


'''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 		_lowercase   ():




     '''simple docstring'''





     __A  :					Optional[Any]						     =       Dataset.from_dict({"i": range(17       )}       )
     __A  :					Any						     =       len(SCREAMING_SNAKE_CASE       )
     __A  :					Tuple						     =       3
     __A  :					str						     =       [
         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 		_lowercase   ():




     '''simple docstring'''





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

     __A  :					Tuple						     =       3
     __A  :					str						     =       IterableDataset.from_generator(SCREAMING_SNAKE_CASE       )
     __A  :					Dict						     =       len(list(SCREAMING_SNAKE_CASE       )       )
     __A  :					List[str]						     =       [
         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 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     def gen(SCREAMING_SNAKE_CASE       ):
          for shard in shards:
               yield from ({"i": i, "shard": shard} for i in range(17       ))

     __A  :					Tuple						     =       3
     __A  :					Optional[Any]						     =       shards_per_node * world_size
     __A  :					List[Any]						     =       {"shards": [f"shard_{shard_idx}.txt" for shard_idx in range(SCREAMING_SNAKE_CASE       )]}
     __A  :					Tuple						     =       IterableDataset.from_generator(SCREAMING_SNAKE_CASE					,				gen_kwargs=SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       len(list(SCREAMING_SNAKE_CASE       )       )
     assert full_ds.n_shards == world_size * shards_per_node
     __A  :					Optional[Any]						     =       [
         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 		_lowercase   ():




     '''simple docstring'''





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

     __A  :					str						     =       2
     __A  :					int						     =       IterableDataset.from_generator(SCREAMING_SNAKE_CASE       )
     __A  :					Optional[int]						     =       len(list(SCREAMING_SNAKE_CASE       )       )

     __A  :					Tuple						     =       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()

     __A  :					Dict						     =       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 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       2
     __A  :					Optional[int]						     =       get_torch_dist_unique_port()
     __A  :					List[Any]						     =       Path(SCREAMING_SNAKE_CASE       ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
     __A  :					str						     =       f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
     __A  :					Tuple						     =       f"\n        --streaming={streaming}\n    ".split()
     __A  :					List[str]						     =       [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 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       True
     __A  :					List[str]						     =       get_torch_dist_unique_port()
     __A  :					Union[str, Any]						     =       Path(SCREAMING_SNAKE_CASE       ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
     __A  :					Any						     =       f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
     __A  :					int						     =       f"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
     __A  :					Dict						     =       [sys.executable] + distributed_args + args
     execute_subprocess_async(SCREAMING_SNAKE_CASE					,				env=os.environ.copy()       )





'''simple docstring'''


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase          =     {
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       {node: {node} for node in graph}

     __A  :					List[str]						     =       {node: graph[node][:] for node in graph}

     while len(SCREAMING_SNAKE_CASE       ) > 2:
          # Choose a random edge.
          __A  :					Tuple						     =       random.choice(list(graph_copy.keys()       )       )
          __A  :					Any						     =       random.choice(graph_copy[u]       )

          # Contract edge (u, v) to new node uv
          __A  :					str						     =       u + v
          __A  :					Any						     =       list(set(graph_copy[u] + graph_copy[v]       )       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          __A  :					Any						     =       uv_neighbors
          for neighbor in uv_neighbors:
               graph_copy[neighbor].append(SCREAMING_SNAKE_CASE       )

          __A  :					str						     =       set(contracted_nodes[u].union(contracted_nodes[v]       )       )

          # Remove nodes u and v.
          del graph_copy[u]
          del graph_copy[v]
          for neighbor in uv_neighbors:
               if u in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )
               if v in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )

    # Find cutset.
     __A  :					int						     =       [contracted_nodes[node] for node in graph_copy]
     return {
         (node, neighbor)
         for node in groups[0]
         for neighbor in graph[node]
         if neighbor in groups[1]
     }


if __name__ == "__main__":
      print(partition_graph(TEST_GRAPH))

