


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      (    ):
     a_     :  Union[str, Any]				      =     Dataset.from_dict({"""i""": range(1_7			)}			)
     a_     :  List[str]				      =     len(__UpperCamelCase			)
     a_     :  Optional[int]				      =     3
     a_     :  List[str]				      =     [
         split_dataset_by_node(__UpperCamelCase	,	rank=__UpperCamelCase	,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
     ]
     assert sum(len(__UpperCamelCase			) 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      (    ):






     def gen():
          return ({"i": i} for i in range(1_7			))

     a_     :  List[str]				      =     3
     a_     :  Optional[int]				      =     IterableDataset.from_generator(__UpperCamelCase			)
     a_     :  Dict				      =     len(list(__UpperCamelCase			)			)
     a_     :  int				      =     [
         split_dataset_by_node(__UpperCamelCase	,	rank=__UpperCamelCase	,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
     ]
     assert sum(len(list(__UpperCamelCase			)			) 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      (    __UpperCamelCase			):






     def gen(__UpperCamelCase			):
          for shard in shards:
               yield from ({"i": i, "shard": shard} for i in range(1_7			))

     a_     :  Optional[Any]				      =     3
     a_     :  Any				      =     shards_per_node * world_size
     a_     :  List[str]				      =     {"""shards""": [F'''shard_{shard_idx}.txt''' for shard_idx in range(__UpperCamelCase			)]}
     a_     :  Union[str, Any]				      =     IterableDataset.from_generator(__UpperCamelCase	,	gen_kwargs=__UpperCamelCase			)
     a_     :  Dict				      =     len(list(__UpperCamelCase			)			)
     assert full_ds.n_shards == world_size * shards_per_node
     a_     :  str				      =     [
         split_dataset_by_node(__UpperCamelCase	,	rank=__UpperCamelCase	,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
     ]
     assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
     assert sum(len(list(__UpperCamelCase			)			) 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      (    ):






     def gen():
          return ({"i": i} for i in range(1_7			))

     a_     :  Optional[int]				      =     2
     a_     :  Any				      =     IterableDataset.from_generator(__UpperCamelCase			)
     a_     :  str				      =     len(list(__UpperCamelCase			)			)

     a_     :  Union[str, Any]				      =     split_dataset_by_node(__UpperCamelCase	,	rank=0	,	world_size=__UpperCamelCase			).shuffle(seed=4_2			)
     assert len(list(__UpperCamelCase			)			) == 1 + full_size // world_size
     with pytest.raises(__UpperCamelCase			):
          split_dataset_by_node(__UpperCamelCase	,	rank=0	,	world_size=__UpperCamelCase			).shuffle()

     a_     :  Optional[Any]				      =     split_dataset_by_node(full_ds.shuffle(seed=4_2			)	,	rank=0	,	world_size=__UpperCamelCase			)
     assert len(list(__UpperCamelCase			)			) == 1 + full_size // world_size
     with pytest.raises(__UpperCamelCase			):
          split_dataset_by_node(full_ds.shuffle()	,	rank=0	,	world_size=__UpperCamelCase			)



@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      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     2
     a_     :  List[str]				      =     get_torch_dist_unique_port()
     a_     :  Optional[int]				      =     Path(__UpperCamelCase			).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
     a_     :  str				      =     F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     a_     :  Dict				      =     F'''
        --streaming={streaming}
    '''.split()
     a_     :  Tuple				      =     [sys.executable] + distributed_args + args
     execute_subprocess_async(__UpperCamelCase	,	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      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     True
     a_     :  Union[str, Any]				      =     get_torch_dist_unique_port()
     a_     :  Dict				      =     Path(__UpperCamelCase			).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
     a_     :  Dict				      =     F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     a_     :  List[Any]				      =     F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
     a_     :  Any				      =     [sys.executable] + distributed_args + args
     execute_subprocess_async(__UpperCamelCase	,	env=os.environ.copy()			)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						List[str]					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






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