





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class        __SCREAMING_SNAKE_CASE      :







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
          return self.get_dummy_input()







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->str:
          if self.block_type == "down":
             return (4, 3_2, 1_6, 1_6)
          elif self.block_type == "mid":
             return (4, 3_2, 3_2, 3_2)
          elif self.block_type == "up":
             return (4, 3_2, 6_4, 6_4)

          raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Tuple=True		, A					:     Optional[int]=False		, A					:     int=False		, A					:     Optional[Any]=False		, )       ->Any:
          lowerCamelCase__       :						Dict        =							4
          lowerCamelCase__       :						str        =							3_2
          lowerCamelCase__       :						Optional[int]        =							(3_2, 3_2)

          lowerCamelCase__       :						Optional[int]        =							torch.manual_seed(0      )
          lowerCamelCase__       :						List[str]        =							torch.device(A      )
          lowerCamelCase__       :						int        =							(batch_size, num_channels) + sizes
          lowerCamelCase__       :						Dict        =							randn_tensor(A		, generator=A		, device=A      )
          lowerCamelCase__       :						str        =							{'''hidden_states''': hidden_states}

          if include_temb:
             lowerCamelCase__       :						Dict        =							1_2_8
             lowerCamelCase__       :						Union[str, Any]        =							randn_tensor((batch_size, temb_channels)		, generator=A		, device=A      )

          if include_res_hidden_states_tuple:
             lowerCamelCase__       :						str        =							torch.manual_seed(1      )
             lowerCamelCase__       :						List[Any]        =							(randn_tensor(A		, generator=A		, device=A      ),)

          if include_encoder_hidden_states:
             lowerCamelCase__       :						str        =							floats_tensor((batch_size, 3_2, 3_2)      ).to(A      )

          if include_skip_sample:
             lowerCamelCase__       :						Optional[Any]        =							randn_tensor(((batch_size, 3) + sizes)		, generator=A		, device=A      )

          return dummy_input







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->str:
          lowerCamelCase__       :						Union[str, Any]        =							{
              '''in_channels''': 3_2,
              '''out_channels''': 3_2,
              '''temb_channels''': 1_2_8,
          }
          if self.block_type == "up":
             lowerCamelCase__       :						Any        =							3_2

          if self.block_type == "mid":
             init_dict.pop('''out_channels'''      )

          lowerCamelCase__       :						Dict        =							self.dummy_input
          return init_dict, inputs_dict







       def 						__lowerCamelCase   (				self					:     Any		, A					:     List[str]      )       ->List[Any]:
          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__       :						Tuple        =							self.block_class(**A      )
          unet_block.to(A      )
          unet_block.eval()

          with torch.no_grad():
             lowerCamelCase__       :						Optional[int]        =							unet_block(**A      )

          if isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							output[0]

          self.assertEqual(output.shape		, self.output_shape      )

          lowerCamelCase__       :						List[Any]        =							output[0, -1, -3:, -3:]
          lowerCamelCase__       :						List[str]        =							torch.tensor(A      ).to(A      )
          assert torch_all_close(output_slice.flatten()		, A		, atol=5e-3      )







       @unittest.skipIf(torch_device == '''mps'''		, '''Training is not supported in mps'''      )
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__       :						List[Any]        =							self.block_class(**A      )
          model.to(A      )
          model.train()
          lowerCamelCase__       :						str        =							model(**A      )

          if isinstance(A		, A      ):
             lowerCamelCase__       :						Any        =							output[0]

          lowerCamelCase__       :						Any        =							torch.device(A      )
          lowerCamelCase__       :						Tuple        =							randn_tensor(output.shape		, device=A      )
          lowerCamelCase__       :						Any        =							torch.nn.functional.mse_loss(A		, A      )
          loss.backward()










import os
from pathlib import Path



def 	_a						(   )    ->    Tuple:







   """simple docstring"""
   from torch.utils.cpp_extension import load

   lowerCamelCase__       :						List[Any]        =							Path(UpperCAmelCase					).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
   lowerCamelCase__       :						Any        =							[
       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'''      ,  UpperCAmelCase      ,  with_cuda=UpperCAmelCase      ,  extra_include_paths=[str(UpperCAmelCase					)]      ,  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



