






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


a_						:       Tuple      			=				16
a_						:       Dict      			=				32





def __snake_case      (   UpperCAmelCase_		:		Accelerator      ,       UpperCAmelCase_		:		int = 16	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained("bert-base-cased"	)
 lowerCamelCase_  				=						load_dataset("glue"      ,       "mrpc"	)

 def tokenize_function(UpperCAmelCase_		:		str	):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase_  				=						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():
  lowerCamelCase_  				=						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
 lowerCamelCase_  				=						tokenized_datasets.rename_column("label"      ,       "labels"	)

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

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

 # Instantiate dataloaders.
 lowerCamelCase_  				=						DataLoader(
     tokenized_datasets["train"]      ,       shuffle=_lowerCamelCase      ,       collate_fn=_lowerCamelCase      ,       batch_size=_lowerCamelCase	)
 lowerCamelCase_  				=						DataLoader(
     tokenized_datasets["validation"]      ,       shuffle=_lowerCamelCase      ,       collate_fn=_lowerCamelCase      ,       batch_size=_lowerCamelCase	)

 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

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





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Any	):


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

 lowerCamelCase_  				=						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=_lowerCamelCase	)
 def inner_training_loop(UpperCAmelCase_		:		Any	):
  # 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(_lowerCamelCase	)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase_  				=						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).
  lowerCamelCase_  				=						model.to(accelerator.device	)

  # Instantiate optimizer
  lowerCamelCase_  				=						AdamW(params=model.parameters()      ,       lr=_lowerCamelCase	)
  lowerCamelCase_  				=						get_dataloaders(_lowerCamelCase      ,       _lowerCamelCase	)

  # Instantiate scheduler
  lowerCamelCase_  				=						get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase      ,       num_warmup_steps=100      ,       num_training_steps=(len(_lowerCamelCase	) * 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.
  lowerCamelCase_  				=						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	)
    lowerCamelCase_  				=						model(**_lowerCamelCase	)
    lowerCamelCase_  				=						outputs.loss
    accelerator.backward(_lowerCamelCase	)
    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():
     lowerCamelCase_  				=						model(**_lowerCamelCase	)
    lowerCamelCase_  				=						outputs.logits.argmax(dim=-1	)
    lowerCamelCase_  				=						accelerator.gather_for_metrics((predictions, batch["labels"])	)
    metric.add_batch(
        predictions=_lowerCamelCase      ,       references=_lowerCamelCase      ,       )

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

    # 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 __snake_case      (   ):
 lowerCamelCase_  				=						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."	)
 lowerCamelCase_  				=						parser.parse_args()
 lowerCamelCase_  				=						{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_lowerCamelCase      ,       _lowerCamelCase	)


if __name__ == "__main__":
     main()







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



