





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[int]       =		(DPMSolverSDEScheduler,)
      snake_case__       :  List[str]       =		10
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    Tuple:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_1_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'noise_sampler_seed': 0,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Any:
             for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,							[0.0002, 0.002, 0.02]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Any:
             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[Any]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	Dict							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  )

             a_      :	str							=						self.dummy_model()
             a_      :	Union[str, Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
             a_      :	List[Any]							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	Dict							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	str							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[int]							=						output.prev_sample

             a_      :	Union[str, Any]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 167.47821044921875  ) < 1E-2
                    assert abs(result_mean.item() - 0.2178705964565277  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 171.59352111816406  ) < 1E-2
                    assert abs(result_mean.item() - 0.22342906892299652  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
                    assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             a_      :	Optional[Any]							=						self.scheduler_classes[0]
             a_      :	Tuple							=						self.get_scheduler_config(prediction_type='v_prediction'  )
             a_      :	List[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	List[str]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
             a_      :	Union[str, Any]							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	int							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Optional[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	List[str]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	str							=						output.prev_sample

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

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 124.77149200439453  ) < 1E-2
                    assert abs(result_mean.item() - 0.16226289014816284  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 128.1663360595703  ) < 1E-2
                    assert abs(result_mean.item() - 0.16688326001167297  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 119.8487548828125  ) < 1E-2
                    assert abs(result_mean.item() - 0.1560530662536621  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Union[str, Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  ,							device=SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma

             for t in scheduler.timesteps:
                    a_      :	Optional[Any]							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Union[str, Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Any							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	List[Any]							=						output.prev_sample

             a_      :	Tuple							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 167.46957397460938  ) < 1E-2
                    assert abs(result_mean.item() - 0.21805934607982635  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 171.59353637695312  ) < 1E-2
                    assert abs(result_mean.item() - 0.22342908382415771  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
                    assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config()
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  ,							use_karras_sigmas=SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  ,							device=SCREAMING_SNAKE_CASE__  )

             a_      :	Dict							=						self.dummy_model()
             a_      :	str							=						self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma
             a_      :	int							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	Optional[int]							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	str							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						output.prev_sample

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 176.66974135742188  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 177.63653564453125  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
             else:
                    assert abs(result_sum.item() - 170.3135223388672  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


