code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				(EulerAncestralDiscreteScheduler,)
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				1_0
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			**A  :			str		):
             _UpperCAmelCase    :			Any              =					{
                 "num_train_timesteps": 1_1_0_0,
                 "beta_start": 0.0_001,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
             }
  
             config.update(**A		)
             return config
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, 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=A  ,			beta_end=A		)
  
  
  
        def 		snake_case_      (  self  :			str		):
             for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=A		)
  
  
  
        def 		snake_case_      (  self  :			int		):
             for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=A		)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Optional[Any]              =					self.scheduler_classes[0]
             _UpperCAmelCase    :			List[Any]              =					self.get_scheduler_config()
             _UpperCAmelCase    :			Union[str, Any]              =					scheduler_class(**A		)
  
             scheduler.set_timesteps(self.num_inference_steps		)
  
             _UpperCAmelCase    :			List[str]              =					torch.manual_seed(0		)
  
             _UpperCAmelCase    :			int              =					self.dummy_model()
             _UpperCAmelCase    :			str              =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _UpperCAmelCase    :			Dict              =					sample.to(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Union[str, Any]              =					scheduler.scale_model_input(A  ,			A		)
  
                  _UpperCAmelCase    :			Any              =					model(A  ,			A		)
  
                  _UpperCAmelCase    :			str              =					scheduler.step(A  ,			A  ,			A  ,			generator=A		)
                  _UpperCAmelCase    :			Optional[Any]              =					output.prev_sample
  
             _UpperCAmelCase    :			List[str]              =					torch.sum(torch.abs(A		)		)
             _UpperCAmelCase    :			List[str]              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_sum.item() - 152.3_192		) < 1e-2
             assert abs(result_mean.item() - 0.1_983		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             _UpperCAmelCase    :			str              =					self.scheduler_classes[0]
             _UpperCAmelCase    :			List[str]              =					self.get_scheduler_config(prediction_type="v_prediction"		)
             _UpperCAmelCase    :			List[Any]              =					scheduler_class(**A		)
  
             scheduler.set_timesteps(self.num_inference_steps		)
  
             _UpperCAmelCase    :			List[str]              =					torch.manual_seed(0		)
  
             _UpperCAmelCase    :			List[Any]              =					self.dummy_model()
             _UpperCAmelCase    :			List[Any]              =					self.dummy_sample_deter * scheduler.init_noise_sigma
             _UpperCAmelCase    :			List[str]              =					sample.to(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Dict              =					scheduler.scale_model_input(A  ,			A		)
  
                  _UpperCAmelCase    :			Optional[Any]              =					model(A  ,			A		)
  
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler.step(A  ,			A  ,			A  ,			generator=A		)
                  _UpperCAmelCase    :			Union[str, Any]              =					output.prev_sample
  
             _UpperCAmelCase    :			Optional[Any]              =					torch.sum(torch.abs(A		)		)
             _UpperCAmelCase    :			str              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_sum.item() - 108.4_439		) < 1e-2
             assert abs(result_mean.item() - 0.1_412		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			str              =					self.scheduler_classes[0]
             _UpperCAmelCase    :			Optional[Any]              =					self.get_scheduler_config()
             _UpperCAmelCase    :			Union[str, Any]              =					scheduler_class(**A		)
  
             scheduler.set_timesteps(self.num_inference_steps  ,			device=A		)
             _UpperCAmelCase    :			Any              =					torch.manual_seed(0		)
  
             _UpperCAmelCase    :			List[str]              =					self.dummy_model()
             _UpperCAmelCase    :			List[str]              =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _UpperCAmelCase    :			List[Any]              =					sample.to(A		)
  
             for t in scheduler.timesteps:
                  _UpperCAmelCase    :			Union[str, Any]              =					scheduler.scale_model_input(A  ,			A		)
  
                  _UpperCAmelCase    :			int              =					model(A  ,			A		)
  
                  _UpperCAmelCase    :			Union[str, Any]              =					scheduler.step(A  ,			A  ,			A  ,			generator=A		)
                  _UpperCAmelCase    :			Tuple              =					output.prev_sample
  
             _UpperCAmelCase    :			Optional[Any]              =					torch.sum(torch.abs(A		)		)
             _UpperCAmelCase    :			Tuple              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_sum.item() - 152.3_192		) < 1e-2
             assert abs(result_mean.item() - 0.1_983		) < 1e-3
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  