




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()   )







from typing import TYPE_CHECKING

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


__snake_case         =     {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileViTV2ForImageClassification''',
         '''MobileViTV2ForSemanticSegmentation''',
         '''MobileViTV2Model''',
         '''MobileViTV2PreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_mobilevitva import (
         MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileViTVaConfig,
         MobileViTVaOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevitva import (
              MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTVaForImageClassification,
              MobileViTVaForSemanticSegmentation,
              MobileViTVaModel,
              MobileViTVaPreTrainedModel,
          )

else:
     import sys

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