






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					(   )							->    List[str]:
 a							=   Dataset.from_dict({'''i''': range(17				)}				)
 a							=   len(a				)
 a							=   3
 a							=   [
     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 		_a					(   )							->    Any:
 def gen():
  return ({"i": i} for i in range(17				))

 a							=   3
 a							=   IterableDataset.from_generator(a				)
 a							=   len(list(a				)				)
 a							=   [
     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 		_a					(   a				:Optional[int]				)							->    List[str]:
 def gen(a				:Optional[int]				):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17				))

 a							=   3
 a							=   shards_per_node * world_size
 a							=   {'''shards''': [F"""shard_{shard_idx}.txt""" for shard_idx in range(a				)]}
 a							=   IterableDataset.from_generator(a			,							gen_kwargs=a				)
 a							=   len(list(a				)				)
 assert full_ds.n_shards == world_size * shards_per_node
 a							=   [
     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 		_a					(   )							->    Optional[int]:
 def gen():
  return ({"i": i} for i in range(17				))

 a							=   2
 a							=   IterableDataset.from_generator(a				)
 a							=   len(list(a				)				)

 a							=   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()

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










from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


