





"""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 		lowercase					(					__a					):





      def __init__(       self      ,						lowercase )		->     Tuple:
            lowerCAmelCase  						=       data





      def __iter__(       self )		->     Optional[int]:

            for element in self.data:
                  yield element







def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str=True			):







      '''simple docstring'''




      lowerCAmelCase  						=       Accelerator(even_batches=lowerCAmelCase_			)
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Accelerator							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						bool = False			):







      '''simple docstring'''




      if iterable:
            lowerCAmelCase  						=       DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_			)			)			)
      else:
            lowerCAmelCase  						=       TensorDataset(torch.as_tensor(range(lowerCAmelCase_			)			)			)

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

      return dl


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Accelerator							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						List[int]							,						SCREAMING_SNAKE_CASE     :						List[int]							,						):







      '''simple docstring'''




      lowerCAmelCase  						=       create_dataloader(accelerator=lowerCAmelCase_							,						dataset_size=lowerCAmelCase_							,						batch_size=lowerCAmelCase_			)

      lowerCAmelCase  						=       [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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       create_accelerator(even_batches=lowerCAmelCase_			)

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

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

      lowerCAmelCase  						=       []
      with accelerator.join_uneven_inputs([ddp_model]			):
            for batch_idx, batch in enumerate(lowerCAmelCase_			):
                  lowerCAmelCase  						=       ddp_model(batch[0].float()			)
                  lowerCAmelCase  						=       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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




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

      with accelerator.join_uneven_inputs([ddp_model]							,						even_batches=lowerCAmelCase_			):
            lowerCAmelCase  						=       train_dl.batch_sampler.even_batches
            lowerCAmelCase  						=       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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       True
      lowerCAmelCase  						=       False
      lowerCAmelCase  						=       create_accelerator(even_batches=lowerCAmelCase_			)
      lowerCAmelCase  						=       torch.nn.Linear(1							,						1			)
      lowerCAmelCase  						=       accelerator.prepare(lowerCAmelCase_			)
      create_dataloader(lowerCAmelCase_							,						dataset_size=3							,						batch_size=1							,						iterable=lowerCAmelCase_			)
      lowerCAmelCase  						=       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_			):
                        lowerCAmelCase  						=       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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       create_accelerator()
      lowerCAmelCase  						=       torch.nn.Linear(1							,						1			)
      lowerCAmelCase  						=       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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       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"""			)
      lowerCAmelCase  						=       accelerator.state.distributed_type
      lowerCAmelCase  						=       DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase_			)
      lowerCAmelCase  						=       original_state


if __name__ == "__main__":
       main()














import math
def 					snake_case_						(   lowerCAmelCase_				:  int					):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5				,				int(math.sqrt(lowerCAmelCase_					) + 1					)				,				6					):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10001					):

							try:
														__lowercase					:							Optional[int]			=   int(lowerCAmelCase_					)
							except (TypeError, ValueError):
														raise TypeError("""Parameter nth must be int or castable to int."""					) from None
							if nth <= 0:
														raise ValueError("""Parameter nth must be greater than or equal to one."""					)
							__lowercase					:							list[int]			=   []
							__lowercase					:							str			=   2
							while len(lowerCAmelCase_					) < nth:
														if is_prime(lowerCAmelCase_					):
																					primes.append(lowerCAmelCase_					)
																					num += 1
														else:
																					num += 1
							return primes[len(lowerCAmelCase_					) - 1]


if __name__ == "__main__":
	print(f'''{solution() = }''')