



"""simple docstring"""






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__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			Dataset.from_dict({'''i''': range(17    )}    )
  _SCREAMING_SNAKE_CASE        =			len(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			3
  _SCREAMING_SNAKE_CASE        =			[
      split_dataset_by_node(UpperCamelCase__       ,							rank=UpperCamelCase__       ,							world_size=UpperCamelCase__    ) for rank in range(UpperCamelCase__    )
  ]
  assert sum(len(UpperCamelCase__    ) 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__ (     ):


  '''simple docstring'''


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

  _SCREAMING_SNAKE_CASE        =			3
  _SCREAMING_SNAKE_CASE        =			IterableDataset.from_generator(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			len(list(UpperCamelCase__    )    )
  _SCREAMING_SNAKE_CASE        =			[
      split_dataset_by_node(UpperCamelCase__       ,							rank=UpperCamelCase__       ,							world_size=UpperCamelCase__    ) for rank in range(UpperCamelCase__    )
  ]
  assert sum(len(list(UpperCamelCase__    )    ) 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__ (     UpperCamelCase__    ):


  '''simple docstring'''


  def gen(UpperCamelCase__    ):
    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(UpperCamelCase__    )]}
  _SCREAMING_SNAKE_CASE        =			IterableDataset.from_generator(UpperCamelCase__       ,							gen_kwargs=UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			len(list(UpperCamelCase__    )    )
  assert full_ds.n_shards == world_size * shards_per_node
  _SCREAMING_SNAKE_CASE        =			[
      split_dataset_by_node(UpperCamelCase__       ,							rank=UpperCamelCase__       ,							world_size=UpperCamelCase__    ) for rank in range(UpperCamelCase__    )
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(UpperCamelCase__    )    ) 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__ (     ):


  '''simple docstring'''


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

  _SCREAMING_SNAKE_CASE        =			2
  _SCREAMING_SNAKE_CASE        =			IterableDataset.from_generator(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			len(list(UpperCamelCase__    )    )

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

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


@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__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			2
  _SCREAMING_SNAKE_CASE        =			get_torch_dist_unique_port()
  _SCREAMING_SNAKE_CASE        =			Path(UpperCamelCase__    ).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(UpperCamelCase__       ,							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__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			True
  _SCREAMING_SNAKE_CASE        =			get_torch_dist_unique_port()
  _SCREAMING_SNAKE_CASE        =			Path(UpperCamelCase__    ).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(UpperCamelCase__       ,							env=os.environ.copy()    )





"""simple docstring"""






import math


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if not isinstance(UpperCamelCase__       ,							UpperCamelCase__    ):
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCamelCase__    )
  if number < 1:
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be a positive integer'''
    raise ValueError(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			[number]
  while number != 1:
    if number % 2 == 0:
      _SCREAMING_SNAKE_CASE        =			math.floor(math.sqrt(UpperCamelCase__    )    )
    else:
      _SCREAMING_SNAKE_CASE        =			math.floor(
          math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    )    )
    sequence.append(UpperCamelCase__    )
  return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()
