





'''simple docstring'''

import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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 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					:		Optional[int]		=							1_6
UpperCAmelCase					:		Any		=							3_2




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = 16	):





      """simple docstring"""



      _snake_case		:	str					     =     AutoTokenizer.from_pretrained('''bert-base-cased'''	)
      _snake_case		:	Tuple					     =     load_dataset('''glue'''			,      '''mrpc'''	)

      def tokenize_function(lowerCAmelCase_	):
            # max_length=None => use the model max length (it's actually the default)
            _snake_case		:	Union[str, Any]					     =     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():
            _snake_case		:	Optional[Any]					     =     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
      _snake_case		:	int					     =     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.
            _snake_case		:	int					     =     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		:	List[str]					     =     16
            elif accelerator.mixed_precision != "no":
                  _snake_case		:	List[Any]					     =     8
            else:
                  _snake_case		:	List[Any]					     =     None

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

      # Instantiate dataloaders.
      _snake_case		:	List[Any]					     =     DataLoader(
          tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase_			,      collate_fn=lowerCAmelCase_			,      batch_size=lowerCAmelCase_			,      drop_last=lowerCAmelCase_	)
      _snake_case		:	str					     =     DataLoader(
          tokenized_datasets['''validation''']			,      shuffle=lowerCAmelCase_			,      collate_fn=lowerCAmelCase_			,      batch_size=lowerCAmelCase_			,      drop_last=(accelerator.mixed_precision == '''fp8''')			,      )

      return train_dataloader, eval_dataloader




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     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		:	Dict					     =     config['''lr''']
      _snake_case		:	Tuple					     =     int(config['''num_epochs''']	)
      _snake_case		:	List[str]					     =     int(config['''seed''']	)
      _snake_case		:	Tuple					     =     int(config['''batch_size''']	)

      _snake_case		:	Optional[Any]					     =     evaluate.load('''glue'''			,      '''mrpc'''	)

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

      set_seed(lowerCAmelCase_	)
      _snake_case		:	int					     =     get_dataloaders(lowerCAmelCase_			,      lowerCAmelCase_	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _snake_case		:	Any					     =     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).
      _snake_case		:	List[str]					     =     model.to(accelerator.device	)
      # Instantiate optimizer
      _snake_case		:	int					     =     AdamW(params=model.parameters()			,      lr=lowerCAmelCase_	)

      # Instantiate scheduler
      _snake_case		:	Optional[Any]					     =     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.

      _snake_case		:	List[str]					     =     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	)
                  _snake_case		:	Union[str, Any]					     =     model(**lowerCAmelCase_	)
                  _snake_case		:	Dict					     =     outputs.loss
                  _snake_case		:	Optional[Any]					     =     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():
                        _snake_case		:	int					     =     model(**lowerCAmelCase_	)
                  _snake_case		:	List[Any]					     =     outputs.logits.argmax(dim=-1	)
                  _snake_case		:	List[str]					     =     accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                  metric.add_batch(
                      predictions=lowerCAmelCase_			,      references=lowerCAmelCase_			,      )

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




def    _a    (							):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     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.'''	)
      _snake_case		:	str					     =     parser.parse_args()
      _snake_case		:	Dict					     =     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(lowerCAmelCase_			,      lowerCAmelCase_	)


if __name__ == "__main__":
   main()









'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase					:		Any		=							TypeVar('T')
UpperCAmelCase					:		str		=							TypeVar('U')





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     key
        _snake_case		:	Optional[int]					     =     val
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''Node: key: {self.key}, val: {self.val}, '''
            F'''has next: {bool(self.next		)}, has prev: {bool(self.prev		)}'''
        )





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self		)		->						None:






        """simple docstring"""





        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case     ,							_snake_case		:	Union[str, Any]					     =     self.rear, self.head







  def __repr__(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     ['''DoubleLinkedList''']
        _snake_case		:	str					     =     self.head
        while node.next is not None:
              rep.append(str(lowercase__		)		)
              _snake_case		:	List[str]					     =     node.next
        rep.append(str(self.rear		)		)
        return ",\n    ".join(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        _snake_case		:	Union[str, Any]					     =     node
        _snake_case		:	Optional[Any]					     =     previous
        _snake_case		:	int					     =     node
        _snake_case		:	Union[str, Any]					     =     self.rear







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						DoubleLinkedListNode[T, U] | None:






        """simple docstring"""





        if node.prev is None or node.next is None:
              return None

        _snake_case		:	Optional[int]					     =     node.next
        _snake_case		:	Any					     =     node.prev
        _snake_case		:	List[str]					     =     None
        _snake_case		:	Optional[int]					     =     None
        return node





class lowerCamelCase						(Generic[T, U]					):
  _lowercase  :	dict[Callable[[T], U], LRUCache[T, U]]       					=				{}







  def __init__(		self     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	DoubleLinkedList[T, U]					     =     DoubleLinkedList()
        _snake_case		:	Union[str, Any]					     =     capacity
        _snake_case		:	int					     =     0
        _snake_case		:	Dict					     =     0
        _snake_case		:	Union[str, Any]					     =     0
        _snake_case		:	dict[T, DoubleLinkedListNode[T, U]]					     =     {}







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
            F'''capacity={self.capacity}, current size={self.num_keys})'''
        )







  def __contains__(		self     ,     lowercase__		)		->						bool:






        """simple docstring"""





        return key in self.cache







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						U | None:






        """simple docstring"""





        if key in self.cache:
              self.hits += 1
              _snake_case		:	DoubleLinkedListNode[T, U]					     =     self.cache[key]
              _snake_case		:	Tuple					     =     self.list.remove(self.cache[key]		)
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(lowercase__		)
              return node.val
        self.miss += 1
        return None







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						None:






        """simple docstring"""





        if key not in self.cache:
              if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case		:	Dict					     =     self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(lowercase__		) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
              _snake_case		:	Optional[int]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
              self.list.add(self.cache[key]		)
              self.num_keys += 1

        else:
              # bump node to the end of the list, update value
              _snake_case		:	Optional[Any]					     =     self.list.remove(self.cache[key]		)
              assert node is not None  # node guaranteed to be in list
              _snake_case		:	Optional[Any]					     =     value
              self.list.add(lowercase__		)







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__ = 128		)		->						Callable[[Callable[[T], U]], Callable[..., U]]:






        """simple docstring"""





        def cache_decorator_inner(lowercase__		) -> Callable[..., U]:
              def cache_decorator_wrapper(*lowercase__		) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                          _snake_case		:	Optional[Any]					     =     LRUCache(lowercase__		)

                    _snake_case		:	Union[str, Any]					     =     cls.decorator_function_to_instance_map[func].get(args[0]		)
                    if result is None:
                          _snake_case		:	Tuple					     =     func(*lowercase__		)
                          cls.decorator_function_to_instance_map[func].put(args[0]     ,     lowercase__		)
                    return result

              def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

              setattr(lowercase__     ,     '''cache_info'''     ,     lowercase__		)  # noqa: B010

              return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


