


"""simple docstring"""


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__       ( )		->		Optional[Any]:
				_A         =	Dataset.from_dict({"i": range(17      )}      )
				_A         =	len(__lowercase      )
				_A         =	3
				_A         =	[
				    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    a__       ( )		->		str:
				def gen():
								return ({"i": i} for i in range(17      ))

				_A         =	3
				_A         =	IterableDataset.from_generator(__lowercase      )
				_A         =	len(list(__lowercase      )      )
				_A         =	[
				    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    a__       ( __lowercase      )		->		Dict:
				def gen(__lowercase      ):
								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(__lowercase      )]}
				_A         =	IterableDataset.from_generator(__lowercase   ,	gen_kwargs=__lowercase      )
				_A         =	len(list(__lowercase      )      )
				assert full_ds.n_shards == world_size * shards_per_node
				_A         =	[
				    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    a__       ( )		->		str:
				def gen():
								return ({"i": i} for i in range(17      ))

				_A         =	2
				_A         =	IterableDataset.from_generator(__lowercase      )
				_A         =	len(list(__lowercase      )      )

				_A         =	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()

				_A         =	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    a__       ( __lowercase      )		->		Optional[int]:
				_A         =	2
				_A         =	get_torch_dist_unique_port()
				_A         =	Path(__lowercase      ).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(__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    a__       ( __lowercase   ,	__lowercase      )		->		Any:
				_A         =	True
				_A         =	get_torch_dist_unique_port()
				_A         =	Path(__lowercase      ).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(__lowercase   ,	env=os.environ.copy()      )



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


a_											=			logging.get_logger(__name__)
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      *a__					:				Union[str, Any]			,      **a__					:				Optional[Any]						)				->  None:





						'''simple docstring'''







						warnings.warn(
						    "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use MobileNetV2ImageProcessor instead."			,      a__			,      )
						super().__init__(*a__			,      **a__						)