





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  __UpperCamelCase    (						)    ->		List[str]:


   '''simple docstring'''







   __A    =  Dataset.from_dict({'''i''': range(1_7      )}      )
   __A    =  len(_A      )
   __A    =  3
   __A    =  [
       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  __UpperCamelCase    (						)    ->		List[str]:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7      ))

   __A    =  3
   __A    =  IterableDataset.from_generator(_A      )
   __A    =  len(list(_A      )      )
   __A    =  [
       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  __UpperCamelCase    (						snake_case      )    ->		Optional[Any]:


   '''simple docstring'''







   def gen(snake_case      ):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(1_7      ))

   __A    =  3
   __A    =  shards_per_node * world_size
   __A    =  {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(_A      )]}
   __A    =  IterableDataset.from_generator(_A							,			gen_kwargs=_A      )
   __A    =  len(list(_A      )      )
   assert full_ds.n_shards == world_size * shards_per_node
   __A    =  [
       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  __UpperCamelCase    (						)    ->		int:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7      ))

   __A    =  2
   __A    =  IterableDataset.from_generator(_A      )
   __A    =  len(list(_A      )      )

   __A    =  split_dataset_by_node(_A							,			rank=0							,			world_size=_A      ).shuffle(seed=4_2      )
   assert len(list(_A      )      ) == 1 + full_size // world_size
   with pytest.raises(_A      ):
      split_dataset_by_node(_A							,			rank=0							,			world_size=_A      ).shuffle()

   __A    =  split_dataset_by_node(full_ds.shuffle(seed=4_2      )							,			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  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   __A    =  2
   __A    =  get_torch_dist_unique_port()
   __A    =  Path(_A      ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A    =  F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A    =  F"\n        --streaming={streaming}\n    ".split()
   __A    =  [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  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Optional[int]:


   '''simple docstring'''







   __A    =  True
   __A    =  get_torch_dist_unique_port()
   __A    =  Path(_A      ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A    =  F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A    =  F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
   __A    =  [sys.executable] + distributed_args + args
   execute_subprocess_async(_A							,			env=os.environ.copy()      )




def      UpperCAmelCase__							(     _A :					int      , _A :					int				):





  '''simple docstring'''







  a__    =1.0
  for i in range(1      , r + 1				):
    ret *= (n + 1 - i) / i
  return round(_A				)




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return choose(8 + n      , _A				) + choose(9 + n      , _A				) - 10




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return sum(non_bouncy_exact(_A				) for i in range(1      , n + 1				)				)




def      UpperCAmelCase__							(     _A :					int = 1_00				):





  '''simple docstring'''







  return non_bouncy_upto(_A				)


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