





"""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 					_SCREAMING_SNAKE_CASE(   A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  data




       def __iter__(						self							)			->      Union[str, Any]:





             """simple docstring"""






             for element in self.data:
                   yield element




def      __lowerCamelCase   (							a_     :		List[Any]=True							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  Accelerator(even_batches=a_							)
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator



def      __lowerCamelCase   (							a_     :		Accelerator ,   a_     :		int ,   a_     :		int ,   a_     :		bool = False							)			->      Union[str, Any]:
      if iterable:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  DummyIterableDataset(torch.as_tensor(range(a_							)							)							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TensorDataset(torch.as_tensor(range(a_							)							)							)

      __SCREAMING_SNAKE_CASE   :int						=  DataLoader(a_ ,   batch_size=a_							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  accelerator.prepare(a_							)

      return dl



def      __lowerCamelCase   (							a_     :		Accelerator ,   a_     :		int ,   a_     :		int ,   a_     :		List[int] ,   a_     :		List[int] ,   )			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  create_dataloader(accelerator=a_ ,   dataset_size=a_ ,   batch_size=a_							)

      __SCREAMING_SNAKE_CASE   :Dict						=  [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   (							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Dict						=  create_accelerator()

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



def      __lowerCamelCase   (							)			->      str:
      __SCREAMING_SNAKE_CASE   :Dict						=  create_accelerator(even_batches=a_							)

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

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



def      __lowerCamelCase   (							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  create_accelerator(even_batches=a_							)

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.nn.Linear(1 ,   1							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  accelerator.prepare(a_							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1							)

      __SCREAMING_SNAKE_CASE   :List[str]						=  []
      with accelerator.join_uneven_inputs([ddp_model]							):
            for batch_idx, batch in enumerate(a_							):
                  __SCREAMING_SNAKE_CASE   :Tuple						=  ddp_model(batch[0].float()							)
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  output.sum()
                  loss.backward()
                  batch_idxs.append(a_							)

      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   (							a_     :		str							)			->      Optional[Any]:
      with warnings.catch_warnings(record=a_							) as w:
            with accelerator.join_uneven_inputs([Mock()]							):
                  pass

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



def      __lowerCamelCase   (							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  True
      __SCREAMING_SNAKE_CASE   :str						=  False
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  create_accelerator(even_batches=a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  torch.nn.Linear(1 ,   1							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  accelerator.prepare(a_							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1							)

      with accelerator.join_uneven_inputs([ddp_model] ,   even_batches=a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  train_dl.batch_sampler.even_batches
            __SCREAMING_SNAKE_CASE   :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   (							)			->      List[Any]:
      __SCREAMING_SNAKE_CASE   :str						=  True
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  False
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  create_accelerator(even_batches=a_							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.nn.Linear(1 ,   1							)
      __SCREAMING_SNAKE_CASE   :Dict						=  accelerator.prepare(a_							)
      create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1 ,   iterable=a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1							)

      with warnings.catch_warnings():
            warnings.filterwarnings('''ignore'''							)
            try:
                  with accelerator.join_uneven_inputs([ddp_model] ,   even_batches=a_							):
                        __SCREAMING_SNAKE_CASE   :Dict						=  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   (							)			->      Optional[Any]:
      __SCREAMING_SNAKE_CASE   :List[str]						=  create_accelerator()
      __SCREAMING_SNAKE_CASE   :Tuple						=  torch.nn.Linear(1 ,   1							)
      __SCREAMING_SNAKE_CASE   :Any						=  accelerator.prepare(a_							)
      create_dataloader(a_ ,   dataset_size=3 ,   batch_size=1 ,   iterable=a_							)

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

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



def      __lowerCamelCase   (							)			->      str:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  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'''							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  accelerator.state.distributed_type
      __SCREAMING_SNAKE_CASE   :Any						=  DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  original_state


if __name__ == "__main__":
  main()






"""simple docstring"""
from typing import Any



def      __lowerCamelCase   (							a_     :		list							)			->      list[Any]:
      if not input_list:
            return []
      __SCREAMING_SNAKE_CASE   :int						=  [input_list.count(a_							) for value in input_list]
      __SCREAMING_SNAKE_CASE   :str						=  max(a_							)  # Gets the maximum count in the input list.
      # Gets values of modes
      return sorted({input_list[i] for i, value in enumerate(a_							) if value == y}							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()