code: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    int								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Dict								=					"MMBTConfig"
  
  
  
  class 			a_					(    nn.Module    ):
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE_			=	config
  				SCREAMING_SNAKE_CASE_			=	encoder
  				SCREAMING_SNAKE_CASE_			=	nn.Linear(config.modal_hidden_size			,    config.hidden_size			)
  				SCREAMING_SNAKE_CASE_			=	embeddings.position_embeddings
  				SCREAMING_SNAKE_CASE_			=	embeddings.token_type_embeddings
  				SCREAMING_SNAKE_CASE_			=	embeddings.word_embeddings
  				SCREAMING_SNAKE_CASE_			=	embeddings.LayerNorm
  				SCREAMING_SNAKE_CASE_			=	nn.Dropout(p=config.hidden_dropout_prob			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.proj_embeddings(self.encoder(SCREAMING_SNAKE_CASE			)			)
  				SCREAMING_SNAKE_CASE_			=	token_embeddings.size(1			)
  
  				if start_token is not None:
  							SCREAMING_SNAKE_CASE_			=	self.word_embeddings(SCREAMING_SNAKE_CASE			)
  							seq_length += 1
  							SCREAMING_SNAKE_CASE_			=	torch.cat([start_token_embeds.unsqueeze(1			), token_embeddings]			,    dim=1			)
  
  				if end_token is not None:
  							SCREAMING_SNAKE_CASE_			=	self.word_embeddings(SCREAMING_SNAKE_CASE			)
  							seq_length += 1
  							SCREAMING_SNAKE_CASE_			=	torch.cat([token_embeddings, end_token_embeds.unsqueeze(1			)]			,    dim=1			)
  
  				if position_ids is None:
  							SCREAMING_SNAKE_CASE_			=	torch.arange(SCREAMING_SNAKE_CASE			,    dtype=torch.long			,    device=input_modal.device			)
  							SCREAMING_SNAKE_CASE_			=	position_ids.unsqueeze(0			).expand(input_modal.size(0			)			,    SCREAMING_SNAKE_CASE			)
  
  				if token_type_ids is None:
  							SCREAMING_SNAKE_CASE_			=	torch.zeros(
  							    (input_modal.size(0			), seq_length)			,    dtype=torch.long			,    device=input_modal.device			)
  
  				SCREAMING_SNAKE_CASE_			=	self.position_embeddings(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.token_type_embeddings(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	token_embeddings + position_embeddings + token_type_embeddings
  				SCREAMING_SNAKE_CASE_			=	self.LayerNorm(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.dropout(SCREAMING_SNAKE_CASE			)
  				return embeddings
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					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"
  
  SCREAMING_SNAKE_CASE__    :    str								=					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.'''     ,						SCREAMING_SNAKE_CASE__     ,						)
  class 			a_					(    nn.Module     ,						SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE_			=	config
  				SCREAMING_SNAKE_CASE_			=	transformer
  				SCREAMING_SNAKE_CASE_			=	ModalEmbeddings(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    transformer.embeddings			)
  
  
  
  
  
  	@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE			)
  	@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE			,    config_class=_CONFIG_FOR_DOC			)
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    )     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	output_attentions if output_attentions is not None else self.config.output_attentions
  				SCREAMING_SNAKE_CASE_			=	(
  				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  				)
  				SCREAMING_SNAKE_CASE_			=	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:
  							SCREAMING_SNAKE_CASE_			=	input_ids.size()
  				elif inputs_embeds is not None:
  							SCREAMING_SNAKE_CASE_			=	inputs_embeds.size()[:-1]
  				else:
  							raise ValueError('You have to specify either input_ids or inputs_embeds'			)
  
  				SCREAMING_SNAKE_CASE_			=	input_ids.device if input_ids is not None else inputs_embeds.device
  
  				SCREAMING_SNAKE_CASE_			=	self.modal_encoder(
  				    SCREAMING_SNAKE_CASE			,    start_token=SCREAMING_SNAKE_CASE			,    end_token=SCREAMING_SNAKE_CASE			,    position_ids=SCREAMING_SNAKE_CASE			,    token_type_ids=SCREAMING_SNAKE_CASE			,    )
  
  				SCREAMING_SNAKE_CASE_			=	modal_embeddings.size()[:-1]
  
  				if token_type_ids is None:
  							SCREAMING_SNAKE_CASE_			=	torch.ones(SCREAMING_SNAKE_CASE			,    dtype=torch.long			,    device=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.transformer.embeddings(
  				    input_ids=SCREAMING_SNAKE_CASE			,    position_ids=SCREAMING_SNAKE_CASE			,    token_type_ids=SCREAMING_SNAKE_CASE			,    inputs_embeds=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.cat([modal_embeddings, txt_embeddings]			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	embedding_output.size()[:-1]
  
  				if attention_mask is None:
  							SCREAMING_SNAKE_CASE_			=	torch.ones(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	torch.cat(
  							    [torch.ones(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			,    dtype=torch.long			), attention_mask]			,    dim=1			)
  				if encoder_attention_mask is None:
  							SCREAMING_SNAKE_CASE_			=	torch.ones(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	torch.cat(
  							    [torch.ones(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			), encoder_attention_mask]			,    dim=1			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_extended_attention_mask(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.invert_attention_mask(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.get_head_mask(SCREAMING_SNAKE_CASE			,    self.config.num_hidden_layers			)
  
  				SCREAMING_SNAKE_CASE_			=	self.transformer.encoder(
  				    SCREAMING_SNAKE_CASE			,    attention_mask=SCREAMING_SNAKE_CASE			,    head_mask=SCREAMING_SNAKE_CASE			,    encoder_hidden_states=SCREAMING_SNAKE_CASE			,    encoder_attention_mask=SCREAMING_SNAKE_CASE			,    output_attentions=SCREAMING_SNAKE_CASE			,    output_hidden_states=SCREAMING_SNAKE_CASE			,    return_dict=SCREAMING_SNAKE_CASE			,    )
  
  				SCREAMING_SNAKE_CASE_			=	encoder_outputs[0]
  				SCREAMING_SNAKE_CASE_			=	self.transformer.pooler(SCREAMING_SNAKE_CASE			)
  
  				if not return_dict:
  							return (sequence_output, pooled_output) + encoder_outputs[1:]
  
  				return BaseModelOutputWithPooling(
  				    last_hidden_state=SCREAMING_SNAKE_CASE			,    pooler_output=SCREAMING_SNAKE_CASE			,    hidden_states=encoder_outputs.hidden_states			,    attentions=encoder_outputs.attentions			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.embeddings.word_embeddings
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	value
  
  
  
  
  @add_start_docstrings(
      '''
      MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
      '''     ,						SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__     ,						)
  class 			a_					(    nn.Module    ):
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  				SCREAMING_SNAKE_CASE_			=	config.num_labels
  
  				SCREAMING_SNAKE_CASE_			=	MMBTModel(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	nn.Dropout(config.hidden_dropout_prob			)
  				SCREAMING_SNAKE_CASE_			=	nn.Linear(config.hidden_size			,    config.num_labels			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    )     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	return_dict if return_dict is not None else self.config.use_return_dict
  
  				SCREAMING_SNAKE_CASE_			=	self.mmbt(
  				    input_modal=SCREAMING_SNAKE_CASE			,    input_ids=SCREAMING_SNAKE_CASE			,    modal_start_tokens=SCREAMING_SNAKE_CASE			,    modal_end_tokens=SCREAMING_SNAKE_CASE			,    attention_mask=SCREAMING_SNAKE_CASE			,    token_type_ids=SCREAMING_SNAKE_CASE			,    modal_token_type_ids=SCREAMING_SNAKE_CASE			,    position_ids=SCREAMING_SNAKE_CASE			,    modal_position_ids=SCREAMING_SNAKE_CASE			,    head_mask=SCREAMING_SNAKE_CASE			,    inputs_embeds=SCREAMING_SNAKE_CASE			,    return_dict=SCREAMING_SNAKE_CASE			,    )
  
  				SCREAMING_SNAKE_CASE_			=	outputs[1]
  
  				SCREAMING_SNAKE_CASE_			=	self.dropout(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.classifier(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	None
  				if labels is not None:
  							if self.num_labels == 1:
  										#  We are doing regression
  										SCREAMING_SNAKE_CASE_			=	MSELoss()
  										SCREAMING_SNAKE_CASE_			=	loss_fct(logits.view(-1			)			,    labels.view(-1			)			)
  							else:
  										SCREAMING_SNAKE_CASE_			=	CrossEntropyLoss()
  										SCREAMING_SNAKE_CASE_			=	loss_fct(logits.view(-1			,    self.num_labels			)			,    labels.view(-1			)			)
  
  				if not return_dict:
  							SCREAMING_SNAKE_CASE_			=	(logits,) + outputs[2:]
  							return ((loss,) + output) if loss is not None else output
  
  				return SequenceClassifierOutput(
  				    loss=SCREAMING_SNAKE_CASE			,    logits=SCREAMING_SNAKE_CASE			,    hidden_states=outputs.hidden_states			,    attentions=outputs.attentions			,    )
  
style_context: 
  
  
  
  
  import gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			for name in unet.attn_processors.keys():
  						SCREAMING_SNAKE_CASE_			=	None if name.endswith('attn1.processor'   ) else unet.config.cross_attention_dim
  						if name.startswith('mid_block'   ):
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[-1]
  						elif name.startswith('up_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('up_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	list(reversed(unet.config.block_out_channels   )   )[block_id]
  						elif name.startswith('down_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('down_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[block_id]
  						SCREAMING_SNAKE_CASE_			=	(
  						    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  						)
  						SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  						    hidden_size=SCREAMING_SNAKE_CASE					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return lora_attn_procs, unet_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			SCREAMING_SNAKE_CASE_			=	(
  			    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  			)
  			for name, module in text_encoder.named_modules():
  						if name.endswith(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  									    hidden_size=module.out_proj.out_features					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			return text_lora_attn_procs
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return text_encoder_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=False   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for _, attn_proc in text_lora_attn_procs.items():
  						# set up.weights
  						for layer_name, layer_module in attn_proc.named_modules():
  									if layer_name.endswith('_lora'   ):
  												SCREAMING_SNAKE_CASE_			=	(
  												    torch.randn_like(layer_module.up.weight   )
  												    if randn_weight
  												    else torch.zeros_like(layer_module.up.weight   )
  												)
  												SCREAMING_SNAKE_CASE_			=	torch.nn.Parameter(SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	UNetaDConditionModel(
  				    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=4			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    cross_attention_dim=32			,    )
  				SCREAMING_SNAKE_CASE_			=	DDIMScheduler(
  				    beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='scaled_linear'			,    clip_sample=SCREAMING_SNAKE_CASE			,    set_alpha_to_one=SCREAMING_SNAKE_CASE			,    steps_offset=1			,    )
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	AutoencoderKL(
  				    block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextConfig(
  				    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextModel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_unet_lora_layers(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet': unet,
  				    'scheduler': scheduler,
  				    'vae': vae,
  				    'text_encoder': text_encoder,
  				    'tokenizer': tokenizer,
  				    'safety_checker': None,
  				    'feature_extractor': None,
  				}
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet_lora_layers': unet_lora_layers,
  				    'text_encoder_lora_layers': text_encoder_lora_layers,
  				    'unet_lora_attn_procs': unet_lora_attn_procs,
  				}
  				return pipeline_components, lora_components
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1
  				SCREAMING_SNAKE_CASE_			=	10
  				SCREAMING_SNAKE_CASE_			=	4
  				SCREAMING_SNAKE_CASE_			=	(32, 32)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	floats_tensor((batch_size, num_channels) + sizes			)
  				SCREAMING_SNAKE_CASE_			=	torch.randint(1			,    SCREAMING_SNAKE_CASE			,    size=(batch_size, sequence_length)			,    generator=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'prompt': 'A painting of a squirrel eating a burger',
  				    'generator': generator,
  				    'num_inference_steps': 2,
  				    'guidance_scale': 6.0,
  				    'output_type': 'numpy',
  				}
  
  				return noise, input_ids, pipeline_inputs
  
  				# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	77
  
  				SCREAMING_SNAKE_CASE_			=	torch.randint(2			,    56			,    size=(1, max_seq_length)			,    generator=torch.manual_seed(0			)			)
  
  				SCREAMING_SNAKE_CASE_			=	{}
  				SCREAMING_SNAKE_CASE_			=	inputs
  				return prepared_inputs
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				LoraLoaderMixin.save_lora_weights(
  				    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  				self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    safe_serialization=SCREAMING_SNAKE_CASE			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.safetensors'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	lora_components['unet_lora_attn_procs']
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.unet
  							unet.set_attn_processor(SCREAMING_SNAKE_CASE			)
  							unet.save_attn_procs(SCREAMING_SNAKE_CASE			)
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with zeroed out up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora outputs should be different to without lora outputs"
  
  				# remove monkey patch
  				pipe._remove_text_encoder_monkey_patch()
  
  				# inference with removed lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora_removed.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			,    cross_attention_kwargs={'scale': 0.5}			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images_with_scale[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(
  				    torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# check if vanilla attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    (AttnProcessor, AttnProcessor2_0)			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													SCREAMING_SNAKE_CASE_			=	(
  													    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,    'scaled_dot_product_attention'			) else LoRAAttnProcessor
  													)
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# enable XFormers
  							sd_pipe.enable_xformers_memory_efficient_attention()
  
  							# check if xFormers attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				# enable XFormers
  				sd_pipe.enable_xformers_memory_efficient_attention()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  