




'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					CommonSchedulerState
       snake_case_			:					jnp.ndarray

       # setable values
       snake_case_			:					jnp.ndarray
       snake_case_			:					jnp.ndarray
       snake_case_			:					Optional[int]   = None

       @classmethod
       def    snake_case__      (   cls ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> Tuple:

           """simple docstring"""



           return cls(
               common=lowerCamelCase__ ,		final_alpha_cumprod=lowerCamelCase__ ,		init_noise_sigma=lowerCamelCase__ ,		timesteps=lowerCamelCase__ ,		)






@dataclass
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					DDIMSchedulerState






class 	SCREAMING_SNAKE_CASE(    __A       ,				__A					):
       snake_case_			:					str   = [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case_			:					jnp.dtype

       @property
       def    snake_case__      (   self					)   -> Union[str, Any]:

           """simple docstring"""



           return True

       @register_to_config
       def __init__(   self ,		lowerCamelCase__ = 1000 ,		lowerCamelCase__ = 0.00_01 ,		lowerCamelCase__ = 0.02 ,		lowerCamelCase__ = "linear" ,		lowerCamelCase__ = None ,		lowerCamelCase__ = True ,		lowerCamelCase__ = 0 ,		lowerCamelCase__ = "epsilon" ,		lowerCamelCase__ = jnp.floataa ,		)   -> List[str]:

           """simple docstring"""



           __lowercase          =						dtype

       def    snake_case__      (   self ,		lowerCamelCase__ = None					)   -> DDIMSchedulerState:

           """simple docstring"""



           if common is None:
               __lowercase          =						CommonSchedulerState.create(self					)

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           __lowercase          =						(
               jnp.array(1.0 ,		dtype=self.dtype					) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

           # standard deviation of the initial noise distribution
           __lowercase          =						jnp.array(1.0 ,		dtype=self.dtype					)

           __lowercase          =						jnp.arange(0 ,		self.config.num_train_timesteps					).round()[::-1]

           return DDIMSchedulerState.create(
               common=lowerCamelCase__ ,		final_alpha_cumprod=lowerCamelCase__ ,		init_noise_sigma=lowerCamelCase__ ,		timesteps=lowerCamelCase__ ,		)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = None					)   -> jnp.ndarray:

           """simple docstring"""



           return sample

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = ()					)   -> DDIMSchedulerState:

           """simple docstring"""



           __lowercase          =						self.config.num_train_timesteps // num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # rounding to avoid issues when num_inference_step is power of 3
           __lowercase          =						(jnp.arange(0 ,		lowerCamelCase__					) * step_ratio).round()[::-1] + self.config.steps_offset

           return state.replace(
               num_inference_steps=lowerCamelCase__ ,		timesteps=lowerCamelCase__ ,		)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> Union[str, Any]:

           """simple docstring"""



           __lowercase          =						state.common.alphas_cumprod[timestep]
           __lowercase          =						jnp.where(
               prev_timestep >= 0 ,		state.common.alphas_cumprod[prev_timestep] ,		state.final_alpha_cumprod					)
           __lowercase          =						1 - alpha_prod_t
           __lowercase          =						1 - alpha_prod_t_prev

           __lowercase          =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = 0.0 ,		lowerCamelCase__ = True ,		)   -> Union[FlaxDDIMSchedulerOutput, Tuple]:

           """simple docstring"""



           if state.num_inference_steps is None:
               raise ValueError(
                   """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""					)

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           __lowercase          =						timestep - self.config.num_train_timesteps // state.num_inference_steps

           __lowercase          =						state.common.alphas_cumprod
           __lowercase          =						state.final_alpha_cumprod

           # 2. compute alphas, betas
           __lowercase          =						alphas_cumprod[timestep]
           __lowercase          =						jnp.where(prev_timestep >= 0 ,		alphas_cumprod[prev_timestep] ,		lowerCamelCase__					)

           __lowercase          =						1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
               __lowercase          =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               __lowercase          =						model_output
           elif self.config.prediction_type == "sample":
               __lowercase          =						model_output
               __lowercase          =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
               __lowercase          =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               __lowercase          =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
               raise ValueError(
                   F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                   """ `v_prediction`"""					)

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           __lowercase          =						self._get_variance(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					)
           __lowercase          =						eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           __lowercase          =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           __lowercase          =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
               return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase__ ,		state=lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> jnp.ndarray:

           """simple docstring"""



           return add_noise_common(state.common ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> jnp.ndarray:

           """simple docstring"""



           return get_velocity_common(state.common ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					)




       def __len__(   self					)   -> Optional[Any]:

           """simple docstring"""



           return self.config.num_train_timesteps










'''simple docstring'''
A							:				int			     =	(1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
A							:				tuple			     =	(-153, -1, 0, 1.2, 200, "A", [], {}, None)
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    __lowercase          =						n
    while temp > 0:
        __lowercase          =						temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Init a "histogram" of the digits
    __lowercase          =						[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    while temp > 0:
        __lowercase    ,__lowercase          =						divmod(a__   ,10      )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(a__   ,range(len(a__      )      )      ):
        total += cnt * i**digit_total

    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False
    __lowercase          =						len(str(a__      )      )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(a__      ) ** expo for i in str(a__      )      )
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						int(input("""Enter an integer to see if it is an Armstrong number: """      ).strip()      )
    print(f'{num} is {"" if armstrong_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if narcissistic_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if pluperfect_number(a__      ) else "not "}an Armstrong number.'      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




