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 			__lowercase     (				)       ->				Tuple:
							__SCREAMING_SNAKE_CASE								=      Dataset.from_dict({'i': range(17     )}     )
							__SCREAMING_SNAKE_CASE								=      len(a__     )
							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      [
							    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 			__lowercase     (				)       ->				List[Any]:
							def gen():
														return ({"i": i} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )
							__SCREAMING_SNAKE_CASE								=      [
							    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 			__lowercase     (				a__     )       ->				Dict:
							def gen(a__     ):
														for shard in shards:
																					yield from ({"i": i, "shard": shard} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      shards_per_node * world_size
							__SCREAMING_SNAKE_CASE								=      {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(a__     )]}
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__							,     gen_kwargs=a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )
							assert full_ds.n_shards == world_size * shards_per_node
							__SCREAMING_SNAKE_CASE								=      [
							    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 			__lowercase     (				)       ->				List[Any]:
							def gen():
														return ({"i": i} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      2
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )

							__SCREAMING_SNAKE_CASE								=      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()

							__SCREAMING_SNAKE_CASE								=      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 			__lowercase     (				a__     )       ->				Tuple:
							__SCREAMING_SNAKE_CASE								=      2
							__SCREAMING_SNAKE_CASE								=      get_torch_dist_unique_port()
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
							__SCREAMING_SNAKE_CASE								=      f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
							__SCREAMING_SNAKE_CASE								=      f"""
        --streaming={streaming}
    """.split()
							__SCREAMING_SNAKE_CASE								=      [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 			__lowercase     (				a__							,     a__     )       ->				List[str]:
							__SCREAMING_SNAKE_CASE								=      True
							__SCREAMING_SNAKE_CASE								=      get_torch_dist_unique_port()
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
							__SCREAMING_SNAKE_CASE								=      f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
							__SCREAMING_SNAKE_CASE								=      f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
							__SCREAMING_SNAKE_CASE								=      [sys.executable] + distributed_args + args
							execute_subprocess_async(a__							,     env=os.environ.copy()     )











def 			__lowercase     (				a__     )       ->				list:
							__SCREAMING_SNAKE_CASE								=      1.3
							__SCREAMING_SNAKE_CASE								=      len(a__     )
							__SCREAMING_SNAKE_CASE								=      False

							while not completed:
														# Update the gap value for a next comb
														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
														if gap <= 1:
																					__SCREAMING_SNAKE_CASE								=      True

														__SCREAMING_SNAKE_CASE								=      0
														while index + gap < len(a__     ):
																					if data[index] > data[index + gap]:
																												# Swap values
																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
																												__SCREAMING_SNAKE_CASE								=      False
																					index += 1

							return data


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
			print(comb_sort(unsorted))






