCase_      			=							loss / gradient_accumulation_steps
                        accelerator.backward(lowerCAmelCase__				)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  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_      			=							model(**lowerCAmelCase__				)
                        UpperCAmelCase_      			=							outputs.logits.argmax(dim=-1				)
                        UpperCAmelCase_			,   UpperCAmelCase_      			=							accelerator.gather_for_metrics((predictions, batch["labels"])				)
                        metric.add_batch(
                            predictions=lowerCAmelCase__     ,      references=lowerCAmelCase__     ,      )

                  UpperCAmelCase_      			=							metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"""epoch {epoch}:"""     ,      lowerCAmelCase__				)

            # New Code #
            # We also run predictions on the test set at the very end
            UpperCAmelCase_      			=							[]
            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_      			=							model(**lowerCAmelCase__				)
                  UpperCAmelCase_      			=							outputs.logits
                  UpperCAmelCase_			,   UpperCAmelCase_      			=							accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  fold_predictions.append(predictions.cpu()				)
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()				)
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(lowerCAmelCase__     ,      dim=0				)				)
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      UpperCAmelCase_      			=							torch.cat(lowerCAmelCase__     ,      dim=0				)
      UpperCAmelCase_      			=							torch.stack(lowerCAmelCase__     ,      dim=0				).sum(dim=0				).div(int(args.num_folds				)				).argmax(dim=-1				)
      UpperCAmelCase_      			=							metric.compute(predictions=lowerCAmelCase__     ,      references=lowerCAmelCase__				)
      accelerator.print("Average test metrics from all folds:"     ,      lowerCAmelCase__				)
def        a__     (				):
      UpperCAmelCase_      			=							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."				)
      # New Code #
      parser.add_argument("--num_folds"     ,      type=lowerCAmelCase__     ,      default=3     ,      help="The number of splits to perform across the dataset"				)
      UpperCAmelCase_      			=							parser.parse_args()
      UpperCAmelCase_      			=							{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(lowerCAmelCase__     ,      lowerCAmelCase__				)


if __name__ == "__main__":
       main()









"""simple docstring"""





def        a__     (				lowerCAmelCase__ = 2000000				):
      UpperCAmelCase_      			=							[0 for i in range(n + 1				)]
      UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							1

      for i in range(2     ,      int(n**0.5				) + 1				):
            if primality_list[i] == 0:
                  for j in range(i * i     ,      n + 1     ,      lowerCAmelCase__				):
                        UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							0
      for i in range(lowerCAmelCase__				):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


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




