






"""simple docstring"""



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A__	:    List[str]			 = [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

A__	:    Union[str, Any]			 = [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

A__	:    Tuple			 = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
						# loop over downblocks/upblocks

						for j in range(2):
												# loop over resnets/attentions for downblocks
												A__	:    Dict			 = f'down_blocks.{i}.resnets.{j}.'
												A__	:    str			 = f'input_blocks.{3*i + j + 1}.0.'
												unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

												if i < 3:
																		# no attention layers in down_blocks.3
																		A__	:    str			 = f'down_blocks.{i}.attentions.{j}.'
																		A__	:    Optional[Any]			 = f'input_blocks.{3*i + j + 1}.1.'
																		unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

						for j in range(3):
												# loop over resnets/attentions for upblocks
												A__	:    List[str]			 = f'up_blocks.{i}.resnets.{j}.'
												A__	:    List[str]			 = f'output_blocks.{3*i + j}.0.'
												unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

												if i > 0:
																		# no attention layers in up_blocks.0
																		A__	:    Tuple			 = f'up_blocks.{i}.attentions.{j}.'
																		A__	:    str			 = f'output_blocks.{3*i + j}.1.'
																		unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

						if i < 3:
												# no downsample in down_blocks.3
												A__	:    str			 = f'down_blocks.{i}.downsamplers.0.conv.'
												A__	:    Tuple			 = f'input_blocks.{3*(i+1)}.0.op.'
												unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

												# no upsample in up_blocks.3
												A__	:    Dict			 = f'up_blocks.{i}.upsamplers.0.'
												A__	:    Dict			 = f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A__	:    List[str]			 = 'mid_block.attentions.0.'
A__	:    str			 = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
						A__	:    str			 = f'mid_block.resnets.{j}.'
						A__	:    str			 = f'middle_block.{2*j}.'
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def 			_snake_case    (					lowerCamelCase__     :   List[Any]  )    ->     Tuple:
							# buyer beware: this is a *brittle* function,
							# and correct output requires that all of these pieces interact in
							# the exact order in which I have arranged them.
							lowerCamelCase_	:							List[Any]									={k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														lowerCamelCase_	:							Optional[Any]									=sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												lowerCamelCase_	:							List[str]									=v.replace(lowerCamelCase__    ,						lowerCamelCase__  )
																					lowerCamelCase_	:							List[str]									=v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					lowerCamelCase_	:							Any									=v.replace(lowerCamelCase__    ,						lowerCamelCase__  )
														lowerCamelCase_	:							Dict									=v
							lowerCamelCase_	:							Optional[Any]									={v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


# ================#
# VAE Conversion #
# ================#

A__	:    Optional[Any]			 = [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
						# down_blocks have two resnets
						for j in range(2):
												A__	:    Optional[int]			 = f'encoder.down_blocks.{i}.resnets.{j}.'
												A__	:    Optional[int]			 = f'encoder.down.{i}.block.{j}.'
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												A__	:    List[str]			 = f'down_blocks.{i}.downsamplers.0.'
												A__	:    List[Any]			 = f'down.{i}.downsample.'
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												A__	:    List[Any]			 = f'up_blocks.{i}.upsamplers.0.'
												A__	:    List[str]			 = f'up.{3-i}.upsample.'
												vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

						# up_blocks have three resnets
						# also, up blocks in hf are numbered in reverse from sd
						for j in range(3):
												A__	:    List[str]			 = f'decoder.up_blocks.{i}.resnets.{j}.'
												A__	:    Optional[Any]			 = f'decoder.up.{3-i}.block.{j}.'
												vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
						A__	:    Optional[int]			 = f'mid_block.resnets.{i}.'
						A__	:    Tuple			 = f'mid.block_{i+1}.'
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A__	:    Tuple			 = [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]

def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     List[str]:
							# convert HF linear weights to SD conv2d weights
							return w.reshape(*w.shape    ,						1    ,						1  )

def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     str:
							lowerCamelCase_	:							List[str]									={k: k for k in vae_state_dict.keys()}
							for k, v in mapping.items():
														for sd_part, hf_part in vae_conversion_map:
																					lowerCamelCase_	:							Optional[int]									=v.replace(lowerCamelCase__    ,						lowerCamelCase__  )
														lowerCamelCase_	:							Optional[int]									=v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												lowerCamelCase_	:							List[Any]									=v.replace(lowerCamelCase__    ,						lowerCamelCase__  )
																					lowerCamelCase_	:							Tuple									=v
							lowerCamelCase_	:							Tuple									={v: vae_state_dict[k] for k, v in mapping.items()}
							lowerCamelCase_	:							Tuple									=["q", "k", "v", "proj_out"]
							for k, v in new_state_dict.items():
														for weight_name in weights_to_convert:
																					if F"""mid.attn_1.{weight_name}.weight""" in k:
																												print(F"""Reshaping {k} for SD format"""  )
																												lowerCamelCase_	:							List[Any]									=reshape_weight_for_sd(lowerCamelCase__  )
							return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A__	:    Any			 = [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A__	:    Optional[int]			 = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A__	:    str			 = re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A__	:    Tuple			 = {'q': 0, 'k': 1, 'v': 2}

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]  )    ->     Dict:
							lowerCamelCase_	:							str									={}
							lowerCamelCase_	:							Optional[int]									={}
							lowerCamelCase_	:							Optional[Any]									={}
							for k, v in text_enc_dict.items():
														if (
														    k.endswith(".self_attn.q_proj.weight"  )
														    or k.endswith(".self_attn.k_proj.weight"  )
														    or k.endswith(".self_attn.v_proj.weight"  )
														):
																					lowerCamelCase_	:							List[Any]									=k[: -len(".q_proj.weight"  )]
																					lowerCamelCase_	:							Any									=k[-len("q_proj.weight"  )]
																					if k_pre not in capture_qkv_weight:
																												lowerCamelCase_	:							Optional[int]									=[None, None, None]
																					lowerCamelCase_	:							List[Any]									=v
																					continue

														if (
														    k.endswith(".self_attn.q_proj.bias"  )
														    or k.endswith(".self_attn.k_proj.bias"  )
														    or k.endswith(".self_attn.v_proj.bias"  )
														):
																					lowerCamelCase_	:							Any									=k[: -len(".q_proj.bias"  )]
																					lowerCamelCase_	:							int									=k[-len("q_proj.bias"  )]
																					if k_pre not in capture_qkv_bias:
																												lowerCamelCase_	:							int									=[None, None, None]
																					lowerCamelCase_	:							Optional[int]									=v
																					continue

														lowerCamelCase_	:							int									=textenc_pattern.sub(lambda lowerCamelCase__  : protected[re.escape(m.group(0  )  )]    ,						lowerCamelCase__  )
														lowerCamelCase_	:							Tuple									=v

							for k_pre, tensors in capture_qkv_weight.items():
														if None in tensors:
																					raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"  )
														lowerCamelCase_	:							str									=textenc_pattern.sub(lambda lowerCamelCase__  : protected[re.escape(m.group(0  )  )]    ,						lowerCamelCase__  )
														lowerCamelCase_	:							Any									=torch.cat(lowerCamelCase__  )

							for k_pre, tensors in capture_qkv_bias.items():
														if None in tensors:
																					raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"  )
														lowerCamelCase_	:							Tuple									=textenc_pattern.sub(lambda lowerCamelCase__  : protected[re.escape(m.group(0  )  )]    ,						lowerCamelCase__  )
														lowerCamelCase_	:							List[str]									=torch.cat(lowerCamelCase__  )

							return new_state_dict

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Union[str, Any]:
							return text_enc_dict


if __name__ == "__main__":
						A__	:    int			 = argparse.ArgumentParser()

						parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
						parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
						)

						A__	:    Dict			 = parser.parse_args()

						assert args.model_path is not None, "Must provide a model path!"

						assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

						# Path for safetensors
						A__	:    List[str]			 = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						A__	:    int			 = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						A__	:    Any			 = osp.join(args.model_path, 'text_encoder', 'model.safetensors')

						# Load models from safetensors if it exists, if it doesn't pytorch
						if osp.exists(unet_path):
												A__	:    Dict			 = load_file(unet_path, device='cpu')
						else:
												A__	:    List[Any]			 = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												A__	:    Dict			 = torch.load(unet_path, map_location='cpu')

						if osp.exists(vae_path):
												A__	:    str			 = load_file(vae_path, device='cpu')
						else:
												A__	:    Optional[int]			 = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
												A__	:    int			 = torch.load(vae_path, map_location='cpu')

						if osp.exists(text_enc_path):
												A__	:    Union[str, Any]			 = load_file(text_enc_path, device='cpu')
						else:
												A__	:    int			 = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
												A__	:    List[str]			 = torch.load(text_enc_path, map_location='cpu')

						# Convert the UNet model
						A__	:    List[str]			 = convert_unet_state_dict(unet_state_dict)
						A__	:    Dict			 = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

						# Convert the VAE model
						A__	:    int			 = convert_vae_state_dict(vae_state_dict)
						A__	:    str			 = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

						# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
						A__	:    List[Any]			 = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

						if is_vaa_model:
												# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
												A__	:    Tuple			 = {'transformer.' + k: v for k, v in text_enc_dict.items()}
												A__	:    List[str]			 = convert_text_enc_state_dict_vaa(text_enc_dict)
												A__	:    str			 = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												A__	:    List[Any]			 = convert_text_enc_state_dict(text_enc_dict)
												A__	:    Optional[int]			 = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

						# Put together new checkpoint
						A__	:    List[Any]			 = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												A__	:    Union[str, Any]			 = {k: v.half() for k, v in state_dict.items()}

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												A__	:    List[Any]			 = {'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)











"""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__      :
			_UpperCAmelCase		:CommonSchedulerState

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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

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

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

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


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=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
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

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

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

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

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

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

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

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


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



