"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							(KDPMaDiscreteScheduler,)
    _snake_case   :     Tuple   							=							10


    def    lowerCAmelCase_     (    self		:     List[Any]      , **__lowerCAmelCase		:     Optional[int]    ):
      _UpperCAmelCase         =					{
          """num_train_timesteps""": 1100,
          """beta_start""": 0.0_001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**__lowerCAmelCase    )
      return config


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      , [0.0_002, 0.002, 0.02]    ):
        self.check_over_configs(beta_start=__lowerCAmelCase      , beta_end=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config(prediction_type="""v_prediction"""    )
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      scheduler.set_timesteps(self.num_inference_steps    )

      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter * scheduler.init_noise_sigma
      _UpperCAmelCase         =					sample.to(__lowerCAmelCase    )

      for i, t in enumerate(scheduler.timesteps    ):
        _UpperCAmelCase         =					scheduler.scale_model_input(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					output.prev_sample

      _UpperCAmelCase         =					torch.sum(torch.abs(__lowerCAmelCase    )    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 4.6_9_3_4e-0_7    ) < 1e-2
        assert abs(result_mean.item() - 6.1_1_1_2e-1_0    ) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7    ) < 1e-2
        assert abs(result_mean.item() - 0.0_002    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      if torch_device == "mps":
        return
      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config()
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      scheduler.set_timesteps(self.num_inference_steps    )

      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter * scheduler.init_noise_sigma
      _UpperCAmelCase         =					sample.to(__lowerCAmelCase    )

      for i, t in enumerate(scheduler.timesteps    ):
        _UpperCAmelCase         =					scheduler.scale_model_input(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					output.prev_sample

      _UpperCAmelCase         =					torch.sum(torch.abs(__lowerCAmelCase    )    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 20.4_125    ) < 1e-2
        assert abs(result_mean.item() - 0.0_266    ) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.4_125    ) < 1e-2
        assert abs(result_mean.item() - 0.0_266    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      if torch_device == "mps":
        return
      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config()
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

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

      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter.to(__lowerCAmelCase    ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        _UpperCAmelCase         =					scheduler.scale_model_input(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					output.prev_sample

      _UpperCAmelCase         =					torch.sum(torch.abs(__lowerCAmelCase    )    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      if str(__lowerCAmelCase    ).startswith("""cpu"""    ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 20.4_125    ) < 1e-2
        assert abs(result_mean.item() - 0.0_266    ) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.4_125    ) < 1e-2
        assert abs(result_mean.item() - 0.0_266    ) < 1e-3

"""simple docstring"""


import warnings


warnings.warn(
    """memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
    """`from accelerate import find_executable_batch_size` to avoid this warning.""",
    FutureWarning,
)
