


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 				UpperCamelCase      (			)     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	Dataset.from_dict({"""i""": range(17  )}  )
		lowercase__      =	len(__magic_name__  )
		lowercase__      =	3
		lowercase__      =	[
		    split_dataset_by_node(__magic_name__					,					rank=__magic_name__					,					world_size=__magic_name__  ) for rank in range(__magic_name__  )
		]
		assert sum(len(__magic_name__  ) 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 				UpperCamelCase      (			)     ->						Dict:
		"""simple docstring"""




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

		lowercase__      =	3
		lowercase__      =	IterableDataset.from_generator(__magic_name__  )
		lowercase__      =	len(list(__magic_name__  )  )
		lowercase__      =	[
		    split_dataset_by_node(__magic_name__					,					rank=__magic_name__					,					world_size=__magic_name__  ) for rank in range(__magic_name__  )
		]
		assert sum(len(list(__magic_name__  )  ) 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 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						str:
		"""simple docstring"""




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

		lowercase__      =	3
		lowercase__      =	shards_per_node * world_size
		lowercase__      =	{"""shards""": [f'''shard_{shard_idx}.txt''' for shard_idx in range(__magic_name__  )]}
		lowercase__      =	IterableDataset.from_generator(__magic_name__					,					gen_kwargs=__magic_name__  )
		lowercase__      =	len(list(__magic_name__  )  )
		assert full_ds.n_shards == world_size * shards_per_node
		lowercase__      =	[
		    split_dataset_by_node(__magic_name__					,					rank=__magic_name__					,					world_size=__magic_name__  ) for rank in range(__magic_name__  )
		]
		assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
		assert sum(len(list(__magic_name__  )  ) 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 				UpperCamelCase      (			)     ->						Tuple:
		"""simple docstring"""




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

		lowercase__      =	2
		lowercase__      =	IterableDataset.from_generator(__magic_name__  )
		lowercase__      =	len(list(__magic_name__  )  )

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

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






@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 				UpperCamelCase      (			__magic_name__  :							str  )     ->						str:
		"""simple docstring"""




		lowercase__      =	2
		lowercase__      =	get_torch_dist_unique_port()
		lowercase__      =	Path(__magic_name__  ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
		lowercase__      =	f'''\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '''.split()
		lowercase__      =	f'''\n        --streaming={streaming}\n    '''.split()
		lowercase__      =	[sys.executable] + distributed_args + args
		execute_subprocess_async(__magic_name__					,					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 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							int  )     ->						str:
		"""simple docstring"""




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





import warnings

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


A :	Optional[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






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




							"""simple docstring"""


							warnings.warn(
							    """The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV1ImageProcessor instead."""   ,      _UpperCAmelCase   ,      )
							super().__init__(*_UpperCAmelCase   ,      **_UpperCAmelCase	)

