






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


SCREAMING_SNAKE_CASE						=	16
SCREAMING_SNAKE_CASE						=	32


def 							lowercase_				(    __A :       Accelerator					,      __A :       int = 1_6			)       ->      List[str]:



 """simple docstring"""




 lowercase					:   Optional[int]				  =AutoTokenizer.from_pretrained('''bert-base-cased'''			)
 lowercase					:   str				  =load_dataset('''glue'''					,      '''mrpc'''			)

 def tokenize_function(__A :       str			):
  # max_length=None => use the model max length (it's actually the default)
  lowercase					:   Tuple				  =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					:   Union[str, Any]				  =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					:   Optional[Any]				  =tokenized_datasets.rename_column('''label'''					,      '''labels'''			)

 def collate_fn(__A :       int			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowercase					:   List[Any]				  =1_2_8 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[Any]				  =1_6
  elif accelerator.mixed_precision != "no":
   lowercase					:   str				  =8
  else:
   lowercase					:   Optional[Any]				  =None

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

 # Instantiate dataloaders.
 lowercase					:   Dict				  =DataLoader(
     tokenized_datasets['''train''']					,      shuffle=__A					,      collate_fn=__A					,      batch_size=__A			)
 lowercase					:   Optional[int]				  =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

      SCREAMING_SNAKE_CASE						=	mocked_dataloaders  # noqa: F811


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      Dict:



 """simple docstring"""




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

 lowercase					:   Union[str, Any]				  =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 :       Tuple			):
  # 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					:   List[Any]				  =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					:   Optional[Any]				  =model.to(accelerator.device			)

  # Instantiate optimizer
  lowercase					:   Any				  =AdamW(params=model.parameters()					,      lr=__A			)
  lowercase  ,		lowercase					:   Tuple				  =get_dataloaders(__A					,      __A			)

  # Instantiate scheduler
  lowercase					:   Any				  =get_linear_schedule_with_warmup(
      optimizer=__A					,      num_warmup_steps=1_0_0					,      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					:   List[Any]				  =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					:   List[str]				  =model(**__A			)
    lowercase					:   int				  =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					:   List[Any]				  =model(**__A			)
    lowercase					:   Optional[Any]				  =outputs.logits.argmax(dim=-1			)
    lowercase  ,		lowercase					:   List[Any]				  =accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
    metric.add_batch(
        predictions=__A					,      references=__A					,      )

   lowercase					:   Optional[Any]				  =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 							lowercase_				(    )       ->      List[str]:



 """simple docstring"""




 lowercase					:   Any				  =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					:   Tuple				  =parser.parse_args()
 lowercase					:   Optional[Any]				  ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
 training_function(__A					,      __A			)


if __name__ == "__main__":
      main()











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



