





"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 		lowercase					:
      _SCREAMING_SNAKE_CASE					=      42

      # setable values
      _SCREAMING_SNAKE_CASE					=      42
      _SCREAMING_SNAKE_CASE					=      42
      _SCREAMING_SNAKE_CASE					=      None





      @classmethod
      def     _snake_case			(       cls      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[Any]:
            return cls(common=lowercase      ,						init_noise_sigma=lowercase      ,						timesteps=lowercase )







@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      [e.name for e in FlaxKarrasDiffusionSchedulers]

      _SCREAMING_SNAKE_CASE					=      42





      @property
      def     _snake_case			(       self )		->     Tuple:
            return True





      @register_to_config
      def __init__(       self      ,						lowercase = 1_000      ,						lowercase = 0.0_001      ,						lowercase = 0.02      ,						lowercase = "linear"      ,						lowercase = None      ,						lowercase = "fixed_small"      ,						lowercase = True      ,						lowercase = "epsilon"      ,						lowercase = jnp.floataa      ,						)		->     List[Any]:
            lowerCAmelCase  						=       dtype





      def     _snake_case			(       self      ,						lowercase = None )		->     DDPMSchedulerState:
            if common is None:
                  lowerCAmelCase  						=       CommonSchedulerState.create(self )

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

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

            return DDPMSchedulerState.create(
                common=lowercase      ,						init_noise_sigma=lowercase      ,						timesteps=lowercase      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None )		->     jnp.ndarray:
            return sample





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = () )		->     DDPMSchedulerState:
            lowerCAmelCase  						=       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
            lowerCAmelCase  						=       (jnp.arange(0      ,						lowercase ) * step_ratio).round()[::-1]

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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=None )		->     Optional[int]:
            lowerCAmelCase  						=       state.common.alphas_cumprod[t]
            lowerCAmelCase  						=       jnp.where(t > 0      ,						state.common.alphas_cumprod[t - 1]      ,						jnp.array(1.0      ,						dtype=self.dtype ) )

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            lowerCAmelCase  						=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

            if variance_type is None:
                  lowerCAmelCase  						=       self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small":
                  lowerCAmelCase  						=       jnp.clip(lowercase      ,						a_min=1e-20 )
            # for rl-diffuser https://arxiv.org/abs/2205.09991
            elif variance_type == "fixed_small_log":
                  lowerCAmelCase  						=       jnp.log(jnp.clip(lowercase      ,						a_min=1e-20 ) )
            elif variance_type == "fixed_large":
                  lowerCAmelCase  						=       state.common.betas[t]
            elif variance_type == "fixed_large_log":
                  # Glide max_log
                  lowerCAmelCase  						=       jnp.log(state.common.betas[t] )
            elif variance_type == "learned":
                  return predicted_variance
            elif variance_type == "learned_range":
                  lowerCAmelCase  						=       variance
                  lowerCAmelCase  						=       state.common.betas[t]
                  lowerCAmelCase  						=       (predicted_variance + 1) / 2
                  lowerCAmelCase  						=       frac * max_log + (1 - frac) * min_log

            return variance





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						lowercase = True      ,						)		->     Union[FlaxDDPMSchedulerOutput, Tuple]:
            lowerCAmelCase  						=       timestep

            if key is None:
                  lowerCAmelCase  						=       jax.random.PRNGKey(0 )

            if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                  lowerCAmelCase						,		lowerCAmelCase  						=       jnp.split(lowercase      ,						sample.shape[1]      ,						axis=1 )
            else:
                  lowerCAmelCase  						=       None

            # 1. compute alphas, betas
            lowerCAmelCase  						=       state.common.alphas_cumprod[t]
            lowerCAmelCase  						=       jnp.where(t > 0      ,						state.common.alphas_cumprod[t - 1]      ,						jnp.array(1.0      ,						dtype=self.dtype ) )
            lowerCAmelCase  						=       1 - alpha_prod_t
            lowerCAmelCase  						=       1 - alpha_prod_t_prev

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                  lowerCAmelCase  						=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                  lowerCAmelCase  						=       model_output
            elif self.config.prediction_type == "v_prediction":
                  lowerCAmelCase  						=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            else:
                  raise ValueError(
                      f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
                      """ for the FlaxDDPMScheduler.""" )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                  lowerCAmelCase  						=       jnp.clip(lowercase      ,						-1      ,						1 )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowerCAmelCase  						=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
            lowerCAmelCase  						=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowerCAmelCase  						=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            def random_variance():
                  lowerCAmelCase  						=       jax.random.split(lowercase      ,						num=1 )
                  lowerCAmelCase  						=       jax.random.normal(lowercase      ,						shape=model_output.shape      ,						dtype=self.dtype )
                  return (self._get_variance(lowercase      ,						lowercase      ,						predicted_variance=lowercase ) ** 0.5) * noise

            lowerCAmelCase  						=       jnp.where(t > 0      ,						random_variance()      ,						jnp.zeros(model_output.shape      ,						dtype=self.dtype ) )

            lowerCAmelCase  						=       pred_prev_sample + variance

            if not return_dict:
                  return (pred_prev_sample, state)

            return FlaxDDPMSchedulerOutput(prev_sample=lowercase      ,						state=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     jnp.ndarray:
            return add_noise_common(state.common      ,						lowercase      ,						lowercase      ,						lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     jnp.ndarray:
            return get_velocity_common(state.common      ,						lowercase      ,						lowercase      ,						lowercase )





      def __len__(       self )		->     str:
            return self.config.num_train_timesteps













"""simple docstring"""





def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      while b:
            lowerCAmelCase						,		lowerCAmelCase  						=       b, a % b
      return a


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNAKE_CASE							,						a % b			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      print(F'euclidean_gcd(3, 5) = {euclidean_gcd(3							,						5			)}'			)
      print(F'euclidean_gcd(5, 3) = {euclidean_gcd(5							,						3			)}'			)
      print(F'euclidean_gcd(1, 3) = {euclidean_gcd(1							,						3			)}'			)
      print(F'euclidean_gcd(3, 6) = {euclidean_gcd(3							,						6			)}'			)
      print(F'euclidean_gcd(6, 3) = {euclidean_gcd(6							,						3			)}'			)

      print(F'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3							,						5			)}'			)
      print(F'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5							,						3			)}'			)
      print(F'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1							,						3			)}'			)
      print(F'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3							,						6			)}'			)
      print(F'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6							,						3			)}'			)


if __name__ == "__main__":
       main()






