
"""simple docstring"""




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					UpperCamelCase     (	UpperCamelCase_				):

    """simple docstring"""



    UpperCAmelCase_    :				Dict						  =    (EulerAncestralDiscreteScheduler,)
    UpperCAmelCase_    :				List[Any]						  =    10


    def 							A (    self		,     **lowercase__      )   ->   Any:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     {
             'num_train_timesteps': 1100,
             'beta_start': 0.0_001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**__SCREAMING_SNAKE_CASE      )
         return config


    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







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


    def 							A (    self      )   ->   List[Any]:
         """simple docstring"""







         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=__SCREAMING_SNAKE_CASE		,     beta_end=__SCREAMING_SNAKE_CASE      )


    def 							A (    self      )   ->   Tuple:
         """simple docstring"""







         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE      )


    def 							A (    self      )   ->   Dict:
         """simple docstring"""







         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE      )


    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps      )

         SCREAMING_SNAKE_CASE           =     torch.manual_seed(0      )

         SCREAMING_SNAKE_CASE           =     self.dummy_model()
         SCREAMING_SNAKE_CASE           =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         SCREAMING_SNAKE_CASE           =     sample.to(__SCREAMING_SNAKE_CASE      )

         for i, t in enumerate(scheduler.timesteps      ):
              SCREAMING_SNAKE_CASE           =     scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     model(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     scheduler.step(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     generator=__SCREAMING_SNAKE_CASE      )
              SCREAMING_SNAKE_CASE           =     output.prev_sample

         SCREAMING_SNAKE_CASE           =     torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
         SCREAMING_SNAKE_CASE           =     torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

         assert abs(result_sum.item() - 152.3192      ) < 1E-2
         assert abs(result_mean.item() - 0.1_983      ) < 1E-3


    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE           =     self.get_scheduler_config(prediction_type='v_prediction'      )
         SCREAMING_SNAKE_CASE           =     scheduler_class(**__SCREAMING_SNAKE_CASE      )

         scheduler.set_timesteps(self.num_inference_steps      )

         SCREAMING_SNAKE_CASE           =     torch.manual_seed(0      )

         SCREAMING_SNAKE_CASE           =     self.dummy_model()
         SCREAMING_SNAKE_CASE           =     self.dummy_sample_deter * scheduler.init_noise_sigma
         SCREAMING_SNAKE_CASE           =     sample.to(__SCREAMING_SNAKE_CASE      )

         for i, t in enumerate(scheduler.timesteps      ):
              SCREAMING_SNAKE_CASE           =     scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     model(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     scheduler.step(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     generator=__SCREAMING_SNAKE_CASE      )
              SCREAMING_SNAKE_CASE           =     output.prev_sample

         SCREAMING_SNAKE_CASE           =     torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
         SCREAMING_SNAKE_CASE           =     torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

         assert abs(result_sum.item() - 108.4439      ) < 1E-2
         assert abs(result_mean.item() - 0.1_412      ) < 1E-3




    def 							A (    self      )   ->   int:
         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps		,     device=__SCREAMING_SNAKE_CASE      )
         SCREAMING_SNAKE_CASE           =     torch.manual_seed(0      )

         SCREAMING_SNAKE_CASE           =     self.dummy_model()
         SCREAMING_SNAKE_CASE           =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         SCREAMING_SNAKE_CASE           =     sample.to(__SCREAMING_SNAKE_CASE      )

         for t in scheduler.timesteps:
              SCREAMING_SNAKE_CASE           =     scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     model(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE      )

              SCREAMING_SNAKE_CASE           =     scheduler.step(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     generator=__SCREAMING_SNAKE_CASE      )
              SCREAMING_SNAKE_CASE           =     output.prev_sample

         SCREAMING_SNAKE_CASE           =     torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
         SCREAMING_SNAKE_CASE           =     torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

         assert abs(result_sum.item() - 152.3192      ) < 1E-2
         assert abs(result_mean.item() - 0.1_983      ) < 1E-3


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
