





import argparse
import os

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
# and perform gradient accumulation
#
# 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             =							16
_UpperCAmelCase             =							32






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Tuple = 16   )      ->   Any:
 __lowerCAmelCase					:	str      =       AutoTokenizer.from_pretrained("""bert-base-cased"""   )
 __lowerCAmelCase					:	List[str]      =       load_dataset("""glue"""   ,   """mrpc"""   )

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

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

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

 # Instantiate dataloaders.
 __lowerCAmelCase					:	Tuple      =       DataLoader(
     tokenized_datasets["""train"""]   ,   shuffle=lowerCAmelCase__   ,   collate_fn=lowerCAmelCase__   ,   batch_size=lowerCAmelCase__   )
 __lowerCAmelCase					:	Optional[Any]      =       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

 _UpperCAmelCase             =							mocked_dataloaders  # noqa: F811






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:str   )      ->   Optional[int]:
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,   lowerCAmelCase__   ) == "1":
  __lowerCAmelCase					:	str      =       2
 # New Code #
 __lowerCAmelCase					:	Dict      =       int(args.gradient_accumulation_steps   )
 # Initialize accelerator
 __lowerCAmelCase					:	Dict      =       Accelerator(
     cpu=args.cpu   ,   mixed_precision=args.mixed_precision   ,   gradient_accumulation_steps=lowerCAmelCase__   )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""   )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase					:	int      =       config["""lr"""]
 __lowerCAmelCase					:	Dict      =       int(config["""num_epochs"""]   )
 __lowerCAmelCase					:	Optional[int]      =       int(config["""seed"""]   )
 __lowerCAmelCase					:	str      =       int(config["""batch_size"""]   )

 __lowerCAmelCase					:	Tuple      =       evaluate.load("""glue"""   ,   """mrpc"""   )

 set_seed(lowerCAmelCase__   )
 __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       get_dataloaders(lowerCAmelCase__   ,   lowerCAmelCase__   )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCAmelCase					:	Optional[int]      =       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					:	str      =       model.to(accelerator.device   )

 # Instantiate optimizer
 __lowerCAmelCase					:	Any      =       AdamW(params=model.parameters()   ,   lr=lowerCAmelCase__   )

 # Instantiate scheduler
 __lowerCAmelCase					:	Optional[Any]      =       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 ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       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   )
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(lowerCAmelCase__   ):
    __lowerCAmelCase					:	Union[str, Any]      =       model(**lowerCAmelCase__   )
    __lowerCAmelCase					:	List[Any]      =       output.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					:	Optional[Any]      =       model(**lowerCAmelCase__   )
   __lowerCAmelCase					:	Union[str, Any]      =       outputs.logits.argmax(dim=-1   )
   __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
   metric.add_batch(
       predictions=lowerCAmelCase__   ,   references=lowerCAmelCase__   ,   )

  __lowerCAmelCase					:	List[Any]      =       metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:'''   ,   lowerCAmelCase__   )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   int:
 __lowerCAmelCase					:	Optional[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."""   ,   )
 # New Code #
 parser.add_argument(
     """--gradient_accumulation_steps"""   ,   type=lowerCAmelCase__   ,   default=1   ,   help="""The number of minibatches to be ran before gradients are accumulated."""   ,   )
 parser.add_argument("""--cpu"""   ,   action="""store_true"""   ,   help="""If passed, will train on the CPU."""   )
 __lowerCAmelCase					:	Dict      =       parser.parse_args()
 __lowerCAmelCase					:	Union[str, Any]      =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(lowerCAmelCase__   ,   lowerCAmelCase__   )


if __name__ == "__main__":
 main()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()