



'''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					(    __lowercase						):






    """simple docstring"""





    def __init__(    self :   Dict    ,		UpperCamelCase__ :   str					):


       """simple docstring"""







       UpperCamelCase      							=    data






    def __iter__(    self :   int					):


       """simple docstring"""







       for element in self.data:
          yield element







def 							__lowerCamelCase							(  A__=True       )			->      Optional[Any]:






   """simple docstring"""
   UpperCamelCase      							=    Accelerator(even_batches=A__       )
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator







def 							__lowerCamelCase							(  A__       ,		A__       ,		A__       ,		A__ = False       )			->      int:






   """simple docstring"""






   if iterable:
      UpperCamelCase      							=    DummyIterableDataset(torch.as_tensor(range(A__       )       )       )
   else:
      UpperCamelCase      							=    TensorDataset(torch.as_tensor(range(A__       )       )       )

   UpperCamelCase      							=    DataLoader(A__       ,		batch_size=A__       )
   UpperCamelCase      							=    accelerator.prepare(A__       )

   return dl







def 							__lowerCamelCase							(  A__       ,		A__       ,		A__       ,		A__       ,		A__       ,		)			->      Optional[int]:






   """simple docstring"""
   UpperCamelCase      							=    create_dataloader(accelerator=A__       ,		dataset_size=A__       ,		batch_size=A__       )

   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 							__lowerCamelCase							(  )			->      Union[str, Any]:






   """simple docstring"""
   UpperCamelCase      							=    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							(  )			->      Any:






   """simple docstring"""
   UpperCamelCase      							=    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]:






   """simple docstring"""
   UpperCamelCase      							=    create_accelerator(even_batches=A__       )

   UpperCamelCase      							=    torch.nn.Linear(1       ,		1       )
   UpperCamelCase      							=    accelerator.prepare(A__       )

   UpperCamelCase      							=    create_dataloader(A__       ,		dataset_size=3       ,		batch_size=1       )

   UpperCamelCase      							=    []
   with accelerator.join_uneven_inputs([ddp_model]       ):
      for batch_idx, batch in enumerate(A__       ):
         UpperCamelCase      							=    ddp_model(batch[0].float()       )
         UpperCamelCase      							=    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__       )			->      Union[str, 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 							__lowerCamelCase							(  )			->      List[Any]:






   """simple docstring"""
   UpperCamelCase      							=    True
   UpperCamelCase      							=    False
   UpperCamelCase      							=    create_accelerator(even_batches=A__       )
   UpperCamelCase      							=    torch.nn.Linear(1       ,		1       )
   UpperCamelCase      							=    accelerator.prepare(A__       )
   UpperCamelCase      							=    create_dataloader(A__       ,		dataset_size=3       ,		batch_size=1       )
   UpperCamelCase      							=    create_dataloader(A__       ,		dataset_size=3       ,		batch_size=1       )

   with accelerator.join_uneven_inputs([ddp_model]       ,		even_batches=A__       ):
      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 							__lowerCamelCase							(  )			->      Tuple:






   """simple docstring"""
   UpperCamelCase      							=    True
   UpperCamelCase      							=    False
   UpperCamelCase      							=    create_accelerator(even_batches=A__       )
   UpperCamelCase      							=    torch.nn.Linear(1       ,		1       )
   UpperCamelCase      							=    accelerator.prepare(A__       )
   create_dataloader(A__       ,		dataset_size=3       ,		batch_size=1       ,		iterable=A__       )
   UpperCamelCase      							=    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      							=    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							(  )			->      int:






   """simple docstring"""
   UpperCamelCase      							=    create_accelerator()
   UpperCamelCase      							=    torch.nn.Linear(1       ,		1       )
   UpperCamelCase      							=    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							(  )			->      Tuple:






   """simple docstring"""
   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(A__       )
   UpperCamelCase      							=    original_state


if __name__ == "__main__":
   main()



from collections.abc import Sequence


def 				__A(lowerCAmelCase = None			)		->					int:

		"""simple docstring"""






		if nums is None or not nums:
				raise ValueError("""Input sequence should not be empty"""			)

		_UpperCamelCase         =						nums[0]
		for i in range(1    ,	len(lowerCAmelCase			)			):
				_UpperCamelCase         =						nums[i]
				_UpperCamelCase         =						max(lowerCAmelCase    ,	ans + num    ,	lowerCAmelCase			)

		return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Try on a sample input from the user
		lowerCamelCase__						=				int(input("Enter number of elements : ").strip())
		lowerCamelCase__						=				list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
		print(max_subsequence_sum(array))



