




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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Dataset.from_dict({'''i''': range(17   )}   )
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    3
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(lowercase_   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Tuple:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    shards_per_node * world_size
    __UpperCamelCase			=    {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(lowercase_   )]}
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_       ,  gen_kwargs=lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    assert full_ds.n_shards == world_size * shards_per_node
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


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

    __UpperCamelCase			=    2
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )

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

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




@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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    2
    __UpperCamelCase			=    get_torch_dist_unique_port()
    __UpperCamelCase			=    Path(lowercase_   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    __UpperCamelCase			=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
    __UpperCamelCase			=    F"\n        --streaming={streaming}\n    ".split()
    __UpperCamelCase			=    [sys.executable] + distributed_args + args
    execute_subprocess_async(lowercase_       ,  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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    True
    __UpperCamelCase			=    get_torch_dist_unique_port()
    __UpperCamelCase			=    Path(lowercase_   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    __UpperCamelCase			=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
    __UpperCamelCase			=    F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
    __UpperCamelCase			=    [sys.executable] + distributed_args + args
    execute_subprocess_async(lowercase_       ,  env=os.environ.copy()   )







"""simple docstring"""

from typing import TYPE_CHECKING

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


__lowercase    =				{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				[
				    '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VideoMAEForPreTraining''',
				    '''VideoMAEModel''',
				    '''VideoMAEPreTrainedModel''',
				    '''VideoMAEForVideoClassification''',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''VideoMAEFeatureExtractor''']
				__lowercase    =				['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
				from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_videomae import (
								    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VideoMAEForPreTraining,
								    VideoMAEForVideoClassification,
								    VideoMAEModel,
								    VideoMAEPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_videomae import VideoMAEFeatureExtractor
								from .image_processing_videomae import VideoMAEImageProcessor

else:
				import sys

				__lowercase    =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



