

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_(			)       -> Optional[int]:
 UpperCAmelCase__							=				Dataset.from_dict({'i': range(17   )}   )
 UpperCAmelCase__							=				len(snake_case__   )
 UpperCAmelCase__							=				3
 UpperCAmelCase__							=				[
     split_dataset_by_node(snake_case__  ,    rank=snake_case__  ,    world_size=snake_case__   ) for rank in range(snake_case__   )
 ]
 assert sum(len(snake_case__   ) 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_(			)       -> Union[str, Any]:
 def gen():
  return ({"i": i} for i in range(17   ))

 UpperCAmelCase__							=				3
 UpperCAmelCase__							=				IterableDataset.from_generator(snake_case__   )
 UpperCAmelCase__							=				len(list(snake_case__   )   )
 UpperCAmelCase__							=				[
     split_dataset_by_node(snake_case__  ,    rank=snake_case__  ,    world_size=snake_case__   ) for rank in range(snake_case__   )
 ]
 assert sum(len(list(snake_case__   )   ) 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__:  str   )       -> int:
 def gen(snake_case__:  Any   ):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17   ))

 UpperCAmelCase__							=				3
 UpperCAmelCase__							=				shards_per_node * world_size
 UpperCAmelCase__							=				{'shards': [f"shard_{shard_idx}.txt" for shard_idx in range(snake_case__   )]}
 UpperCAmelCase__							=				IterableDataset.from_generator(snake_case__  ,    gen_kwargs=snake_case__   )
 UpperCAmelCase__							=				len(list(snake_case__   )   )
 assert full_ds.n_shards == world_size * shards_per_node
 UpperCAmelCase__							=				[
     split_dataset_by_node(snake_case__  ,    rank=snake_case__  ,    world_size=snake_case__   ) for rank in range(snake_case__   )
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(snake_case__   )   ) 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_(			)       -> str:
 def gen():
  return ({"i": i} for i in range(17   ))

 UpperCAmelCase__							=				2
 UpperCAmelCase__							=				IterableDataset.from_generator(snake_case__   )
 UpperCAmelCase__							=				len(list(snake_case__   )   )

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

 UpperCAmelCase__							=				split_dataset_by_node(full_ds.shuffle(seed=42   )  ,    rank=0  ,    world_size=snake_case__   )
 assert len(list(snake_case__   )   ) == 1 + full_size // world_size
 with pytest.raises(snake_case__   ):
  split_dataset_by_node(full_ds.shuffle()  ,    rank=0  ,    world_size=snake_case__   )
@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__:  Tuple   )       -> str:
 UpperCAmelCase__							=				2
 UpperCAmelCase__							=				get_torch_dist_unique_port()
 UpperCAmelCase__							=				Path(snake_case__   ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
 UpperCAmelCase__							=				f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
 UpperCAmelCase__							=				f"\n        --streaming={streaming}\n    ".split()
 UpperCAmelCase__							=				[sys.executable] + distributed_args + args
 execute_subprocess_async(snake_case__  ,    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__:  List[Any]  ,    snake_case__:  Optional[int]   )       -> int:
 UpperCAmelCase__							=				True
 UpperCAmelCase__							=				get_torch_dist_unique_port()
 UpperCAmelCase__							=				Path(snake_case__   ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
 UpperCAmelCase__							=				f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
 UpperCAmelCase__							=				f"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
 UpperCAmelCase__							=				[sys.executable] + distributed_args + args
 execute_subprocess_async(snake_case__  ,    env=os.environ.copy()   )






import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

 with open(snake_case__   ) as in_file:
  UpperCAmelCase__							=				[[int(snake_case__   ) for i in line.split()] for line in in_file]

 while len(snake_case__   ) != 1:
  UpperCAmelCase__							=				triangle.pop()
  UpperCAmelCase__							=				triangle[-1]
  for j in range(len(snake_case__   ) - 1   ):
   curr_row[j] += max(last_row[j]  ,    last_row[j + 1]   )
 return triangle[0][0]


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



