


'''simple docstring'''

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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							:   Union[str, Any]  						=	16
lowerCamelCase							:   int  						=	32

def _SCREAMING_SNAKE_CASE      (A  , A = 16       )       ->       str:






       """simple docstring"""







       lowercase__      			= AutoTokenizer.from_pretrained('''bert-base-cased'''       )
       lowercase__      			= load_dataset('''glue'''  , '''mrpc'''       )

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

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

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

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

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 lowerCamelCase							:   List[Any]  						=	mocked_dataloaders  # noqa: F811

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Tuple:






       """simple docstring"""







       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  , A       ) == "1":
              lowercase__      			= 2
       # Initialize accelerator
       lowercase__      			= Accelerator(cpu=args.cpu  , mixed_precision=args.mixed_precision       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__      			= config['''lr''']
       lowercase__      			= int(config['''num_epochs''']       )
       lowercase__      			= int(config['''seed''']       )
       lowercase__      			= int(config['''batch_size''']       )

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

       # New Code #
       # We now can define an inner training loop function. It should take a batch size as the only parameter,
       # and build the dataloaders in there.
       # It also gets our decorator
       @find_executable_batch_size(starting_batch_size=A       )
       def inner_training_loop(A       ):
              # And now just move everything below under this function
              # We need to bring in the Accelerator object from earlier
              nonlocal accelerator
              # And reset all of its attributes that could hold onto any memory:
              accelerator.free_memory()

              # Then we can declare the model, optimizer, and everything else:
              set_seed(A       )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowercase__      			= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  , return_dict=A       )

              # 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__      			= model.to(accelerator.device       )

              # Instantiate optimizer
              lowercase__      			= AdamW(params=model.parameters()  , lr=A       )
              lowercase__			,lowercase__      			= get_dataloaders(A  , A       )

              # Instantiate scheduler
              lowercase__      			= get_linear_schedule_with_warmup(
                  optimizer=A  , num_warmup_steps=100  , num_training_steps=(len(A       ) * num_epochs)  , )

              # 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__			,lowercase__			,lowercase__			,lowercase__			,lowercase__      			= accelerator.prepare(
                  A  , A  , A  , A  , A       )

              # Now we train the model
              for epoch in range(A       ):
                     model.train()
                     for step, batch in enumerate(A       ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device       )
                            lowercase__      			= model(**A       )
                            lowercase__      			= outputs.loss
                            accelerator.backward(A       )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()

def _SCREAMING_SNAKE_CASE      ()       ->       Dict:






       """simple docstring"""







       lowercase__      			= argparse.ArgumentParser(description='''Simple example of training script.'''       )
       parser.add_argument(
           '''--mixed_precision'''  , type=A  , default=A  , 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.'''       )
       lowercase__      			= parser.parse_args()
       lowercase__      			= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(A  , A       )


if __name__ == "__main__":
 main()








def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


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



