






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def      __lowercase					( snake_case,  snake_case=1 ):







				"""simple docstring"""






				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
				else:
								return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )

def      __lowercase					( snake_case,  snake_case=0 ):







				"""simple docstring"""






				__magic_name__		:Dict        =      []
				for old_item in old_list:
								__magic_name__		:Optional[int]        =      old_item.replace('''in_layers.0''',  '''norm1''' )
								__magic_name__		:Optional[Any]        =      new_item.replace('''in_layers.2''',  '''conv1''' )

								__magic_name__		:Optional[Any]        =      new_item.replace('''out_layers.0''',  '''norm2''' )
								__magic_name__		:Dict        =      new_item.replace('''out_layers.3''',  '''conv2''' )

								__magic_name__		:List[str]        =      new_item.replace('''emb_layers.1''',  '''time_emb_proj''' )
								__magic_name__		:List[Any]        =      new_item.replace('''skip_connection''',  '''conv_shortcut''' )

								__magic_name__		:Any        =      shave_segments(snake_case,  n_shave_prefix_segments=snake_case )

								mapping.append({'''old''': old_item, '''new''': new_item} )

				return mapping

def      __lowercase					( snake_case,  snake_case=0 ):







				"""simple docstring"""






				__magic_name__		:Any        =      []
				for old_item in old_list:
								__magic_name__		:Union[str, Any]        =      old_item

								__magic_name__		:str        =      new_item.replace('''norm.weight''',  '''group_norm.weight''' )
								__magic_name__		:List[str]        =      new_item.replace('''norm.bias''',  '''group_norm.bias''' )

								__magic_name__		:Tuple        =      new_item.replace('''proj_out.weight''',  '''proj_attn.weight''' )
								__magic_name__		:List[str]        =      new_item.replace('''proj_out.bias''',  '''proj_attn.bias''' )

								__magic_name__		:int        =      shave_segments(snake_case,  n_shave_prefix_segments=snake_case )

								mapping.append({'''old''': old_item, '''new''': new_item} )

				return mapping

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case=None,  snake_case=None,  snake_case=None ):







				"""simple docstring"""






				assert isinstance(snake_case,  snake_case ), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												__magic_name__		:Optional[Any]        =      old_checkpoint[path]
												__magic_name__		:Any        =      old_tensor.shape[0] // 3

												__magic_name__		:Union[str, Any]        =      (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

												__magic_name__		:int        =      old_tensor.shape[0] // config['''num_head_channels'''] // 3

												__magic_name__		:str        =      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
												__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      old_tensor.split(channels // num_heads,  dim=1 )

												__magic_name__		:Dict        =      query.reshape(snake_case )
												__magic_name__		:int        =      key.reshape(snake_case )
												__magic_name__		:List[Any]        =      value.reshape(snake_case )

				for path in paths:
								__magic_name__		:List[str]        =      path['''new''']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								__magic_name__		:Union[str, Any]        =      new_path.replace('''middle_block.0''',  '''mid_block.resnets.0''' )
								__magic_name__		:List[str]        =      new_path.replace('''middle_block.1''',  '''mid_block.attentions.0''' )
								__magic_name__		:str        =      new_path.replace('''middle_block.2''',  '''mid_block.resnets.1''' )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																__magic_name__		:List[Any]        =      new_path.replace(replacement['''old'''],  replacement['''new'''] )

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												__magic_name__		:Union[str, Any]        =      old_checkpoint[path['''old''']][:, :, 0]
								else:
												__magic_name__		:str        =      old_checkpoint[path['''old''']]

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      {}

				__magic_name__		:Dict        =      checkpoint['''time_embed.0.weight''']
				__magic_name__		:str        =      checkpoint['''time_embed.0.bias''']
				__magic_name__		:str        =      checkpoint['''time_embed.2.weight''']
				__magic_name__		:int        =      checkpoint['''time_embed.2.bias''']

				__magic_name__		:Tuple        =      checkpoint['''input_blocks.0.0.weight''']
				__magic_name__		:Tuple        =      checkpoint['''input_blocks.0.0.bias''']

				__magic_name__		:Tuple        =      checkpoint['''out.0.weight''']
				__magic_name__		:str        =      checkpoint['''out.0.bias''']
				__magic_name__		:Optional[int]        =      checkpoint['''out.2.weight''']
				__magic_name__		:int        =      checkpoint['''out.2.bias''']

				# Retrieves the keys for the input blocks only
				__magic_name__		:Any        =      len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''input_blocks''' in layer} )
				__magic_name__		:Dict        =      {
				    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(snake_case )
				}

				# Retrieves the keys for the middle blocks only
				__magic_name__		:Optional[int]        =      len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''middle_block''' in layer} )
				__magic_name__		:List[Any]        =      {
				    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
				    for layer_id in range(snake_case )
				}

				# Retrieves the keys for the output blocks only
				__magic_name__		:List[str]        =      len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''output_blocks''' in layer} )
				__magic_name__		:Union[str, Any]        =      {
				    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(snake_case )
				}

				for i in range(1,  snake_case ):
								__magic_name__		:int        =      (i - 1) // (config['''num_res_blocks'''] + 1)
								__magic_name__		:Union[str, Any]        =      (i - 1) % (config['''num_res_blocks'''] + 1)

								__magic_name__		:int        =      [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
								__magic_name__		:Union[str, Any]        =      [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

								if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
												__magic_name__		:Tuple        =      checkpoint[
												    f'''input_blocks.{i}.0.op.weight'''
												]
												__magic_name__		:Optional[Any]        =      checkpoint[
												    f'''input_blocks.{i}.0.op.bias'''
												]
												continue

								__magic_name__		:Optional[Any]        =      renew_resnet_paths(snake_case )
								__magic_name__		:Optional[int]        =      {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								__magic_name__		:Dict        =      {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
								assign_to_checkpoint(
								    snake_case,  snake_case,  snake_case,  additional_replacements=[meta_path, resnet_op],  config=snake_case )

								if len(snake_case ):
												__magic_name__		:Optional[Any]        =      renew_attention_paths(snake_case )
												__magic_name__		:Dict        =      {
												    '''old''': f'''input_blocks.{i}.1''',
												    '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												__magic_name__		:Union[str, Any]        =      {
												    f'''input_blocks.{i}.1.qkv.bias''': {
												        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''input_blocks.{i}.1.qkv.weight''': {
												        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    snake_case,  snake_case,  snake_case,  additional_replacements=[meta_path],  attention_paths_to_split=snake_case,  config=snake_case,  )

				__magic_name__		:int        =      middle_blocks[0]
				__magic_name__		:Dict        =      middle_blocks[1]
				__magic_name__		:Optional[int]        =      middle_blocks[2]

				__magic_name__		:Any        =      renew_resnet_paths(snake_case )
				assign_to_checkpoint(snake_case,  snake_case,  snake_case,  config=snake_case )

				__magic_name__		:Optional[int]        =      renew_resnet_paths(snake_case )
				assign_to_checkpoint(snake_case,  snake_case,  snake_case,  config=snake_case )

				__magic_name__		:Optional[int]        =      renew_attention_paths(snake_case )
				__magic_name__		:Optional[int]        =      {
				    '''middle_block.1.qkv.bias''': {
				        '''key''': '''mid_block.attentions.0.key.bias''',
				        '''query''': '''mid_block.attentions.0.query.bias''',
				        '''value''': '''mid_block.attentions.0.value.bias''',
				    },
				    '''middle_block.1.qkv.weight''': {
				        '''key''': '''mid_block.attentions.0.key.weight''',
				        '''query''': '''mid_block.attentions.0.query.weight''',
				        '''value''': '''mid_block.attentions.0.value.weight''',
				    },
				}
				assign_to_checkpoint(
				    snake_case,  snake_case,  snake_case,  attention_paths_to_split=snake_case,  config=snake_case )

				for i in range(snake_case ):
								__magic_name__		:Optional[int]        =      i // (config['''num_res_blocks'''] + 1)
								__magic_name__		:Tuple        =      i % (config['''num_res_blocks'''] + 1)
								__magic_name__		:Dict        =      [shave_segments(snake_case,  2 ) for name in output_blocks[i]]
								__magic_name__		:str        =      {}

								for layer in output_block_layers:
												__magic_name__     ,				__magic_name__		:Optional[int]        =      layer.split('''.''' )[0], shave_segments(snake_case,  1 )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(snake_case )
												else:
																__magic_name__		:Optional[Any]        =      [layer_name]

								if len(snake_case ) > 1:
												__magic_name__		:List[Any]        =      [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
												__magic_name__		:Union[str, Any]        =      [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

												__magic_name__		:List[str]        =      renew_resnet_paths(snake_case )
												__magic_name__		:List[Any]        =      renew_resnet_paths(snake_case )

												__magic_name__		:List[Any]        =      {'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(snake_case,  snake_case,  snake_case,  additional_replacements=[meta_path],  config=snake_case )

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																__magic_name__		:Union[str, Any]        =      list(output_block_list.values() ).index(['''conv.weight''', '''conv.bias'''] )
																__magic_name__		:int        =      checkpoint[
																    f'''output_blocks.{i}.{index}.conv.weight'''
																]
																__magic_name__		:Optional[int]        =      checkpoint[
																    f'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(snake_case ) == 2:
																				__magic_name__		:Optional[Any]        =      []

												if len(snake_case ):
																__magic_name__		:List[Any]        =      renew_attention_paths(snake_case )
																__magic_name__		:int        =      {
																    '''old''': f'''output_blocks.{i}.1''',
																    '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																__magic_name__		:int        =      {
																    f'''output_blocks.{i}.1.qkv.bias''': {
																        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																    },
																    f'''output_blocks.{i}.1.qkv.weight''': {
																        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																    },
																}
																assign_to_checkpoint(
																    snake_case,  snake_case,  snake_case,  additional_replacements=[meta_path],  attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions ) else None,  config=snake_case,  )
								else:
												__magic_name__		:List[str]        =      renew_resnet_paths(snake_case,  n_shave_prefix_segments=1 )
												for path in resnet_0_paths:
																__magic_name__		:List[Any]        =      '''.'''.join(['''output_blocks''', str(snake_case ), path['''old''']] )
																__magic_name__		:str        =      '''.'''.join(['''up_blocks''', str(snake_case ), '''resnets''', str(snake_case ), path['''new''']] )

																__magic_name__		:List[Any]        =      checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)

					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					SCREAMING_SNAKE_CASE__      :			Tuple							=		parser.parse_args()

					SCREAMING_SNAKE_CASE__      :			List[Any]							=		torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										SCREAMING_SNAKE_CASE__      :			List[str]							=		json.loads(f.read())

					SCREAMING_SNAKE_CASE__      :			List[Any]							=		convert_ldm_checkpoint(checkpoint, config)

					if "ldm" in config:
										del config["ldm"]

					SCREAMING_SNAKE_CASE__      :			Tuple							=		UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										SCREAMING_SNAKE_CASE__      :			Optional[int]							=		DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
										SCREAMING_SNAKE_CASE__      :			Dict							=		VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										SCREAMING_SNAKE_CASE__      :			Dict							=		LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)














# 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 						lowerCamelCase_				:
				a__					       =  42

				# setable values
				a__					       =  42
				a__					       =  42
				a__					       =  None







				@classmethod
				def 			A	(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return cls(common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







@dataclass
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  42







class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

				a__					       =  42







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return True







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase = 1_0_0_0       ,					__lowerCAmelCase = 0.0001       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase = "linear"       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "fixed_small"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "epsilon"       ,					__lowerCAmelCase = jnp.floataa       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      dtype







				def 			A	(						self       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if common is None:
												__magic_name__		:Dict        =      CommonSchedulerState.create(self     )

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

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

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								return sample







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = ()     ):
								"""simple docstring"""
								__magic_name__		:int        =      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
								__magic_name__		:List[Any]        =      (jnp.arange(0       ,					__lowerCAmelCase     ) * step_ratio).round()[::-1]

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      state.common.alphas_cumprod[t]
								__magic_name__		:Optional[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
								__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												__magic_name__		:Optional[Any]        =      self.config.variance_type

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

								return variance







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      timestep

								if key is None:
												__magic_name__		:Union[str, Any]        =      jax.random.PRNGKey(0     )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												__magic_name__     ,				__magic_name__		:Dict        =      jnp.split(__lowerCAmelCase       ,					sample.shape[1]       ,					axis=1     )
								else:
												__magic_name__		:Optional[int]        =      None

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

								# 6. Add noise
								def random_variance():
												__magic_name__		:Tuple        =      jax.random.split(__lowerCAmelCase       ,					num=1     )
												__magic_name__		:Dict        =      jax.random.normal(__lowerCAmelCase       ,					shape=model_output.shape       ,					dtype=self.dtype     )
												return (self._get_variance(__lowerCAmelCase       ,					__lowerCAmelCase       ,					predicted_variance=__lowerCAmelCase     ) ** 0.5) * noise

								__magic_name__		:List[str]        =      jnp.where(t > 0       ,					random_variance()       ,					jnp.zeros(model_output.shape       ,					dtype=self.dtype     )     )

								__magic_name__		:int        =      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,					state=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return add_noise_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return get_velocity_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps






