




"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment






class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  inspect.getfile(accelerate.test_utils		)
   _snake_case							:		List[str]								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_script.py"""]		)
   _snake_case							:		Optional[Any]								=  os.path.sep.join(
       mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]		)
   _snake_case							:		int								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_ops.py"""]		)


 @require_multi_gpu
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   print(f"Found {torch.cuda.device_count()} devices."		)
   _snake_case							:		Optional[int]								=  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
   with patch_environment(omp_num_threads=1		):
     execute_subprocess_async(a_,      env=os.environ.copy()		)


 @require_multi_gpu
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   print(f"Found {torch.cuda.device_count()} devices."		)
   _snake_case							:		Optional[int]								=  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
   print(f"Command: {cmd}"		)
   with patch_environment(omp_num_threads=1		):
     execute_subprocess_async(a_,      env=os.environ.copy()		)


 @require_multi_gpu
 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		int								=  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__		)]
   with patch_environment(omp_num_threads=1		):
     execute_subprocess_async(a_,      env=os.environ.copy()		)


 @require_multi_gpu
 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"		)
   _snake_case							:		Tuple								=  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
   with patch_environment(omp_num_threads=1,      cuda_visible_devices="""0,1"""		):
     execute_subprocess_async(a_,      env=os.environ.copy()		)


if __name__ == "__main__":
     A_						=				Accelerator()
     A_						=				(accelerator.state.process_index + 2, 10)
     A_						=				torch.randint(0, 10, shape).to(accelerator.device)

     A_						=				''''''

     A_						=				accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     A_						=				accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     A_						=				accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)








"""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_						=				16
A_						=				32


def 					UpperCAmelCase__       (snake_case__							:					Accelerator		,   snake_case__							:					int = 16    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  AutoTokenizer.from_pretrained("""bert-base-cased"""    )
  _snake_case							:		Any								=  load_dataset("""glue"""		,   """mrpc"""    )

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

  def collate_fn(snake_case__							:					int    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _snake_case							:		Optional[int]								=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _snake_case							:		str								=  16
    elif accelerator.mixed_precision != "no":
      _snake_case							:		Optional[int]								=  8
    else:
      _snake_case							:		Optional[int]								=  None

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

  # Instantiate dataloaders.
  _snake_case							:		Optional[int]								=  DataLoader(
      tokenized_datasets["""train"""]		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__    )
  _snake_case							:		Dict								=  DataLoader(
      tokenized_datasets["""validation"""]		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__    )

  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_						=				mocked_dataloaders  # noqa: F811


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any    ):
  """simple docstring"""

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

  _snake_case							:		List[str]								=  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=snake_case__    )
  def inner_training_loop(snake_case__							:					Union[str, 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(snake_case__    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _snake_case							:		List[Any]								=  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,   return_dict=snake_case__    )

    # 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).
    _snake_case							:		Tuple								=  model.to(accelerator.device    )

    # Instantiate optimizer
    _snake_case							:		str								=  AdamW(params=model.parameters()		,   lr=snake_case__    )
    _snake_case		,					_snake_case							:		Optional[int]								=  get_dataloaders(snake_case__		,   snake_case__    )

    # Instantiate scheduler
    _snake_case							:		str								=  get_linear_schedule_with_warmup(
        optimizer=snake_case__		,   num_warmup_steps=1_00		,   num_training_steps=(len(snake_case__    ) * 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.
    _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  accelerator.prepare(
        snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )

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

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

      _snake_case							:		str								=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"		,   snake_case__    )

    # 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 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  argparse.ArgumentParser(description="""Simple example of training script."""    )
  parser.add_argument(
      """--mixed_precision"""		,   type=snake_case__		,   default=snake_case__		,   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."""    )
  _snake_case							:		Dict								=  parser.parse_args()
  _snake_case							:		int								=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(snake_case__		,   snake_case__    )


if __name__ == "__main__":
     main()


