






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							__UpperCamelCase				):
	lowercase__						:					Tuple											=   (DDPMParallelScheduler,)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				int:
							__magic_name__      :       str       						= {
							    'num_train_timesteps': 10_00,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**lowerCAmelCase__			)
							return config
	def       __magic_name__    (    self			)							->				Dict:
							for timesteps in [1, 5, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]			):
													self.check_over_configs(beta_start=lowerCAmelCase__      ,       beta_end=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Dict:
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				str:
							self.check_over_configs(thresholding=lowerCAmelCase__			)
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=lowerCAmelCase__      ,       prediction_type=lowerCAmelCase__      ,       sample_max_value=lowerCAmelCase__      ,       )
	def       __magic_name__    (    self			)							->				Optional[int]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							for t in [0, 5_00, 9_99]:
													self.check_over_forward(time_step=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       List[str]       						= self.scheduler_classes[0]
							__magic_name__      :       Any       						= self.get_scheduler_config()
							__magic_name__      :       Any       						= scheduler_class(**lowerCAmelCase__			)

							assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.0_0_9_7_9			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.0_2			)			) < 1e-5
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Tuple       						= self.scheduler_classes[0]
							__magic_name__      :       Optional[Any]       						= self.get_scheduler_config()
							__magic_name__      :       str       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= len(lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= self.dummy_model()
							__magic_name__      :       Optional[int]       						= self.dummy_sample_deter
							__magic_name__      :       int       						= self.dummy_sample_deter + 0.1
							__magic_name__      :       Union[str, Any]       						= self.dummy_sample_deter - 0.1

							__magic_name__      :       str       						= samplea.shape[0]
							__magic_name__      :       int       						= torch.stack([samplea, samplea, samplea]      ,       dim=0			)
							__magic_name__      :       Optional[Any]       						= torch.arange(lowerCAmelCase__			)[0:3, None].repeat(1      ,       lowerCAmelCase__			)

							__magic_name__      :       Dict       						= model(samples.flatten(0      ,       1			)      ,       timesteps.flatten(0      ,       1			)			)
							__magic_name__      :       Optional[Any]       						= scheduler.batch_step_no_noise(lowerCAmelCase__      ,       timesteps.flatten(0      ,       1			)      ,       samples.flatten(0      ,       1			)			)

							__magic_name__      :       int       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       Union[str, Any]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
							assert abs(result_mean.item() - 0.5_0_0_5			) < 1e-3
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       int       						= self.scheduler_classes[0]
							__magic_name__      :       str       						= self.get_scheduler_config()
							__magic_name__      :       str       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       int       						= len(lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= self.dummy_model()
							__magic_name__      :       List[str]       						= self.dummy_sample_deter
							__magic_name__      :       Union[str, Any]       						= torch.manual_seed(0			)

							for t in reversed(range(lowerCAmelCase__			)			):
													# 1. predict noise residual
													__magic_name__      :       int       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)

													# 2. predict previous mean of sample x_t-1
													__magic_name__      :       Union[str, Any]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       generator=lowerCAmelCase__			).prev_sample

													__magic_name__      :       Any       						= pred_prev_sample

							__magic_name__      :       int       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       Dict       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2			) < 1e-3
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Tuple       						= self.scheduler_classes[0]
							__magic_name__      :       Tuple       						= self.get_scheduler_config(prediction_type="""v_prediction"""			)
							__magic_name__      :       Any       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= len(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= self.dummy_model()
							__magic_name__      :       Optional[Any]       						= self.dummy_sample_deter
							__magic_name__      :       List[Any]       						= torch.manual_seed(0			)

							for t in reversed(range(lowerCAmelCase__			)			):
													# 1. predict noise residual
													__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)

													# 2. predict previous mean of sample x_t-1
													__magic_name__      :       List[Any]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       generator=lowerCAmelCase__			).prev_sample

													__magic_name__      :       Tuple       						= pred_prev_sample

							__magic_name__      :       int       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       List[str]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1			) < 1e-3
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Any       						= self.scheduler_classes[0]
							__magic_name__      :       List[Any]       						= self.get_scheduler_config()
							__magic_name__      :       Optional[int]       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= scheduler.timesteps

							for i, timestep in enumerate(lowerCAmelCase__			):
													if i == len(lowerCAmelCase__			) - 1:
																			__magic_name__      :       Dict       						= -1
													else:
																			__magic_name__      :       List[str]       						= timesteps[i + 1]

													__magic_name__      :       Any       						= scheduler.previous_timestep(lowerCAmelCase__			)
													__magic_name__      :       Union[str, Any]       						= prev_t.item()

													self.assertEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       int       						= self.scheduler_classes[0]
							__magic_name__      :       int       						= self.get_scheduler_config()
							__magic_name__      :       str       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= [1_00, 87, 50, 51, 0]

							with self.assertRaises(lowerCAmelCase__      ,       msg="""`custom_timesteps` must be in descending order."""			):
													scheduler.set_timesteps(timesteps=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Any       						= self.scheduler_classes[0]
							__magic_name__      :       Union[str, Any]       						= self.get_scheduler_config()
							__magic_name__      :       List[str]       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       Dict       						= [1_00, 87, 50, 1, 0]
							__magic_name__      :       List[str]       						= len(lowerCAmelCase__			)

							with self.assertRaises(lowerCAmelCase__      ,       msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""			):
													scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__      ,       timesteps=lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Optional[Any]       						= self.scheduler_classes[0]
							__magic_name__      :       List[str]       						= self.get_scheduler_config()
							__magic_name__      :       int       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    lowerCAmelCase__      ,       msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""      ,       ):
													scheduler.set_timesteps(timesteps=lowerCAmelCase__			)








import os
from pathlib import Path

def    UpperCamelCase						(	):






						"""simple docstring"""







						from torch.utils.cpp_extension import load

						__magic_name__      :       Dict       						= Path(_A			).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
						__magic_name__      :       Optional[int]       						= [
						    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
