import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 						__snake_case						(			A_		):





      '''simple docstring'''






      _snake_case     		=							(EulerAncestralDiscreteScheduler,)
      _snake_case     		=							10



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	**_UpperCamelCase  :					int)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    {
              'num_train_timesteps': 1100,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
          }

          config.update(**__lowerCamelCase)
          return config



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->str:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Optional[int]:


          """simple docstring"""


          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]):
              self.check_over_configs(beta_start=__lowerCamelCase     ,	beta_end=__lowerCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->List[str]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->List[str]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		Optional[Any]			    =    self.get_scheduler_config()
          _lowerCamelCase  :		Tuple			    =    scheduler_class(**__lowerCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase  :		int			    =    torch.manual_seed(0)

          _lowerCamelCase  :		str			    =    self.dummy_model()
          _lowerCamelCase  :		Union[str, Any]			    =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase  :		Tuple			    =    sample.to(__lowerCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		Optional[int]			    =    scheduler.scale_model_input(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		Optional[Any]			    =    model(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		Union[str, Any]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	generator=__lowerCamelCase)
              _lowerCamelCase  :		Optional[Any]			    =    output.prev_sample

          _lowerCamelCase  :		Optional[int]			    =    torch.sum(torch.abs(__lowerCamelCase))
          _lowerCamelCase  :		Tuple			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_sum.item() - 152.3192) < 1E-2
          assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		Dict			    =    self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase  :		Tuple			    =    scheduler_class(**__lowerCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase  :		List[str]			    =    torch.manual_seed(0)

          _lowerCamelCase  :		Optional[int]			    =    self.dummy_model()
          _lowerCamelCase  :		Optional[int]			    =    self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase  :		int			    =    sample.to(__lowerCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		Any			    =    scheduler.scale_model_input(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		List[str]			    =    model(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		List[Any]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	generator=__lowerCamelCase)
              _lowerCamelCase  :		Union[str, Any]			    =    output.prev_sample

          _lowerCamelCase  :		List[Any]			    =    torch.sum(torch.abs(__lowerCamelCase))
          _lowerCamelCase  :		Optional[Any]			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_sum.item() - 108.4439) < 1E-2
          assert abs(result_mean.item() - 0.1_4_1_2) < 1E-3




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		str			    =    self.get_scheduler_config()
          _lowerCamelCase  :		Dict			    =    scheduler_class(**__lowerCamelCase)

          scheduler.set_timesteps(self.num_inference_steps     ,	device=__lowerCamelCase)
          _lowerCamelCase  :		str			    =    torch.manual_seed(0)

          _lowerCamelCase  :		Tuple			    =    self.dummy_model()
          _lowerCamelCase  :		List[str]			    =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase  :		List[Any]			    =    sample.to(__lowerCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase  :		str			    =    scheduler.scale_model_input(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		Dict			    =    model(__lowerCamelCase     ,	__lowerCamelCase)

              _lowerCamelCase  :		Optional[Any]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	generator=__lowerCamelCase)
              _lowerCamelCase  :		Dict			    =    output.prev_sample

          _lowerCamelCase  :		List[str]			    =    torch.sum(torch.abs(__lowerCamelCase))
          _lowerCamelCase  :		int			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_sum.item() - 152.3192) < 1E-2
          assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3





"""simple docstring"""







import pytest

from datasets.utils.version import Version




@pytest.mark.parametrize(
    'other, expected_equality'  ,       [
        (Version('1.0.0'						), True),
        ('1.0.0', True),
        (Version('2.0.0'						), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]  ,       )
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Tuple								=		Version('1.0.0'						)
     assert (version == other) is expected_equality
     assert (version != other) is not expected_equality
     assert (hash(_SCREAMING_SNAKE_CASE						) == hash(_SCREAMING_SNAKE_CASE						)) is expected_equality



