





# 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(						snake_case__ ):

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						data



     def __iter__(      self      ):





            """simple docstring"""

            for element in self.data:
                   yield element






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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = False					):
       if iterable:
              _UpperCamelCase              =						DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase					)					)					)
       else:
              _UpperCamelCase              =						TensorDataset(torch.as_tensor(range(lowerCAmelCase					)					)					)

       _UpperCamelCase              =						DataLoader(lowerCAmelCase    ,       batch_size=lowerCAmelCase					)
       _UpperCamelCase              =						accelerator.prepare(lowerCAmelCase					)

       return dl


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       ):
       _UpperCamelCase              =						create_dataloader(accelerator=lowerCAmelCase    ,       dataset_size=lowerCAmelCase    ,       batch_size=lowerCAmelCase					)

       _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     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						create_accelerator()

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


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						create_accelerator(even_batches=lowerCAmelCase					)

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

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


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						create_accelerator(even_batches=lowerCAmelCase					)

       _UpperCamelCase              =						torch.nn.Linear(1    ,       1					)
       _UpperCamelCase              =						accelerator.prepare(lowerCAmelCase					)

       _UpperCamelCase              =						create_dataloader(lowerCAmelCase    ,       dataset_size=3    ,       batch_size=1					)

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

       accelerator.wait_for_everyone()

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


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

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


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						True
       _UpperCamelCase              =						False
       _UpperCamelCase              =						create_accelerator(even_batches=lowerCAmelCase					)
       _UpperCamelCase              =						torch.nn.Linear(1    ,       1					)
       _UpperCamelCase              =						accelerator.prepare(lowerCAmelCase					)
       _UpperCamelCase              =						create_dataloader(lowerCAmelCase    ,       dataset_size=3    ,       batch_size=1					)
       _UpperCamelCase              =						create_dataloader(lowerCAmelCase    ,       dataset_size=3    ,       batch_size=1					)

       with accelerator.join_uneven_inputs([ddp_model]    ,       even_batches=lowerCAmelCase					):
              _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     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						True
       _UpperCamelCase              =						False
       _UpperCamelCase              =						create_accelerator(even_batches=lowerCAmelCase					)
       _UpperCamelCase              =						torch.nn.Linear(1    ,       1					)
       _UpperCamelCase              =						accelerator.prepare(lowerCAmelCase					)
       create_dataloader(lowerCAmelCase    ,       dataset_size=3    ,       batch_size=1    ,       iterable=lowerCAmelCase					)
       _UpperCamelCase              =						create_dataloader(lowerCAmelCase    ,       dataset_size=3    ,       batch_size=1					)

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

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

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


def     SCREAMING_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(lowerCAmelCase					)
       _UpperCamelCase              =						original_state


if __name__ == "__main__":
   main()













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						0
       for ch in input_str:
              _UpperCamelCase              =						ord(lowerCAmelCase					)
              _UpperCamelCase              =						pow(2    ,       lowerCAmelCase					)

              # If we already turned on bit for current character's unicode
              if bitmap >> ch_unicode & 1 == 1:
                     return False
              bitmap |= ch_bit_index_on
       return True


if __name__ == "__main__":
       import doctest

       doctest.testmod()






