code: 
  
  
  
  
  
  
  '''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    _lowerCamelCase	(	)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[int]										= Dataset.from_dict({"""i""": range(17					)}					)
       UpperCAmelCase__			:     List[Any]										= len(__snake_case					)
       UpperCAmelCase__			:     List[Any]										= 3
       UpperCAmelCase__			:     Dict										= [
           split_dataset_by_node(__snake_case   ,       rank=__snake_case   ,       world_size=__snake_case					) for rank in range(__snake_case					)
       ]
       assert sum(len(__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    _lowerCamelCase	(	)   -> Dict:
  
       '''simple docstring'''
       def gen():
            return ({"i": i} for i in range(17					))
  
       UpperCAmelCase__			:     Optional[int]										= 3
       UpperCAmelCase__			:     Any										= IterableDataset.from_generator(__snake_case					)
       UpperCAmelCase__			:     Optional[Any]										= len(list(__snake_case					)					)
       UpperCAmelCase__			:     str										= [
           split_dataset_by_node(__snake_case   ,       rank=__snake_case   ,       world_size=__snake_case					) for rank in range(__snake_case					)
       ]
       assert sum(len(list(__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    _lowerCamelCase	(	__lowerCamelCase					)   -> List[Any]:
  
       '''simple docstring'''
       def gen(__lowerCamelCase					):
            for shard in shards:
                 yield from ({"i": i, "shard": shard} for i in range(17					))
  
       UpperCAmelCase__			:     str										= 3
       UpperCAmelCase__			:     str										= shards_per_node * world_size
       UpperCAmelCase__			:     Dict										= {"""shards""": [F"shard_{shard_idx}.txt" for shard_idx in range(__snake_case					)]}
       UpperCAmelCase__			:     List[Any]										= IterableDataset.from_generator(__snake_case   ,       gen_kwargs=__snake_case					)
       UpperCAmelCase__			:     Tuple										= len(list(__snake_case					)					)
       assert full_ds.n_shards == world_size * shards_per_node
       UpperCAmelCase__			:     Dict										= [
           split_dataset_by_node(__snake_case   ,       rank=__snake_case   ,       world_size=__snake_case					) for rank in range(__snake_case					)
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(__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    _lowerCamelCase	(	)   -> int:
  
       '''simple docstring'''
       def gen():
            return ({"i": i} for i in range(17					))
  
       UpperCAmelCase__			:     List[str]										= 2
       UpperCAmelCase__			:     Optional[int]										= IterableDataset.from_generator(__snake_case					)
       UpperCAmelCase__			:     str										= len(list(__snake_case					)					)
  
       UpperCAmelCase__			:     Optional[Any]										= split_dataset_by_node(__snake_case   ,       rank=0   ,       world_size=__snake_case					).shuffle(seed=42					)
       assert len(list(__snake_case					)					) == 1 + full_size // world_size
       with pytest.raises(__snake_case					):
            split_dataset_by_node(__snake_case   ,       rank=0   ,       world_size=__snake_case					).shuffle()
  
       UpperCAmelCase__			:     int										= split_dataset_by_node(full_ds.shuffle(seed=42					)   ,       rank=0   ,       world_size=__snake_case					)
       assert len(list(__snake_case					)					) == 1 + full_size // world_size
       with pytest.raises(__snake_case					):
            split_dataset_by_node(full_ds.shuffle()   ,       rank=0   ,       world_size=__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    _lowerCamelCase	(	__lowerCamelCase					)   -> str:
  
       '''simple docstring'''
       UpperCAmelCase__			:     int										= 2
       UpperCAmelCase__			:     List[str]										= get_torch_dist_unique_port()
       UpperCAmelCase__			:     List[str]										= Path(__snake_case					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
       UpperCAmelCase__			:     Any										= F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
       UpperCAmelCase__			:     str										= F"\n        --streaming={streaming}\n    ".split()
       UpperCAmelCase__			:     Any										= [sys.executable] + distributed_args + args
       execute_subprocess_async(__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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Any:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Any										= True
       UpperCAmelCase__			:     str										= get_torch_dist_unique_port()
       UpperCAmelCase__			:     Optional[int]										= Path(__snake_case					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
       UpperCAmelCase__			:     Optional[Any]										= F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
       UpperCAmelCase__			:     Optional[int]										= F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
       UpperCAmelCase__			:     Optional[int]										= [sys.executable] + distributed_args + args
       execute_subprocess_async(__snake_case   ,       env=os.environ.copy()					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            warnings.warn(
                """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
            super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  
  
  