



'''simple docstring'''



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 							UpperCAmelCase__	(     UpperCAmelCase_):



 def __init__(	self		,						lowercase      )	->				Optional[Any]:
  __UpperCamelCase					   =	data




 def __iter__(	self      )	->				Dict:




  for element in self.data:
   yield element







def _lowercase (	__A=True							):



 '''simple docstring'''



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







def _lowercase (	__A				,__A				,__A				,__A = False							):



 '''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 _lowercase (	__A				,__A				,__A				,__A				,__A				,):



 '''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 _lowercase (	):



 '''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 _lowercase (	):



 '''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 _lowercase (	):



 '''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 _lowercase (	__A							):



 '''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 _lowercase (	):



 '''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 _lowercase (	):



 '''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 _lowercase (	):



 '''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 _lowercase (	):



 '''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()








'''simple docstring'''



UpperCAmelCase					:			Tuple       =	range(2, 2_0 + 1)
UpperCAmelCase					:			int       =	[1_0**k for k in range(ks[-1] + 1)]
UpperCAmelCase					:			dict[int, dict[int, list[list[int]]]]       =	{}







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = sum(a_i[j] for j in range(a__ ,       len(a__		)		)		)
     __SCREAMING_SNAKE_CASE      = sum(a_i[j] * base[j] for j in range(min(len(a__		) ,       a__		)		)		)

     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = 0, 0
     __SCREAMING_SNAKE_CASE      = n - i

     __SCREAMING_SNAKE_CASE      = memo.get(a__		)

     if sub_memo is not None:
          __SCREAMING_SNAKE_CASE      = sub_memo.get(a__		)

          if jumps is not None and len(a__		) > 0:
               # find and make the largest jump without going over
               __SCREAMING_SNAKE_CASE      = -1
               for _k in range(len(a__		) - 1 ,       -1 ,       -1		):
                    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                         __SCREAMING_SNAKE_CASE      = _k
                         break

               if max_jump >= 0:
                    __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = jumps[max_jump]
                    # since the difference between jumps is cached, add c
                    __SCREAMING_SNAKE_CASE      = diff + c
                    for j in range(min(a__ ,       len(a__		)		)		):
                         __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
                    if new_c > 0:
                         add(a__ ,       a__ ,       a__		)

          else:
               __SCREAMING_SNAKE_CASE      = []
     else:
          __SCREAMING_SNAKE_CASE      = {c: []}
          __SCREAMING_SNAKE_CASE      = sub_memo

     if dn >= max_dn or c + diff >= base[k]:
          return diff, dn

     if k > ks[0]:
          while True:
               # keep doing smaller jumps
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = next_term(a__ ,       k - 1 ,       i + dn ,       a__		)
               diff += _diff
               dn += terms_jumped

               if dn >= max_dn or c + diff >= base[k]:
                    break
     else:
          # would be too small a jump, just compute sequential terms instead
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = compute(a__ ,       a__ ,       i + dn ,       a__		)
          diff += _diff
          dn += terms_jumped

     __SCREAMING_SNAKE_CASE      = sub_memo[c]

     # keep jumps sorted by # of terms skipped
     __SCREAMING_SNAKE_CASE      = 0
     while j < len(a__		):
          if jumps[j][1] > dn:
               break
          j += 1

     # cache the jump for this value digitsum(b) and c
     sub_memo[c].insert(a__ ,       (diff, dn, k)		)
     return (diff, dn)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     if i >= n:
          return 0, i
     if k > len(a__		):
          a_i.extend([0 for _ in range(k - len(a__		)		)]		)

     # note: a_i -> b * 10^k + c
     # ds_b -> digitsum(b)
     # ds_c -> digitsum(c)
     __SCREAMING_SNAKE_CASE      = i
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = 0, 0, 0
     for j in range(len(a__		)		):
          if j >= k:
               ds_b += a_i[j]
          else:
               ds_c += a_i[j]

     while i < n:
          i += 1
          __SCREAMING_SNAKE_CASE      = ds_c + ds_b
          diff += addend
          __SCREAMING_SNAKE_CASE      = 0
          for j in range(a__		):
               __SCREAMING_SNAKE_CASE      = a_i[j] + addend
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)

               ds_c += a_i[j]

          if addend > 0:
               break

     if addend > 0:
          add(a__ ,       a__ ,       a__		)
     return diff, i - start_i







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     for j in range(a__ ,       len(a__		)		):
          __SCREAMING_SNAKE_CASE      = digits[j] + addend
          if s >= 10:
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
               __SCREAMING_SNAKE_CASE      = addend // 10 + quotient
          else:
               __SCREAMING_SNAKE_CASE      = s
               __SCREAMING_SNAKE_CASE      = addend // 10

          if addend == 0:
               break

     while addend > 0:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
          digits.append(a__		)







def   a__			(    a__ = 10**15		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = [1]
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 0
     while True:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = next_term(a__ ,       20 ,       i + dn ,       a__		)
          dn += terms_jumped
          if dn == n - i:
               break

     __SCREAMING_SNAKE_CASE      = 0
     for j in range(len(a__		)		):
          a_n += digits[j] * 10**j
     return a_n


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






