

'''simple docstring'''






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[str]  ,  SCREAMING_SNAKE_CASE_     :	Dict    )							->   Optional[Any]:
      """simple docstring"""

      sequence.append(SCREAMING_SNAKE_CASE_    )

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       0
      while sequence[index] != target:
            index += 1

      sequence.pop()

      if index == len(SCREAMING_SNAKE_CASE_    ):
            return None

      return index


if __name__ == "__main__":
      snake_case_       =       input('Enter numbers separated by comma:\n').strip()
      snake_case_       =       [int(item) for item in user_input.split(',')]

      snake_case_       =       input('Enter a single number to be found in the list:\n')
      snake_case_       =       int(target_input)
      snake_case_       =       sentinel_linear_search(sequence, target)
      if result is not None:
            print(F'''{target} found at positions: {result}''')
      else:
            print('Not found')




'''simple docstring'''






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(EulerAncestralDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          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=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Any     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       torch.manual_seed(0			)

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

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			str     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.manual_seed(0			)

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

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[Any]     =       model(lowercase__				,     lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 108.4439			) < 1e-2
          assert abs(result_mean.item() - 0.1412			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

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

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

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

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3

