





'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       (DPMSolverSinglestepScheduler,)
 __lowerCamelCase    :				int								=       (("num_inference_steps", 25),)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
         "prediction_type": "epsilon",
         "thresholding": False,
         "sample_max_value": 1.0,
         "algorithm_type": "dpmsolver++",
         "solver_type": "midpoint",
         "lambda_min_clipped": -float("inf"		),
         "variance_type": None,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)
         # copy over dummy past residuals
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   scheduler_class.from_pretrained(_lowerCAmelCase		)
             new_scheduler.set_timesteps(_lowerCAmelCase		)
             # copy over dummy past residuals
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase      ,      _lowerCAmelCase						      =   sample, sample
         for t in range(_lowerCAmelCase      ,  time_step + scheduler.config.solver_order + 1		):
             _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
             _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self		)     ->				int:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config()
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)

         # copy over dummy past residuals (must be after setting timesteps)
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

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

             # copy over dummy past residual (must be after setting timesteps)
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
         _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Tuple:
     if scheduler is None:
         _lowerCAmelCase						      =   self.scheduler_classes[0]
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     return sample




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   50
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2574		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3

     _lowerCAmelCase						      =   DEISMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   UniPCMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for order in [1, 2, 3]:
         for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  algorithm_type="dpmsolver++"      ,  solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     _lowerCAmelCase						      =   self.full_loop(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(variance_type=_lowerCAmelCase		)
     self.check_over_configs(variance_type="learned_range"		)




 def 				_snake_case (						self		)     ->				int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=_lowerCAmelCase      ,  time_step=0		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop()
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2248		) < 1E-3




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.1453		) < 1E-3




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"      ,  use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.0649		) < 1E-3





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(thresholding=_lowerCAmelCase      ,  dynamic_thresholding_ratio=0		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     assert sample.dtype == torch.floataa










# 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.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


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