
'''simple docstring'''


from __future__ import annotations

import random
import string




class     __snake_case			:




		def __init__(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:str | None = None)					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     passcode or self.__passcode_creator()
								UpperCamelCase_     :	str					     =     self.__make_key_list()
								UpperCamelCase_     :	int					     =     self.__make_shift_key()




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






								'''simple docstring'''






								return "".join(self.__passcode)




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:list[int])					->			int:






								'''simple docstring'''






								for i in range(1			,    len(UpperCamelCase_)			,    2):
														iterlist[i] *= -1
								return iterlist




		def 	_snake_case	(  self					:List[str])					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     string.ascii_letters + string.digits
								UpperCamelCase_     :	Union[str, Any]					     =     [random.choice(UpperCamelCase_) for _ in range(random.randint(1_0			,    2_0))]
								return password




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






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     (
								    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
								)

								UpperCamelCase_     :	Optional[Any]					     =     []

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								UpperCamelCase_     :	str					     =     sorted(set(self.__passcode))
								UpperCamelCase_     :	list[str]					     =     []

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
														temp_list.extend(UpperCamelCase_)

														# checking breakpoints at which to pivot temporary sublist and add it into
														# keys_l
														if i in breakpoints or i == key_list_options[-1]:
																				keys_l.extend(temp_list[::-1])
																				temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l




		def 	_snake_case	(  self					:Optional[int])					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     sum(self.__neg_pos([ord(UpperCamelCase_) for x in self.__passcode]))
								return num if num > 0 else len(self.__passcode)




		def 	_snake_case	(  self					:str			,    _SCREAMING_SNAKE_CASE					:str)					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     ''''''

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
														UpperCamelCase_     :	List[Any]					     =     self.__key_list.index(UpperCamelCase_)
														decoded_message += self.__key_list[
														    (position - self.__shift_key) % -len(self.__key_list)
														]

								return decoded_message





		def 	_snake_case	(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:str)					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     ''''''

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
														UpperCamelCase_     :	int					     =     self.__key_list.index(UpperCamelCase_)
														encoded_message += self.__key_list[
														    (position + self.__shift_key) % len(self.__key_list)
														]

								return encoded_message







def 					__magic_name__(       UpperCAmelCase__ :       str = "Hello, this is a modified Caesar cipher"	):
						UpperCamelCase_     :	Optional[int]					     =     ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(_snake_case	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()








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


def   _snake_case    (    _snake_case					:      nn.Module      ):
      lowerCAmelCase       :						List[str]							= {}
      lowerCAmelCase       :						Optional[Any]							= (
          LoRAAttnProcessor2_0 if hasattr(_snake_case			, '''scaled_dot_product_attention'''      ) else LoRAAttnProcessor
      )
      for name, module in text_encoder.named_modules():
            if name.endswith(_snake_case      ):
                  lowerCAmelCase       :						Dict							= lora_attn_processor_class(
                      hidden_size=module.out_proj.out_features			, cross_attention_dim=_snake_case      )
      return text_lora_attn_procs


def   _snake_case    (    _snake_case					:      nn.Module      ):
      lowerCAmelCase       :						Tuple							= create_text_encoder_lora_attn_procs(_snake_case      )
      lowerCAmelCase       :						Optional[int]							= AttnProcsLayers(_snake_case      )
      return text_encoder_lora_layers


def   _snake_case    (    _snake_case					:      Optional[Any]			, _snake_case					:      Any=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'''      ):
                        lowerCAmelCase       :						Dict							= (
                            torch.randn_like(layer_module.up.weight      )
                            if randn_weight
                            else torch.zeros_like(layer_module.up.weight      )
                        )
                        lowerCAmelCase       :						Optional[int]							= torch.nn.Parameter(_snake_case      )






class 		snake_case_(							unittest.TestCase      ):






     def   lowerCamelCase__  (						self	:       int					):
           torch.manual_seed(0					)
           lowerCAmelCase       :						str							= 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       ,					)
           lowerCAmelCase       :						Dict							= DDIMScheduler(
               beta_start=0.00_085       ,					beta_end=0.012       ,					beta_schedule='''scaled_linear'''       ,					clip_sample=UpperCamelCase_       ,					set_alpha_to_one=UpperCamelCase_       ,					steps_offset=1       ,					)
           torch.manual_seed(0					)
           lowerCAmelCase       :						Tuple							= 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       ,					)
           lowerCAmelCase       :						str							= CLIPTextConfig(
               bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					)
           lowerCAmelCase       :						List[Any]							= CLIPTextModel(UpperCamelCase_					)
           lowerCAmelCase       :						Dict							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

           lowerCAmelCase,   lowerCAmelCase       :						List[str]							= create_unet_lora_layers(UpperCamelCase_					)
           lowerCAmelCase       :						int							= create_text_encoder_lora_layers(UpperCamelCase_					)

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

           lowerCAmelCase       :						Optional[Any]							= torch.manual_seed(0					)
           lowerCAmelCase       :						Any							= floats_tensor((batch_size, num_channels) + sizes					)
           lowerCAmelCase       :						Tuple							= torch.randint(1       ,					UpperCamelCase_       ,					size=(batch_size, sequence_length)       ,					generator=UpperCamelCase_					)

           lowerCAmelCase       :						Optional[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	:       Optional[Any]					):
           lowerCAmelCase       :						str							= 7_7

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

           lowerCAmelCase       :						Union[str, Any]							= {}
           lowerCAmelCase       :						List[str]							= inputs
           return prepared_inputs






     def   lowerCamelCase__  (						self	:       int       ,					UpperCamelCase_	:       Optional[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Tuple							= self.get_dummy_components()
           LoraLoaderMixin.save_lora_weights(
               save_directory=UpperCamelCase_       ,					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(UpperCamelCase_       ,					'''pytorch_lora_weights.bin'''					)					)					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase,   lowerCAmelCase       :						Union[str, Any]							= self.get_dummy_components()
           lowerCAmelCase       :						str							= StableDiffusionPipeline(**UpperCamelCase_					)
           lowerCAmelCase       :						Union[str, Any]							= sd_pipe.to(UpperCamelCase_					)
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						Any							= self.get_dummy_inputs()

           lowerCAmelCase       :						Tuple							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						int							= original_images[0, -3:, -3:, -1]

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

           lowerCAmelCase       :						Optional[Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Tuple							= lora_images[0, -3:, -3:, -1]

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






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

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						Any							= self.get_dummy_inputs()

           lowerCAmelCase       :						Union[str, Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Union[str, Any]							= original_images[0, -3:, -3:, -1]

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

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

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






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Tuple							= self.get_dummy_components()
           lowerCAmelCase       :						Dict							= lora_components['''unet_lora_attn_procs''']
           lowerCAmelCase       :						str							= StableDiffusionPipeline(**UpperCamelCase_					)
           lowerCAmelCase       :						str							= sd_pipe.to(UpperCamelCase_					)
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						List[str]							= self.get_dummy_inputs()

           lowerCAmelCase       :						List[str]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Optional[Any]							= original_images[0, -3:, -3:, -1]

           with tempfile.TemporaryDirectory() as tmpdirname:
                 lowerCAmelCase       :						Tuple							= sd_pipe.unet
                 unet.set_attn_processor(UpperCamelCase_					)
                 unet.save_attn_procs(UpperCamelCase_					)
                 self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase_       ,					'''pytorch_lora_weights.bin'''					)					)					)
                 sd_pipe.load_lora_weights(UpperCamelCase_					)

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

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






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						str							= self.get_dummy_components()
           lowerCAmelCase       :						Optional[int]							= StableDiffusionPipeline(**UpperCamelCase_					)

           lowerCAmelCase       :						Union[str, Any]							= self.get_dummy_tokens()

           # inference without lora
           lowerCAmelCase       :						Optional[int]							= pipe.text_encoder(**UpperCamelCase_					)[0]
           assert outputs_without_lora.shape == (1, 7_7, 3_2)

           # create lora_attn_procs with zeroed out up.weights
           lowerCAmelCase       :						Tuple							= create_text_encoder_lora_attn_procs(pipe.text_encoder					)
           set_lora_up_weights(UpperCamelCase_       ,					randn_weight=UpperCamelCase_					)

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

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

           assert torch.allclose(
               UpperCamelCase_       ,					UpperCamelCase_					), "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
           lowerCAmelCase       :						Any							= create_text_encoder_lora_attn_procs(pipe.text_encoder					)
           set_lora_up_weights(UpperCamelCase_       ,					randn_weight=UpperCamelCase_					)

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

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

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






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Optional[Any]							= self.get_dummy_components()
           lowerCAmelCase       :						Optional[int]							= StableDiffusionPipeline(**UpperCamelCase_					)

           lowerCAmelCase       :						Dict							= self.get_dummy_tokens()

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

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

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

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

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

           # remove monkey patch
           pipe._remove_text_encoder_monkey_patch()

           # inference with removed lora
           lowerCAmelCase       :						List[str]							= pipe.text_encoder(**UpperCamelCase_					)[0]
           assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

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






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

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

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

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

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

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






     def   lowerCamelCase__  (						self	:       List[str]					):
           with tempfile.TemporaryDirectory() as tmpdirname:
                 self.create_lora_weight_file(UpperCamelCase_					)

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

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

            # load LoRA weight file
                 sd_pipe.load_lora_weights(UpperCamelCase_					)

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






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

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

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

            # load LoRA weight file
                 sd_pipe.load_lora_weights(UpperCamelCase_					)

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






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

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

           # enable XFormers
           sd_pipe.enable_xformers_memory_efficient_attention()

           lowerCAmelCase       :						List[Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Optional[int]							= original_images[0, -3:, -3:, -1]

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

           lowerCAmelCase       :						Any							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Optional[Any]							= lora_images[0, -3:, -3:, -1]

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


