code: 
  
  '''simple docstring'''
  
  
  
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  __A          =    {
      "vit_b32": {
          "embed_dim": 512,
          "image_resolution": 768,
          "context_length": 16,
          "vocab_size": 49_408,
          "vision_layers": 12,
          "vision_width": 768,
          "vision_patch_size": 32,
          "transformer_width": 512,
          "transformer_heads": 8,
          "transformer_layers": 12,
      },
      "vit_b16": {
          "embed_dim": 512,
          "image_resolution": 768,
          "context_length": 16,
          "vocab_size": 49_408,
          "vision_layers": 12,
          "vision_width": 768,
          "vision_patch_size": 16,
          "transformer_width": 512,
          "transformer_heads": 8,
          "transformer_layers": 12,
      },
      "vit_l14": {
          "embed_dim": 768,
          "image_resolution": 840,
          "context_length": 16,
          "vocab_size": 49_408,
          "vision_layers": 24,
          "vision_width": 1_024,
          "vision_patch_size": 14,
          "transformer_width": 768,
          "transformer_heads": 12,
          "transformer_layers": 12,
      },
  }
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=""		,		lowercase__="/"	):
  						lowercase__							 =       []
  
  						for k, v in params.items():
  												lowercase__							 =       parent_key + sep + k if parent_key else k
  
  												if isinstance(lowercase__		,		collections.MutableMapping	):
  																		items.extend(flatten_nested_dict(lowercase__		,		lowercase__		,		sep=lowercase__	).items()	)
  												else:
  																		items.append((new_key, v)	)
  						return dict(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						return jax.tree_util.tree_map(lambda lowercase__	: x.astype(jnp.floataa	) if x.dtype == jnp.bfloataa else x		,		lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__ ,						lowercase__ ,						lowercase__							 =       pt_attn_layer.in_proj_weight.chunk(3		,		dim=0	)
  						lowercase__ ,						lowercase__ ,						lowercase__							 =       pt_attn_layer.in_proj_bias.chunk(3		,		dim=0	)
  
  						lowercase__							 =       pt_attn_layer.out_proj.weight
  						lowercase__							 =       pt_attn_layer.out_proj.bias
  
  						lowercase__							 =       q_proj
  						lowercase__							 =       q_proj_bias
  
  						lowercase__							 =       k_proj
  						lowercase__							 =       k_proj_bias
  
  						lowercase__							 =       v_proj
  						lowercase__							 =       v_proj_bias
  
  						lowercase__							 =       out_proj_weights
  						lowercase__							 =       out_proj_bias
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						copy_linear(hf_mlp.fca		,		pt_mlp.c_fc	)
  						copy_linear(hf_mlp.fca		,		pt_mlp.c_proj	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       pt_linear.weight
  						lowercase__							 =       pt_linear.bias
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						# copy layer norms
  						copy_linear(hf_layer.layer_norma		,		pt_layer.ln_a	)
  						copy_linear(hf_layer.layer_norma		,		pt_layer.ln_a	)
  
  						# copy MLP
  						copy_mlp(hf_layer.mlp		,		pt_layer.mlp	)
  
  						# copy attn
  						copy_attn_layer(hf_layer.self_attn		,		pt_layer.attn	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						for hf_layer, pt_layer in zip(lowercase__		,		lowercase__	):
  												copy_layer(lowercase__		,		lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						# copy  embeds
  						lowercase__							 =       pt_model.token_embedding.weight
  						lowercase__							 =       pt_model.positional_embedding
  
  						# copy layer norm
  						copy_linear(hf_encoder.final_layer_norm		,		pt_model.ln_final	)
  
  						# copy hidden layers
  						copy_layers(hf_encoder.encoder.layers		,		pt_model.transformer.resblocks	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						# copy projection
  						lowercase__							 =       pt_model.text_projection.data.T
  
  						# copy text encoder
  						copy_encoder(hf_model.text_model		,		lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						# copy projection
  						lowercase__							 =       pt_model.visual.proj.data.T
  
  						# copy layer norms
  						copy_linear(hf_model.vision_model.pre_layernorm		,		pt_model.visual.ln_pre	)
  						copy_linear(hf_model.vision_model.post_layernorm		,		pt_model.visual.ln_post	)
  
  						# copy embeds
  						lowercase__							 =       pt_model.visual.conva.weight.data
  						lowercase__							 =       pt_model.visual.class_embedding
  						lowercase__							 =       pt_model.visual.positional_embedding.data
  
  						# copy encoder
  						copy_layers(hf_model.vision_model.encoder.layers		,		pt_model.visual.transformer.resblocks	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]	)
  
  						lowercase__							 =       torch.from_numpy(flax_class_token_params["""scale"""]	)
  						lowercase__							 =       torch.from_numpy(flax_class_token_params["""bias"""]	)
  						lowercase__							 =       nn.Parameter(lowercase__	)
  						lowercase__							 =       nn.Parameter(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       hf_model.state_dict()
  						lowercase__							 =       {}
  
  						# Rename class prediction head flax params to pytorch HF
  						lowercase__							 =       flatten_nested_dict(flax_params["""class_head"""]	)
  
  						for flax_key, v in flax_class_params.items():
  												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
  												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)
  												lowercase__							 =       torch_key.replace("""Dense_0"""		,		"""dense0"""	)
  												lowercase__							 =       """class_head.""" + torch_key
  
  												if "weight" in torch_key and v.ndim == 2:
  																		lowercase__							 =       v.T
  
  												lowercase__							 =       nn.Parameter(torch.from_numpy(lowercase__	)	)
  
  						# Rename box prediction box flax params to pytorch HF
  						lowercase__							 =       flatten_nested_dict(flax_params["""obj_box_head"""]	)
  
  						for flax_key, v in flax_box_params.items():
  												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
  												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)
  												lowercase__							 =       torch_key.replace("""_"""		,		""""""	).lower()
  												lowercase__							 =       """box_head.""" + torch_key
  
  												if "weight" in torch_key and v.ndim == 2:
  																		lowercase__							 =       v.T
  
  												lowercase__							 =       nn.Parameter(torch.from_numpy(lowercase__	)	)
  
  						# Copy flax params to PyTorch params
  						for name, param in new_params.items():
  												if name in pt_params.keys():
  																		pt_params[name].copy_(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						for k, v in flax_attn_params.items():
  												if k.startswith("""transformer"""	):
  																		lowercase__							 =       k.replace("""transformer.resblocks"""		,		"""text_model.encoder.layers"""	)
  												else:
  																		lowercase__							 =       k.replace("""visual.transformer.resblocks"""		,		"""vision_model.encoder.layers"""	)
  
  												lowercase__							 =       torch_key.replace("""attn"""		,		"""self_attn"""	)
  												lowercase__							 =       torch_key.replace("""key"""		,		"""k_proj"""	)
  												lowercase__							 =       torch_key.replace("""value"""		,		"""v_proj"""	)
  												lowercase__							 =       torch_key.replace("""query"""		,		"""q_proj"""	)
  												lowercase__							 =       torch_key.replace("""out"""		,		"""out_proj"""	)
  
  												if "bias" in torch_key and v.ndim == 2:
  																		lowercase__							 =       v.shape[0] * v.shape[1]
  																		lowercase__							 =       v.reshape(lowercase__	)
  
  												if "weight" in torch_key and "out" in torch_key:
  																		lowercase__							 =       (v.shape[0] * v.shape[1], v.shape[2])
  																		lowercase__							 =       v.reshape(lowercase__	).T
  
  												if "weight" in torch_key and "out" not in torch_key:
  																		lowercase__							 =       (v.shape[0], v.shape[1] * v.shape[2])
  																		lowercase__							 =       v.reshape(lowercase__	).T
  
  												# Copy flax CLIP attn params to HF PyTorch params
  												lowercase__							 =       torch.from_numpy(lowercase__	)
  												hf_backbone.state_dict()[torch_key].copy_(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						lowercase__							 =       {}
  						lowercase__							 =       []
  
  						for k, v in params.items():
  												if "attn." in k:
  																		lowercase__							 =       k[: k.rindex("""attn."""	) + 5]
  																		if base in processed_attn_layers:
  																								continue
  
  																		processed_attn_layers.append(lowercase__	)
  																		lowercase__							 =       params[base + """out.weight"""].shape[-1]
  																		lowercase__							 =       params[base + """out.weight"""].reshape(lowercase__		,		lowercase__	).T
  																		lowercase__							 =       params[base + """out.bias"""]
  												else:
  																		lowercase__							 =       v
  						return new_params
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       CLIP(**lowercase__	)
  						torch_model.eval()
  						lowercase__							 =       torch_model.state_dict()
  
  						lowercase__							 =       flatten_nested_dict(flax_params["""backbone"""]["""clip"""]	)
  						lowercase__							 =       {}
  
  						for flax_key, v in flax_clip_params.items():
  												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
  												lowercase__							 =       torch_key.replace("""text.token_embedding.embedding"""		,		"""token_embedding.kernel"""	)
  
  												if (
  												    torch_key.startswith("""text.transformer"""	)
  												    or torch_key.startswith("""text.text_projection"""	)
  												    or torch_key.startswith("""text.ln_final"""	)
  												    or torch_key.startswith("""text.positional_embedding"""	)
  												):
  																		lowercase__							 =       torch_key[5:]
  
  												lowercase__							 =       torch_key.replace("""text_projection.kernel"""		,		"""text_projection"""	)
  												lowercase__							 =       torch_key.replace("""visual.proj.kernel"""		,		"""visual.proj"""	)
  												lowercase__							 =       torch_key.replace(""".scale"""		,		""".weight"""	)
  												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)
  
  												if "conv" in torch_key or "downsample.0.weight" in torch_key:
  																		lowercase__							 =       v.transpose(3		,		2		,		0		,		1	)
  
  												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
  																		# Fully connected layers are transposed, embeddings are not
  																		lowercase__							 =       v.T
  
  												lowercase__							 =       v
  
  						lowercase__							 =       _convert_attn_layers(lowercase__	)
  						new_torch_params.update(lowercase__	)
  						lowercase__							 =       {}
  
  						# Copy flax CLIP backbone params to PyTorch params
  						for name, param in new_torch_params.items():
  												if name in torch_clip_params.keys():
  																		lowercase__							 =       torch.from_numpy(new_torch_params[name]	)
  																		torch_clip_params[name].copy_(lowercase__	)
  												else:
  																		lowercase__							 =       param
  
  						return torch_clip_params, torch_model, attn_params
  
  
  
  
  
  @torch.no_grad()
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None	):
  						lowercase__							 =       Repository(lowercase__		,		clone_from=f'''google/{pytorch_dump_folder_path}'''	)
  						repo.git_pull()
  
  						if config_path is not None:
  												lowercase__							 =       OwlViTConfig.from_pretrained(lowercase__	)
  						else:
  												lowercase__							 =       OwlViTConfig()
  
  						lowercase__							 =       OwlViTModel(lowercase__	).eval()
  						lowercase__							 =       OwlViTForObjectDetection(lowercase__	).eval()
  
  						copy_text_model_and_projection(lowercase__		,		lowercase__	)
  						copy_vision_model_and_projection(lowercase__		,		lowercase__	)
  						lowercase__							 =       pt_backbone.logit_scale
  						copy_flax_attn_params(lowercase__		,		lowercase__	)
  
  						lowercase__							 =       hf_backbone
  						copy_class_merge_token(lowercase__		,		lowercase__	)
  						copy_class_box_heads(lowercase__		,		lowercase__	)
  
  						# Save HF model
  						hf_model.save_pretrained(repo.local_dir	)
  
  						# Initialize image processor
  						lowercase__							 =       OwlViTImageProcessor(
  						    size=config.vision_config.image_size		,		crop_size=config.vision_config.image_size	)
  						# Initialize tokenizer
  						lowercase__							 =       CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""		,		pad_token="""!"""		,		model_max_length=16	)
  
  						# Initialize processor
  						lowercase__							 =       OwlViTProcessor(image_processor=lowercase__		,		tokenizer=lowercase__	)
  						image_processor.save_pretrained(repo.local_dir	)
  						processor.save_pretrained(repo.local_dir	)
  
  						repo.git_add()
  						repo.git_commit("""Upload model and processor"""	)
  						repo.git_push()
  
  
  if __name__ == "__main__":
  				__A          =    argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    "--owlvit_version",
  				    default=None,
  				    type=str,
  				    required=True,
  				    help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
  				)
  				parser.add_argument(
  				    "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
  				)
  				parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
  				parser.add_argument(
  				    "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
  				)
  				__A          =    parser.parse_args()
  
  				# Initialize PyToch clip model
  				__A          =    args.owlvit_version
  				if model_name == "clip_b16":
  								__A          =    CONFIGS["vit_b16"]
  				elif model_name == "clip_b32":
  								__A          =    CONFIGS["vit_b32"]
  				elif model_name == "clip_l14":
  								__A          =    CONFIGS["vit_l14"]
  
  				# Load from checkpoint and convert params to float-32
  				__A          =    checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
  				__A          =    jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  				del variables
  
  				# Convert CLIP backbone
  				__A    ,       __A    ,       __A          =    convert_clip_backbone(flax_params, torch_config)
  
  				convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  
  @flax.struct.dataclass
  class 			A							:
  			# setable values
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls()
  
  
  
  
  
  @dataclass
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							ScoreSdeVeSchedulerState
  			lowerCamelCase							:							jnp.ndarray
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return True
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ScoreSdeVeSchedulerState.create()
  									return self.set_sigmas(
  									    lowerCamelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return state.replace(timesteps=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
  									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  									if state.timesteps is None:
  															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									lowercase__							 =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )       ,     jnp.log(lowerCamelCase__       )       ,     lowerCamelCase__       )       )
  									lowercase__							 =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )
  
  									return state.replace(discrete_sigmas=lowerCamelCase__       ,     sigmas=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									lowercase__							 =       timestep * jnp.ones(
  									    sample.shape[0]       ,     )
  									lowercase__							 =       (timestep * (len(state.timesteps       ) - 1)).long()
  
  									lowercase__							 =       state.discrete_sigmas[timesteps]
  									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
  									lowercase__							 =       (sigma**2 - adjacent_sigma**2) ** 0.5
  
  									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  									# also equation 47 shows the analog from SDE models to ancestral sampling methods
  									lowercase__							 =       diffusion.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       drift - diffusion**2 * model_output
  
  									#  equation 6: sample noise for the diffusion term of
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
  									# TODO is the variable diffusion the correct scaling term for the noise?
  									lowercase__							 =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  									if not return_dict:
  															return (prev_sample, prev_sample_mean, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     prev_sample_mean=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  									# sample noise for correction
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  
  									# compute step size from the model_output, the noise, and the snr
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  									lowercase__							 =       step_size * jnp.ones(sample.shape[0]       )
  
  									# compute corrected sample: model_output term and noise term
  									lowercase__							 =       step_size.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       sample + step_size * model_output
  									lowercase__							 =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  									if not return_dict:
  															return (prev_sample, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def __len__(					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.config.num_train_timesteps
  
  
  
  
  
  
  