
"""simple docstring"""







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 				A__ (			a__):
		A_		: Union[str, Any]            =			(DDPMParallelScheduler,)

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   {
			    '''num_train_timesteps''': 10_00,
			    '''beta_start''': 0.0001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			    '''variance_type''': '''fixed_small''',
			    '''clip_sample''': True,
			}

			config.update(**lowercase__				)
			return config

		def 							__lowerCamelCase							(   self				):





			for timesteps in [1, 5, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=lowercase__				)

		def 							__lowerCamelCase							(   self				):





			for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,	[0.002, 0.02, 0.2, 2]				):
				self.check_over_configs(beta_start=lowercase__ ,	beta_end=lowercase__				)

		def 							__lowerCamelCase							(   self				):





			for schedule in ["linear", "squaredcos_cap_v2"]:
				self.check_over_configs(beta_schedule=lowercase__				)

		def 							__lowerCamelCase							(   self				):





			for variance in ["fixed_small", "fixed_large", "other"]:
				self.check_over_configs(variance_type=lowercase__				)

		def 							__lowerCamelCase							(   self				):





			for clip_sample in [True, False]:
				self.check_over_configs(clip_sample=lowercase__				)

		def 							__lowerCamelCase							(   self				):
			self.check_over_configs(thresholding=lowercase__				)
			for threshold in [0.5, 1.0, 2.0]:
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
					self.check_over_configs(
					    thresholding=lowercase__ ,	prediction_type=lowercase__ ,	sample_max_value=lowercase__ ,	)

		def 							__lowerCamelCase							(   self				):





			for prediction_type in ["epsilon", "sample", "v_prediction"]:
				self.check_over_configs(prediction_type=lowercase__				)

		def 							__lowerCamelCase							(   self				):





			for t in [0, 5_00, 9_99]:
				self.check_over_forward(time_step=lowercase__				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		Any		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Any		    =   scheduler_class(**lowercase__				)

			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_0979				)				) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99				) - 0.02				)				) < 1E-5

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		Tuple		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Optional[Any]		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		Optional[int]		    =   len(lowercase__				)

			__lowerCAmelCase    :		Dict		    =   self.dummy_model()
			__lowerCAmelCase    :		Dict		    =   self.dummy_sample_deter
			__lowerCAmelCase    :		List[Any]		    =   self.dummy_sample_deter + 0.1
			__lowerCAmelCase    :		Dict		    =   self.dummy_sample_deter - 0.1

			__lowerCAmelCase    :		str		    =   samplea.shape[0]
			__lowerCAmelCase    :		Optional[int]		    =   torch.stack([samplea, samplea, samplea] ,	dim=0				)
			__lowerCAmelCase    :		List[str]		    =   torch.arange(lowercase__				)[0:3, None].repeat(1 ,	lowercase__				)

			__lowerCAmelCase    :		int		    =   model(samples.flatten(0 ,	1				) ,	timesteps.flatten(0 ,	1				)				)
			__lowerCAmelCase    :		str		    =   scheduler.batch_step_no_noise(lowercase__ ,	timesteps.flatten(0 ,	1				) ,	samples.flatten(0 ,	1				)				)

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

			assert abs(result_sum.item() - 11_53.18_33				) < 1E-2
			assert abs(result_mean.item() - 0.5005				) < 1E-3

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		List[str]		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Optional[int]		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		List[str]		    =   len(lowercase__				)

			__lowerCAmelCase    :		Optional[Any]		    =   self.dummy_model()
			__lowerCAmelCase    :		Dict		    =   self.dummy_sample_deter
			__lowerCAmelCase    :		Optional[int]		    =   torch.manual_seed(0				)

			for t in reversed(range(lowercase__				)				):
				# 1. predict noise residual
				__lowerCAmelCase    :		Union[str, Any]		    =   model(lowercase__ ,	lowercase__				)

				# 2. predict previous mean of sample x_t-1
				__lowerCAmelCase    :		int		    =   scheduler.step(lowercase__ ,	lowercase__ ,	lowercase__ ,	generator=lowercase__				).prev_sample

				__lowerCAmelCase    :		List[str]		    =   pred_prev_sample

			__lowerCAmelCase    :		int		    =   torch.sum(torch.abs(lowercase__				)				)
			__lowerCAmelCase    :		Tuple		    =   torch.mean(torch.abs(lowercase__				)				)

			assert abs(result_sum.item() - 258.9606				) < 1E-2
			assert abs(result_mean.item() - 0.3372				) < 1E-3

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		str		    =   self.get_scheduler_config(prediction_type='v_prediction'				)
			__lowerCAmelCase    :		Dict		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		Dict		    =   len(lowercase__				)

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

			for t in reversed(range(lowercase__				)				):
				# 1. predict noise residual
				__lowerCAmelCase    :		List[str]		    =   model(lowercase__ ,	lowercase__				)

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

				__lowerCAmelCase    :		Optional[Any]		    =   pred_prev_sample

			__lowerCAmelCase    :		List[Any]		    =   torch.sum(torch.abs(lowercase__				)				)
			__lowerCAmelCase    :		str		    =   torch.mean(torch.abs(lowercase__				)				)

			assert abs(result_sum.item() - 202.0296				) < 1E-2
			assert abs(result_mean.item() - 0.2631				) < 1E-3

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		Optional[Any]		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Union[str, Any]		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		str		    =   [1_00, 87, 50, 1, 0]

			scheduler.set_timesteps(timesteps=lowercase__				)

			__lowerCAmelCase    :		List[str]		    =   scheduler.timesteps

			for i, timestep in enumerate(lowercase__				):
				if i == len(lowercase__				) - 1:
					__lowerCAmelCase    :		int		    =   -1
				else:
					__lowerCAmelCase    :		Optional[Any]		    =   timesteps[i + 1]

				__lowerCAmelCase    :		str		    =   scheduler.previous_timestep(lowercase__				)
				__lowerCAmelCase    :		Tuple		    =   prev_t.item()

				self.assertEqual(lowercase__ ,	lowercase__				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		int		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		int		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		Dict		    =   [1_00, 87, 50, 51, 0]

			with self.assertRaises(lowercase__ ,	msg='`custom_timesteps` must be in descending order.'				):
				scheduler.set_timesteps(timesteps=lowercase__				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		Any		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Any		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		Tuple		    =   [1_00, 87, 50, 1, 0]
			__lowerCAmelCase    :		List[Any]		    =   len(lowercase__				)

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







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.scheduler_classes[0]
			__lowerCAmelCase    :		int		    =   self.get_scheduler_config()
			__lowerCAmelCase    :		Union[str, Any]		    =   scheduler_class(**lowercase__				)

			__lowerCAmelCase    :		Union[str, Any]		    =   [scheduler.config.num_train_timesteps]

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

"""simple docstring"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip