

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 				lowercase       ( _UpperCAmelCase    ):
    __SCREAMING_SNAKE_CASE      :				List[str]     =    (CMStochasticIterativeScheduler,)
    __SCREAMING_SNAKE_CASE      :				int     =    10




    def 					a							(      self   ,     **snake_case ):
           snake_case_           =					{
               """num_train_timesteps""": 201,
               """sigma_min""": 0.0_02,
               """sigma_max""": 80.0,
           }

           config.update(**lowercase_ )
           return config




    def 					a							(      self ):
           snake_case_           =					10

           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					self.scheduler_classes[0](**lowercase_ )

           scheduler.set_timesteps(lowercase_ )

           snake_case_           =					scheduler.timesteps[0]
           snake_case_           =					scheduler.timesteps[1]

           snake_case_           =					self.dummy_sample
           snake_case_           =					0.1 * sample

           snake_case_           =					scheduler.step(lowercase_   ,     lowercase_   ,     lowercase_ ).prev_sample
           snake_case_           =					scheduler.step(lowercase_   ,     lowercase_   ,     lowercase_ ).prev_sample

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




    def 					a							(      self ):
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=lowercase_ )




    def 					a							(      self ):
           for clip_denoised in [True, False]:
                  self.check_over_configs(clip_denoised=lowercase_ )




    def 					a							(      self ):
           snake_case_           =					self.scheduler_classes[0]
           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					scheduler_class(**lowercase_ )

           snake_case_           =					1
           scheduler.set_timesteps(lowercase_ )
           snake_case_           =					scheduler.timesteps

           snake_case_           =					torch.manual_seed(0 )

           snake_case_           =					self.dummy_model()
           snake_case_           =					self.dummy_sample_deter * scheduler.init_noise_sigma

           for i, t in enumerate(lowercase_ ):
                  # 1. scale model input
                  snake_case_           =					scheduler.scale_model_input(lowercase_   ,     lowercase_ )

                  # 2. predict noise residual
                  snake_case_           =					model(lowercase_   ,     lowercase_ )

                  # 3. predict previous sample x_t-1
                  snake_case_           =					scheduler.step(lowercase_   ,     lowercase_   ,     lowercase_   ,     generator=lowercase_ ).prev_sample

                  snake_case_           =					pred_prev_sample

           snake_case_           =					torch.sum(torch.abs(lowercase_ ) )
           snake_case_           =					torch.mean(torch.abs(lowercase_ ) )

           assert abs(result_sum.item() - 192.7614 ) < 1e-2
           assert abs(result_mean.item() - 0.25_10 ) < 1e-3




    def 					a							(      self ):
           snake_case_           =					self.scheduler_classes[0]
           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					scheduler_class(**lowercase_ )

           snake_case_           =					[106, 0]
           scheduler.set_timesteps(timesteps=lowercase_ )
           snake_case_           =					scheduler.timesteps

           snake_case_           =					torch.manual_seed(0 )

           snake_case_           =					self.dummy_model()
           snake_case_           =					self.dummy_sample_deter * scheduler.init_noise_sigma

           for t in timesteps:
                  # 1. scale model input
                  snake_case_           =					scheduler.scale_model_input(lowercase_   ,     lowercase_ )

                  # 2. predict noise residual
                  snake_case_           =					model(lowercase_   ,     lowercase_ )

                  # 3. predict previous sample x_t-1
                  snake_case_           =					scheduler.step(lowercase_   ,     lowercase_   ,     lowercase_   ,     generator=lowercase_ ).prev_sample

                  snake_case_           =					pred_prev_sample

           snake_case_           =					torch.sum(torch.abs(lowercase_ ) )
           snake_case_           =					torch.mean(torch.abs(lowercase_ ) )

           assert abs(result_sum.item() - 347.6357 ) < 1e-2
           assert abs(result_mean.item() - 0.45_27 ) < 1e-3




    def 					a							(      self ):
           snake_case_           =					self.scheduler_classes[0]
           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					scheduler_class(**lowercase_ )

           snake_case_           =					[39, 30, 12, 15, 0]

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




    def 					a							(      self ):
           snake_case_           =					self.scheduler_classes[0]
           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					scheduler_class(**lowercase_ )

           snake_case_           =					[39, 30, 12, 1, 0]
           snake_case_           =					len(lowercase_ )

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




    def 					a							(      self ):
           snake_case_           =					self.scheduler_classes[0]
           snake_case_           =					self.get_scheduler_config()
           snake_case_           =					scheduler_class(**lowercase_ )

           snake_case_           =					[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_ )







# 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,
)




