






# 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     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  data




  def __iter__(  self			)      ->			Tuple:
     for element in self.data:
        yield element






def      _snake_case     (__lowercase=True):
   UpperCamelCase_						=  Accelerator(even_batches=__lowercase)
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase = False):
   if iterable:
      UpperCamelCase_						=  DummyIterableDataset(torch.as_tensor(range(__lowercase)))
   else:
      UpperCamelCase_						=  TensorDataset(torch.as_tensor(range(__lowercase)))

   UpperCamelCase_						=  DataLoader(__lowercase     ,   batch_size=__lowercase)
   UpperCamelCase_						=  accelerator.prepare(__lowercase)

   return dl






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   ):
   UpperCamelCase_						=  create_dataloader(accelerator=__lowercase     ,   dataset_size=__lowercase     ,   batch_size=__lowercase)

   UpperCamelCase_						=  [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      _snake_case     ():
   UpperCamelCase_						=  create_accelerator()

   # without padding, we would expect a different number of batches
   verify_dataloader_batch_sizes(
       __lowercase     ,   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(
       __lowercase     ,   dataset_size=7     ,   batch_size=2     ,   process_0_expected_batch_sizes=[2, 2]     ,   process_1_expected_batch_sizes=[2, 2]     ,   )






def      _snake_case     ():
   UpperCamelCase_						=  create_accelerator(even_batches=__lowercase)

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

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






def      _snake_case     ():
   UpperCamelCase_						=  create_accelerator(even_batches=__lowercase)

   UpperCamelCase_						=  torch.nn.Linear(1     ,   1)
   UpperCamelCase_						=  accelerator.prepare(__lowercase)

   UpperCamelCase_						=  create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1)

   UpperCamelCase_						=  []
   with accelerator.join_uneven_inputs([ddp_model]):
      for batch_idx, batch in enumerate(__lowercase):
         UpperCamelCase_						=  ddp_model(batch[0].float())
         UpperCamelCase_						=  output.sum()
         loss.backward()
         batch_idxs.append(__lowercase)

   accelerator.wait_for_everyone()

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






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

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






def      _snake_case     ():
   UpperCamelCase_						=  True
   UpperCamelCase_						=  False
   UpperCamelCase_						=  create_accelerator(even_batches=__lowercase)
   UpperCamelCase_						=  torch.nn.Linear(1     ,   1)
   UpperCamelCase_						=  accelerator.prepare(__lowercase)
   UpperCamelCase_						=  create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1)
   UpperCamelCase_						=  create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1)

   with accelerator.join_uneven_inputs([ddp_model]     ,   even_batches=__lowercase):
      UpperCamelCase_						=  train_dl.batch_sampler.even_batches
      UpperCamelCase_						=  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      _snake_case     ():
   UpperCamelCase_						=  True
   UpperCamelCase_						=  False
   UpperCamelCase_						=  create_accelerator(even_batches=__lowercase)
   UpperCamelCase_						=  torch.nn.Linear(1     ,   1)
   UpperCamelCase_						=  accelerator.prepare(__lowercase)
   create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1     ,   iterable=__lowercase)
   UpperCamelCase_						=  create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1)

   with warnings.catch_warnings():
      warnings.filterwarnings('ignore')
      try:
         with accelerator.join_uneven_inputs([ddp_model]     ,   even_batches=__lowercase):
            UpperCamelCase_						=  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      _snake_case     ():
   UpperCamelCase_						=  create_accelerator()
   UpperCamelCase_						=  torch.nn.Linear(1     ,   1)
   UpperCamelCase_						=  accelerator.prepare(__lowercase)
   create_dataloader(__lowercase     ,   dataset_size=3     ,   batch_size=1     ,   iterable=__lowercase)

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

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






def      _snake_case     ():
   UpperCamelCase_						=  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')
   UpperCamelCase_						=  accelerator.state.distributed_type
   UpperCamelCase_						=  DistributedType.FSDP
   test_join_raises_warning_for_non_ddp_distributed(__lowercase)
   UpperCamelCase_						=  original_state


if __name__ == "__main__":
 main()













import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'			)
        UpperCamelCase_						=  input_file.read()
        UpperCamelCase_						=  regexp.search(_UpperCAmelCase			)

     return match




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'		,		re.DOTALL			)
        UpperCamelCase_						=  input_file.read()
        # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
        UpperCamelCase_						=  regexp.finditer(_UpperCAmelCase			)

     UpperCamelCase_						=  [match for match in matches if match is not None and match.group(1			) is not None]
     return matches[0] if matches else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_encoding_on_file_open(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_print_statements(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""			)





