'''simple docstring'''
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @property
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	3
         __UpperCamelCase						:						Tuple		     =	(3_2, 3_2)

         __UpperCamelCase						:						List[Any]		     =	floats_tensor((batch_size, num_channels) + sizes  ,      rng=random.Random(0     )     ).to(_lowerCamelCase     )
         return image







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	UNetaDModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=6  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
         return model







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						List[str]		     =	VQModel(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=3  ,      )
         return model







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	'cpu'
         __UpperCamelCase						:						Union[str, Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						List[str]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         __UpperCamelCase						:						Tuple		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.Generator(device=_lowerCamelCase     ).manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						str		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         __UpperCamelCase						:						str		     =	np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]     )

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







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						Optional[int]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         # put models in fp16
         __UpperCamelCase						:						str		     =	unet.half()
         __UpperCamelCase						:						Optional[Any]		     =	vqvae.half()

         __UpperCamelCase						:						Optional[int]		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[Any]		     =	ldm(_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         assert image.shape == (1, 6_4, 6_4, 3)






@slow
@require_torch
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool.png'     )
         __UpperCamelCase						:						List[Any]		     =	init_image.resize((6_4, 6_4)  ,      resample=PIL_INTERPOLATION['lanczos']     )

         __UpperCamelCase						:						List[str]		     =	LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'  ,      device_map='auto'     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2_0  ,      output_type='numpy'     ).images

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

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         __UpperCamelCase						:						str		     =	np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]     )

         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 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	{}
    __UpperCamelCase						:						List[str]		     =	(
        LoRAAttnProcessor2_0 if hasattr(_a					,  'scaled_dot_product_attention'      ) else LoRAAttnProcessor
    )
    for name, module in text_encoder.named_modules():
        if name.endswith(_a      ):
            __UpperCamelCase						:						List[Any]		     =	lora_attn_processor_class(
                hidden_size=module.out_proj.out_features					,  cross_attention_dim=_a      )
    return text_lora_attn_procs







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	create_text_encoder_lora_attn_procs(_a      )
    __UpperCamelCase						:						Any		     =	AttnProcsLayers(_a      )
    return text_encoder_lora_layers







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						List[Any]=False      ):




    """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'      ):
                __UpperCamelCase						:						List[str]		     =	(
                    torch.randn_like(layer_module.up.weight      )
                    if randn_weight
                    else torch.zeros_like(layer_module.up.weight      )
                )
                __UpperCamelCase						:						Any		     =	torch.nn.Parameter(_a      )






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,      cross_attention_dim=3_2  ,      )
         __UpperCamelCase						:						int		     =	DDIMScheduler(
             beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      beta_schedule='scaled_linear'  ,      clip_sample=_lowerCamelCase  ,      set_alpha_to_one=_lowerCamelCase  ,      steps_offset=1  ,      )
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	AutoencoderKL(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=4  ,      )
         __UpperCamelCase						:						str		     =	CLIPTextConfig(
             bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=3_2  ,      intermediate_size=3_7  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_0_0_0  ,      )
         __UpperCamelCase						:						List[str]		     =	CLIPTextModel(_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	create_unet_lora_layers(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_layers(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         __UpperCamelCase						:						List[str]		     =	{
             '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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	1_0
         __UpperCamelCase						:						Dict		     =	4
         __UpperCamelCase						:						Optional[Any]		     =	(3_2, 3_2)

         __UpperCamelCase						:						Any		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	floats_tensor((batch_size, num_channels) + sizes     )
         __UpperCamelCase						:						List[str]		     =	torch.randint(1  ,      _lowerCamelCase  ,      size=(batch_size, sequence_length)  ,      generator=_lowerCamelCase     )

         __UpperCamelCase						:						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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	7_7

         __UpperCamelCase						:						Any		     =	torch.randint(2  ,      5_6  ,      size=(1, max_seq_length)  ,      generator=torch.manual_seed(0     )     )

         __UpperCamelCase						:						List[str]		     =	{}
         __UpperCamelCase						:						Optional[int]		     =	inputs
         return prepared_inputs







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_components()
         LoraLoaderMixin.save_lora_weights(
             save_directory=_lowerCamelCase  ,      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(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[str]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Dict		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

         __UpperCamelCase						:						Any		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						int		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_components()
         __UpperCamelCase						:						str		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[Any]		     =	lora_components['unet_lora_attn_procs']
         __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.unet
             unet.set_attn_processor(_lowerCamelCase     )
             unet.save_attn_procs(_lowerCamelCase     )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[Any]		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	self.get_dummy_tokens()

         # inference without lora
         __UpperCamelCase						:						List[str]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with zeroed out up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

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

         # inference with lora
         __UpperCamelCase						:						Tuple		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

         assert torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "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
         __UpperCamelCase						:						Optional[int]		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

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

         # inference with lora
         __UpperCamelCase						:						Optional[int]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

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

         # inference without lora
         __UpperCamelCase						:						str		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with randn up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

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

         # inference with lora
         __UpperCamelCase						:						Any		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

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

         # remove monkey patch
         pipe._remove_text_encoder_monkey_patch()

         # inference with removed lora
         __UpperCamelCase						:						Dict		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_inputs()

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

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

         __UpperCamelCase						:						List[str]		     =	sd_pipe(**_lowerCamelCase  ,      cross_attention_kwargs={'scale': 0.5}     ).images
         __UpperCamelCase						:						Union[str, Any]		     =	lora_images_with_scale[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_lowerCamelCase     )

             __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
             __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						Tuple		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	(
                         LoRAAttnProcessor2_0 if hasattr(_lowerCamelCase  ,      'scaled_dot_product_attention'     ) else LoRAAttnProcessor
                     )
                     self.assertIsInstance(module.processor  ,      _lowerCamelCase     )







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test is supposed to run on GPU'     )
     def lowerCAmelCase  (		self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_lowerCamelCase     )

             __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
             __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

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







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test is supposed to run on GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_inputs()

         # enable XFormers
         sd_pipe.enable_xformers_memory_efficient_attention()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

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



