






# 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"""



		snake_case    =   42

		# setable values
		snake_case    =   42
		snake_case    =   42
		snake_case    =   None
		@classmethod
		def       A(  cls		:       int						,_SCREAMING_SNAKE_CASE		:       CommonSchedulerState						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray		)    ->   Dict:


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


@dataclass
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   42


class      UpperCamelCase (				snake_case__			,       snake_case__ ):







		"""simple docstring"""



		snake_case    =   [e.name for e in FlaxKarrasDiffusionSchedulers]

		snake_case    =   42
		@property
		def       A(  self		:       str		)    ->   Optional[Any]:


						'''simple docstring'''
						return True
		@register_to_config
		def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int = 1_0_0_0						,_SCREAMING_SNAKE_CASE		:       float = 0.0001						,_SCREAMING_SNAKE_CASE		:       float = 0.02						,_SCREAMING_SNAKE_CASE		:       str = "linear"						,_SCREAMING_SNAKE_CASE		:       Optional[jnp.ndarray] = None						,_SCREAMING_SNAKE_CASE		:       str = "fixed_small"						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       str = "epsilon"						,_SCREAMING_SNAKE_CASE		:       jnp.dtype = jnp.floataa						,)    ->   int:


						'''simple docstring'''
						A	 =      dtype
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[CommonSchedulerState] = None		)    ->   DDPMSchedulerState:


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

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

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

						return DDPMSchedulerState.create(
						    common=_SCREAMING_SNAKE_CASE						,init_noise_sigma=_SCREAMING_SNAKE_CASE						,timesteps=_SCREAMING_SNAKE_CASE						,)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None		)    ->   jnp.ndarray:


						'''simple docstring'''
						return sample
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple = ()		)    ->   DDPMSchedulerState:


						'''simple docstring'''
						A	 =      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
						A	 =      (jnp.arange(0						,_SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

						return state.replace(
						    num_inference_steps=_SCREAMING_SNAKE_CASE						,timesteps=_SCREAMING_SNAKE_CASE						,)
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None		)    ->   int:


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

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

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

						return variance
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       Optional[jax.random.KeyArray] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,)    ->   Union[FlaxDDPMSchedulerOutput, Tuple]:


						'''simple docstring'''
						A	 =      timestep

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

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

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

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

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

						A	 =      pred_prev_sample + variance

						if not return_dict:
										return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE						,state=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,)    ->   jnp.ndarray:


						'''simple docstring'''
						return add_noise_common(state.common						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       DDPMSchedulerState						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,_SCREAMING_SNAKE_CASE		:       jnp.ndarray						,)    ->   jnp.ndarray:


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







		def __len__(  self		:       List[Any]		)    ->   Dict:


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













def      snake_case		(							UpperCAmelCase	:       int   ):
				A	 =      0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum







def      snake_case		(							UpperCAmelCase	:       int = 1_00   ):
				A	 =      1
				A	 =      2

				for i in range(2,						max_n + 1   ):
								A	 =      pre_numerator
								A	 =      2 * i // 3 if i % 3 == 0 else 1
								A	 =      cur_numerator
								A	 =      e_cont * pre_numerator + temp

				return sum_digits(UpperCAmelCase   )


if __name__ == "__main__":
					print(f'''{solution() = }''')





