





"""simple docstring"""



import os
from pathlib import Path







def 		__lowercase					(       )       ->Optional[int]:



    '''simple docstring'''







    from torch.utils.cpp_extension import load

    __A   :						Tuple									=					Path(snake_case_						).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
    __A   :						List[str]									=					[
        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











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




