




'''simple docstring'''


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_snake_case : Any  				=						4
_snake_case : Any  				=						3

class      A       (						_a       ):
							pass



def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							for shard in shards:
														for i in range(UpperCamelCase ):
																					yield {"i": i, "shard": shard}
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							int(os.environ['''RANK'''] )
							_a      	=							int(os.environ['''WORLD_SIZE'''] )

							_a      	=							ArgumentParser()
							parser.add_argument('''--streaming'''					,		type=UpperCamelCase )
							parser.add_argument('''--local_rank'''					,		type=UpperCamelCase )
							parser.add_argument('''--num_workers'''					,		type=UpperCamelCase					,		default=0 )
							_a      	=							parser.parse_args()
							_a      	=							args.streaming
							_a      	=							args.num_workers

							_a      	=							{'''shards''': [f'shard_{shard_idx}' for shard_idx in range(UpperCamelCase )]}
							_a      	=							IterableDataset.from_generator(UpperCamelCase					,		gen_kwargs=UpperCamelCase )
							if not streaming:
														_a      	=							Dataset.from_list(list(UpperCamelCase ) )

							_a      	=							split_dataset_by_node(UpperCamelCase					,		rank=UpperCamelCase					,		world_size=UpperCamelCase )
							_a      	=							torch.utils.data.DataLoader(UpperCamelCase					,		num_workers=UpperCamelCase )

							_a      	=							NUM_SHARDS * NUM_ITEMS_PER_SHARD
							_a      	=							full_size // world_size
							expected_local_size += int(rank < (full_size % world_size) )

							_a      	=							sum(1 for _ in dataloader )
							if local_size != expected_local_size:
														raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}' )


if __name__ == "__main__":
				main()


'''simple docstring'''






import os
from pathlib import Path





def  _SCREAMING_SNAKE_CASE		(      ):
     from torch.utils.cpp_extension import load

     _A    		=  Path(__snake_case				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
     _A    		=  [
         root / filename
         for filename in [
             'vision.cpp',
             os.path.join('cpu'   ,		'ms_deform_attn_cpu.cpp'				),
             os.path.join('cuda'   ,		'ms_deform_attn_cuda.cu'				),
         ]
     ]

     load(
         'MultiScaleDeformableAttention'   ,		__snake_case   ,		with_cuda=__snake_case   ,		extra_include_paths=[str(__snake_case				)]   ,		extra_cflags=['-DWITH_CUDA=1']   ,		extra_cuda_cflags=[
             '-DCUDA_HAS_FP16=1',
             '-D__CUDA_NO_HALF_OPERATORS__',
             '-D__CUDA_NO_HALF_CONVERSIONS__',
             '-D__CUDA_NO_HALF2_OPERATORS__',
         ]   ,		)

     import MultiScaleDeformableAttention as MSDA

     return MSDA



