


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							(     ):
     __UpperCamelCase       				=Dataset.from_dict({'i': range(17							)}							)
     __UpperCamelCase       				=len(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=3
     __UpperCamelCase       				=[
         split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,	rank=SCREAMING_SNAKE_CASE__     ,	world_size=SCREAMING_SNAKE_CASE__							) for rank in range(SCREAMING_SNAKE_CASE__							)
     ]
     assert sum(len(SCREAMING_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							(     ):
     def gen():
          return ({"i": i} for i in range(17							))

     __UpperCamelCase       				=3
     __UpperCamelCase       				=IterableDataset.from_generator(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=len(list(SCREAMING_SNAKE_CASE__							)							)
     __UpperCamelCase       				=[
         split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,	rank=SCREAMING_SNAKE_CASE__     ,	world_size=SCREAMING_SNAKE_CASE__							) for rank in range(SCREAMING_SNAKE_CASE__							)
     ]
     assert sum(len(list(SCREAMING_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							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     def gen(SCREAMING_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(SCREAMING_SNAKE_CASE__							)]}
     __UpperCamelCase       				=IterableDataset.from_generator(SCREAMING_SNAKE_CASE__     ,	gen_kwargs=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=len(list(SCREAMING_SNAKE_CASE__							)							)
     assert full_ds.n_shards == world_size * shards_per_node
     __UpperCamelCase       				=[
         split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,	rank=SCREAMING_SNAKE_CASE__     ,	world_size=SCREAMING_SNAKE_CASE__							) for rank in range(SCREAMING_SNAKE_CASE__							)
     ]
     assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
     assert sum(len(list(SCREAMING_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							(     ):
     def gen():
          return ({"i": i} for i in range(17							))

     __UpperCamelCase       				=2
     __UpperCamelCase       				=IterableDataset.from_generator(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=len(list(SCREAMING_SNAKE_CASE__							)							)

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

     __UpperCamelCase       				=split_dataset_by_node(full_ds.shuffle(seed=42							)     ,	rank=0     ,	world_size=SCREAMING_SNAKE_CASE__							)
     assert len(list(SCREAMING_SNAKE_CASE__							)							) == 1 + full_size // world_size
     with pytest.raises(SCREAMING_SNAKE_CASE__							):
          split_dataset_by_node(full_ds.shuffle()     ,	rank=0     ,	world_size=SCREAMING_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							(     SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=2
     __UpperCamelCase       				=get_torch_dist_unique_port()
     __UpperCamelCase       				=Path(SCREAMING_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(SCREAMING_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							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __UpperCamelCase       				=True
     __UpperCamelCase       				=get_torch_dist_unique_port()
     __UpperCamelCase       				=Path(SCREAMING_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(SCREAMING_SNAKE_CASE__     ,	env=os.environ.copy()							)









def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=int((lower_bound + upper_bound) // 2							)
     if array[r] == value:
          return r
     if lower_bound >= upper_bound:
          return -1
     if array[r] < value:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	r + 1     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     else:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	r - 1     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   list							):
     __UpperCamelCase       				=0
     if matrix[index][0] == value:
          return [index, 0]
     while index < len(SCREAMING_SNAKE_CASE__							) and matrix[index][0] < value:
          __UpperCamelCase       				=binary_search(matrix[index]     ,	0     ,	len(matrix[index]							) - 1     ,	SCREAMING_SNAKE_CASE__							)
          if r != -1:
               return [index, r]
          index += 1
     return [-1, -1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





