






'''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      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Dict            =				data





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






def 				snake_case       (  a_				:			Union[str, Any]=True						)							->					Union[str, Any]:





      """simple docstring"""

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



def 				snake_case       (  a_				:			Accelerator     , a_				:			int     , a_				:			int     , a_				:			bool = False						)							->					Optional[Any]:





      """simple docstring"""

      if iterable:
            UpperCamelCase_					: Optional[int]            =				DummyIterableDataset(torch.as_tensor(range(a_						)						)						)
      else:
            UpperCamelCase_					: Optional[Any]            =				TensorDataset(torch.as_tensor(range(a_						)						)						)

      UpperCamelCase_					: Any            =				DataLoader(a_     , batch_size=a_						)
      UpperCamelCase_					: Tuple            =				accelerator.prepare(a_						)

      return dl



def 				snake_case       (  a_				:			Accelerator     , a_				:			int     , a_				:			int     , a_				:			List[int]     , a_				:			List[int]     , )							->					str:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				create_dataloader(accelerator=a_     , dataset_size=a_     , batch_size=a_						)

      UpperCamelCase_					: List[Any]            =				[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       (  )							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: 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 				snake_case       (  )							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				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 				snake_case       (  )							->					str:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				create_accelerator(even_batches=a_						)

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

      UpperCamelCase_					: List[Any]            =				create_dataloader(a_     , dataset_size=3     , batch_size=1						)

      UpperCamelCase_					: Optional[int]            =				[]
      with accelerator.join_uneven_inputs([ddp_model]						):
            for batch_idx, batch in enumerate(a_						):
                  UpperCamelCase_					: Optional[Any]            =				ddp_model(batch[0].float()						)
                  UpperCamelCase_					: 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 				snake_case       (  a_				:			Tuple						)							->					List[Any]:





      """simple docstring"""

      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 				snake_case       (  )							->					Optional[int]:





      """simple docstring"""

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

      with accelerator.join_uneven_inputs([ddp_model]     , even_batches=a_						):
            UpperCamelCase_					: Union[str, Any]            =				train_dl.batch_sampler.even_batches
            UpperCamelCase_					: Optional[Any]            =				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       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				True
      UpperCamelCase_					: Tuple            =				False
      UpperCamelCase_					: Tuple            =				create_accelerator(even_batches=a_						)
      UpperCamelCase_					: str            =				torch.nn.Linear(1     , 1						)
      UpperCamelCase_					: Dict            =				accelerator.prepare(a_						)
      create_dataloader(a_     , dataset_size=3     , batch_size=1     , iterable=a_						)
      UpperCamelCase_					: Tuple            =				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_						):
                        UpperCamelCase_					: List[str]            =				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       (  )							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: int            =				create_accelerator()
      UpperCamelCase_					: Optional[Any]            =				torch.nn.Linear(1     , 1						)
      UpperCamelCase_					: str            =				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 				snake_case       (  )							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				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_					: Union[str, Any]            =				accelerator.state.distributed_type
      UpperCamelCase_					: List[str]            =				DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(a_						)
      UpperCamelCase_					: Dict            =				original_state


if __name__ == "__main__":
      main()














'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






