






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCamelCase     :							int    =[
    # (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'''),
]

lowerCamelCase     :							str    =[
    # (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'''),
]

lowerCamelCase     :							Optional[int]    =[]
# 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
		lowerCamelCase     :							List[str]    =F"""down_blocks.{i}.resnets.{j}."""
		lowerCamelCase     :							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
			lowerCamelCase     :							Optional[int]    =F"""down_blocks.{i}.attentions.{j}."""
			lowerCamelCase     :							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
		lowerCamelCase     :							Optional[Any]    =F"""up_blocks.{i}.resnets.{j}."""
		lowerCamelCase     :							Optional[Any]    =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
			lowerCamelCase     :							Tuple    =F"""up_blocks.{i}.attentions.{j}."""
			lowerCamelCase     :							int    =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
		lowerCamelCase     :							Optional[Any]    =F"""down_blocks.{i}.downsamplers.0.conv."""
		lowerCamelCase     :							Union[str, Any]    =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
		lowerCamelCase     :							int    =F"""up_blocks.{i}.upsamplers.0."""
		lowerCamelCase     :							Union[str, Any]    =F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase     :							int    ='''mid_block.attentions.0.'''
lowerCamelCase     :							Optional[Any]    ='''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	lowerCamelCase     :							List[str]    =F"""mid_block.resnets.{j}."""
	lowerCamelCase     :							Optional[int]    =F"""middle_block.{2*j}."""
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:
					# 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.
					UpperCamelCase__      :       List[str]	    =  {k: k for k in unet_state_dict.keys()}
					for sd_name, hf_name in unet_conversion_map:
										UpperCamelCase__      :       List[str]	    =  sd_name
					for k, v in mapping.items():
										if "resnets" in k:
															for sd_part, hf_part in unet_conversion_map_resnet:
																				UpperCamelCase__      :       List[Any]	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
															UpperCamelCase__      :       str	    =  v
					for k, v in mapping.items():
										for sd_part, hf_part in unet_conversion_map_layer:
															UpperCamelCase__      :       str	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
										UpperCamelCase__      :       Any	    =  v
					UpperCamelCase__      :       int	    =  {v: unet_state_dict[k] for k, v in mapping.items()}
					return new_state_dict


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

lowerCamelCase     :							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):
		lowerCamelCase     :							List[str]    =F"""encoder.down_blocks.{i}.resnets.{j}."""
		lowerCamelCase     :							Optional[int]    =F"""encoder.down.{i}.block.{j}."""
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		lowerCamelCase     :							Tuple    =F"""down_blocks.{i}.downsamplers.0."""
		lowerCamelCase     :							Tuple    =F"""down.{i}.downsample."""
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		lowerCamelCase     :							str    =F"""up_blocks.{i}.upsamplers.0."""
		lowerCamelCase     :							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):
		lowerCamelCase     :							Union[str, Any]    =F"""decoder.up_blocks.{i}.resnets.{j}."""
		lowerCamelCase     :							Tuple    =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):
	lowerCamelCase     :							List[Any]    =F"""mid_block.resnets.{i}."""
	lowerCamelCase     :							List[str]    =F"""mid.block_{i+1}."""
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
					# convert HF linear weights to SD conv2d weights
					return w.reshape(*w.shape ,   1 ,   1      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Dict:
					UpperCamelCase__      :       Dict	    =  {k: k for k in vae_state_dict.keys()}
					for k, v in mapping.items():
										for sd_part, hf_part in vae_conversion_map:
															UpperCamelCase__      :       int	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
										UpperCamelCase__      :       List[str]	    =  v
					for k, v in mapping.items():
										if "attentions" in k:
															for sd_part, hf_part in vae_conversion_map_attn:
																				UpperCamelCase__      :       List[str]	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
															UpperCamelCase__      :       Union[str, Any]	    =  v
					UpperCamelCase__      :       Tuple	    =  {v: vae_state_dict[k] for k, v in mapping.items()}
					UpperCamelCase__      :       List[str]	    =  ["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'      )
																				UpperCamelCase__      :       Any	    =  reshape_weight_for_sd(__lowerCAmelCase      )
					return new_state_dict


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


lowerCamelCase     :							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'''),
]
lowerCamelCase     :							Optional[Any]    ={re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase     :							List[Any]    =re.compile('''|'''.join(protected.keys()))

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     str:
					UpperCamelCase__      :       Union[str, Any]	    =  {}
					UpperCamelCase__      :       str	    =  {}
					UpperCamelCase__      :       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"      )
										):
															UpperCamelCase__      :       List[str]	    =  k[: -len(".q_proj.weight"      )]
															UpperCamelCase__      :       Tuple	    =  k[-len("q_proj.weight"      )]
															if k_pre not in capture_qkv_weight:
																				UpperCamelCase__      :       Tuple	    =  [None, None, None]
															UpperCamelCase__      :       Optional[int]	    =  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"      )
										):
															UpperCamelCase__      :       List[Any]	    =  k[: -len(".q_proj.bias"      )]
															UpperCamelCase__      :       Optional[int]	    =  k[-len("q_proj.bias"      )]
															if k_pre not in capture_qkv_bias:
																				UpperCamelCase__      :       int	    =  [None, None, None]
															UpperCamelCase__      :       Optional[int]	    =  v
															continue

										UpperCamelCase__      :       Dict	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
										UpperCamelCase__      :       str	    =  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"      )
										UpperCamelCase__      :       Any	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
										UpperCamelCase__      :       List[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"      )
										UpperCamelCase__      :       List[str]	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
										UpperCamelCase__      :       int	    =  torch.cat(__lowerCAmelCase      )

					return new_state_dict







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
					return text_enc_dict


if __name__ == "__main__":
	lowerCamelCase     :							Optional[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.'''
	)

	lowerCamelCase     :							Tuple    =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
	lowerCamelCase     :							Tuple    =osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase     :							List[Any]    =osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase     :							List[str]    =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):
		lowerCamelCase     :							Dict    =load_file(unet_path, device='''cpu''')
	else:
		lowerCamelCase     :							Dict    =osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
		lowerCamelCase     :							Any    =torch.load(unet_path, map_location='''cpu''')

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

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

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

	# Convert the VAE model
	lowerCamelCase     :							str    =convert_vae_state_dict(vae_state_dict)
	lowerCamelCase     :							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
	lowerCamelCase     :							str    ='''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
		lowerCamelCase     :							Any    ={'''transformer.''' + k: v for k, v in text_enc_dict.items()}
		lowerCamelCase     :							List[str]    =convert_text_enc_state_dict_vaa(text_enc_dict)
		lowerCamelCase     :							int    ={'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
	else:
		lowerCamelCase     :							Optional[Any]    =convert_text_enc_state_dict(text_enc_dict)
		lowerCamelCase     :							Dict    ={'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

	# Put together new checkpoint
	lowerCamelCase     :							Tuple    ={**unet_state_dict, **vae_state_dict, **text_enc_dict}
	if args.half:
		lowerCamelCase     :							Optional[int]    ={k: v.half() for k, v in state_dict.items()}

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		lowerCamelCase     :							Optional[int]    ={'''state_dict''': state_dict}
		torch.save(state_dict, args.checkpoint_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       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

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







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






      return cls(common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE						)







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






      if common is None:
           UpperCamelCase__      :       Tuple	    =  CommonSchedulerState.create(self						)

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

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

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







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      UpperCamelCase__      :       Optional[int]	    =  (jnp.arange(0							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  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__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

      if key is None:
           UpperCamelCase__      :       str	    =  jax.random.PRNGKey(0						)

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

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

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

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

      UpperCamelCase__      :       List[str]	    =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE							,       state=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps