

'''simple docstring'''






snake_case_       =       [0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
snake_case_       =       3
snake_case_       =       5

# Buffer Space (with Parameters Space)
snake_case_       =       []
snake_case_       =       []

# Machine Time
snake_case_       =       0


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any    )							->   Optional[int]:
      """simple docstring"""

      global buffer_space, params_space, machine_time, K, m, t

      # Choosing Dynamical Systems (All)
      for key, value in enumerate(SCREAMING_SNAKE_CASE_    ):
            # Evolution Parameter
            SCREAMING_SNAKE_CASE_      :			int     =       float(seed / value    )

            # Control Theory: Orbit Change
            SCREAMING_SNAKE_CASE_      :			Dict     =       (buffer_space[(key + 1) % m] + e) % 1

            # Control Theory: Trajectory Change
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       (params_space[key] + e) % 1 + 3

            # Modification (Transition Function) - Jumps
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       round(float(r * value * (1 - value)    )  ,  1_0    )
            SCREAMING_SNAKE_CASE_      :			Any     =       r  # Saving to Parameters Space

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

      # Machine Time
      machine_time += 1


def 					__lowerCamelCase			(      )							->   Union[str, Any]:
      """simple docstring"""

      global buffer_space, params_space, machine_time, K, m, t

      # PRNG (Xorshift by George Marsaglia)
      def xorshift(SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]    ):
            x ^= y >> 1_3
            y ^= x << 1_7
            x ^= y >> 5
            return x

      # Choosing Dynamical Systems (Increment)
      SCREAMING_SNAKE_CASE_      :			int     =       machine_time % m

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

            # Modification (Transition Function) - Flow
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       round(float(r * value * (1 - value)    )  ,  1_0    )
            SCREAMING_SNAKE_CASE_      :			Dict     =       (machine_time * 0.01 + r * 1.01) % 1 + 3

      # Choosing Chaotic Data
      SCREAMING_SNAKE_CASE_      :			Any     =       int(buffer_space[(key + 2) % m] * (1_0**1_0)    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       int(buffer_space[(key - 2) % m] * (1_0**1_0)    )

      # Machine Time
      machine_time += 1

      return xorshift(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ) % 0xFF_FF_FF_FF


def 					__lowerCamelCase			(      )							->   Optional[int]:
      """simple docstring"""

      global buffer_space, params_space, machine_time, K, m, t

      SCREAMING_SNAKE_CASE_      :			List[str]     =       K
      SCREAMING_SNAKE_CASE_      :			Dict     =       [0] * m
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0


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

      # Pushing Data (Input)
      import random

      snake_case_       =       random.sample(range(0XFFFF_FFFF), 1_0_0)
      for chunk in message:
            push(chunk)

      # for controlling
      snake_case_       =       ''

      # Pulling Data (Output)
      while inp in ("e", "E"):
            print(F'''{format(pull(), "#04x")}''')
            print(buffer_space)
            print(params_space)
            snake_case_       =       input('(e)exit? ').strip()




'''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

