






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Dict									=(LMSDiscreteScheduler,)
      UpperCamelCase_    : Dict									=10
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Dict:
          UpperCamelCase	:Union[str, Any]     							=       {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**SCREAMING_SNAKE_CASE_			)
          return config
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          for beta_start, beta_end in zip([0.0_0001, 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    UpperCAmelCase						(    self			)    ->				List[str]:
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				str:
          for t in [0, 500, 800]:
              self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Dict     							=       self.scheduler_classes[0]
          UpperCamelCase	:List[str]     							=       self.get_scheduler_config()
          UpperCamelCase	:List[Any]     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

          UpperCamelCase	:Tuple     							=       self.dummy_model()
          UpperCamelCase	:Union[str, Any]     							=       self.dummy_sample_deter * scheduler.init_noise_sigma

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase	:Dict     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Tuple     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Optional[Any]     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:List[str]     							=       output.prev_sample

          UpperCamelCase	:Any     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:List[str]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1006.388			) < 1e-2
          assert abs(result_mean.item() - 1.31			) < 1e-3
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Tuple     							=       self.scheduler_classes[0]
          UpperCamelCase	:List[Any]     							=       self.get_scheduler_config(prediction_type='''v_prediction'''			)
          UpperCamelCase	:List[Any]     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

          UpperCamelCase	:Optional[Any]     							=       self.dummy_model()
          UpperCamelCase	:Tuple     							=       self.dummy_sample_deter * scheduler.init_noise_sigma

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase	:Optional[Any]     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Optional[Any]     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Any     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:int     							=       output.prev_sample

          UpperCamelCase	:Any     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Optional[Any]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 0.0017			) < 1e-2
          assert abs(result_mean.item() - 2.2676e-06			) < 1e-3
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Any     							=       self.scheduler_classes[0]
          UpperCamelCase	:Optional[Any]     							=       self.get_scheduler_config()
          UpperCamelCase	:Union[str, Any]     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:Any     							=       self.dummy_model()
          UpperCamelCase	:int     							=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCamelCase	:Dict     							=       sample.to(SCREAMING_SNAKE_CASE_			)

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase	:int     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Tuple     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Union[str, Any]     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Optional[int]     							=       output.prev_sample

          UpperCamelCase	:Dict     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Tuple     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1006.388			) < 1e-2
          assert abs(result_mean.item() - 1.31			) < 1e-3




      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          UpperCamelCase	:Optional[int]     							=       self.scheduler_classes[0]
          UpperCamelCase	:int     							=       self.get_scheduler_config()
          UpperCamelCase	:int     							=       scheduler_class(**SCREAMING_SNAKE_CASE_   ,    use_karras_sigmas=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:Dict     							=       self.dummy_model()
          UpperCamelCase	:List[Any]     							=       self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
          UpperCamelCase	:Optional[Any]     							=       sample.to(SCREAMING_SNAKE_CASE_			)

          for t in scheduler.timesteps:
              UpperCamelCase	:Dict     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:str     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Optional[int]     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:str     							=       output.prev_sample

          UpperCamelCase	:Tuple     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Dict     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 3812.9927			) < 2e-2
          assert abs(result_mean.item() - 4.9648			) < 1e-3













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





