






'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 							lowerCAmelCase					(							UpperCamelCase_							,      unittest.TestCase   ):







   """simple docstring"""







   _A						=   PaintByExamplePipeline
   _A						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   _A						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _A						=   frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    torch.manual_seed(0			)
    __a    :	Optional[int]									=						UNetaDConditionModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=9   ,   out_channels=4   ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,   cross_attention_dim=32   ,   )
    __a    :	Any									=						PNDMScheduler(skip_prk_steps=__UpperCamelCase			)
    torch.manual_seed(0			)
    __a    :	int									=						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   ,   )
    torch.manual_seed(0			)
    __a    :	Tuple									=						CLIPVisionConfig(
        hidden_size=32   ,   projection_dim=32   ,   intermediate_size=37   ,   layer_norm_eps=1E-0_5   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   image_size=32   ,   patch_size=4   ,   )
    __a    :	int									=						PaintByExampleImageEncoder(__UpperCamelCase   ,   proj_size=32			)
    __a    :	int									=						CLIPImageProcessor(crop_size=32   ,   size=32			)

    __a    :	List[str]									=						{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''image_encoder''': image_encoder,
        '''safety_checker''': None,
        '''feature_extractor''': feature_extractor,
    }
    return components



   def    __magic_name__    ( self   ,   _A			)    ->   Dict:
    __a    :	Tuple									=						np.array(image.convert('RGB'			)			)
    __a    :	List[Any]									=						image[None].transpose(0   ,   3   ,   1   ,   2			)
    __a    :	List[Any]									=						torch.from_numpy(__UpperCamelCase			).to(dtype=torch.floataa			) / 127.5 - 1.0
    return image



   def    __magic_name__    ( self   ,   _A="cpu"   ,   _A=0			)    ->   Any:
    __a    :	Optional[int]									=						floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)
    __a    :	List[str]									=						image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]
    __a    :	str									=						Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	Union[str, Any]									=						Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	Tuple									=						Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			).resize((32, 32)			)

    if str(__UpperCamelCase			).startswith('mps'			):
     __a    :	Optional[Any]									=						torch.manual_seed(__UpperCamelCase			)
    else:
     __a    :	Optional[int]									=						torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)
    __a    :	Dict									=						{
        '''example_image''': example_image,
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def    __magic_name__    ( self			)    ->   Dict:
    __a    :	List[Any]									=						self.get_dummy_components()

    # make sure here that pndm scheduler skips prk
    __a    :	List[Any]									=						PaintByExamplePipeline(**__UpperCamelCase			)
    __a    :	Dict									=						pipe.to('cpu'			)
    pipe.set_progress_bar_config(disable=__UpperCamelCase			)

    __a    :	Optional[int]									=						self.get_dummy_inputs()
    __a    :	Optional[int]									=						pipe(**__UpperCamelCase			)
    __a    :	Dict									=						output.images

    __a    :	List[Any]									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    __a    :	int									=						np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Optional[int]									=						'''cpu'''
    __a    :	Any									=						self.get_dummy_inputs()
    inputs.pop('mask_image'			)
    __a    :	Dict									=						self.convert_to_pt(inputs.pop('image'			)			)
    __a    :	str									=						image.clamp(0   ,   1			) / 2

    # make sure here that pndm scheduler skips prk
    __a    :	Any									=						PaintByExamplePipeline(**self.get_dummy_components()			)
    __a    :	Any									=						pipe.to(__UpperCamelCase			)
    pipe.set_progress_bar_config(disable=__UpperCamelCase			)

    __a    :	Union[str, Any]									=						pipe(image=__UpperCamelCase   ,   mask_image=mask_image[:, 0]   ,   **__UpperCamelCase			)
    __a    :	Dict									=						output.images

    __a    :	Union[str, Any]									=						image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]
    __a    :	Optional[int]									=						mask_image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]

    __a    :	List[Any]									=						Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			)
    __a    :	str									=						Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			)

    __a    :	Optional[Any]									=						pipe(**self.get_dummy_inputs()			)
    __a    :	Optional[int]									=						output.images

    assert out_a.shape == (1, 64, 64, 3)
    assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5E-2



   def    __magic_name__    ( self			)    ->   Any:
    super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def    __magic_name__    ( self			)    ->   Optional[Any]:
    __a    :	List[Any]									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/dog_in_bucket.png'			)
    __a    :	str									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/mask.png'			)
    __a    :	Tuple									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/panda.jpg'			)

    __a    :	Tuple									=						PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'			)
    __a    :	Optional[Any]									=						pipe.to(__UpperCamelCase			)
    pipe.set_progress_bar_config(disable=__UpperCamelCase			)

    __a    :	Any									=						torch.manual_seed(321			)
    __a    :	Optional[Any]									=						pipe(
        image=__UpperCamelCase   ,   mask_image=__UpperCamelCase   ,   example_image=__UpperCamelCase   ,   generator=__UpperCamelCase   ,   guidance_scale=5.0   ,   num_inference_steps=50   ,   output_type='np'   ,   )

    __a    :	Optional[Any]									=						output.images

    __a    :	Optional[int]									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    __a    :	List[str]									=						np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2









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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Dict									=						{}
 __a    :	int									=						(
     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__		):
   __a    :	Dict									=						lora_attn_processor_class(
       hidden_size=module.out_proj.out_features       ,		cross_attention_dim=SCREAMING_SNAKE_CASE__		)
 return text_lora_attn_procs






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Tuple									=						create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE__		)
 __a    :	Dict									=						AttnProcsLayers(SCREAMING_SNAKE_CASE__		)
 return text_encoder_lora_layers






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=False		):
 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    :	Optional[int]									=						(
        torch.randn_like(layer_module.up.weight		)
        if randn_weight
        else torch.zeros_like(layer_module.up.weight		)
    )
    __a    :	int									=						torch.nn.Parameter(SCREAMING_SNAKE_CASE__		)




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   int:
    torch.manual_seed(0			)
    __a    :	str									=						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    :	List[Any]									=						DDIMScheduler(
        beta_start=0.00_085   ,   beta_end=0.012   ,   beta_schedule='scaled_linear'   ,   clip_sample=_A   ,   set_alpha_to_one=_A   ,   steps_offset=1   ,   )
    torch.manual_seed(0			)
    __a    :	Optional[int]									=						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    :	Union[str, Any]									=						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=1000   ,   )
    __a    :	List[Any]									=						CLIPTextModel(_A			)
    __a    :	Dict									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

    __a			,    __a    :	Optional[int]									=						create_unet_lora_layers(_A			)
    __a    :	List[Any]									=						create_text_encoder_lora_layers(_A			)

    __a    :	str									=						{
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    __a    :	Optional[Any]									=						{
        '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    __magic_name__    ( self			)    ->   Optional[Any]:
    __a    :	int									=						1
    __a    :	Tuple									=						10
    __a    :	int									=						4
    __a    :	List[str]									=						(32, 32)

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

    __a    :	Union[str, Any]									=						{
        '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    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	Optional[Any]									=						77

    __a    :	Union[str, Any]									=						torch.randint(2   ,   56   ,   size=(1, max_seq_length)   ,   generator=torch.manual_seed(0			)			)

    __a    :	Optional[int]									=						{}
    __a    :	Tuple									=						inputs
    return prepared_inputs



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[int]:
    __a			,    __a    :	Dict									=						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    __magic_name__    ( self			)    ->   str:
    __a			,    __a    :	Dict									=						self.get_dummy_components()
    __a    :	List[Any]									=						StableDiffusionPipeline(**_A			)
    __a    :	List[Any]									=						sd_pipe.to(_A			)
    sd_pipe.set_progress_bar_config(disable=_A			)

    __a			,    __a			,    __a    :	List[Any]									=						self.get_dummy_inputs()

    __a    :	Dict									=						sd_pipe(**_A			).images
    __a    :	Optional[int]									=						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    :	str									=						sd_pipe(**_A			).images
    __a    :	Optional[Any]									=						lora_images[0, -3:, -3:, -1]

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



   def    __magic_name__    ( self			)    ->   Tuple:
    __a			,    __a    :	List[str]									=						self.get_dummy_components()
    __a    :	Any									=						StableDiffusionPipeline(**_A			)
    __a    :	str									=						sd_pipe.to(_A			)
    sd_pipe.set_progress_bar_config(disable=_A			)

    __a			,    __a			,    __a    :	str									=						self.get_dummy_inputs()

    __a    :	Optional[Any]									=						sd_pipe(**_A			).images
    __a    :	Tuple									=						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    :	str									=						sd_pipe(**_A			).images
    __a    :	int									=						lora_images[0, -3:, -3:, -1]

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



   def    __magic_name__    ( self			)    ->   Optional[Any]:
    __a			,    __a    :	Optional[int]									=						self.get_dummy_components()
    __a    :	List[Any]									=						lora_components['unet_lora_attn_procs']
    __a    :	Union[str, Any]									=						StableDiffusionPipeline(**_A			)
    __a    :	Optional[Any]									=						sd_pipe.to(_A			)
    sd_pipe.set_progress_bar_config(disable=_A			)

    __a			,    __a			,    __a    :	Union[str, Any]									=						self.get_dummy_inputs()

    __a    :	List[Any]									=						sd_pipe(**_A			).images
    __a    :	Union[str, Any]									=						original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
     __a    :	Optional[Any]									=						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    :	Optional[Any]									=						sd_pipe(**_A			).images
    __a    :	Any									=						lora_images[0, -3:, -3:, -1]

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



   def    __magic_name__    ( self			)    ->   int:
    __a			,    __a    :	Dict									=						self.get_dummy_components()
    __a    :	Optional[int]									=						StableDiffusionPipeline(**_A			)

    __a    :	List[Any]									=						self.get_dummy_tokens()

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

    # create lora_attn_procs with zeroed out up.weights
    __a    :	int									=						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    :	Union[str, Any]									=						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    :	Optional[int]									=						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    :	List[str]									=						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    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a			,    __a    :	List[str]									=						self.get_dummy_components()
    __a    :	str									=						StableDiffusionPipeline(**_A			)

    __a    :	Tuple									=						self.get_dummy_tokens()

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

    # create lora_attn_procs with randn up.weights
    __a    :	Optional[int]									=						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    :	int									=						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    :	Union[str, Any]									=						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    __magic_name__    ( self			)    ->   Dict:
    __a			,    __a    :	Dict									=						self.get_dummy_components()
    __a    :	Optional[Any]									=						StableDiffusionPipeline(**_A			)
    __a    :	int									=						sd_pipe.to(_A			)
    sd_pipe.set_progress_bar_config(disable=_A			)

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

    __a    :	Optional[int]									=						sd_pipe(**_A   ,   cross_attention_kwargs={'scale': 0.5}			).images
    __a    :	str									=						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    __magic_name__    ( self			)    ->   str:
    with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(_A			)

     __a			,    __a    :	Union[str, Any]									=						self.get_dummy_components()
     __a    :	List[str]									=						StableDiffusionPipeline(**_A			)
     __a    :	List[str]									=						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    :	str									=						(
           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    __magic_name__    ( self			)    ->   Union[str, Any]:
    with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(_A			)

     __a			,    __a    :	Optional[int]									=						self.get_dummy_components()
     __a    :	Tuple									=						StableDiffusionPipeline(**_A			)
     __a    :	Optional[int]									=						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    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a			,    __a    :	Union[str, Any]									=						self.get_dummy_components()
    __a    :	Tuple									=						StableDiffusionPipeline(**_A			)
    __a    :	str									=						sd_pipe.to(_A			)
    sd_pipe.set_progress_bar_config(disable=_A			)

    __a			,    __a			,    __a    :	Optional[Any]									=						self.get_dummy_inputs()

    # enable XFormers
    sd_pipe.enable_xformers_memory_efficient_attention()

    __a    :	str									=						sd_pipe(**_A			).images
    __a    :	str									=						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    :	Union[str, Any]									=						sd_pipe(**_A			).images
    __a    :	str									=						lora_images[0, -3:, -3:, -1]

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




