
"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Dict   			=  data



       def __iter__(  self							)  ->						Tuple:





         """simple docstring"""




         for element in self.data:
           yield element




def 			_lowerCAmelCase		(		_UpperCamelCase=True    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  Accelerator(even_batches=_UpperCamelCase    )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = False    ):






  """simple docstring"""



  if iterable:
    _lowercase:      Optional[Any]   			=  DummyIterableDataset(torch.as_tensor(range(_UpperCamelCase    )    )    )
  else:
    _lowercase:      Tuple   			=  TensorDataset(torch.as_tensor(range(_UpperCamelCase    )    )    )

  _lowercase:      Any   			=  DataLoader(_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      Tuple   			=  accelerator.prepare(_UpperCamelCase    )

  return dl




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  create_dataloader(accelerator=_UpperCamelCase			,					dataset_size=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  _lowercase:      str   			=  [len(batch[0]    ) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      _UpperCamelCase			,					dataset_size=3			,					batch_size=1			,					process_0_expected_batch_sizes=[1, 1]			,					process_1_expected_batch_sizes=[1, 1]			,					)

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      _UpperCamelCase			,					dataset_size=7			,					batch_size=2			,					process_0_expected_batch_sizes=[2, 2]			,					process_1_expected_batch_sizes=[2, 2]			,					)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  create_accelerator(even_batches=_UpperCamelCase    )

  verify_dataloader_batch_sizes(
      _UpperCamelCase			,					dataset_size=3			,					batch_size=1			,					process_0_expected_batch_sizes=[1, 1]			,					process_1_expected_batch_sizes=[1]			,					)

  verify_dataloader_batch_sizes(
      _UpperCamelCase			,					dataset_size=7			,					batch_size=2			,					process_0_expected_batch_sizes=[2, 2]			,					process_1_expected_batch_sizes=[2, 1]			,					)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      str   			=  create_accelerator(even_batches=_UpperCamelCase    )

  _lowercase:      Union[str, Any]   			=  torch.nn.Linear(1			,					1    )
  _lowercase:      Any   			=  accelerator.prepare(_UpperCamelCase    )

  _lowercase:      Optional[int]   			=  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1    )

  _lowercase:      Union[str, Any]   			=  []
  with accelerator.join_uneven_inputs([ddp_model]    ):
    for batch_idx, batch in enumerate(_UpperCamelCase    ):
      _lowercase:      Dict   			=  ddp_model(batch[0].float()    )
      _lowercase:      List[str]   			=  output.sum()
      loss.backward()
      batch_idxs.append(_UpperCamelCase    )

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  with warnings.catch_warnings(record=_UpperCamelCase    ) as w:
    with accelerator.join_uneven_inputs([Mock()]    ):
      pass

    assert issubclass(w[-1].category			,					_UpperCamelCase    )
    assert "only supported for multi-GPU" in str(w[-1].message    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  True
  _lowercase:      Optional[int]   			=  False
  _lowercase:      Dict   			=  create_accelerator(even_batches=_UpperCamelCase    )
  _lowercase:      Any   			=  torch.nn.Linear(1			,					1    )
  _lowercase:      Tuple   			=  accelerator.prepare(_UpperCamelCase    )
  _lowercase:      Any   			=  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1    )
  _lowercase:      List[Any]   			=  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1    )

  with accelerator.join_uneven_inputs([ddp_model]			,					even_batches=_UpperCamelCase    ):
    _lowercase:      int   			=  train_dl.batch_sampler.even_batches
    _lowercase:      Optional[int]   			=  valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  True
  _lowercase:      int   			=  False
  _lowercase:      List[str]   			=  create_accelerator(even_batches=_UpperCamelCase    )
  _lowercase:      Tuple   			=  torch.nn.Linear(1			,					1    )
  _lowercase:      Optional[int]   			=  accelerator.prepare(_UpperCamelCase    )
  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1			,					iterable=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1    )

  with warnings.catch_warnings():
    warnings.filterwarnings('''ignore'''    )
    try:
      with accelerator.join_uneven_inputs([ddp_model]			,					even_batches=_UpperCamelCase    ):
        _lowercase:      Union[str, Any]   			=  batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  create_accelerator()
  _lowercase:      Dict   			=  torch.nn.Linear(1			,					1    )
  _lowercase:      Tuple   			=  accelerator.prepare(_UpperCamelCase    )
  create_dataloader(_UpperCamelCase			,					dataset_size=3			,					batch_size=1			,					iterable=_UpperCamelCase    )

  with warnings.catch_warnings(record=_UpperCamelCase    ) as w:
    with accelerator.join_uneven_inputs([ddp_model]			,					even_batches=_UpperCamelCase    ):
      pass

    assert issubclass(w[-1].category			,					_UpperCamelCase    )
    assert "only supported for map-style datasets" in str(w[-1].message    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Tuple   			=  create_accelerator()

  accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''    )
  test_default_ensures_even_batch_sizes()

  accelerator.print('''Run tests with even_batches disabled'''    )
  test_can_disable_even_batches()

  accelerator.print('''Test joining uneven inputs'''    )
  test_can_join_uneven_inputs()

  accelerator.print('''Test overriding even_batches when joining uneven inputs'''    )
  test_join_can_override_even_batches()

  accelerator.print('''Test overriding even_batches for mixed dataloader types'''    )
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''    )
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print('''Test join with non DDP distributed raises warning'''    )
  _lowercase:      Optional[int]   			=  accelerator.state.distributed_type
  _lowercase:      int   			=  DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(_UpperCamelCase    )
  _lowercase:      str   			=  original_state


if __name__ == "__main__":
  main()


"""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__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    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:      Tuple   			=  datasets.map(
        _UpperCamelCase			,					batched=_UpperCamelCase			,					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(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:      Union[str, Any]   			=  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":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

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

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  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 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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

  _lowercase:      Optional[int]   			=  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=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # 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(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,					return_dict=_UpperCamelCase    )

    # 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:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * 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:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

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

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

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # 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 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
  parser.add_argument(
      '''--mixed_precision'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					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:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
