code: 
  '''simple docstring'''
  
  
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
  from ....modeling_utils import ModuleUtilsMixin
  from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    "MMBTConfig"
  
  
  
  
  
  class 			A							(       nn.Module				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__()
  									lowercase__							 =       config
  									lowercase__							 =       encoder
  									lowercase__							 =       nn.Linear(config.modal_hidden_size       ,     config.hidden_size       )
  									lowercase__							 =       embeddings.position_embeddings
  									lowercase__							 =       embeddings.token_type_embeddings
  									lowercase__							 =       embeddings.word_embeddings
  									lowercase__							 =       embeddings.LayerNorm
  									lowercase__							 =       nn.Dropout(p=config.hidden_dropout_prob       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.proj_embeddings(self.encoder(lowerCamelCase__       )       )
  									lowercase__							 =       token_embeddings.size(1       )
  
  									if start_token is not None:
  															lowercase__							 =       self.word_embeddings(lowerCamelCase__       )
  															seq_length += 1
  															lowercase__							 =       torch.cat([start_token_embeds.unsqueeze(1       ), token_embeddings]       ,     dim=1       )
  
  									if end_token is not None:
  															lowercase__							 =       self.word_embeddings(lowerCamelCase__       )
  															seq_length += 1
  															lowercase__							 =       torch.cat([token_embeddings, end_token_embeds.unsqueeze(1       )]       ,     dim=1       )
  
  									if position_ids is None:
  															lowercase__							 =       torch.arange(lowerCamelCase__       ,     dtype=torch.long       ,     device=input_modal.device       )
  															lowercase__							 =       position_ids.unsqueeze(0       ).expand(input_modal.size(0       )       ,     lowerCamelCase__       )
  
  									if token_type_ids is None:
  															lowercase__							 =       torch.zeros(
  															    (input_modal.size(0       ), seq_length)       ,     dtype=torch.long       ,     device=input_modal.device       )
  
  									lowercase__							 =       self.position_embeddings(lowerCamelCase__       )
  									lowercase__							 =       self.token_type_embeddings(lowerCamelCase__       )
  									lowercase__							 =       token_embeddings + position_embeddings + token_type_embeddings
  									lowercase__							 =       self.LayerNorm(lowerCamelCase__       )
  									lowercase__							 =       self.dropout(lowerCamelCase__       )
  									return embeddings
  
  
  
  
  __A          =    r"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"
  
  __A          =    r"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
  
  
  
  
  
  @add_start_docstrings(
      """The bare MMBT Model outputting raw hidden-states without any specific head on top."""		,						__UpperCAmelCase		,						)
  class 			A							(       nn.Module		,						__UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__()
  									lowercase__							 =       config
  									lowercase__							 =       transformer
  									lowercase__							 =       ModalEmbeddings(lowerCamelCase__       ,     lowerCamelCase__       ,     transformer.embeddings       )
  
  
  
  			@add_start_docstrings_to_model_forward(lowerCamelCase__       )
  			@replace_return_docstrings(output_type=lowerCamelCase__       ,     config_class=_CONFIG_FOR_DOC       )
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       output_attentions if output_attentions is not None else self.config.output_attentions
  									lowercase__							 =       (
  									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  									)
  									lowercase__							 =       return_dict if return_dict is not None else self.config.use_return_dict
  
  									if input_ids is not None and inputs_embeds is not None:
  															raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""       )
  									elif input_ids is not None:
  															lowercase__							 =       input_ids.size()
  									elif inputs_embeds is not None:
  															lowercase__							 =       inputs_embeds.size()[:-1]
  									else:
  															raise ValueError("""You have to specify either input_ids or inputs_embeds"""       )
  
  									lowercase__							 =       input_ids.device if input_ids is not None else inputs_embeds.device
  
  									lowercase__							 =       self.modal_encoder(
  									    lowerCamelCase__       ,     start_token=lowerCamelCase__       ,     end_token=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     )
  
  									lowercase__							 =       modal_embeddings.size()[:-1]
  
  									if token_type_ids is None:
  															lowercase__							 =       torch.ones(lowerCamelCase__       ,     dtype=torch.long       ,     device=lowerCamelCase__       )
  
  									lowercase__							 =       self.transformer.embeddings(
  									    input_ids=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     inputs_embeds=lowerCamelCase__       )
  
  									lowercase__							 =       torch.cat([modal_embeddings, txt_embeddings]       ,     1       )
  
  									lowercase__							 =       embedding_output.size()[:-1]
  
  									if attention_mask is None:
  															lowercase__							 =       torch.ones(lowerCamelCase__       ,     device=lowerCamelCase__       )
  									else:
  															lowercase__							 =       torch.cat(
  															    [torch.ones(lowerCamelCase__       ,     device=lowerCamelCase__       ,     dtype=torch.long       ), attention_mask]       ,     dim=1       )
  									if encoder_attention_mask is None:
  															lowercase__							 =       torch.ones(lowerCamelCase__       ,     device=lowerCamelCase__       )
  									else:
  															lowercase__							 =       torch.cat(
  															    [torch.ones(lowerCamelCase__       ,     device=lowerCamelCase__       ), encoder_attention_mask]       ,     dim=1       )
  
  									lowercase__							 =       self.get_extended_attention_mask(lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       self.invert_attention_mask(lowerCamelCase__       )
  									lowercase__							 =       self.get_head_mask(lowerCamelCase__       ,     self.config.num_hidden_layers       )
  
  									lowercase__							 =       self.transformer.encoder(
  									    lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     head_mask=lowerCamelCase__       ,     encoder_hidden_states=lowerCamelCase__       ,     encoder_attention_mask=lowerCamelCase__       ,     output_attentions=lowerCamelCase__       ,     output_hidden_states=lowerCamelCase__       ,     return_dict=lowerCamelCase__       ,     )
  
  									lowercase__							 =       encoder_outputs[0]
  									lowercase__							 =       self.transformer.pooler(lowerCamelCase__       )
  
  									if not return_dict:
  															return (sequence_output, pooled_output) + encoder_outputs[1:]
  
  									return BaseModelOutputWithPooling(
  									    last_hidden_state=lowerCamelCase__       ,     pooler_output=lowerCamelCase__       ,     hidden_states=encoder_outputs.hidden_states       ,     attentions=encoder_outputs.attentions       ,     )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.embeddings.word_embeddings
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       value
  
  
  
  
  
  @add_start_docstrings(
      """
      MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
      """		,						__UpperCAmelCase		,						__UpperCAmelCase		,						)
  class 			A							(       nn.Module				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__()
  									lowercase__							 =       config.num_labels
  
  									lowercase__							 =       MMBTModel(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       nn.Dropout(config.hidden_dropout_prob       )
  									lowercase__							 =       nn.Linear(config.hidden_size       ,     config.num_labels       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       return_dict if return_dict is not None else self.config.use_return_dict
  
  									lowercase__							 =       self.mmbt(
  									    input_modal=lowerCamelCase__       ,     input_ids=lowerCamelCase__       ,     modal_start_tokens=lowerCamelCase__       ,     modal_end_tokens=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     modal_token_type_ids=lowerCamelCase__       ,     position_ids=lowerCamelCase__       ,     modal_position_ids=lowerCamelCase__       ,     head_mask=lowerCamelCase__       ,     inputs_embeds=lowerCamelCase__       ,     return_dict=lowerCamelCase__       ,     )
  
  									lowercase__							 =       outputs[1]
  
  									lowercase__							 =       self.dropout(lowerCamelCase__       )
  									lowercase__							 =       self.classifier(lowerCamelCase__       )
  
  									lowercase__							 =       None
  									if labels is not None:
  															if self.num_labels == 1:
  																					#  We are doing regression
  																					lowercase__							 =       MSELoss()
  																					lowercase__							 =       loss_fct(logits.view(-1       )       ,     labels.view(-1       )       )
  															else:
  																					lowercase__							 =       CrossEntropyLoss()
  																					lowercase__							 =       loss_fct(logits.view(-1       ,     self.num_labels       )       ,     labels.view(-1       )       )
  
  									if not return_dict:
  															lowercase__							 =       (logits,) + outputs[2:]
  															return ((loss,) + output) if loss is not None else output
  
  									return SequenceClassifierOutput(
  									    loss=lowerCamelCase__       ,     logits=lowerCamelCase__       ,     hidden_states=outputs.hidden_states       ,     attentions=outputs.attentions       ,     )
  
  
  
  
  
  
  
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
  
  
  
  
  
  
  