




'''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 			_UpperCamelCase ()->   Optional[Any]:




     '''simple docstring'''





     __snake_case    					=  Dataset.from_dict({'''i''': range(17	)}	)
     __snake_case    					=  len(_lowerCamelCase	)
     __snake_case    					=  3
     __snake_case    					=  [
         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 			_UpperCamelCase ()->   Union[str, Any]:




     '''simple docstring'''





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

     __snake_case    					=  3
     __snake_case    					=  IterableDataset.from_generator(_lowerCamelCase	)
     __snake_case    					=  len(list(_lowerCamelCase	)	)
     __snake_case    					=  [
         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 			_UpperCamelCase (_lowerCamelCase							:     Dict	)->   Any:




     '''simple docstring'''





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

     __snake_case    					=  3
     __snake_case    					=  shards_per_node * world_size
     __snake_case    					=  {'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(_lowerCamelCase	)]}
     __snake_case    					=  IterableDataset.from_generator(_lowerCamelCase   ,	gen_kwargs=_lowerCamelCase	)
     __snake_case    					=  len(list(_lowerCamelCase	)	)
     assert full_ds.n_shards == world_size * shards_per_node
     __snake_case    					=  [
         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 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





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

     __snake_case    					=  2
     __snake_case    					=  IterableDataset.from_generator(_lowerCamelCase	)
     __snake_case    					=  len(list(_lowerCamelCase	)	)

     __snake_case    					=  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()

     __snake_case    					=  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 			_UpperCamelCase (_lowerCamelCase							:     List[Any]	)->   Union[str, Any]:




     '''simple docstring'''





     __snake_case    					=  2
     __snake_case    					=  get_torch_dist_unique_port()
     __snake_case    					=  Path(_lowerCamelCase	).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     __snake_case    					=  f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     __snake_case    					=  f'''
        --streaming={streaming}
    '''.split()
     __snake_case    					=  [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 			_UpperCamelCase (_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Optional[int]	)->   Dict:




     '''simple docstring'''





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












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


UpperCAmelCase_       :  int      =		logging.get_logger(__name__)





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  None:



            '''simple docstring'''




            warnings.warn(
                '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MaskFormerImageProcessor instead.'''    ,  __SCREAMING_SNAKE_CASE    ,  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






