
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					(      )	->     int:
			UpperCamelCase__    :		List[Any]     =				Dataset.from_dict({'i': range(17 )} )
			UpperCamelCase__    :		Optional[int]     =				len(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Dict     =				3
			UpperCamelCase__    :		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       _lowercase					(      )	->     List[Any]:
			def gen():
						return ({"i": i} for i in range(17 ))

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

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

			UpperCamelCase__    :		Any     =				2
			UpperCamelCase__    :		Union[str, Any]     =				IterableDataset.from_generator(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[Any]     =				len(list(__SCREAMING_SNAKE_CASE ) )

			UpperCamelCase__    :		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()

			UpperCamelCase__    :		int     =				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 )	->     Optional[int]:
			UpperCamelCase__    :		int     =				2
			UpperCamelCase__    :		List[Any]     =				get_torch_dist_unique_port()
			UpperCamelCase__    :		List[str]     =				Path(__SCREAMING_SNAKE_CASE ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCamelCase__    :		Union[str, Any]     =				F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCamelCase__    :		Tuple     =				F"""
        --streaming={streaming}
    """.split()
			UpperCamelCase__    :		Dict     =				[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 )	->     Optional[Any]:
			UpperCamelCase__    :		Optional[int]     =				True
			UpperCamelCase__    :		str     =				get_torch_dist_unique_port()
			UpperCamelCase__    :		List[str]     =				Path(__SCREAMING_SNAKE_CASE ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCamelCase__    :		Tuple     =				F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCamelCase__    :		List[Any]     =				F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCamelCase__    :		List[Any]     =				[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,
)


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


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__				:  List[Any]       					=				[
							    '''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

							UpperCAmelCase__				:  List[str]       					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



