


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       (     )  ->   int:





     '''simple docstring'''





     UpperCamelCase__							:  List[Any]								=     Dataset.from_dict({'''i''': range(17			)}			)
     UpperCamelCase__							:  List[str]								=     len(__lowerCamelCase			)
     UpperCamelCase__							:  int								=     3
     UpperCamelCase__							:  int								=     [
         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 						_lowercase       (     )  ->   Tuple:





     '''simple docstring'''





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

     UpperCamelCase__							:  Any								=     3
     UpperCamelCase__							:  Optional[int]								=     IterableDataset.from_generator(__lowerCamelCase			)
     UpperCamelCase__							:  str								=     len(list(__lowerCamelCase			)			)
     UpperCamelCase__							:  str								=     [
         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 						_lowercase       (     __lowerCamelCase			:					int			)  ->   str:





     '''simple docstring'''





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

     UpperCamelCase__							:  Dict								=     3
     UpperCamelCase__							:  Dict								=     shards_per_node * world_size
     UpperCamelCase__							:  str								=     {'''shards''': [F'shard_{shard_idx}.txt' for shard_idx in range(__lowerCamelCase			)]}
     UpperCamelCase__							:  str								=     IterableDataset.from_generator(__lowerCamelCase		,gen_kwargs=__lowerCamelCase			)
     UpperCamelCase__							:  List[Any]								=     len(list(__lowerCamelCase			)			)
     assert full_ds.n_shards == world_size * shards_per_node
     UpperCamelCase__							:  List[Any]								=     [
         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 						_lowercase       (     )  ->   Union[str, Any]:





     '''simple docstring'''





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

     UpperCamelCase__							:  Tuple								=     2
     UpperCamelCase__							:  Optional[int]								=     IterableDataset.from_generator(__lowerCamelCase			)
     UpperCamelCase__							:  Optional[int]								=     len(list(__lowerCamelCase			)			)

     UpperCamelCase__							:  Optional[Any]								=     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()

     UpperCamelCase__							:  Union[str, Any]								=     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 						_lowercase       (     __lowerCamelCase			:					List[Any]			)  ->   List[str]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     2
     UpperCamelCase__							:  List[Any]								=     get_torch_dist_unique_port()
     UpperCamelCase__							:  List[Any]								=     Path(__lowerCamelCase			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     UpperCamelCase__							:  str								=     F'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
     UpperCamelCase__							:  List[str]								=     F'\n        --streaming={streaming}\n    '.split()
     UpperCamelCase__							:  Optional[int]								=     [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 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Union[str, Any]			)  ->   List[str]:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     True
     UpperCamelCase__							:  Dict								=     get_torch_dist_unique_port()
     UpperCamelCase__							:  List[Any]								=     Path(__lowerCamelCase			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     UpperCamelCase__							:  str								=     F'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
     UpperCamelCase__							:  List[str]								=     F'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
     UpperCamelCase__							:  List[str]								=     [sys.executable] + distributed_args + args
     execute_subprocess_async(__lowerCamelCase		,env=os.environ.copy()			)








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




