


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Tuple       =						(DDIMScheduler,)
     UpperCAmelCase__			:	Optional[int]       =						(("eta", 0.0), ("num_inference_steps", 50))





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]     ,						**SCREAMING_SNAKE_CASE_       :					Dict				):
        __lowerCamelCase:					int	 =						{
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**SCREAMING_SNAKE_CASE_				)
        return config





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						**SCREAMING_SNAKE_CASE_       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						self.scheduler_classes[0]
        __lowerCamelCase:					List[str]	 =						self.get_scheduler_config(**SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[int]	 =						scheduler_class(**SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase ,  __lowerCamelCase:					str	 =						10, 0.0

        __lowerCamelCase:					int	 =						self.dummy_model()
        __lowerCamelCase:					Tuple	 =						self.dummy_sample_deter

        scheduler.set_timesteps(SCREAMING_SNAKE_CASE_				)

        for t in scheduler.timesteps:
           __lowerCamelCase:					str	 =						model(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					Tuple	 =						scheduler.step(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				).prev_sample

        return sample





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for timesteps in [100, 500, 1000]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Tuple	 =						self.scheduler_classes[0]
        __lowerCamelCase:					Optional[int]	 =						self.get_scheduler_config(steps_offset=1				)
        __lowerCamelCase:					List[str]	 =						scheduler_class(**SCREAMING_SNAKE_CASE_				)
        scheduler.set_timesteps(5				)
        assert torch.equal(scheduler.timesteps     ,						torch.LongTensor([801, 601, 401, 201, 1]				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,						[0.002, 0.02, 0.2, 2]				):
           self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_     ,						beta_end=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_				)
        for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(
                  thresholding=SCREAMING_SNAKE_CASE_     ,						prediction_type=SCREAMING_SNAKE_CASE_     ,						sample_max_value=SCREAMING_SNAKE_CASE_     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for t in [1, 10, 49]:
           self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        for t, num_inference_steps in zip([1, 10, 50]     ,						[10, 50, 500]				):
           self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_     ,						num_inference_steps=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        for t, eta in zip([1, 10, 49]     ,						[0.0, 0.5, 1.0]				):
           self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_     ,						eta=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase:					Any	 =						self.scheduler_classes[0]
        __lowerCamelCase:					Tuple	 =						self.get_scheduler_config()
        __lowerCamelCase:					List[Any]	 =						scheduler_class(**SCREAMING_SNAKE_CASE_				)

        assert torch.sum(torch.abs(scheduler._get_variance(0     ,						0				) - 0.0				)				) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420     ,						400				) - 0.1_4771				)				) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980     ,						960				) - 0.3_2460				)				) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0     ,						0				) - 0.0				)				) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487     ,						486				) - 0.0_0979				)				) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999     ,						998				) - 0.02				)				) < 1E-5





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase:					List[Any]	 =						self.full_loop()

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

        assert abs(result_sum.item() - 172.0067				) < 1E-2
        assert abs(result_mean.item() - 0.22_3967				) < 1E-3





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					Tuple	 =						self.full_loop(prediction_type="""v_prediction"""				)

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

        assert abs(result_sum.item() - 52.5302				) < 1E-2
        assert abs(result_mean.item() - 0.0684				) < 1E-3





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        # We specify different beta, so that the first alpha is 0.99
        __lowerCamelCase:					Optional[Any]	 =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_     ,						beta_start=0.01				)
        __lowerCamelCase:					int	 =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_				)				)
        __lowerCamelCase:					Optional[int]	 =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_				)				)

        assert abs(result_sum.item() - 149.8295				) < 1E-2
        assert abs(result_mean.item() - 0.1951				) < 1E-3







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        # We specify different beta, so that the first alpha is 0.99
        __lowerCamelCase:					int	 =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_     ,						beta_start=0.01				)
        __lowerCamelCase:					Tuple	 =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_				)				)
        __lowerCamelCase:					Any	 =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_				)				)

        assert abs(result_sum.item() - 149.0784				) < 1E-2
        assert abs(result_mean.item() - 0.1941				) < 1E-3






_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


