
# 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 UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					CommonSchedulerState
   __UpperCamelCase	:					jnp.ndarray

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


   @classmethod
   def 				__magic_name__       (      cls  :							Any    ,    lowerCAmelCase_  :							CommonSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							jnp.ndarray    ,    ):

      """simple docstring"""
      return cls(
          common=lowerCAmelCase_    ,    final_alpha_cumprod=lowerCAmelCase_    ,    init_noise_sigma=lowerCAmelCase_    ,    timesteps=lowerCAmelCase_    ,    )






@dataclass
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''
   __UpperCamelCase	:					DDIMSchedulerState






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





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

   __UpperCamelCase	:					jnp.dtype


   @property
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      return True


   @register_to_config
   def __init__(      self  :							Any    ,    lowerCAmelCase_  :							int = 1_0_0_0    ,    lowerCAmelCase_  :							float = 0.0001    ,    lowerCAmelCase_  :							float = 0.02    ,    lowerCAmelCase_  :							str = "linear"    ,    lowerCAmelCase_  :							Optional[jnp.ndarray] = None    ,    lowerCAmelCase_  :							bool = True    ,    lowerCAmelCase_  :							int = 0    ,    lowerCAmelCase_  :							str = "epsilon"    ,    lowerCAmelCase_  :							jnp.dtype = jnp.floataa    ,    ):

      """simple docstring"""
      _A:						str	    = dtype


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							Optional[CommonSchedulerState] = None  ):

      """simple docstring"""
      if common is None:
         _A:						Union[str, Any]	    = 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:						Any	    = (
          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:						Optional[int]	    = jnp.array(1.0    ,    dtype=self.dtype  )

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

      return DDIMSchedulerState.create(
          common=lowerCAmelCase_    ,    final_alpha_cumprod=lowerCAmelCase_    ,    init_noise_sigma=lowerCAmelCase_    ,    timesteps=lowerCAmelCase_    ,    )


   def 				__magic_name__       (      self  :							Tuple    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							Optional[int] = None  ):

      """simple docstring"""
      return sample


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							Tuple = ()  ):

      """simple docstring"""
      _A:						Optional[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:						Any	    = (jnp.arange(0    ,    lowerCAmelCase_  ) * step_ratio).round()[::-1] + self.config.steps_offset

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


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    lowerCAmelCase_  :							List[Any]    ,    lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = state.common.alphas_cumprod[timestep]
      _A:						Optional[int]	    = jnp.where(
          prev_timestep >= 0    ,    state.common.alphas_cumprod[prev_timestep]    ,    state.final_alpha_cumprod  )
      _A:						Union[str, Any]	    = 1 - alpha_prod_t
      _A:						Dict	    = 1 - alpha_prod_t_prev

      _A:						List[Any]	    = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							float = 0.0    ,    lowerCAmelCase_  :							bool = True    ,    ):

      """simple docstring"""
      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:						Tuple	    = timestep - self.config.num_train_timesteps // state.num_inference_steps

      _A:						Any	    = state.common.alphas_cumprod
      _A:						Union[str, Any]	    = state.final_alpha_cumprod

      # 2. compute alphas, betas
      _A:						Tuple	    = alphas_cumprod[timestep]
      _A:						Dict	    = jnp.where(prev_timestep >= 0    ,    alphas_cumprod[prev_timestep]    ,    lowerCAmelCase_  )

      _A:						List[Any]	    = 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:						int	    = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _A:						Optional[int]	    = model_output
      elif self.config.prediction_type == "sample":
         _A:						str	    = model_output
         _A:						Tuple	    = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
         _A:						List[Any]	    = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _A:						Union[str, Any]	    = (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:						str	    = self._get_variance(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )
      _A:						List[str]	    = eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _A:						Optional[int]	    = (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:						Tuple	    = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
         return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCAmelCase_    ,    state=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							jnp.ndarray    ,    ):

      """simple docstring"""
      return add_noise_common(state.common    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							DDIMSchedulerState    ,    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





import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


UpperCAmelCase__	:  Optional[int]      =   logging.get_logger(__name__)






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    *lowerCAmelCase_  :							Union[str, Any]    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      warnings.warn(
          '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ConvNextImageProcessor instead.'''    ,    lowerCAmelCase_    ,    )
      super().__init__(*lowerCAmelCase_    ,    **lowerCAmelCase_  )



