


'''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]:




     '''simple docstring'''



     _a         =				Dataset.from_dict({'i': range(17		)}		)
     _a         =				len(lowerCAmelCase__		)
     _a         =				3
     _a         =				[
         split_dataset_by_node(lowerCAmelCase__					,    rank=lowerCAmelCase__					,    world_size=lowerCAmelCase__		) for rank in range(lowerCAmelCase__		)
     ]
     assert sum(len(lowerCAmelCase__		) 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					() ->	List[Any]:




     '''simple docstring'''



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

     _a         =				3
     _a         =				IterableDataset.from_generator(lowerCAmelCase__		)
     _a         =				len(list(lowerCAmelCase__		)		)
     _a         =				[
         split_dataset_by_node(lowerCAmelCase__					,    rank=lowerCAmelCase__					,    world_size=lowerCAmelCase__		) for rank in range(lowerCAmelCase__		)
     ]
     assert sum(len(list(lowerCAmelCase__		)		) 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					(lowerCAmelCase__	:Tuple		) ->	Union[str, Any]:




     '''simple docstring'''



     def gen(lowerCAmelCase__	:Optional[int]		):
          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(lowerCAmelCase__		)]}
     _a         =				IterableDataset.from_generator(lowerCAmelCase__					,    gen_kwargs=lowerCAmelCase__		)
     _a         =				len(list(lowerCAmelCase__		)		)
     assert full_ds.n_shards == world_size * shards_per_node
     _a         =				[
         split_dataset_by_node(lowerCAmelCase__					,    rank=lowerCAmelCase__					,    world_size=lowerCAmelCase__		) for rank in range(lowerCAmelCase__		)
     ]
     assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
     assert sum(len(list(lowerCAmelCase__		)		) 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					() ->	Dict:




     '''simple docstring'''



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

     _a         =				2
     _a         =				IterableDataset.from_generator(lowerCAmelCase__		)
     _a         =				len(list(lowerCAmelCase__		)		)

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

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






@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					(lowerCAmelCase__	:List[Any]		) ->	List[str]:




     '''simple docstring'''



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




     '''simple docstring'''



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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

