




from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)


@flax.struct.dataclass
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		jnp.ndarray
     UpperCamelCase_							:		jnp.ndarray





class  SCREAMING_SNAKE_CASE			(			nn.Module					):
     '''simple docstring'''


     UpperCamelCase_							:		int
     UpperCamelCase_							:		Tuple[int]			=	(1_6, 3_2, 9_6, 2_5_6)
     UpperCamelCase_							:		jnp.dtype			=	jnp.floataa


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    nn.Conv(
           self.block_out_channels[0]					, kernel_size=(3, 3)					, padding=((1, 1), (1, 1))					, dtype=self.dtype					, )

       SCREAMING_SNAKE_CASE       :      str							=    []
       for i in range(len(self.block_out_channels      ) - 1      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.block_out_channels[i]
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.block_out_channels[i + 1]
         SCREAMING_SNAKE_CASE       :      str							=    nn.Conv(
             UpperCAmelCase_					, kernel_size=(3, 3)					, padding=((1, 1), (1, 1))					, dtype=self.dtype					, )
         blocks.append(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Dict							=    nn.Conv(
             UpperCAmelCase_					, kernel_size=(3, 3)					, strides=(2, 2)					, padding=((1, 1), (1, 1))					, dtype=self.dtype					, )
         blocks.append(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    blocks

       SCREAMING_SNAKE_CASE       :      str							=    nn.Conv(
           self.conditioning_embedding_channels					, kernel_size=(3, 3)					, padding=((1, 1), (1, 1))					, kernel_init=nn.initializers.zeros_init()					, bias_init=nn.initializers.zeros_init()					, dtype=self.dtype					, )






     def __call__(				self       :	int					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.conv_in(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    nn.silu(UpperCAmelCase_      )

       for block in self.blocks:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    block(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.silu(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Dict							=    self.conv_out(UpperCAmelCase_      )

       return embedding





@flax_register_to_config
class  SCREAMING_SNAKE_CASE			(			nn.Module      ,		lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		int			=	3_2
     UpperCamelCase_							:		int			=	4
     UpperCamelCase_							:		Tuple[str]			=	(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     UpperCamelCase_							:		Union[bool, Tuple[bool]]			=	False
     UpperCamelCase_							:		Tuple[int]			=	(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
     UpperCamelCase_							:		int			=	2
     UpperCamelCase_							:		Union[int, Tuple[int]]			=	8
     UpperCamelCase_							:		Optional[Union[int, Tuple[int]]]			=	None
     UpperCamelCase_							:		int			=	1_2_8_0
     UpperCamelCase_							:		float			=	0.0
     UpperCamelCase_							:		bool			=	False
     UpperCamelCase_							:		jnp.dtype			=	jnp.floataa
     UpperCamelCase_							:		bool			=	True
     UpperCamelCase_							:		int			=	0
     UpperCamelCase_							:		str			=	"rgb"
     UpperCamelCase_							:		Tuple[int]			=	(1_6, 3_2, 9_6, 2_5_6)


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	jax.random.KeyArray      ):
       # init input tensors
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (1, self.in_channels, self.sample_size, self.sample_size)
       SCREAMING_SNAKE_CASE       :      Tuple							=    jnp.zeros(UpperCAmelCase_					, dtype=jnp.floataa      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    jnp.ones((1,)					, dtype=jnp.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    jnp.zeros((1, 1, self.cross_attention_dim)					, dtype=jnp.floataa      )
       SCREAMING_SNAKE_CASE       :      Dict							=    (1, 3, self.sample_size * 8, self.sample_size * 8)
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    jnp.zeros(UpperCAmelCase_					, dtype=jnp.floataa      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    jax.random.split(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {"params": params_rng, "dropout": dropout_rng}

       return self.init(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )["params"]


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.block_out_channels
       SCREAMING_SNAKE_CASE       :      List[str]							=    block_out_channels[0] * 4

       # If `num_attention_heads` is not defined (which is the case for most models)
       # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
       # The reason for this behavior is to correct for incorrectly named variables that were introduced
       # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
       # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
       # which is why we correct for the naming here.
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.num_attention_heads or self.attention_head_dim

       # input
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.Conv(
           block_out_channels[0]					, kernel_size=(3, 3)					, strides=(1, 1)					, padding=((1, 1), (1, 1))					, dtype=self.dtype					, )

       # time
       SCREAMING_SNAKE_CASE       :      Dict							=    FlaxTimesteps(
           block_out_channels[0]					, flip_sin_to_cos=self.flip_sin_to_cos					, freq_shift=self.config.freq_shift      )
       SCREAMING_SNAKE_CASE       :      str							=    FlaxTimestepEmbedding(UpperCAmelCase_					, dtype=self.dtype      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    FlaxControlNetConditioningEmbedding(
           conditioning_embedding_channels=block_out_channels[0]					, block_out_channels=self.conditioning_embedding_out_channels					, )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.only_cross_attention
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    (only_cross_attention,) * len(self.down_block_types      )

       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    (num_attention_heads,) * len(self.down_block_types      )

       # down
       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    block_out_channels[0]

       SCREAMING_SNAKE_CASE       :      Tuple							=    nn.Conv(
           UpperCAmelCase_					, kernel_size=(1, 1)					, padding="VALID"					, kernel_init=nn.initializers.zeros_init()					, bias_init=nn.initializers.zeros_init()					, dtype=self.dtype					, )
       controlnet_down_blocks.append(UpperCAmelCase_      )

       for i, down_block_type in enumerate(self.down_block_types      ):
         SCREAMING_SNAKE_CASE       :      str							=    output_channel
         SCREAMING_SNAKE_CASE       :      List[Any]							=    block_out_channels[i]
         SCREAMING_SNAKE_CASE       :      List[str]							=    i == len(UpperCAmelCase_      ) - 1

         if down_block_type == "CrossAttnDownBlock2D":
           SCREAMING_SNAKE_CASE       :      Any							=    FlaxCrossAttnDownBlockaD(
               in_channels=UpperCAmelCase_					, out_channels=UpperCAmelCase_					, dropout=self.dropout					, num_layers=self.layers_per_block					, num_attention_heads=num_attention_heads[i]					, add_downsample=not is_final_block					, use_linear_projection=self.use_linear_projection					, only_cross_attention=only_cross_attention[i]					, dtype=self.dtype					, )
         else:
           SCREAMING_SNAKE_CASE       :      int							=    FlaxDownBlockaD(
               in_channels=UpperCAmelCase_					, out_channels=UpperCAmelCase_					, dropout=self.dropout					, num_layers=self.layers_per_block					, add_downsample=not is_final_block					, dtype=self.dtype					, )

         down_blocks.append(UpperCAmelCase_      )

         for _ in range(self.layers_per_block      ):
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.Conv(
               UpperCAmelCase_					, kernel_size=(1, 1)					, padding="VALID"					, kernel_init=nn.initializers.zeros_init()					, bias_init=nn.initializers.zeros_init()					, dtype=self.dtype					, )
           controlnet_down_blocks.append(UpperCAmelCase_      )

         if not is_final_block:
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    nn.Conv(
               UpperCAmelCase_					, kernel_size=(1, 1)					, padding="VALID"					, kernel_init=nn.initializers.zeros_init()					, bias_init=nn.initializers.zeros_init()					, dtype=self.dtype					, )
           controlnet_down_blocks.append(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      str							=    down_blocks
       SCREAMING_SNAKE_CASE       :      List[Any]							=    controlnet_down_blocks

       # mid
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    block_out_channels[-1]
       SCREAMING_SNAKE_CASE       :      Any							=    FlaxUNetMidBlockaDCrossAttn(
           in_channels=UpperCAmelCase_					, dropout=self.dropout					, num_attention_heads=num_attention_heads[-1]					, use_linear_projection=self.use_linear_projection					, dtype=self.dtype					, )

       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.Conv(
           UpperCAmelCase_					, kernel_size=(1, 1)					, padding="VALID"					, kernel_init=nn.initializers.zeros_init()					, bias_init=nn.initializers.zeros_init()					, dtype=self.dtype					, )






     def __call__(				self       :	Tuple					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	float = 1.0					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = False					, ):
       SCREAMING_SNAKE_CASE       :      int							=    self.controlnet_conditioning_channel_order
       if channel_order == "bgr":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    jnp.flip(UpperCAmelCase_					, axis=1      )

       # 1. time
       if not isinstance(UpperCAmelCase_					, jnp.ndarray      ):
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    jnp.array([timesteps]					, dtype=jnp.intaa      )
       elif isinstance(UpperCAmelCase_					, jnp.ndarray      ) and len(timesteps.shape      ) == 0:
         SCREAMING_SNAKE_CASE       :      Any							=    timesteps.astype(dtype=jnp.floataa      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    jnp.expand_dims(UpperCAmelCase_					, 0      )

       SCREAMING_SNAKE_CASE       :      Dict							=    self.time_proj(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Dict							=    self.time_embedding(UpperCAmelCase_      )

       # 2. pre-process
       SCREAMING_SNAKE_CASE       :      Dict							=    jnp.transpose(UpperCAmelCase_					, (0, 2, 3, 1)      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.conv_in(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    jnp.transpose(UpperCAmelCase_					, (0, 2, 3, 1)      )
       SCREAMING_SNAKE_CASE       :      Any							=    self.controlnet_cond_embedding(UpperCAmelCase_      )
       sample += controlnet_cond

       # 3. down
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample,)
       for down_block in self.down_blocks:
         if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    down_block(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, deterministic=not train      )
         else:
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    down_block(UpperCAmelCase_					, UpperCAmelCase_					, deterministic=not train      )
         down_block_res_samples += res_samples

       # 4. mid
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.mid_block(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, deterministic=not train      )

       # 5. contronet blocks
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ()
       for down_block_res_sample, controlnet_block in zip(UpperCAmelCase_					, self.controlnet_down_blocks      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    controlnet_block(UpperCAmelCase_      )
         controlnet_down_block_res_samples += (down_block_res_sample,)

       SCREAMING_SNAKE_CASE       :      List[Any]							=    controlnet_down_block_res_samples

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.controlnet_mid_block(UpperCAmelCase_      )

       # 6. scaling
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [sample * conditioning_scale for sample in down_block_res_samples]
       mid_block_res_sample *= conditioning_scale

       if not return_dict:
         return (down_block_res_samples, mid_block_res_sample)

       return FlaxControlNetOutput(
           down_block_res_samples=UpperCAmelCase_					, mid_block_res_sample=UpperCAmelCase_      )














# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor
     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None





def   lowerCamelCase__			(     lowercase						,     lowercase=0.999						,     lowercase="cosine"						,     ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE       :      List[str]							=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)						,     lowercase				)				)
  return torch.tensor(lowercase						,     dtype=torch.floataa				)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	1


     @register_to_config
     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	int = 1000					, UpperCAmelCase_       :	float = 0.0_001					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	Optional[Union[np.ndarray, List[float]]] = None					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	int = 0					, UpperCAmelCase_       :	str = "epsilon"					, UpperCAmelCase_       :	float = 1.0					, **UpperCAmelCase_       :	Dict					, ):
       if kwargs.get("set_alpha_to_one"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      int							=    (
             "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
         )
         deprecate("set_alpha_to_one"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs["set_alpha_to_one"]
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.tensor(UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             torch.linspace(beta_start**0.5					, beta_end**0.5					, UpperCAmelCase_					, dtype=torch.floataa      ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE       :      int							=    betas_for_alpha_bar(UpperCAmelCase_      )
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1.0 - self.betas
       SCREAMING_SNAKE_CASE       :      Any							=    torch.cumprod(self.alphas					, dim=0      )

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE       :      List[str]							=    1.0

       # setable values
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.from_numpy(np.arange(0					, UpperCAmelCase_      ).copy().astype(np.intaa      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[int] = None      ):
       return sample


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, torch.device] = None      ):

       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
             f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
             f''' maximal {self.config.num_train_timesteps} timesteps.'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    num_inference_steps
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE       :      str							=    (np.arange(0					, UpperCAmelCase_      ) * step_ratio).round().copy().astype(np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )
       self.timesteps += self.config.steps_offset


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       # 1. get previous step value (=t+1)
       SCREAMING_SNAKE_CASE       :      Tuple							=    timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.alphas_cumprod[timestep]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       SCREAMING_SNAKE_CASE       :      Tuple							=    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":
         SCREAMING_SNAKE_CASE       :      Any							=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_output
       elif self.config.prediction_type == "sample":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_output
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         SCREAMING_SNAKE_CASE       :      Tuple							=    (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. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         SCREAMING_SNAKE_CASE       :      List[str]							=    pred_original_sample.clamp(
             -self.config.clip_sample_range					, self.config.clip_sample_range      )

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

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

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_      )






     def __len__(				self       :	Dict      ):
       return self.config.num_train_timesteps






