






# 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    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42

				# setable values
				lowercase__       							= 42
				lowercase__       							= 42
				lowercase__       							= None




				@classmethod
				def        lowercase__     (  cls		:					Tuple,						lowerCamelCase		:					CommonSchedulerState,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					jnp.ndarray						):

								'''simple docstring'''
								return cls(common=lowerCamelCase,						init_noise_sigma=lowerCamelCase,						timesteps=lowerCamelCase						)



@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= [e.name for e in FlaxKarrasDiffusionSchedulers]

				lowercase__       							= 42




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return True




				@register_to_config
				def __init__(  self		:					Any,						lowerCamelCase		:					int = 1_000,						lowerCamelCase		:					float = 0.0001,						lowerCamelCase		:					float = 0.02,						lowerCamelCase		:					str = "linear",						lowerCamelCase		:					Optional[jnp.ndarray] = None,						lowerCamelCase		:					str = "fixed_small",						lowerCamelCase		:					bool = True,						lowerCamelCase		:					str = "epsilon",						lowerCamelCase		:					jnp.dtype = jnp.floataa,						):

								'''simple docstring'''
								lowercase__     				=    dtype




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					Optional[CommonSchedulerState] = None						):

								'''simple docstring'''
								if common is None:
												lowercase__     				=    CommonSchedulerState.create(self						)

								# 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 DDPMSchedulerState.create(
								    common=lowerCamelCase,						init_noise_sigma=lowerCamelCase,						timesteps=lowerCamelCase,						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					int,						lowerCamelCase		:					Tuple = ()						):

								'''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]

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




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					int,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					Optional[int]=None						):

								'''simple docstring'''
								lowercase__     				=    state.common.alphas_cumprod[t]
								lowercase__     				=    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
								lowercase__     				=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

								return variance




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					int,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					Optional[jax.random.KeyArray] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								lowercase__     				=    timestep

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

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

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

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

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

								lowercase__     				=    pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase,						state=lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					jnp.ndarray,						):

								'''simple docstring'''
								return add_noise_common(state.common,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					DDPMSchedulerState,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					jnp.ndarray,						lowerCamelCase		:					jnp.ndarray,						):

								'''simple docstring'''
								return get_velocity_common(state.common,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def __len__(  self		:					Dict						):

								'''simple docstring'''
								return self.config.num_train_timesteps












from __future__ import annotations






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								if resistor <= 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative or zero value!"""
												raise ValueError(lowerCamelCase_       )
								first_sum += 1 / float(lowerCamelCase_       )
								index += 1
				return 1 / first_sum






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								sum_r += resistor
								if resistor < 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative value!"""
												raise ValueError(lowerCamelCase_       )
								index += 1
				return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()




