


'''simple docstring'''




_a      :      List[Any]       				=      [0.33, 0.44, 0.55, 0.44, 0.33]
_a      :      Union[str, Any]       				=      3
_a      :      Optional[Any]       				=      5

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

# Machine Time
_a      :      str       				=      0





def      _a		(lowercase__ :      Optional[int]	) -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

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

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

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

          # Modification (Transition Function) - Jumps
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	r  # Saving to Parameters Space

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

     # Machine Time
     machine_time += 1





def      _a		() -> Tuple:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(lowercase__ :      Tuple       ,      lowercase__ :      int	):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __snake_case    			=	machine_time % m

     # Evolution (Time Length)
     for _ in range(0       ,      lowercase__	):
          # Variables (Position + Parameters)
          __snake_case    			=	params_space[key]
          __snake_case    			=	buffer_space[key]

          # Modification (Transition Function) - Flow
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	(machine_time * 0.01 + r * 1.01) % 1 + 3

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

     # Machine Time
     machine_time += 1

     return xorshift(lowercase__       ,      lowercase__	) % 0xF_F_F_F_F_F_F_F





def      _a		() -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     __snake_case    			=	K
     __snake_case    			=	[0] * m
     __snake_case    			=	0


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

       # Pushing Data (Input)
       import random

       _a      :      Tuple       				=      random.sample(range(0xFFFF_FFFF), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       _a      :      List[Any]       				=      ""

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








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        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 a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

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

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

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

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

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

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




