






import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def    a__    (	):
   lowercase__    :						int       =			Dataset.from_dict({"""i""": range(17       )}       )
   lowercase__    :						List[Any]       =			len(A_       )
   lowercase__    :						Optional[int]       =			3
   lowercase__    :						List[str]       =			[
       split_dataset_by_node(A_		,					rank=A_		,					world_size=A_       ) for rank in range(A_       )
   ]
   assert sum(len(A_       ) for ds in datasets_per_rank       ) == full_size
   assert len({tuple(x.values()       ) for ds in datasets_per_rank for x in ds}       ) == full_size
def    a__    (	):

   def gen():
      return ({"i": i} for i in range(17       ))

   lowercase__    :						Any       =			3
   lowercase__    :						Optional[Any]       =			IterableDataset.from_generator(A_       )
   lowercase__    :						List[str]       =			len(list(A_       )       )
   lowercase__    :						Dict       =			[
       split_dataset_by_node(A_		,					rank=A_		,					world_size=A_       ) for rank in range(A_       )
   ]
   assert sum(len(list(A_       )       ) for ds in datasets_per_rank       ) == full_size
   assert len({tuple(x.values()       ) for ds in datasets_per_rank for x in ds}       ) == full_size
@pytest.mark.parametrize("""shards_per_node"""		,					[1, 2, 3]       )
def    a__    (	A       ):

   def gen(A       ):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(17       ))

   lowercase__    :						List[Any]       =			3
   lowercase__    :						int       =			shards_per_node * world_size
   lowercase__    :						Union[str, Any]       =			{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(A_       )]}
   lowercase__    :						Optional[Any]       =			IterableDataset.from_generator(A_		,					gen_kwargs=A_       )
   lowercase__    :						List[Any]       =			len(list(A_       )       )
   assert full_ds.n_shards == world_size * shards_per_node
   lowercase__    :						Union[str, Any]       =			[
       split_dataset_by_node(A_		,					rank=A_		,					world_size=A_       ) for rank in range(A_       )
   ]
   assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
   assert sum(len(list(A_       )       ) for ds in datasets_per_rank       ) == full_size
   assert len({tuple(x.values()       ) for ds in datasets_per_rank for x in ds}       ) == full_size
def    a__    (	):

   def gen():
      return ({"i": i} for i in range(17       ))

   lowercase__    :						Union[str, Any]       =			2
   lowercase__    :						List[str]       =			IterableDataset.from_generator(A_       )
   lowercase__    :						Optional[int]       =			len(list(A_       )       )

   lowercase__    :						Dict       =			split_dataset_by_node(A_		,					rank=0		,					world_size=A_       ).shuffle(seed=42       )
   assert len(list(A_       )       ) == 1 + full_size // world_size
   with pytest.raises(A_       ):
      split_dataset_by_node(A_		,					rank=0		,					world_size=A_       ).shuffle()

   lowercase__    :						int       =			split_dataset_by_node(full_ds.shuffle(seed=42       )		,					rank=0		,					world_size=A_       )
   assert len(list(A_       )       ) == 1 + full_size // world_size
   with pytest.raises(A_       ):
      split_dataset_by_node(full_ds.shuffle()		,					rank=0		,					world_size=A_       )
@pytest.mark.parametrize("""streaming"""		,					[False, True]       )
@require_torch
@pytest.mark.skipif(os.name == """nt"""		,					reason="""execute_subprocess_async doesn\'t support windows"""       )
@pytest.mark.integration
def    a__    (	A       ):
   lowercase__    :						Optional[int]       =			2
   lowercase__    :						str       =			get_torch_dist_unique_port()
   lowercase__    :						Optional[Any]       =			Path(A_       ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
   lowercase__    :						Tuple       =			F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
   lowercase__    :						Tuple       =			F'''
        --streaming={streaming}
    '''.split()
   lowercase__    :						Any       =			[sys.executable] + distributed_args + args
   execute_subprocess_async(A_		,					env=os.environ.copy()       )






@pytest.mark.parametrize(
    """nproc_per_node, num_workers"""		,					[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]		,					)
@require_torch
@pytest.mark.skipif(os.name == """nt"""		,					reason="""execute_subprocess_async doesn\'t support windows"""       )
@pytest.mark.integration
def    a__    (	A		,					A       ):
   lowercase__    :						Union[str, Any]       =			True
   lowercase__    :						List[str]       =			get_torch_dist_unique_port()
   lowercase__    :						List[str]       =			Path(A_       ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
   lowercase__    :						List[Any]       =			F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
   lowercase__    :						int       =			F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
   lowercase__    :						Optional[Any]       =			[sys.executable] + distributed_args + args
   execute_subprocess_async(A_		,					env=os.environ.copy()       )










"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




