
"""simple docstring"""




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class     _lowercase						(       __SCREAMING_SNAKE_CASE				):


     '''simple docstring'''





     _A								=							42







class     _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase=3 ,			__UpperCamelCase=3 ,			__UpperCamelCase=("DownEncoderBlock2D",) ,			__UpperCamelCase=(64,) ,			__UpperCamelCase=2 ,			__UpperCamelCase=32 ,			__UpperCamelCase="silu" ,			__UpperCamelCase=True ,			)->					Dict:
          super().__init__()
          UpperCAmelCase__							: Optional[Any] 							=						layers_per_block

          UpperCAmelCase__							: List[str] 							=						torch.nn.Convad(
              __snake_case ,			block_out_channels[0] ,			kernel_size=3 ,			stride=1 ,			padding=1 ,			)

          UpperCAmelCase__							: Optional[int] 							=						None
          UpperCAmelCase__							: str 							=						nn.ModuleList([]  )

          # down
          UpperCAmelCase__							: Tuple 							=						block_out_channels[0]
          for i, down_block_type in enumerate(__snake_case  ):
               UpperCAmelCase__							: Union[str, Any] 							=						output_channel
               UpperCAmelCase__							: Tuple 							=						block_out_channels[i]
               UpperCAmelCase__							: Dict 							=						i == len(__snake_case  ) - 1

               UpperCAmelCase__							: int 							=						get_down_block(
                   __snake_case ,			num_layers=self.layers_per_block ,			in_channels=__snake_case ,			out_channels=__snake_case ,			add_downsample=not is_final_block ,			resnet_eps=1E-6 ,			downsample_padding=0 ,			resnet_act_fn=__snake_case ,			resnet_groups=__snake_case ,			attention_head_dim=__snake_case ,			temb_channels=__snake_case ,			)
               self.down_blocks.append(__snake_case  )

          # mid
          UpperCAmelCase__							: List[Any] 							=						UNetMidBlockaD(
              in_channels=block_out_channels[-1] ,			resnet_eps=1E-6 ,			resnet_act_fn=__snake_case ,			output_scale_factor=1 ,			resnet_time_scale_shift="default" ,			attention_head_dim=block_out_channels[-1] ,			resnet_groups=__snake_case ,			temb_channels=__snake_case ,			)

          # out
          UpperCAmelCase__							: Optional[int] 							=						nn.GroupNorm(num_channels=block_out_channels[-1] ,			num_groups=__snake_case ,			eps=1E-6  )
          UpperCAmelCase__							: Union[str, Any] 							=						nn.SiLU()

          UpperCAmelCase__							: Any 							=						2 * out_channels if double_z else out_channels
          UpperCAmelCase__							: Optional[int] 							=						nn.Convad(block_out_channels[-1] ,			__snake_case ,			3 ,			padding=1  )

          UpperCAmelCase__							: str 							=						False



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							: Optional[int] 							=						x
          UpperCAmelCase__							: Optional[Any] 							=						self.conv_in(__snake_case  )

          if self.training and self.gradient_checkpointing:

               def create_custom_forward(__UpperCamelCase  ):
                    def custom_forward(*__UpperCamelCase  ):
                         return module(*__snake_case  )

                    return custom_forward

               # down
               if is_torch_version(">=" ,			"1.11.0"  ):
                    for down_block in self.down_blocks:
                         UpperCAmelCase__							: List[str] 							=						torch.utils.checkpoint.checkpoint(
                             create_custom_forward(__snake_case  ) ,			__snake_case ,			use_reentrant=__snake_case  )
                    # middle
                    UpperCAmelCase__							: int 							=						torch.utils.checkpoint.checkpoint(
                        create_custom_forward(self.mid_block  ) ,			__snake_case ,			use_reentrant=__snake_case  )
               else:
                    for down_block in self.down_blocks:
                         UpperCAmelCase__							: Optional[int] 							=						torch.utils.checkpoint.checkpoint(create_custom_forward(__snake_case  ) ,			__snake_case  )
                    # middle
                    UpperCAmelCase__							: Tuple 							=						torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block  ) ,			__snake_case  )

          else:
               # down
               for down_block in self.down_blocks:
                    UpperCAmelCase__							: Optional[Any] 							=						down_block(__snake_case  )

               # middle
               UpperCAmelCase__							: List[str] 							=						self.mid_block(__snake_case  )

          # post-process
          UpperCAmelCase__							: Tuple 							=						self.conv_norm_out(__snake_case  )
          UpperCAmelCase__							: List[str] 							=						self.conv_act(__snake_case  )
          UpperCAmelCase__							: Tuple 							=						self.conv_out(__snake_case  )

          return sample







class     _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase=3 ,			__UpperCamelCase=3 ,			__UpperCamelCase=("UpDecoderBlock2D",) ,			__UpperCamelCase=(64,) ,			__UpperCamelCase=2 ,			__UpperCamelCase=32 ,			__UpperCamelCase="silu" ,			__UpperCamelCase="group" ,			)->					Tuple:
          super().__init__()
          UpperCAmelCase__							: str 							=						layers_per_block

          UpperCAmelCase__							: Tuple 							=						nn.Convad(
              __snake_case ,			block_out_channels[-1] ,			kernel_size=3 ,			stride=1 ,			padding=1 ,			)

          UpperCAmelCase__							: Union[str, Any] 							=						None
          UpperCAmelCase__							: Tuple 							=						nn.ModuleList([]  )

          UpperCAmelCase__							: str 							=						in_channels if norm_type == '''spatial''' else None

          # mid
          UpperCAmelCase__							: str 							=						UNetMidBlockaD(
              in_channels=block_out_channels[-1] ,			resnet_eps=1E-6 ,			resnet_act_fn=__snake_case ,			output_scale_factor=1 ,			resnet_time_scale_shift="default" if norm_type == "group" else norm_type ,			attention_head_dim=block_out_channels[-1] ,			resnet_groups=__snake_case ,			temb_channels=__snake_case ,			)

          # up
          UpperCAmelCase__							: Union[str, Any] 							=						list(reversed(__snake_case  )  )
          UpperCAmelCase__							: Optional[Any] 							=						reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__snake_case  ):
               UpperCAmelCase__							: Optional[Any] 							=						output_channel
               UpperCAmelCase__							: str 							=						reversed_block_out_channels[i]

               UpperCAmelCase__							: int 							=						i == len(__snake_case  ) - 1

               UpperCAmelCase__							: List[Any] 							=						get_up_block(
                   __snake_case ,			num_layers=self.layers_per_block + 1 ,			in_channels=__snake_case ,			out_channels=__snake_case ,			prev_output_channel=__snake_case ,			add_upsample=not is_final_block ,			resnet_eps=1E-6 ,			resnet_act_fn=__snake_case ,			resnet_groups=__snake_case ,			attention_head_dim=__snake_case ,			temb_channels=__snake_case ,			resnet_time_scale_shift=__snake_case ,			)
               self.up_blocks.append(__snake_case  )
               UpperCAmelCase__							: Optional[Any] 							=						output_channel

          # out
          if norm_type == "spatial":
               UpperCAmelCase__							: Union[str, Any] 							=						SpatialNorm(block_out_channels[0] ,			__snake_case  )
          else:
               UpperCAmelCase__							: str 							=						nn.GroupNorm(num_channels=block_out_channels[0] ,			num_groups=__snake_case ,			eps=1E-6  )
          UpperCAmelCase__							: Any 							=						nn.SiLU()
          UpperCAmelCase__							: Optional[int] 							=						nn.Convad(block_out_channels[0] ,			__snake_case ,			3 ,			padding=1  )

          UpperCAmelCase__							: str 							=						False



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=None  )->					List[str]:
          UpperCAmelCase__							: str 							=						z
          UpperCAmelCase__							: List[Any] 							=						self.conv_in(__snake_case  )

          UpperCAmelCase__							: int 							=						next(iter(self.up_blocks.parameters()  )  ).dtype
          if self.training and self.gradient_checkpointing:

               def create_custom_forward(__UpperCamelCase  ):
                    def custom_forward(*__UpperCamelCase  ):
                         return module(*__snake_case  )

                    return custom_forward

               if is_torch_version(">=" ,			"1.11.0"  ):
                    # middle
                    UpperCAmelCase__							: int 							=						torch.utils.checkpoint.checkpoint(
                        create_custom_forward(self.mid_block  ) ,			__snake_case ,			__snake_case ,			use_reentrant=__snake_case  )
                    UpperCAmelCase__							: Optional[int] 							=						sample.to(__snake_case  )

                    # up
                    for up_block in self.up_blocks:
                         UpperCAmelCase__							: List[Any] 							=						torch.utils.checkpoint.checkpoint(
                             create_custom_forward(__snake_case  ) ,			__snake_case ,			__snake_case ,			use_reentrant=__snake_case  )
               else:
                    # middle
                    UpperCAmelCase__							: str 							=						torch.utils.checkpoint.checkpoint(
                        create_custom_forward(self.mid_block  ) ,			__snake_case ,			__snake_case  )
                    UpperCAmelCase__							: Tuple 							=						sample.to(__snake_case  )

                    # up
                    for up_block in self.up_blocks:
                         UpperCAmelCase__							: str 							=						torch.utils.checkpoint.checkpoint(create_custom_forward(__snake_case  ) ,			__snake_case ,			__snake_case  )
          else:
               # middle
               UpperCAmelCase__							: str 							=						self.mid_block(__snake_case ,			__snake_case  )
               UpperCAmelCase__							: Optional[int] 							=						sample.to(__snake_case  )

               # up
               for up_block in self.up_blocks:
                    UpperCAmelCase__							: Optional[Any] 							=						up_block(__snake_case ,			__snake_case  )

        # post-process
          if latent_embeds is None:
               UpperCAmelCase__							: Optional[int] 							=						self.conv_norm_out(__snake_case  )
          else:
               UpperCAmelCase__							: List[Any] 							=						self.conv_norm_out(__snake_case ,			__snake_case  )
          UpperCAmelCase__							: Tuple 							=						self.conv_act(__snake_case  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.conv_out(__snake_case  )

          return sample







class     _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase="random" ,			__UpperCamelCase=False ,			__UpperCamelCase=True  )->					Any:
          super().__init__()
          UpperCAmelCase__							: List[str] 							=						n_e
          UpperCAmelCase__							: Dict 							=						vq_embed_dim
          UpperCAmelCase__							: int 							=						beta
          UpperCAmelCase__							: Optional[int] 							=						legacy

          UpperCAmelCase__							: Tuple 							=						nn.Embedding(self.n_e ,			self.vq_embed_dim  )
          self.embedding.weight.data.uniform_(-1.0 / self.n_e ,			1.0 / self.n_e  )

          UpperCAmelCase__							: Dict 							=						remap
          if self.remap is not None:
               self.register_buffer("used" ,			torch.tensor(np.load(self.remap  )  )  )
               UpperCAmelCase__							: Tuple 							=						self.used.shape[0]
               UpperCAmelCase__							: Tuple 							=						unknown_index  # "random" or "extra" or integer
               if self.unknown_index == "extra":
                    UpperCAmelCase__							: List[Any] 							=						self.re_embed
                    UpperCAmelCase__							: str 							=						self.re_embed + 1
               print(
                   F"Remapping {self.n_e} indices to {self.re_embed} indices. "
                   F"Using {self.unknown_index} for unknown indices."  )
          else:
               UpperCAmelCase__							: Dict 							=						n_e

          UpperCAmelCase__							: Optional[Any] 							=						sane_index_shape



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Dict 							=						inds.shape
          assert len(__snake_case  ) > 1
          UpperCAmelCase__							: List[str] 							=						inds.reshape(ishape[0] ,			-1  )
          UpperCAmelCase__							: List[Any] 							=						self.used.to(__snake_case  )
          UpperCAmelCase__							: Any 							=						(inds[:, :, None] == used[None, None, ...]).long()
          UpperCAmelCase__							: int 							=						match.argmax(-1  )
          UpperCAmelCase__							: Any 							=						match.sum(2  ) < 1
          if self.unknown_index == "random":
               UpperCAmelCase__							: Optional[int] 							=						torch.randint(0 ,			self.re_embed ,			size=new[unknown].shape  ).to(device=new.device  )
          else:
               UpperCAmelCase__							: List[Any] 							=						self.unknown_index
          return new.reshape(__snake_case  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: Tuple 							=						inds.shape
          assert len(__snake_case  ) > 1
          UpperCAmelCase__							: Optional[Any] 							=						inds.reshape(ishape[0] ,			-1  )
          UpperCAmelCase__							: List[Any] 							=						self.used.to(__snake_case  )
          if self.re_embed > self.used.shape[0]:  # extra token
               UpperCAmelCase__							: Union[str, Any] 							=						0  # simply set to zero
          UpperCAmelCase__							: Union[str, Any] 							=						torch.gather(used[None, :][inds.shape[0] * [0], :] ,			1 ,			__snake_case  )
          return back.reshape(__snake_case  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          # reshape z -> (batch, height, width, channel) and flatten
          UpperCAmelCase__							: Any 							=						z.permute(0 ,			2 ,			3 ,			1  ).contiguous()
          UpperCAmelCase__							: Optional[int] 							=						z.view(-1 ,			self.vq_embed_dim  )

          # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
          UpperCAmelCase__							: int 							=						torch.argmin(torch.cdist(__snake_case ,			self.embedding.weight  ) ,			dim=1  )

          UpperCAmelCase__							: List[str] 							=						self.embedding(__snake_case  ).view(z.shape  )
          UpperCAmelCase__							: Optional[int] 							=						None
          UpperCAmelCase__							: Tuple 							=						None

          # compute loss for embedding
          if not self.legacy:
               UpperCAmelCase__							: Optional[Any] 							=						self.beta * torch.mean((z_q.detach() - z) ** 2  ) + torch.mean((z_q - z.detach()) ** 2  )
          else:
               UpperCAmelCase__							: Tuple 							=						torch.mean((z_q.detach() - z) ** 2  ) + self.beta * torch.mean((z_q - z.detach()) ** 2  )

          # preserve gradients
          UpperCAmelCase__							: List[Any] 							=						z + (z_q - z).detach()

          # reshape back to match original input shape
          UpperCAmelCase__							: Optional[int] 							=						z_q.permute(0 ,			3 ,			1 ,			2  ).contiguous()

          if self.remap is not None:
               UpperCAmelCase__							: Dict 							=						min_encoding_indices.reshape(z.shape[0] ,			-1  )  # add batch axis
               UpperCAmelCase__							: int 							=						self.remap_to_used(__snake_case  )
               UpperCAmelCase__							: Union[str, Any] 							=						min_encoding_indices.reshape(-1 ,			1  )  # flatten

          if self.sane_index_shape:
               UpperCAmelCase__							: Tuple 							=						min_encoding_indices.reshape(z_q.shape[0] ,			z_q.shape[2] ,			z_q.shape[3]  )

          return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          # shape specifying (batch, height, width, channel)
          if self.remap is not None:
               UpperCAmelCase__							: Optional[int] 							=						indices.reshape(shape[0] ,			-1  )  # add batch axis
               UpperCAmelCase__							: Tuple 							=						self.unmap_to_all(__snake_case  )
               UpperCAmelCase__							: List[Any] 							=						indices.reshape(-1  )  # flatten again

          # get quantized latent vectors
          UpperCAmelCase__							: Union[str, Any] 							=						self.embedding(__snake_case  )

          if shape is not None:
               UpperCAmelCase__							: Optional[Any] 							=						z_q.view(__snake_case  )
               # reshape back to match original input shape
               UpperCAmelCase__							: Optional[int] 							=						z_q.permute(0 ,			3 ,			1 ,			2  ).contiguous()

          return z_q







class     _lowercase						(       __SCREAMING_SNAKE_CASE				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						parameters
          UpperCAmelCase__							: List[str] 							=						torch.chunk(__snake_case ,			2 ,			dim=1  )
          UpperCAmelCase__							: List[Any] 							=						torch.clamp(self.logvar ,			-30.0 ,			20.0  )
          UpperCAmelCase__							: str 							=						deterministic
          UpperCAmelCase__							: Dict 							=						torch.exp(0.5 * self.logvar  )
          UpperCAmelCase__							: Optional[int] 							=						torch.exp(self.logvar  )
          if self.deterministic:
               UpperCAmelCase__							: Optional[int] 							=						torch.zeros_like(
                   self.mean ,			device=self.parameters.device ,			dtype=self.parameters.dtype  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					int:
          # make sure sample is on the same device as the parameters and has same dtype
          UpperCAmelCase__							: Tuple 							=						randn_tensor(
              self.mean.shape ,			generator=__snake_case ,			device=self.parameters.device ,			dtype=self.parameters.dtype  )
          UpperCAmelCase__							: str 							=						self.mean + self.std * sample
          return x



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase=None  )->					str:
          if self.deterministic:
               return torch.Tensor([0.0]  )
          else:
               if other is None:
                    return 0.5 * torch.sum(torch.pow(self.mean ,			2  ) + self.var - 1.0 - self.logvar ,			dim=[1, 2, 3]  )
               else:
                    return 0.5 * torch.sum(
                        torch.pow(self.mean - other.mean ,			2  ) / other.var
                        + self.var / other.var
                        - 1.0
                        - self.logvar
                        + other.logvar ,			dim=[1, 2, 3] ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=[1, 2, 3]  )->					List[Any]:
          if self.deterministic:
               return torch.Tensor([0.0]  )
          UpperCAmelCase__							: List[str] 							=						np.log(2.0 * np.pi  )
          return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean ,			2  ) / self.var ,			dim=__snake_case  )



     def  lowerCAmelCase__       (      self  )->					str:
          return self.mean





"""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   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

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

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

          return DDPMSchedulerState.create(
              common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						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
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						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
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

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

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

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

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

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

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

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

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



