


"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 __snake_case		:







   """simple docstring"""




   lowerCAmelCase_				:   CommonSchedulerState
   lowerCAmelCase_				:   jnp.ndarray

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




   @classmethod
   def SCREAMING_SNAKE_CASE_      (		cls							:Optional[int]				,  UpperCamelCase__							:CommonSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
          return cls(
              common=UpperCamelCase__				,  final_alpha_cumprod=UpperCamelCase__				,  init_noise_sigma=UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )






@dataclass
class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   DDIMSchedulerState






class __snake_case		(					_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   Union[str, Any]     	=			[e.name for e in FlaxKarrasDiffusionSchedulers]

   lowerCAmelCase_				:   jnp.dtype




   @property
   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
          return True




   @register_to_config
   def __init__(		self							:int				,  UpperCamelCase__							:int = 1_000				,  UpperCamelCase__							:float = 0.0001				,  UpperCamelCase__							:float = 0.02				,  UpperCamelCase__							:str = "linear"				,  UpperCamelCase__							:Optional[jnp.ndarray] = None				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:int = 0				,  UpperCamelCase__							:str = "epsilon"				,  UpperCamelCase__							:jnp.dtype = jnp.floataa				,  ):
          _a          =				dtype




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:Optional[CommonSchedulerState] = None       ):
          if common is None:
                 _a          =				CommonSchedulerState.create(self       )

          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _a          =				(
              jnp.array(1.0				,  dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )

          # 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 DDIMSchedulerState.create(
              common=UpperCamelCase__				,  final_alpha_cumprod=UpperCamelCase__				,  init_noise_sigma=UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:Optional[int] = None       ):
          return sample




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:int				,  UpperCamelCase__							:Tuple = ()       ):
          _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				,  UpperCamelCase__       ) * step_ratio).round()[::-1] + self.config.steps_offset

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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:Optional[Any]       ):
          _a          =				state.common.alphas_cumprod[timestep]
          _a          =				jnp.where(
              prev_timestep >= 0				,  state.common.alphas_cumprod[prev_timestep]				,  state.final_alpha_cumprod       )
          _a          =				1 - alpha_prod_t
          _a          =				1 - alpha_prod_t_prev

          _a          =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:int				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:float = 0.0				,  UpperCamelCase__							:bool = True				,  ):
          if state.num_inference_steps is None:
                 raise ValueError(
                     "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding

          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"

          # 1. get previous step value (=t-1)
          _a          =				timestep - self.config.num_train_timesteps // state.num_inference_steps

          _a          =				state.common.alphas_cumprod
          _a          =				state.final_alpha_cumprod

          # 2. compute alphas, betas
          _a          =				alphas_cumprod[timestep]
          _a          =				jnp.where(prev_timestep >= 0				,  alphas_cumprod[prev_timestep]				,  UpperCamelCase__       )

          _a          =				1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
                 _a          =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 _a          =				model_output
          elif self.config.prediction_type == "sample":
                 _a          =				model_output
                 _a          =				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
                 _a          =				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 _a          =				(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
                 raise ValueError(
                     f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                     " `v_prediction`"       )

          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _a          =				self._get_variance(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				eta * variance ** 0.5

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _a          =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _a          =				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
                 return (prev_sample, state)

          return FlaxDDIMSchedulerOutput(prev_sample=UpperCamelCase__				,  state=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
          return add_noise_common(state.common				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
          return get_velocity_common(state.common				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )




   def __len__(		self							:Optional[Any]       ):
          return self.config.num_train_timesteps




"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				data
          _a          =				None




   def __str__(		self							:Any       ):
          return f'{self.data}'






class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Any       ):
          _a          =				None
          _a          =				None




   def __iter__(		self							:List[str]       ):
          _a          =				self.front
          while node:
                 yield node.data
                 _a          =				node.next




   def __len__(		self							:List[str]       ):
          return len(tuple(iter(self       )       )       )




   def __str__(		self							:Union[str, Any]       ):
          return " <- ".join(str(UpperCamelCase__       ) for item in self       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          return len(self       ) == 0




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
          _a          =				Node(UpperCamelCase__       )
          if self.is_empty():
                 _a          =				_a          =				node
          else:
                 assert isinstance(self.rear				,  UpperCamelCase__       )
                 _a          =				node
                 _a          =				node




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          if self.is_empty():
                 raise IndexError("dequeue from empty queue"       )
          assert isinstance(self.front				,  UpperCamelCase__       )
          _a          =				self.front
          _a          =				self.front.next
          if self.front is None:
                 _a          =				None
          return node.data




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				_a          =				None


if __name__ == "__main__":
   from doctest import testmod

   testmod()
