

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Optional[Any]      		=       (DDPMParallelScheduler,)





      def 		__A	(   self							:				List[Any]						,			**UpperCAmelCase							:				Optional[int]	):
            A_						  =				{
                "num_train_timesteps": 1000,
                "beta_start": 0.0_001,
                "beta_end": 0.02,
                "beta_schedule": "linear",
                "variance_type": "fixed_small",
                "clip_sample": True,
            }

            config.update(**UpperCAmelCase	)
            return config





      def 		__A	(   self							:				Optional[Any]	):
            for timesteps in [1, 5, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase	)





      def 		__A	(   self							:				Dict	):
            for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,			[0.002, 0.02, 0.2, 2]	):
                  self.check_over_configs(beta_start=UpperCAmelCase						,			beta_end=UpperCAmelCase	)





      def 		__A	(   self							:				int	):
            for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            for variance in ["fixed_small", "fixed_large", "other"]:
                  self.check_over_configs(variance_type=UpperCAmelCase	)





      def 		__A	(   self							:				int	):
            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]	):
            self.check_over_configs(thresholding=UpperCAmelCase	)
            for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=UpperCAmelCase						,			prediction_type=UpperCAmelCase						,			sample_max_value=UpperCAmelCase						,			)





      def 		__A	(   self							:				Optional[int]	):
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(prediction_type=UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            for t in [0, 500, 999]:
                  self.check_over_forward(time_step=UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.00_979	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.02	)	) < 1E-5





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				len(UpperCAmelCase	)

            A_						  =				self.dummy_model()
            A_						  =				self.dummy_sample_deter
            A_						  =				self.dummy_sample_deter + 0.1
            A_						  =				self.dummy_sample_deter - 0.1

            A_						  =				samplea.shape[0]
            A_						  =				torch.stack([samplea, samplea, samplea]						,			dim=0	)
            A_						  =				torch.arange(UpperCAmelCase	)[0:3, None].repeat(1						,			UpperCAmelCase	)

            A_						  =				model(samples.flatten(0						,			1	)						,			timesteps.flatten(0						,			1	)	)
            A_						  =				scheduler.batch_step_no_noise(UpperCAmelCase						,			timesteps.flatten(0						,			1	)						,			samples.flatten(0						,			1	)	)

            A_						  =				torch.sum(torch.abs(UpperCAmelCase	)	)
            A_						  =				torch.mean(torch.abs(UpperCAmelCase	)	)

            assert abs(result_sum.item() - 1_153.1_833	) < 1E-2
            assert abs(result_mean.item() - 0.5_005	) < 1E-3





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				len(UpperCAmelCase	)

            A_						  =				self.dummy_model()
            A_						  =				self.dummy_sample_deter
            A_						  =				torch.manual_seed(0	)

            for t in reversed(range(UpperCAmelCase	)	):
                  # 1. predict noise residual
                  A_						  =				model(UpperCAmelCase						,			UpperCAmelCase	)

                  # 2. predict previous mean of sample x_t-1
                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			generator=UpperCAmelCase	).prev_sample

                  A_						  =				pred_prev_sample

            A_						  =				torch.sum(torch.abs(UpperCAmelCase	)	)
            A_						  =				torch.mean(torch.abs(UpperCAmelCase	)	)

            assert abs(result_sum.item() - 258.9_606	) < 1E-2
            assert abs(result_mean.item() - 0.3_372	) < 1E-3





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config(prediction_type="v_prediction"	)
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				len(UpperCAmelCase	)

            A_						  =				self.dummy_model()
            A_						  =				self.dummy_sample_deter
            A_						  =				torch.manual_seed(0	)

            for t in reversed(range(UpperCAmelCase	)	):
                  # 1. predict noise residual
                  A_						  =				model(UpperCAmelCase						,			UpperCAmelCase	)

                  # 2. predict previous mean of sample x_t-1
                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			generator=UpperCAmelCase	).prev_sample

                  A_						  =				pred_prev_sample

            A_						  =				torch.sum(torch.abs(UpperCAmelCase	)	)
            A_						  =				torch.mean(torch.abs(UpperCAmelCase	)	)

            assert abs(result_sum.item() - 202.0_296	) < 1E-2
            assert abs(result_mean.item() - 0.2_631	) < 1E-3





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				[100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=UpperCAmelCase	)

            A_						  =				scheduler.timesteps

            for i, timestep in enumerate(UpperCAmelCase	):
                  if i == len(UpperCAmelCase	) - 1:
                        A_						  =				-1
                  else:
                        A_						  =				timesteps[i + 1]

                  A_						  =				scheduler.previous_timestep(UpperCAmelCase	)
                  A_						  =				prev_t.item()

                  self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				[100, 87, 50, 51, 0]

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





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				[100, 87, 50, 1, 0]
            A_						  =				len(UpperCAmelCase	)

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







      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config()
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				[scheduler.config.num_train_timesteps]

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




"""simple docstring"""

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)





