



"""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   =	1_6
_UpperCAmelCase   =	3_2





def    __magic_name__ (							lowercase							,	lowercase = 16       ):
   SCREAMING_SNAKE_CASE_:       Optional[Any]   	=AutoTokenizer.from_pretrained("""bert-base-cased"""       )
   SCREAMING_SNAKE_CASE_:       Any   	=load_dataset("""glue"""							,	"""mrpc"""       )

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

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

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_:       str   	=DataLoader(
       tokenized_datasets["""train"""]							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_							,	batch_size=lowerCamelCase_							,	drop_last=lowerCamelCase_       )
   SCREAMING_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    __magic_name__ (							lowercase							,	lowercase       ):
   # Initialize accelerator
   SCREAMING_SNAKE_CASE_:       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
   SCREAMING_SNAKE_CASE_:       Tuple   	=config['lr']
   SCREAMING_SNAKE_CASE_:       Any   	=int(config["""num_epochs"""]       )
   SCREAMING_SNAKE_CASE_:       Optional[int]   	=int(config["""seed"""]       )
   SCREAMING_SNAKE_CASE_:       List[str]   	=int(config["""batch_size"""]       )

   SCREAMING_SNAKE_CASE_:       List[Any]   	=evaluate.load("""glue"""							,	"""mrpc"""       )

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

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

   # Instantiate scheduler
   SCREAMING_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.

   SCREAMING_SNAKE_CASE_:       int   	=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       )
         SCREAMING_SNAKE_CASE_:       str   	=model(**lowerCamelCase_       )
         SCREAMING_SNAKE_CASE_:       Tuple   	=outputs.loss
         SCREAMING_SNAKE_CASE_:       str   	=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():
            SCREAMING_SNAKE_CASE_:       List[str]   	=model(**lowerCamelCase_       )
         SCREAMING_SNAKE_CASE_:       Dict   	=outputs.logits.argmax(dim=-1       )
         SCREAMING_SNAKE_CASE_:       str   	=accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
         metric.add_batch(
             predictions=lowerCamelCase_							,	references=lowerCamelCase_							,	)

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





def    __magic_name__ (							):
   SCREAMING_SNAKE_CASE_:       List[Any]   	=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."""       )
   SCREAMING_SNAKE_CASE_:       Optional[int]   	=parser.parse_args()
   SCREAMING_SNAKE_CASE_:       Dict   	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCamelCase_							,	lowerCamelCase_       )


if __name__ == "__main__":
   main()







def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							Tuple      		=		(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total



def 				UpperCamelCase_(      )    ->						Optional[int]:
    print(sum_of_series(1							, 1							, 10 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


