


'''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
#
########################################################################


UpperCAmelCase    =					16
UpperCAmelCase    =					32






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = 16      )->						List[Any]:




      """simple docstring"""

      snake_case_ =   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
      snake_case_ =   DatasetDict(
          {
              '''train''': dataset['''train'''].select(SCREAMING_SNAKE_CASE      ),
              '''validation''': dataset['''train'''].select(SCREAMING_SNAKE_CASE      ),
              '''test''': dataset['''validation'''],
          }      )

      def tokenize_function(SCREAMING_SNAKE_CASE      ):
            # max_length=None => use the model max length (it's actually the default)
            snake_case_ =   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
      # starting with the main process first:
      with accelerator.main_process_first():
            snake_case_ =   datasets.map(
                SCREAMING_SNAKE_CASE      ,      batched=SCREAMING_SNAKE_CASE      ,      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
      snake_case_ =   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.
            snake_case_ =   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":
                  snake_case_ =   16
            elif accelerator.mixed_precision != "no":
                  snake_case_ =   8
            else:
                  snake_case_ =   None

            return tokenizer.pad(
                SCREAMING_SNAKE_CASE      ,      padding='''longest'''      ,      max_length=SCREAMING_SNAKE_CASE      ,      pad_to_multiple_of=SCREAMING_SNAKE_CASE      ,      return_tensors='''pt'''      ,      )

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

      snake_case_ =   DataLoader(
          tokenized_datasets['''test''']      ,      shuffle=SCREAMING_SNAKE_CASE      ,      collate_fn=SCREAMING_SNAKE_CASE      ,      batch_size=SCREAMING_SNAKE_CASE      )

      return train_dataloader, eval_dataloader, test_dataloader






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   []
      # Download the dataset
      snake_case_ =   load_dataset('''glue'''      ,      '''mrpc'''      )
      # Create our splits
      snake_case_ =   StratifiedKFold(n_splits=int(args.num_folds      )      )
      # Initialize accelerator
      snake_case_ =   Accelerator(cpu=args.cpu      ,      mixed_precision=args.mixed_precision      )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case_ =   config['''lr''']
      snake_case_ =   int(config['''num_epochs''']      )
      snake_case_ =   int(config['''seed''']      )
      snake_case_ =   int(config['''batch_size''']      )

      snake_case_ =   evaluate.load('''glue'''      ,      '''mrpc'''      )

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

      set_seed(SCREAMING_SNAKE_CASE      )

      # New Code #
      # Create our folds:
      snake_case_ =   kfold.split(np.zeros(datasets['''train'''].num_rows      )      ,      datasets['''train''']['''label''']      )
      snake_case_ =   []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(SCREAMING_SNAKE_CASE      ):
            snake_case_ ,					snake_case_ ,					snake_case_ =   get_fold_dataloaders(
                SCREAMING_SNAKE_CASE      ,      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)
            snake_case_ =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''      ,      return_dict=SCREAMING_SNAKE_CASE      )

            # 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).
            snake_case_ =   model.to(accelerator.device      )

            # Instantiate optimizer
            snake_case_ =   AdamW(params=model.parameters()      ,      lr=SCREAMING_SNAKE_CASE      )

            # Instantiate scheduler
            snake_case_ =   get_linear_schedule_with_warmup(
                optimizer=SCREAMING_SNAKE_CASE      ,      num_warmup_steps=100      ,      num_training_steps=(len(SCREAMING_SNAKE_CASE      ) * 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.
            snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ =   accelerator.prepare(
                SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

                  model.eval()
                  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():
                              snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
                        snake_case_ =   outputs.logits.argmax(dim=-1      )
                        snake_case_ ,					snake_case_ =   accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
                        metric.add_batch(
                            predictions=SCREAMING_SNAKE_CASE      ,      references=SCREAMING_SNAKE_CASE      ,      )

                  snake_case_ =   metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f'''epoch {epoch}:'''      ,      SCREAMING_SNAKE_CASE      )

            # New Code #
            # We also run predictions on the test set at the very end
            snake_case_ =   []
            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():
                        snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
                  snake_case_ =   outputs.logits
                  snake_case_ ,					snake_case_ =   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(SCREAMING_SNAKE_CASE      ,      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:
      snake_case_ =   torch.cat(SCREAMING_SNAKE_CASE      ,      dim=0      )
      snake_case_ =   torch.stack(SCREAMING_SNAKE_CASE      ,      dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
      snake_case_ =   metric.compute(predictions=SCREAMING_SNAKE_CASE      ,      references=SCREAMING_SNAKE_CASE      )
      accelerator.print('''Average test metrics from all folds:'''      ,      SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   argparse.ArgumentParser(description='''Simple example of training script.'''      )
      parser.add_argument(
          '''--mixed_precision'''      ,      type=SCREAMING_SNAKE_CASE      ,      default=SCREAMING_SNAKE_CASE      ,      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=SCREAMING_SNAKE_CASE      ,      default=3      ,      help='''The number of splits to perform across the dataset'''      )
      snake_case_ =   parser.parse_args()
      snake_case_ =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   main()



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))