




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				(				)     ->			Dict:
       lowerCamelCase       :				Any      			= Dataset.from_dict({"i": range(17   )}   )
       lowerCamelCase       :				List[Any]      			= len(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= 3
       lowerCamelCase       :				List[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 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 					A				(				)     ->			Optional[Any]:
       def gen():
              return ({"i": i} for i in range(17   ))

       lowerCamelCase       :				Any      			= 3
       lowerCamelCase       :				Optional[int]      			= IterableDataset.from_generator(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[int]      			= len(list(_SCREAMING_SNAKE_CASE   )   )
       lowerCamelCase       :				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 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Optional[Any]:
       def gen(_SCREAMING_SNAKE_CASE   ):
              for shard in shards:
                     yield from ({"i": i, "shard": shard} for i in range(17   ))

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

       lowerCamelCase       :				Union[str, Any]      			= 2
       lowerCamelCase       :				Optional[int]      			= IterableDataset.from_generator(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= len(list(_SCREAMING_SNAKE_CASE   )   )

       lowerCamelCase       :				Optional[int]      			= 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()

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__	:				Dict  =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       ['OneFormerImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OneFormerForUniversalSegmentation',
           'OneFormerModel',
           'OneFormerPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
       from .processing_oneformer import OneFormerProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_oneformer import OneFormerImageProcessor
       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_oneformer import (
                  ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OneFormerForUniversalSegmentation,
                  OneFormerModel,
                  OneFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				str  =       _LazyModule(__name__, globals()['__file__'], _import_structure)






