


'''simple docstring'''



A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
A__	:      Dict				=						3
A__	:      Union[str, Any]				=						5

# Buffer Space (with Parameters Space)
A__	:      list[float]				=						[]
A__	:      list[float]				=						[]

# Machine Time
A__	:      Dict				=						0



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(UpperCAmelCase_							):
          # Evolution Parameter
          __lowerCamelCase     :			List[str]              =	float(seed / value							)

          # Control Theory: Orbit Change
          __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space

     # Logistic Map
     assert max(UpperCAmelCase_							) < 1
     assert max(UpperCAmelCase_							) < 4

     # Machine Time
     machine_time += 1



def     UpperCAmelCase__      (			)  ->	Optional[int]:
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
          x ^= y >> 13
          y ^= x << 17
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __lowerCamelCase     :			List[Any]              =	machine_time % m

     # Evolution (Time Length)
     for _ in range(0   ,					UpperCAmelCase_							):
          # Variables (Position + Parameters)
          __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
          __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]

          # Modification (Transition Function) - Flow
          __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
     __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)

     # Machine Time
     machine_time += 1

     return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff



def     UpperCAmelCase__      (			)  ->	Any:
     global buffer_space, params_space, machine_time, K, m, t

     __lowerCamelCase     :			Union[str, Any]              =	K
     __lowerCamelCase     :			Optional[int]              =	[0] * m
     __lowerCamelCase     :			Tuple              =	0


if __name__ == "__main__":
      # Initialization
      reset()

      # Pushing Data (Input)
      import random

      A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
      for chunk in message:
            push(chunk)

      # for controlling
      A__	:      Tuple				=						""""""

      # Pulling Data (Output)
      while inp in ("e", "E"):
            print(f'''{format(pull(), '#04x')}''')
            print(buffer_space)
            print(params_space)
            A__	:      Optional[Any]				=						input("""(e)exit? """).strip()










'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase						:      Tuple							  =    1_0







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         __lowerCamelCase     :			Union[str, Any]              =	{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**SCREAMING_SNAKE_CASE_			)
         return config







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Dict:
         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=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  int:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
         __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

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

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

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

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

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3







    def    lowercase_       (							self			)       ->  List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
         __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

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

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

              __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Tuple              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3







    def    lowercase_       (							self			)       ->  Dict:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			str              =	self.get_scheduler_config()
         __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
         __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

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

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

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3






