


# 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 	A_ :




      '''simple docstring'''


      __snake_case 	=       42

      # setable values
      __snake_case 	=       42
      __snake_case 	=       42
      __snake_case 	=       None




      @classmethod
      def 					_snake_case     (    cls:	Tuple						,				a:	CommonSchedulerState						,				a:	jnp.ndarray						,				a:	jnp.ndarray    ):
       return cls(common=a						,				init_noise_sigma=a						,				timesteps=a    )



@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       42



class 	A_ (	__UpperCamelCase ,				__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [e.name for e in FlaxKarrasDiffusionSchedulers]

      __snake_case 	=       42




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return True




      @register_to_config
      def __init__(    self:	int						,				a:	int = 1000						,				a:	float = 0.0_0_0_1						,				a:	float = 0.0_2						,				a:	str = "linear"						,				a:	Optional[jnp.ndarray] = None						,				a:	str = "fixed_small"						,				a:	bool = True						,				a:	str = "epsilon"						,				a:	jnp.dtype = jnp.floataa						,				):
       __lowerCamelCase		:							Optional[Any]		 =						dtype




      def 					_snake_case     (    self:	int						,				a:	Optional[CommonSchedulerState] = None    ):
       if common is None:
        __lowerCamelCase		:							Dict		 =						CommonSchedulerState.create(self    )

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

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

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




      def 					_snake_case     (    self:	str						,				a:	DDPMSchedulerState						,				a:	jnp.ndarray						,				a:	Optional[int] = None    ):
       return sample




      def 					_snake_case     (    self:	Dict						,				a:	DDPMSchedulerState						,				a:	int						,				a:	Tuple = ()    ):
       __lowerCamelCase		:							List[str]		 =						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		:							Optional[Any]		 =						(jnp.arange(0						,				a    ) * step_ratio).round()[::-1]

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




      def 					_snake_case     (    self:	Tuple						,				a:	DDPMSchedulerState						,				a:	List[str]						,				a:	Dict=None						,				a:	str=None    ):
       __lowerCamelCase		:							str		 =						state.common.alphas_cumprod[t]
       __lowerCamelCase		:							int		 =						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		:							Any		 =						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
        __lowerCamelCase		:							Any		 =						self.config.variance_type

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

       return variance




      def 					_snake_case     (    self:	Dict						,				a:	DDPMSchedulerState						,				a:	jnp.ndarray						,				a:	int						,				a:	jnp.ndarray						,				a:	Optional[jax.random.KeyArray] = None						,				a:	bool = True						,				):
       __lowerCamelCase		:							List[str]		 =						timestep

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

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

       # 1. compute alphas, betas
       __lowerCamelCase		:							List[str]		 =						state.common.alphas_cumprod[t]
       __lowerCamelCase		:							int		 =						jnp.where(t > 0						,				state.common.alphas_cumprod[t - 1]						,				jnp.array(1.0						,				dtype=self.dtype    )    )
       __lowerCamelCase		:							str		 =						1 - alpha_prod_t
       __lowerCamelCase		:							str		 =						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		:							str		 =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
        __lowerCamelCase		:							str		 =						model_output
       elif self.config.prediction_type == "v_prediction":
        __lowerCamelCase		:							str		 =						(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		:							Optional[Any]		 =						jnp.clip(a						,				-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		:							Optional[Any]		 =						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       __lowerCamelCase		:							Any		 =						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		:							int		 =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
        __lowerCamelCase		:							Optional[int]		 =						jax.random.split(a						,				num=1    )
        __lowerCamelCase		:							Dict		 =						jax.random.normal(a						,				shape=model_output.shape						,				dtype=self.dtype    )
        return (self._get_variance(a						,				a						,				predicted_variance=a    ) ** 0.5) * noise

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

       __lowerCamelCase		:							Any		 =						pred_prev_sample + variance

       if not return_dict:
        return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=a						,				state=a    )




      def 					_snake_case     (    self:	Dict						,				a:	DDPMSchedulerState						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				):
       return add_noise_common(state.common						,				a						,				a						,				a    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	DDPMSchedulerState						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				):
       return get_velocity_common(state.common						,				a						,				a						,				a    )




      def __len__(    self:	List[str]    ):
       return self.config.num_train_timesteps





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						0.00
 __lowerCamelCase		:							Tuple		 =						0
 for resistor in resistors:
  if resistor <= 0:
   __lowerCamelCase		:							Union[str, Any]		 =						f'Resistor at index {index} has a negative or zero value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  first_sum += 1 / float(SCREAMING_SNAKE_CASE__						)
  index += 1
 return 1 / first_sum
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						0.00
 __lowerCamelCase		:							str		 =						0
 for resistor in resistors:
  sum_r += resistor
  if resistor < 0:
   __lowerCamelCase		:							Any		 =						f'Resistor at index {index} has a negative value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  index += 1
 return sum_r


if __name__ == "__main__":
       import doctest

       doctest.testmod()

