





"""simple docstring"""




import os
from pathlib import Path




def       __UpperCamelCase  ()    ->       Dict:
  from torch.utils.cpp_extension import load

  lowercase__           =  Path(_a		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
  lowercase__           =  [
      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'		,						_a		,						with_cuda=_a		,						extra_include_paths=[str(_a		)]		,						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









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    List[str]      ,					a      :    Callable      ,					a      :    Optional[Features] = None      ,					a      :    str = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    Optional[dict] = None      ,					a      :    Optional[int] = None      ,					**a      :    str      ,					)->       Tuple:





     """simple docstring"""







     super().__init__(
         features=a      ,					cache_dir=a      ,					keep_in_memory=a      ,					streaming=a      ,					num_proc=a      ,					**a      ,					)
     lowercase__           =  Generator(
         cache_dir=a      ,					features=a      ,					generator=a      ,					gen_kwargs=a      ,					**a      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """simple docstring"""







     if self.streaming:
       lowercase__           =  self.builder.as_streaming_dataset(split='train'	)
     # Build regular (map-style) dataset
     else:
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None

       self.builder.download_and_prepare(
           download_config=a      ,					download_mode=a      ,					verification_mode=a      ,					base_path=a      ,					num_proc=self.num_proc      ,					)
       lowercase__           =  self.builder.as_dataset(
           split='train'      ,					verification_mode=a      ,					in_memory=self.keep_in_memory	)
     return dataset


