
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__    :		List[str]          =   1_6
lowerCamelCase__    :		Optional[Any]          =   3_2

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = 16   )		->	Optional[Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      AutoTokenizer.from_pretrained("""bert-base-cased"""   )
  lowercase__  :   List[str]    		=      DatasetDict(
      {
          """train""": dataset["""train"""].select(lowercase_   ),
          """validation""": dataset["""train"""].select(lowercase_   ),
          """test""": dataset["""validation"""],
      }   )

  def tokenize_function(lowercase_   ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase__  :   str    		=      tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase_ , max_length=lowercase_   )
    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():
    lowercase__  :   Optional[int]    		=      datasets.map(
        lowercase_ , batched=lowercase_ , 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
  lowercase__  :   Union[str, Any]    		=      tokenized_datasets.rename_column("""label""" , """labels"""   )

  def collate_fn(lowercase_   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowercase__  :   Any    		=      1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowercase__  :   Optional[int]    		=      16
    elif accelerator.mixed_precision != "no":
      lowercase__  :   Tuple    		=      8
    else:
      lowercase__  :   Optional[Any]    		=      None

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

  # Instantiate dataloaders.
  lowercase__  :   str    		=      DataLoader(
      tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )
  lowercase__  :   str    		=      DataLoader(
      tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )

  lowercase__  :   Dict    		=      DataLoader(
      tokenized_datasets["""test"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )

  return train_dataloader, eval_dataloader, test_dataloader

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Optional[Any]:


  '''simple docstring'''






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

  lowercase__  :   List[str]    		=      evaluate.load("""glue""" , """mrpc"""   )

  # If the batch size is too big we use gradient accumulation
  lowercase__  :   Dict    		=      1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowercase__  :   int    		=      batch_size // MAX_GPU_BATCH_SIZE
    lowercase__  :   List[str]    		=      MAX_GPU_BATCH_SIZE

  set_seed(lowercase_   )

  # New Code #
  # Create our folds:
  lowercase__  :   Optional[Any]    		=      kfold.split(np.zeros(datasets["""train"""].num_rows   ) , datasets["""train"""]["""label"""]   )
  lowercase__  :   Optional[int]    		=      []
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(lowercase_   ):
    lowercase__  :   Optional[int]    		=      get_fold_dataloaders(
        lowercase_ , lowercase_ , lowercase_ , lowercase_ , )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowercase__  :   Union[str, Any]    		=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase_   )

    # 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).
    lowercase__  :   int    		=      model.to(accelerator.device   )

    # Instantiate optimizer
    lowercase__  :   Any    		=      AdamW(params=model.parameters() , lr=lowercase_   )

    # Instantiate scheduler
    lowercase__  :   Dict    		=      get_linear_schedule_with_warmup(
        optimizer=lowercase_ , num_warmup_steps=1_00 , num_training_steps=(len(lowercase_   ) * 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.
    lowercase__  :   List[str]    		=      accelerator.prepare(
        lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_   )

    # Now we train the model
    for epoch in range(lowercase_   ):
      model.train()
      for step, batch in enumerate(lowercase_   ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device   )
        lowercase__  :   List[str]    		=      model(**lowercase_   )
        lowercase__  :   Dict    		=      outputs.loss
        lowercase__  :   List[Any]    		=      loss / gradient_accumulation_steps
        accelerator.backward(lowercase_   )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(lowercase_   ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device   )
        with torch.no_grad():
          lowercase__  :   int    		=      model(**lowercase_   )
        lowercase__  :   Any    		=      outputs.logits.argmax(dim=-1   )
        lowercase__  :   Any    		=      accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
        metric.add_batch(
            predictions=lowercase_ , references=lowercase_ , )

      lowercase__  :   Optional[Any]    		=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'epoch {epoch}:' , lowercase_   )

    # New Code #
    # We also run predictions on the test set at the very end
    lowercase__  :   Dict    		=      []
    for step, batch in enumerate(lowercase_   ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device   )
      with torch.no_grad():
        lowercase__  :   Union[str, Any]    		=      model(**lowercase_   )
      lowercase__  :   str    		=      outputs.logits
      lowercase__  :   List[str]    		=      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(lowercase_ , 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:
  lowercase__  :   Dict    		=      torch.cat(lowercase_ , dim=0   )
  lowercase__  :   Union[str, Any]    		=      torch.stack(lowercase_ , dim=0   ).sum(dim=0   ).div(int(args.num_folds   )   ).argmax(dim=-1   )
  lowercase__  :   Any    		=      metric.compute(predictions=lowercase_ , references=lowercase_   )
  accelerator.print("""Average test metrics from all folds:""" , lowercase_   )

def 		UpperCamelCase	(      )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      argparse.ArgumentParser(description="""Simple example of training script."""   )
  parser.add_argument(
      """--mixed_precision""" , type=lowercase_ , default=lowercase_ , 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=lowercase_ , default=3 , help="""The number of splits to perform across the dataset"""   )
  lowercase__  :   Any    		=      parser.parse_args()
  lowercase__  :   Union[str, Any]    		=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase_ , lowercase_   )


if __name__ == "__main__":
     main()






from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      data
   lowercase__  :   Node | None    		=      None



class       _snake_case   :


 def __init__(					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      None
   lowercase__  :   int    		=      None


 def __iter__(					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self.head
   while self.head:
     yield node.data
     lowercase__  :   str    		=      node.next
     if node == self.head:
       break


 def __len__(					self):


   '''simple docstring'''




   return sum(1 for _ in self)


 def __repr__(					self):


   '''simple docstring'''




   return "->".join(str(SCREAMING_SNAKE_CASE_) for item in iter(self))


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(len(self)					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(0					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if index < 0 or index > len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   Optional[int]    		=      Node(SCREAMING_SNAKE_CASE_)
   if self.head is None:
     lowercase__  :   Union[str, Any]    		=      new_node  # first node points itself
     lowercase__  :   Dict    		=      new_node
   elif index == 0:  # insert at head
     lowercase__  :   int    		=      self.head
     lowercase__  :   int    		=      new_node
   else:
     lowercase__  :   List[str]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   Union[str, Any]    		=      temp.next
     lowercase__  :   int    		=      temp.next
     lowercase__  :   Optional[Any]    		=      new_node
     if index == len(self) - 1:  # insert at tail
       lowercase__  :   Union[str, Any]    		=      new_node


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(0)


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(len(self) - 1)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_ = 0):


   '''simple docstring'''




   if not 0 <= index < len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   str    		=      self.head
   if self.head == self.tail:  # just one node
     lowercase__  :   List[Any]    		=      None
   elif index == 0:  # delete head node
     lowercase__  :   str    		=      self.tail.next.next
     lowercase__  :   List[Any]    		=      self.head.next
   else:
     lowercase__  :   Optional[int]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   List[Any]    		=      temp.next
     lowercase__  :   Optional[Any]    		=      temp.next
     lowercase__  :   Dict    		=      temp.next.next
     if index == len(self) - 1:  # delete at tail
       lowercase__  :   Tuple    		=      temp
   return delete_node.data



 def      lowercase__     (					self):


   '''simple docstring'''




   return len(self) == 0

def 		UpperCamelCase	(      )		->	None:


  '''simple docstring'''






  lowercase__  :   int    		=      CircularLinkedList()
  assert len(lowercase_   ) == 0
  assert circular_linked_list.is_empty() is True
  assert str(lowercase_   ) == ""

  try:
    circular_linked_list.delete_front()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_tail()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_nth(-1   )
    raise AssertionError
  except IndexError:
    assert True

  try:
    circular_linked_list.delete_nth(0   )
    raise AssertionError
  except IndexError:
    assert True

  assert circular_linked_list.is_empty() is True
  for i in range(5   ):
    assert len(lowercase_   ) == i
    circular_linked_list.insert_nth(lowercase_ , i + 1   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  circular_linked_list.insert_tail(6   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 7   )   )
  circular_linked_list.insert_head(0   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(0 , 7   )   )

  assert circular_linked_list.delete_front() == 0
  assert circular_linked_list.delete_tail() == 6
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )
  assert circular_linked_list.delete_nth(2   ) == 3

  circular_linked_list.insert_nth(2 , 3   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
     import doctest

     doctest.testmod()






