





'''simple docstring'''



import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


snake_case_   :  Optional[int]								=      logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    42
			_lowerCAmelCase						=    None




def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : List[str]=0.999 ,   _UpperCamelCase  : Union[str, Any]="cosine" ,   )->   Tuple:






					"""simple docstring"""

					if alpha_transform_type == "cosine":

										def alpha_bar_fn(_UpperCamelCase  : List[str]				):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(_UpperCamelCase  : int				):
															return math.exp(t * -12.0				)

					else:
										raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

					_UpperCamelCase											=   []
					for i in range(_UpperCamelCase				):
										_UpperCamelCase											=   i / num_diffusion_timesteps
										_UpperCamelCase											=   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(_UpperCamelCase				) / alpha_bar_fn(_UpperCamelCase				) ,   _UpperCamelCase				)				)
					return torch.tensor(_UpperCamelCase ,   dtype=torch.floataa				)

class     A_ (  lowerCAmelCase_    ,			lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    [e.name for e in KarrasDiffusionSchedulers]
			_lowerCAmelCase						=    1




			@register_to_config
			def __init__(					self			,     A_ = 10_00			,     A_ = 0.0001			,     A_ = 0.02			,     A_ = "linear"			,     A_ = None			,     A_ = "epsilon"			,     A_ = "linear"			,     A_ = False			,     A_ = "linspace"			,     A_ = 0			,     ):
								if trained_betas is not None:
													_UpperCamelCase											=   torch.tensor(A_			,     dtype=torch.floataa   )
								elif beta_schedule == "linear":
													_UpperCamelCase											=   torch.linspace(A_			,     A_			,     A_			,     dtype=torch.floataa   )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase											=   (
													    torch.linspace(beta_start**0.5			,     beta_end**0.5			,     A_			,     dtype=torch.floataa   ) ** 2
													)
								elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase											=   betas_for_alpha_bar(A_   )
								else:
													raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

								_UpperCamelCase											=   1.0 - self.betas
								_UpperCamelCase											=   torch.cumprod(self.alphas			,     dim=0   )

								_UpperCamelCase											=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
								_UpperCamelCase											=   np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
								_UpperCamelCase											=   torch.from_numpy(A_   )

								# setable values
								_UpperCamelCase											=   None
								_UpperCamelCase											=   np.linspace(0			,     num_train_timesteps - 1			,     A_			,     dtype=A_   )[::-1].copy()
								_UpperCamelCase											=   torch.from_numpy(A_   )
								_UpperCamelCase											=   False
								_UpperCamelCase											=   use_karras_sigmas




			@property
			def       a	(					self   ):
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5




			def       a	(					self			,     A_			,     A_   ):
								if isinstance(A_			,     torch.Tensor   ):
													_UpperCamelCase											=   timestep.to(self.timesteps.device   )
								_UpperCamelCase											=   (self.timesteps == timestep).nonzero().item()
								_UpperCamelCase											=   self.sigmas[step_index]

								_UpperCamelCase											=   sample / ((sigma**2 + 1) ** 0.5)

								_UpperCamelCase											=   True
								return sample




			def       a	(					self			,     A_			,     A_ = None   ):
								_UpperCamelCase											=   num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
													_UpperCamelCase											=   np.linspace(0			,     self.config.num_train_timesteps - 1			,     A_			,     dtype=A_   )[
													    ::-1
													].copy()
								elif self.config.timestep_spacing == "leading":
													_UpperCamelCase											=   self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase											=   (np.arange(0			,     A_   ) * step_ratio).round()[::-1].copy().astype(A_   )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase											=   self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase											=   (np.arange(self.config.num_train_timesteps			,     0			,     -step_ratio   )).round().copy().astype(A_   )
													timesteps -= 1
								else:
													raise ValueError(
													    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

								_UpperCamelCase											=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
								_UpperCamelCase											=   np.log(A_   )

								if self.config.interpolation_type == "linear":
													_UpperCamelCase											=   np.interp(A_			,     np.arange(0			,     len(A_   )   )			,     A_   )
								elif self.config.interpolation_type == "log_linear":
													_UpperCamelCase											=   torch.linspace(np.log(sigmas[-1]   )			,     np.log(sigmas[0]   )			,     num_inference_steps + 1   ).exp()
								else:
													raise ValueError(
													    F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
													    " 'linear' or 'log_linear'"   )

								if self.use_karras_sigmas:
													_UpperCamelCase											=   self._convert_to_karras(in_sigmas=A_			,     num_inference_steps=self.num_inference_steps   )
													_UpperCamelCase											=   np.array([self._sigma_to_t(A_			,     A_   ) for sigma in sigmas]   )

								_UpperCamelCase											=   np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
								_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_   )
								if str(A_   ).startswith("mps"   ):
													# mps does not support float64
													_UpperCamelCase											=   torch.from_numpy(A_   ).to(A_			,     dtype=torch.floataa   )
								else:
													_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_   )




			def       a	(					self			,     A_			,     A_   ):
								# get log sigma
								_UpperCamelCase											=   np.log(A_   )

								# get distribution
								_UpperCamelCase											=   log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								_UpperCamelCase											=   np.cumsum((dists >= 0)			,     axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
								_UpperCamelCase											=   low_idx + 1

								_UpperCamelCase											=   log_sigmas[low_idx]
								_UpperCamelCase											=   log_sigmas[high_idx]

								# interpolate sigmas
								_UpperCamelCase											=   (low - log_sigma) / (low - high)
								_UpperCamelCase											=   np.clip(A_			,     0			,     1   )

								# transform interpolation to time range
								_UpperCamelCase											=   (1 - w) * low_idx + w * high_idx
								_UpperCamelCase											=   t.reshape(sigma.shape   )
								return t




			def       a	(					self			,     A_			,     A_   ):
								_UpperCamelCase											=   in_sigmas[-1].item()
								_UpperCamelCase											=   in_sigmas[0].item()

								_UpperCamelCase											=   7.0  # 7.0 is the value used in the paper
								_UpperCamelCase											=   np.linspace(0			,     1			,     A_   )
								_UpperCamelCase											=   sigma_min ** (1 / rho)
								_UpperCamelCase											=   sigma_max ** (1 / rho)
								_UpperCamelCase											=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




			def       a	(					self			,     A_			,     A_			,     A_			,     A_ = 0.0			,     A_ = 0.0			,     A_ = float("inf"   )			,     A_ = 1.0			,     A_ = None			,     A_ = True			,     ):

								if (
								    isinstance(A_			,     A_   )
								    or isinstance(A_			,     torch.IntTensor   )
								    or isinstance(A_			,     torch.LongTensor   )
								):
													raise ValueError(
													    (
													        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
													        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
													        " one of the `scheduler.timesteps` as a timestep."
													    )			,     )

								if not self.is_scale_input_called:
													logger.warning(
													    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
													    "See `StableDiffusionPipeline` for a usage example."   )

								if isinstance(A_			,     torch.Tensor   ):
													_UpperCamelCase											=   timestep.to(self.timesteps.device   )

								_UpperCamelCase											=   (self.timesteps == timestep).nonzero().item()
								_UpperCamelCase											=   self.sigmas[step_index]

								_UpperCamelCase											=   min(s_churn / (len(self.sigmas   ) - 1)			,     2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

								_UpperCamelCase											=   randn_tensor(
								    model_output.shape			,     dtype=model_output.dtype			,     device=model_output.device			,     generator=A_   )

								_UpperCamelCase											=   noise * s_noise
								_UpperCamelCase											=   sigma * (gamma + 1)

								if gamma > 0:
													_UpperCamelCase											=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													_UpperCamelCase											=   model_output
								elif self.config.prediction_type == "epsilon":
													_UpperCamelCase											=   sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													_UpperCamelCase											=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
													raise ValueError(
													    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

								# 2. Convert to an ODE derivative
								_UpperCamelCase											=   (sample - pred_original_sample) / sigma_hat

								_UpperCamelCase											=   self.sigmas[step_index + 1] - sigma_hat

								_UpperCamelCase											=   sample + derivative * dt

								if not return_dict:
													return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=A_			,     pred_original_sample=A_   )




			def       a	(					self			,     A_			,     A_			,     A_			,     ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								_UpperCamelCase											=   self.sigmas.to(device=original_samples.device			,     dtype=original_samples.dtype   )
								if original_samples.device.type == "mps" and torch.is_floating_point(A_   ):
													# mps does not support float64
													_UpperCamelCase											=   self.timesteps.to(original_samples.device			,     dtype=torch.floataa   )
													_UpperCamelCase											=   timesteps.to(original_samples.device			,     dtype=torch.floataa   )
								else:
													_UpperCamelCase											=   self.timesteps.to(original_samples.device   )
													_UpperCamelCase											=   timesteps.to(original_samples.device   )

								_UpperCamelCase											=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								_UpperCamelCase											=   sigmas[step_indices].flatten()
								while len(sigma.shape   ) < len(original_samples.shape   ):
													_UpperCamelCase											=   sigma.unsqueeze(-1   )

								_UpperCamelCase											=   original_samples + noise * sigma
								return noisy_samples




			def __len__(					self   ):
								return self.config.num_train_timesteps










'''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  lowercase__(		_UpperCamelCase  : nn.Module				)->   Any:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   List[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   {}
					_UpperCamelCase											=   (
					    LoRAAttnProcessor2_0 if hasattr(_UpperCamelCase ,   "scaled_dot_product_attention"				) else LoRAAttnProcessor
					)
					for name, module in text_encoder.named_modules():
										if name.endswith(_UpperCamelCase				):
															_UpperCamelCase											=   lora_attn_processor_class(
															    hidden_size=module.out_proj.out_features ,   cross_attention_dim=_UpperCamelCase				)
					return text_lora_attn_procs



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   create_text_encoder_lora_attn_procs(_UpperCamelCase				)
					_UpperCamelCase											=   AttnProcsLayers(_UpperCamelCase				)
					return text_encoder_lora_layers



def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : Optional[int]=False				)->   Optional[int]:






					"""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											=   (
																				    torch.randn_like(layer_module.up.weight				)
																				    if randn_weight
																				    else torch.zeros_like(layer_module.up.weight				)
																				)
																				_UpperCamelCase											=   torch.nn.Parameter(_UpperCamelCase				)

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   DDIMScheduler(
								    beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=A_			,     set_alpha_to_one=A_			,     steps_offset=1			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   CLIPTextModel(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

								_UpperCamelCase				,	_UpperCamelCase											=   create_unet_lora_layers(A_   )
								_UpperCamelCase											=   create_text_encoder_lora_layers(A_   )

								_UpperCamelCase											=   {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								_UpperCamelCase											=   {
								    "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   ):
								_UpperCamelCase											=   1
								_UpperCamelCase											=   10
								_UpperCamelCase											=   4
								_UpperCamelCase											=   (32, 32)

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

								_UpperCamelCase											=   {
								    "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   ):
								_UpperCamelCase											=   77

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

								_UpperCamelCase											=   {}
								_UpperCamelCase											=   inputs
								return prepared_inputs




			def       a	(					self			,     A_   ):
								_UpperCamelCase				,	_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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_   )

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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_   )

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   lora_components["unet_lora_attn_procs"]
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
													_UpperCamelCase											=   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_   )

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

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

								# create lora_attn_procs with zeroed out up.weights
								_UpperCamelCase											=   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
								_UpperCamelCase											=   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
								_UpperCamelCase											=   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
								_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

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

								# create lora_attn_procs with randn up.weights
								_UpperCamelCase											=   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
								_UpperCamelCase											=   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
								_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   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_   )

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

								_UpperCamelCase											=   sd_pipe(**A_			,     cross_attention_kwargs={"scale": 0.5}   ).images
								_UpperCamelCase											=   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   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   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_   ):
																							_UpperCamelCase											=   (
																							    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   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   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   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   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_   )

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

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



