


'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class  _lowercase   :
   # setable values
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   @classmethod
   def a     (	cls					:			Optional[int]			)   ->       Optional[int]:
        return cls()




@dataclass
class  _lowercase   (  __UpperCAmelCase       ):
   _SCREAMING_SNAKE_CASE     :     ScoreSdeVeSchedulerState
   _SCREAMING_SNAKE_CASE     :     jnp.ndarray
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None




class  _lowercase   (  __UpperCAmelCase	,				__UpperCAmelCase       ):
   @property
   def a     (	self					:			List[Any]			)   ->       Tuple:
        return True
   @register_to_config
   def __init__(	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			int = 2000					,      SCREAMING_SNAKE_CASE_					:			Dict = 0.1_5					,      SCREAMING_SNAKE_CASE_					:			str = 0.0_1					,      SCREAMING_SNAKE_CASE_					:			Optional[Any] = 1348.0					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any] = 1e-5					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = 1					,      )   ->       str:
        pass
   def a     (	self					:			List[Any]			)   ->       List[Any]:
        __snake_case    			=	ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            lowerCamelCase__					,      self.config.num_train_timesteps					,      self.config.sigma_min					,      self.config.sigma_max					,      self.config.sampling_eps					,      )
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Any = ()					,      SCREAMING_SNAKE_CASE_					:			Any = None			)   ->       ScoreSdeVeSchedulerState:
        __snake_case    			=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        __snake_case    			=	jnp.linspace(1					,      lowerCamelCase__					,      lowerCamelCase__			)
        return state.replace(timesteps=lowerCamelCase__			)
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any] = None					,      SCREAMING_SNAKE_CASE_					:			Any = None					,      SCREAMING_SNAKE_CASE_					:			int = None					,      )   ->       ScoreSdeVeSchedulerState:
        __snake_case    			=	sigma_min if sigma_min is not None else self.config.sigma_min
        __snake_case    			=	sigma_max if sigma_max is not None else self.config.sigma_max
        __snake_case    			=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             __snake_case    			=	self.set_timesteps(lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__			)

        __snake_case    			=	jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__			)					,      jnp.log(lowerCamelCase__			)					,      lowerCamelCase__			)			)
        __snake_case    			=	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

        return state.replace(discrete_sigmas=lowerCamelCase__					,      sigmas=lowerCamelCase__			)
   def a     (	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       List[str]:
        return jnp.where(timesteps == 0					,      jnp.zeros_like(lowerCamelCase__			)					,      state.discrete_sigmas[timesteps - 1]			)
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[Any] = True					,      )   ->       Union[FlaxSdeVeOutput, Tuple]:



        if state.timesteps is None:
             raise ValueError(
                 '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

        __snake_case    			=	timestep * jnp.ones(
            sample.shape[0]					,      )
        __snake_case    			=	(timestep * (len(state.timesteps			) - 1)).long()

        __snake_case    			=	state.discrete_sigmas[timesteps]
        __snake_case    			=	self.get_adjacent_sigma(lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__			)
        __snake_case    			=	jnp.zeros_like(lowerCamelCase__			)
        __snake_case    			=	(sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        __snake_case    			=	diffusion.flatten()
        __snake_case    			=	broadcast_to_shape_from_left(lowerCamelCase__					,      sample.shape			)
        __snake_case    			=	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        __snake_case    			=	random.split(lowerCamelCase__					,      num=1			)
        __snake_case    			=	random.normal(key=lowerCamelCase__					,      shape=sample.shape			)
        __snake_case    			=	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        __snake_case    			=	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
             return (prev_sample, prev_sample_mean, state)

        return FlaxSdeVeOutput(prev_sample=lowerCamelCase__					,      prev_sample_mean=lowerCamelCase__					,      state=lowerCamelCase__			)
   def a     (	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any] = True					,      )   ->       Union[FlaxSdeVeOutput, Tuple]:



        if state.timesteps is None:
             raise ValueError(
                 '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        __snake_case    			=	random.split(lowerCamelCase__					,      num=1			)
        __snake_case    			=	random.normal(key=lowerCamelCase__					,      shape=sample.shape			)

        # compute step size from the model_output, the noise, and the snr
        __snake_case    			=	jnp.linalg.norm(lowerCamelCase__			)
        __snake_case    			=	jnp.linalg.norm(lowerCamelCase__			)
        __snake_case    			=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        __snake_case    			=	step_size * jnp.ones(sample.shape[0]			)

        # compute corrected sample: model_output term and noise term
        __snake_case    			=	step_size.flatten()
        __snake_case    			=	broadcast_to_shape_from_left(lowerCamelCase__					,      sample.shape			)
        __snake_case    			=	sample + step_size * model_output
        __snake_case    			=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=lowerCamelCase__					,      state=lowerCamelCase__			)





   def __len__(	self					:			Union[str, Any]			)   ->       Union[str, Any]:
        return self.config.num_train_timesteps








'''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()




