


'''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 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		CommonSchedulerState

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


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  return cls(common=A_      ,      init_noise_sigma=A_      ,      timesteps=A_						)


@dataclass
class 						_lowerCamelCase					(    __snake_case				):




 '''simple docstring'''
 __lowercase				:		DDPMSchedulerState


class 						_lowerCamelCase					(    __snake_case    ,		__snake_case				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						[e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowercase				:		jnp.dtype


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return True


 @register_to_config
 def __init__(      self      ,      __lowercase = 1_000      ,      __lowercase = 0.0_0_0_1      ,      __lowercase = 0.0_2      ,      __lowercase = "linear"      ,      __lowercase = None      ,      __lowercase = "fixed_small"      ,      __lowercase = True      ,      __lowercase = "epsilon"      ,      __lowercase = jnp.floataa      ,      ):

  """simple docstring"""







  __A  :		int       =			dtype


 def  snake_case__   (      self      ,      __lowercase = None						):

  """simple docstring"""







  if common is None:
   __A  :		List[Any]       =			CommonSchedulerState.create(self						)

  # standard deviation of the initial noise distribution
  __A  :		List[Any]       =			jnp.array(1.0      ,      dtype=self.dtype						)

  __A  :		int       =			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      ,      __lowercase      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  return sample


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = ()						):

  """simple docstring"""







  __A  :		List[Any]       =			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  :		Union[str, Any]       =			(jnp.arange(0      ,      A_						) * step_ratio).round()[::-1]

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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		Optional[int]       =			state.common.alphas_cumprod[t]
  __A  :		List[str]       =			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  :		str       =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

  if variance_type is None:
   __A  :		str       =			self.config.variance_type

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

  return variance


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = True      ,      ):

  """simple docstring"""







  __A  :		Union[str, Any]       =			timestep

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

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

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

  # 6. Add noise
  def random_variance():
   __A  :		Dict       =			jax.random.split(A_      ,      num=1						)
   __A  :		List[Any]       =			jax.random.normal(A_      ,      shape=model_output.shape      ,      dtype=self.dtype						)
   return (self._get_variance(A_      ,      A_      ,      predicted_variance=A_						) ** 0.5) * noise

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

  __A  :		str       =			pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample, state)

  return FlaxDDPMSchedulerOutput(prev_sample=A_      ,      state=A_						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







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







 def __len__(      self						):

  """simple docstring"""







  return self.config.num_train_timesteps










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			0.0

  # Start at index 1 and do index 0 at the end.
  for i in range(1      ,      self.order + 1						):
   result += (
       self.b_coeffs[i] * self.input_history[i - 1]
       - self.a_coeffs[i] * self.output_history[i - 1]
   )

  __A  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






