code: 
  
  
  
  
  
  
  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__  (				)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=Dataset.from_dict({"""i""": range(17		)}		)
         __UpperCAmelCase    							=len(A_		)
         __UpperCAmelCase    							=3
         __UpperCAmelCase    							=[
             split_dataset_by_node(A_     ,			rank=A_     ,			world_size=A_		) for rank in range(A_		)
         ]
         assert sum(len(A_		) 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__  (				)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         def gen():
                return ({"i": i} for i in range(17		))
  
         __UpperCAmelCase    							=3
         __UpperCAmelCase    							=IterableDataset.from_generator(A_		)
         __UpperCAmelCase    							=len(list(A_		)		)
         __UpperCAmelCase    							=[
             split_dataset_by_node(A_     ,			rank=A_     ,			world_size=A_		) for rank in range(A_		)
         ]
         assert sum(len(list(A_		)		) 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__  (				A_:	Optional[Any]		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         def gen(A_:	str		):
                for shard in shards:
                       yield from ({"i": i, "shard": shard} for i in range(17		))
  
         __UpperCAmelCase    							=3
         __UpperCAmelCase    							=shards_per_node * world_size
         __UpperCAmelCase    							={"""shards""": [F'''shard_{shard_idx}.txt''' for shard_idx in range(A_		)]}
         __UpperCAmelCase    							=IterableDataset.from_generator(A_     ,			gen_kwargs=A_		)
         __UpperCAmelCase    							=len(list(A_		)		)
         assert full_ds.n_shards == world_size * shards_per_node
         __UpperCAmelCase    							=[
             split_dataset_by_node(A_     ,			rank=A_     ,			world_size=A_		) for rank in range(A_		)
         ]
         assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
         assert sum(len(list(A_		)		) 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__  (				)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         def gen():
                return ({"i": i} for i in range(17		))
  
         __UpperCAmelCase    							=2
         __UpperCAmelCase    							=IterableDataset.from_generator(A_		)
         __UpperCAmelCase    							=len(list(A_		)		)
  
         __UpperCAmelCase    							=split_dataset_by_node(A_     ,			rank=0     ,			world_size=A_		).shuffle(seed=42		)
         assert len(list(A_		)		) == 1 + full_size // world_size
         with pytest.raises(A_		):
                split_dataset_by_node(A_     ,			rank=0     ,			world_size=A_		).shuffle()
  
         __UpperCAmelCase    							=split_dataset_by_node(full_ds.shuffle(seed=42		)     ,			rank=0     ,			world_size=A_		)
         assert len(list(A_		)		) == 1 + full_size // world_size
         with pytest.raises(A_		):
                split_dataset_by_node(full_ds.shuffle()     ,			rank=0     ,			world_size=A_		)
  
  
  
  
  
  
  
  @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__  (				A_:	Dict		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=2
         __UpperCAmelCase    							=get_torch_dist_unique_port()
         __UpperCAmelCase    							=Path(A_		).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
         __UpperCAmelCase    							=F'''
          -m torch.distributed.launch
          --nproc_per_node={nproc_per_node}
          --master_port={master_port}
          {test_script}
      '''.split()
         __UpperCAmelCase    							=F'''
          --streaming={streaming}
      '''.split()
         __UpperCAmelCase    							=[sys.executable] + distributed_args + args
         execute_subprocess_async(A_     ,			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__  (				A_:	Tuple     ,			A_:	Optional[Any]		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=True
         __UpperCAmelCase    							=get_torch_dist_unique_port()
         __UpperCAmelCase    							=Path(A_		).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
         __UpperCAmelCase    							=F'''
          -m torch.distributed.launch
          --nproc_per_node={nproc_per_node}
          --master_port={master_port}
          {test_script}
      '''.split()
         __UpperCAmelCase    							=F'''
          --streaming={streaming}
          --num_workers={num_workers}
      '''.split()
         __UpperCAmelCase    							=[sys.executable] + distributed_args + args
         execute_subprocess_async(A_     ,			env=os.environ.copy()		)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections import namedtuple
  
  __A  							=  namedtuple("from_to", "from_ to")
  
  __A  							=  {
      "millimeter": "mm",
      "centimeter": "cm",
      "meter": "m",
      "kilometer": "km",
      "inch": "in",
      "inche": "in",  # Trailing 's' has been stripped off
      "feet": "ft",
      "foot": "ft",
      "yard": "yd",
      "mile": "mi",
  }
  
  __A  							=  {
      "mm": from_to(0.001, 10_00),
      "cm": from_to(0.01, 1_00),
      "m": from_to(1, 1),
      "km": from_to(10_00, 0.001),
      "in": from_to(0.0254, 39.3701),
      "ft": from_to(0.3048, 3.2_8084),
      "yd": from_to(0.9144, 1.0_9361),
      "mi": from_to(1609.34, 0.0_0062_1371),
  }
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	float     ,			A_:	str     ,			A_:	str		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=from_type.lower().rstrip("""s"""		)
         __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
         __UpperCAmelCase    							=to_type.lower().rstrip("""s"""		)
         __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
         if new_from not in METRIC_CONVERSION:
                __UpperCAmelCase    							=(
                    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
                    F'''Conversion abbreviations are: {", ".join(A_		)}'''
                )
                raise ValueError(A_		)
         if new_to not in METRIC_CONVERSION:
                __UpperCAmelCase    							=(
                    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
                    F'''Conversion abbreviations are: {", ".join(A_		)}'''
                )
                raise ValueError(A_		)
         return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  