





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  str			   =   (PNDMScheduler,)
       _UpperCAmelCase				:  Dict			   =   (("num_inference_steps", 5_0),)







       def 						__lowerCamelCase   (				self					:     Any		, **A					:     Any      )       ->Tuple:
          lowerCamelCase__       :						Optional[int]        =							{
              '''num_train_timesteps''': 1_0_0_0,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**A      )
          return config







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Tuple=0		, **A					:     str      )       ->List[str]:
          lowerCamelCase__       :						int        =							dict(self.forward_default_kwargs      )
          lowerCamelCase__       :						Dict        =							kwargs.pop('''num_inference_steps'''		, A      )
          lowerCamelCase__       :						int        =							self.dummy_sample
          lowerCamelCase__       :						Union[str, Any]        =							0.1 * sample
          lowerCamelCase__       :						str        =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Any        =							self.get_scheduler_config(**A      )
             lowerCamelCase__       :						Optional[int]        =							scheduler_class(**A      )
             scheduler.set_timesteps(A      )
             # copy over dummy past residuals
             lowerCamelCase__       :						Tuple        =							dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A      )
                lowerCamelCase__       :						List[str]        =							scheduler_class.from_pretrained(A      )
                new_scheduler.set_timesteps(A      )
                # copy over dummy past residuals
                lowerCamelCase__       :						int        =							dummy_past_residuals[:]

             lowerCamelCase__       :						Union[str, Any]        =							scheduler.step_prk(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						List[str]        =							new_scheduler.step_prk(A		, A		, A		, **A      ).prev_sample

             assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

             lowerCamelCase__       :						Optional[Any]        =							scheduler.step_plms(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						Union[str, Any]        =							new_scheduler.step_plms(A		, A		, A		, **A      ).prev_sample

             assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Union[str, Any]:
          pass







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Optional[int]=0		, **A					:     Any      )       ->Tuple:
          lowerCamelCase__       :						Union[str, Any]        =							dict(self.forward_default_kwargs      )
          lowerCamelCase__       :						List[str]        =							kwargs.pop('''num_inference_steps'''		, A      )
          lowerCamelCase__       :						List[str]        =							self.dummy_sample
          lowerCamelCase__       :						Tuple        =							0.1 * sample
          lowerCamelCase__       :						str        =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Optional[int]        =							self.get_scheduler_config()
             lowerCamelCase__       :						Union[str, Any]        =							scheduler_class(**A      )
             scheduler.set_timesteps(A      )

             # copy over dummy past residuals (must be after setting timesteps)
             lowerCamelCase__       :						Dict        =							dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A      )
                lowerCamelCase__       :						Dict        =							scheduler_class.from_pretrained(A      )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(A      )

                # copy over dummy past residual (must be after setting timesteps)
                lowerCamelCase__       :						Tuple        =							dummy_past_residuals[:]

             lowerCamelCase__       :						Tuple        =							scheduler.step_prk(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						List[Any]        =							new_scheduler.step_prk(A		, A		, A		, **A      ).prev_sample

             assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

             lowerCamelCase__       :						Any        =							scheduler.step_plms(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						Optional[Any]        =							new_scheduler.step_plms(A		, A		, A		, **A      ).prev_sample

             assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"







       def 						__lowerCamelCase   (				self					:     str		, **A					:     str      )       ->Any:
          lowerCamelCase__       :						Dict        =							self.scheduler_classes[0]
          lowerCamelCase__       :						List[Any]        =							self.get_scheduler_config(**A      )
          lowerCamelCase__       :						List[Any]        =							scheduler_class(**A      )

          lowerCamelCase__       :						Tuple        =							1_0
          lowerCamelCase__       :						Dict        =							self.dummy_model()
          lowerCamelCase__       :						Dict        =							self.dummy_sample_deter
          scheduler.set_timesteps(A      )

          for i, t in enumerate(scheduler.prk_timesteps      ):
             lowerCamelCase__       :						Optional[int]        =							model(A		, A      )
             lowerCamelCase__       :						List[str]        =							scheduler.step_prk(A		, A		, A      ).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps      ):
             lowerCamelCase__       :						Any        =							model(A		, A      )
             lowerCamelCase__       :						Tuple        =							scheduler.step_plms(A		, A		, A      ).prev_sample

          return sample







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->int:
          lowerCamelCase__       :						int        =							dict(self.forward_default_kwargs      )

          lowerCamelCase__       :						List[str]        =							kwargs.pop('''num_inference_steps'''		, A      )

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						str        =							self.get_scheduler_config()
             lowerCamelCase__       :						Any        =							scheduler_class(**A      )

             lowerCamelCase__       :						int        =							self.dummy_sample
             lowerCamelCase__       :						Optional[int]        =							0.1 * sample

             if num_inference_steps is not None and hasattr(A		, '''set_timesteps'''      ):
                scheduler.set_timesteps(A      )
             elif num_inference_steps is not None and not hasattr(A		, '''set_timesteps'''      ):
                lowerCamelCase__       :						Tuple        =							num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             lowerCamelCase__       :						Tuple        =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             lowerCamelCase__       :						List[Any]        =							dummy_past_residuals[:]

             lowerCamelCase__       :						Optional[int]        =							scheduler.step_prk(A		, 0		, A		, **A      ).prev_sample
             lowerCamelCase__       :						int        =							scheduler.step_prk(A		, 1		, A		, **A      ).prev_sample

             self.assertEqual(output_a.shape		, sample.shape      )
             self.assertEqual(output_a.shape		, output_a.shape      )

             lowerCamelCase__       :						Tuple        =							scheduler.step_plms(A		, 0		, A		, **A      ).prev_sample
             lowerCamelCase__       :						Union[str, Any]        =							scheduler.step_plms(A		, 1		, A		, **A      ).prev_sample

             self.assertEqual(output_a.shape		, sample.shape      )
             self.assertEqual(output_a.shape		, output_a.shape      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          for timesteps in [1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=A      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A      )

          lowerCamelCase__       :						str        =							self.scheduler_classes[0]
          lowerCamelCase__       :						List[Any]        =							self.get_scheduler_config(steps_offset=1      )
          lowerCamelCase__       :						str        =							scheduler_class(**A      )
          scheduler.set_timesteps(1_0      )
          assert torch.equal(
              scheduler.timesteps		, torch.LongTensor(
                  [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]      )		, )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          for beta_start, beta_end in zip([0.00_01, 0.0_01]		, [0.0_02, 0.02]      ):
             self.check_over_configs(beta_start=A		, beta_end=A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          for t in [1, 5, 1_0]:
             self.check_over_forward(time_step=A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->str:
          for t, num_inference_steps in zip([1, 5, 1_0]		, [1_0, 5_0, 1_0_0]      ):
             self.check_over_forward(num_inference_steps=A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->List[str]:
          # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
          lowerCamelCase__       :						Tuple        =							2_7

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Union[str, Any]        =							self.dummy_sample
             lowerCamelCase__       :						Union[str, Any]        =							0.1 * sample

             lowerCamelCase__       :						Optional[int]        =							self.get_scheduler_config()
             lowerCamelCase__       :						Optional[int]        =							scheduler_class(**A      )

             scheduler.set_timesteps(A      )

             # before power of 3 fix, would error on first step, so we only need to do two
             for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
                lowerCamelCase__       :						Tuple        =							scheduler.step_prk(A		, A		, A      ).prev_sample







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          with self.assertRaises(A      ):
             lowerCamelCase__       :						Dict        =							self.scheduler_classes[0]
             lowerCamelCase__       :						Optional[Any]        =							self.get_scheduler_config()
             lowerCamelCase__       :						Optional[Any]        =							scheduler_class(**A      )

             scheduler.step_plms(self.dummy_sample		, 1		, self.dummy_sample      ).prev_sample







       def 						__lowerCamelCase   (				self					:     int      )       ->int:
          lowerCamelCase__       :						Union[str, Any]        =							self.full_loop()
          lowerCamelCase__       :						Optional[int]        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						Optional[int]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 1_98.13_18      ) < 1e-2
          assert abs(result_mean.item() - 0.25_80      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							self.full_loop(prediction_type='''v_prediction'''      )
          lowerCamelCase__       :						Tuple        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						Union[str, Any]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 67.39_86      ) < 1e-2
          assert abs(result_mean.item() - 0.08_78      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Union[str, Any]:
          # We specify different beta, so that the first alpha is 0.99
          lowerCamelCase__       :						Any        =							self.full_loop(set_alpha_to_one=A		, beta_start=0.01      )
          lowerCamelCase__       :						Optional[int]        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						List[Any]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 2_30.03_99      ) < 1e-2
          assert abs(result_mean.item() - 0.29_95      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     str      )       ->str:
          # We specify different beta, so that the first alpha is 0.99
          lowerCamelCase__       :						str        =							self.full_loop(set_alpha_to_one=A		, beta_start=0.01      )
          lowerCamelCase__       :						Union[str, Any]        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						str        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 1_86.94_82      ) < 1e-2
          assert abs(result_mean.item() - 0.24_34      ) < 1e-3










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



