


"""simple docstring"""


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a_											=     logging.get_logger(__name__)  # pylint: disable=invalid-name






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

						self.register_modules(
						    prior=a__     , tokenizer=a__     , text_encoder=a__     , prior_scheduler=a__     , )







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_A       	=      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
														logger.warning(
														    "The following part of your input was truncated because CLIP can only handle sequences up to"
														    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""		)
														_A       	=      text_input_ids[:, : self.tokenizer.model_max_length]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , dim=0		)

										# done duplicates

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_hf_hook"		)
										    and hasattr(module._hf_hook     , "execution_device"		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		def 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

						for i, t in enumerate(self.progress_bar(a__		)		):
										# expand the latents if we are doing classifier free guidance
										_A       	=      torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output



"""simple docstring"""


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   a__			(					__lowercase )						->						Optional[Any]:
				_A       	=      {}
				for name in unet.attn_processors.keys():
								_A       	=      None if name.endswith("attn1.processor" ) else unet.config.cross_attention_dim
								if name.startswith("mid_block" ):
												_A       	=      unet.config.block_out_channels[-1]
								elif name.startswith("up_blocks" ):
												_A       	=      int(name[len("up_blocks." )] )
												_A       	=      list(reversed(unet.config.block_out_channels ) )[block_id]
								elif name.startswith("down_blocks" ):
												_A       	=      int(name[len("down_blocks." )] )
												_A       	=      unet.config.block_out_channels[block_id]
								_A       	=      (
								    LoRAAttnProcessor2_0 if hasattr(__lowercase  ,      "scaled_dot_product_attention" ) else LoRAAttnProcessor
								)
								_A       	=      lora_attn_processor_class(
								    hidden_size=__lowercase  ,      cross_attention_dim=__lowercase )
				_A       	=      AttnProcsLayers(__lowercase )
				return lora_attn_procs, unet_lora_layers






def   a__			(					__lowercase )						->						Optional[Any]:
				_A       	=      {}
				_A       	=      (
				    LoRAAttnProcessor2_0 if hasattr(__lowercase  ,      "scaled_dot_product_attention" ) else LoRAAttnProcessor
				)
				for name, module in text_encoder.named_modules():
								if name.endswith(__lowercase ):
												_A       	=      lora_attn_processor_class(
												    hidden_size=module.out_proj.out_features  ,      cross_attention_dim=__lowercase )
				return text_lora_attn_procs






def   a__			(					__lowercase )						->						str:
				_A       	=      create_text_encoder_lora_attn_procs(__lowercase )
				_A       	=      AttnProcsLayers(__lowercase )
				return text_encoder_lora_layers






def   a__			(					__lowercase  ,      __lowercase=False )						->						Dict:
				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" ):
																_A       	=      (
																    torch.randn_like(layer_module.up.weight )
																    if randn_weight
																    else torch.zeros_like(layer_module.up.weight )
																)
																_A       	=      torch.nn.Parameter(__lowercase )




class snake_case   (	unittest.TestCase):







		def 			a_			(       self   :							Any		)							->    Any:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A       	=      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     , )
						_A       	=      DDIMScheduler(
						    beta_start=0.0_0_0_8_5     , beta_end=0.0_1_2     , beta_schedule="scaled_linear"     , clip_sample=a__     , set_alpha_to_one=a__     , steps_offset=1     , )
						torch.manual_seed(0		)
						_A       	=      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     , )
						_A       	=      CLIPTextConfig(
						    bos_token_id=0     , eos_token_id=2     , hidden_size=32     , intermediate_size=37     , layer_norm_eps=1E-0_5     , num_attention_heads=4     , num_hidden_layers=5     , pad_token_id=1     , vocab_size=10_00     , )
						_A       	=      CLIPTextModel(a__		)
						_A       	=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A	,		_A       	=      create_unet_lora_layers(a__		)
						_A       	=      create_text_encoder_lora_layers(a__		)

						_A       	=      {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						_A       	=      {
						    "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		)							->    Any:





						'''simple docstring'''







						_A       	=      1
						_A       	=      10
						_A       	=      4
						_A       	=      (32, 32)

						_A       	=      torch.manual_seed(0		)
						_A       	=      floats_tensor((batch_size, num_channels) + sizes		)
						_A       	=      torch.randint(1     , a__     , size=(batch_size, sequence_length)     , generator=a__		)

						_A       	=      {
						    "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   :							Dict		)							->    List[Any]:





						'''simple docstring'''







						_A       	=      77

						_A       	=      torch.randint(2     , 56     , size=(1, max_seq_length)     , generator=torch.manual_seed(0		)		)

						_A       	=      {}
						_A       	=      inputs
						return prepared_inputs







		def 			a_			(       self   :							Union[str, Any]     , a__   :							int		)							->    List[Any]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						LoraLoaderMixin.save_lora_weights(
						    save_directory=a__     , 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(a__     , "pytorch_lora_weights.bin"		)		)		)







		def 			a_			(       self   :							List[Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , 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(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Union[str, Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , safe_serialization=a__     , )
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.safetensors"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Optional[Any]		)							->    Tuple:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      lora_components["unet_lora_attn_procs"]
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										_A       	=      sd_pipe.unet
										unet.set_attn_processor(a__		)
										unet.save_attn_procs(a__		)
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)

						_A       	=      self.get_dummy_tokens()

						# inference without lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with zeroed out up.weights
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert torch.allclose(
						    a__     , a__		), "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
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    a__     , a__		), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







		def 			a_			(       self   :							Dict		)							->    int:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)

						_A       	=      self.get_dummy_tokens()

						# inference without lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with randn up.weights
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    a__     , a__		), "lora outputs should be different to without lora outputs"

						# remove monkey patch
						pipe._remove_text_encoder_monkey_patch()

						# inference with removed lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora_removed.shape == (1, 77, 32)

						assert torch.allclose(
						    a__     , a__		), "remove lora monkey patch should restore the original outputs"







		def 			a_			(       self   :							Union[str, Any]		)							->    Union[str, Any]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , 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(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						_A       	=      sd_pipe(**a__     , cross_attention_kwargs={"scale": 0.5}		).images
						_A       	=      lora_images_with_scale[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(
						    torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Union[str, Any]		)							->    Any:





						'''simple docstring'''







						with tempfile.TemporaryDirectory() as tmpdirname:
										self.create_lora_weight_file(a__		)

										_A	,		_A       	=      self.get_dummy_components()
										_A       	=      StableDiffusionPipeline(**a__		)
										_A       	=      sd_pipe.to(a__		)
										sd_pipe.set_progress_bar_config(disable=a__		)

										# check if vanilla attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		self.assertIsInstance(module.processor     , (AttnProcessor, AttnProcessor2_0)		)

            # load LoRA weight file
										sd_pipe.load_lora_weights(a__		)

										# check if lora attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		_A       	=      (
																		    LoRAAttnProcessor2_0 if hasattr(a__     , "scaled_dot_product_attention"		) else LoRAAttnProcessor
																		)
																		self.assertIsInstance(module.processor     , a__		)







		@unittest.skipIf(torch_device != "cuda"     , "This test is supposed to run on GPU"		)
		def 			a_			(       self   :							Dict		)							->    List[str]:





						'''simple docstring'''







						with tempfile.TemporaryDirectory() as tmpdirname:
										self.create_lora_weight_file(a__		)

										_A	,		_A       	=      self.get_dummy_components()
										_A       	=      StableDiffusionPipeline(**a__		)
										_A       	=      sd_pipe.to(a__		)
										sd_pipe.set_progress_bar_config(disable=a__		)

										# 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(a__     , a__		):
																		self.assertIsInstance(module.processor     , a__		)

            # load LoRA weight file
										sd_pipe.load_lora_weights(a__		)

										# check if lora attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		self.assertIsInstance(module.processor     , a__		)







		@unittest.skipIf(torch_device != "cuda"     , "This test is supposed to run on GPU"		)
		def 			a_			(       self   :							List[str]		)							->    Union[str, Any]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						# enable XFormers
						sd_pipe.enable_xformers_memory_efficient_attention()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , 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(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)