code: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    str								=					logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   42
  	A						       =   None
  
  
  
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=0.999					,						SCREAMING_SNAKE_CASE="cosine"					,						) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if alpha_transform_type == "cosine":
  
  						def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
  									return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2
  
  			elif alpha_transform_type == "exp":
  
  						def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
  									return math.exp(t * -12.0   )
  
  			else:
  						raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'   )
  
  			SCREAMING_SNAKE_CASE_			=	[]
  			for i in range(SCREAMING_SNAKE_CASE   ):
  						SCREAMING_SNAKE_CASE_			=	i / num_diffusion_timesteps
  						SCREAMING_SNAKE_CASE_			=	(i + 1) / num_diffusion_timesteps
  						betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE   ) / alpha_bar_fn(SCREAMING_SNAKE_CASE   )					,						SCREAMING_SNAKE_CASE   )   )
  			return torch.tensor(SCREAMING_SNAKE_CASE					,						dtype=torch.floataa   )
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__    ):
  	A						       =   [e.name for e in KarrasDiffusionSchedulers]
  	A						       =   1
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = 1000			,    SCREAMING_SNAKE_CASE = 0.0_0_0_1			,    SCREAMING_SNAKE_CASE = 0.0_2			,    SCREAMING_SNAKE_CASE = "linear"			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "epsilon"			,    SCREAMING_SNAKE_CASE = "linear"			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = "linspace"			,    SCREAMING_SNAKE_CASE = 0			,    )     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if trained_betas is not None:
  							SCREAMING_SNAKE_CASE_			=	torch.tensor(SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
  				elif beta_schedule == "linear":
  							SCREAMING_SNAKE_CASE_			=	torch.linspace(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
  				elif beta_schedule == "scaled_linear":
  							# this schedule is very specific to the latent diffusion model.
  							SCREAMING_SNAKE_CASE_			=	(
  							    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			) ** 2
  							)
  				elif beta_schedule == "squaredcos_cap_v2":
  							# Glide cosine schedule
  							SCREAMING_SNAKE_CASE_			=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE			)
  				else:
  							raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'			)
  
  				SCREAMING_SNAKE_CASE_			=	1.0 - self.betas
  				SCREAMING_SNAKE_CASE_			=	torch.cumprod(self.alphas			,    dim=0			)
  
  				SCREAMING_SNAKE_CASE_			=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			)
  
  				# setable values
  				SCREAMING_SNAKE_CASE_			=	None
  				SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    num_train_timesteps - 1			,    SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)[::-1].copy()
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	False
  				SCREAMING_SNAKE_CASE_			=	use_karras_sigmas
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.config.timestep_spacing in ["linspace", "trailing"]:
  							return self.sigmas.max()
  
  				return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							SCREAMING_SNAKE_CASE_			=	timestep.to(self.timesteps.device			)
  				SCREAMING_SNAKE_CASE_			=	(self.timesteps == timestep).nonzero().item()
  				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index]
  
  				SCREAMING_SNAKE_CASE_			=	sample / ((sigma**2 + 1) ** 0.5)
  
  				SCREAMING_SNAKE_CASE_			=	True
  				return sample
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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":
  							SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    self.config.num_train_timesteps - 1			,    SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)[
  							    ::-1
  							].copy()
  				elif self.config.timestep_spacing == "leading":
  							SCREAMING_SNAKE_CASE_			=	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
  							SCREAMING_SNAKE_CASE_			=	(np.arange(0			,    SCREAMING_SNAKE_CASE			) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE			)
  							timesteps += self.config.steps_offset
  				elif self.config.timestep_spacing == "trailing":
  							SCREAMING_SNAKE_CASE_			=	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
  							SCREAMING_SNAKE_CASE_			=	(np.arange(self.config.num_train_timesteps			,    0			,    -step_ratio			)).round().copy().astype(SCREAMING_SNAKE_CASE			)
  							timesteps -= 1
  				else:
  							raise ValueError(
  							    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)
  
  				SCREAMING_SNAKE_CASE_			=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
  				SCREAMING_SNAKE_CASE_			=	np.log(SCREAMING_SNAKE_CASE			)
  
  				if self.config.interpolation_type == "linear":
  							SCREAMING_SNAKE_CASE_			=	np.interp(SCREAMING_SNAKE_CASE			,    np.arange(0			,    len(SCREAMING_SNAKE_CASE			)			)			,    SCREAMING_SNAKE_CASE			)
  				elif self.config.interpolation_type == "log_linear":
  							SCREAMING_SNAKE_CASE_			=	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:
  							SCREAMING_SNAKE_CASE_			=	self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE			,    num_inference_steps=self.num_inference_steps			)
  							SCREAMING_SNAKE_CASE_			=	np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for sigma in sigmas]			)
  
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(device=SCREAMING_SNAKE_CASE			)
  				if str(SCREAMING_SNAKE_CASE			).startswith('mps'			):
  							# mps does not support float64
  							SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(device=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	np.log(SCREAMING_SNAKE_CASE			)
  
  				# get distribution
  				SCREAMING_SNAKE_CASE_			=	log_sigma - log_sigmas[:, np.newaxis]
  
  				# get sigmas range
  				SCREAMING_SNAKE_CASE_			=	np.cumsum((dists >= 0)			,    axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
  				SCREAMING_SNAKE_CASE_			=	low_idx + 1
  
  				SCREAMING_SNAKE_CASE_			=	log_sigmas[low_idx]
  				SCREAMING_SNAKE_CASE_			=	log_sigmas[high_idx]
  
  				# interpolate sigmas
  				SCREAMING_SNAKE_CASE_			=	(low - log_sigma) / (low - high)
  				SCREAMING_SNAKE_CASE_			=	np.clip(SCREAMING_SNAKE_CASE			,    0			,    1			)
  
  				# transform interpolation to time range
  				SCREAMING_SNAKE_CASE_			=	(1 - w) * low_idx + w * high_idx
  				SCREAMING_SNAKE_CASE_			=	t.reshape(sigma.shape			)
  				return t
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	in_sigmas[-1].item()
  				SCREAMING_SNAKE_CASE_			=	in_sigmas[0].item()
  
  				SCREAMING_SNAKE_CASE_			=	7.0  # 7.0 is the value used in the paper
  				SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    1			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sigma_min ** (1 / rho)
  				SCREAMING_SNAKE_CASE_			=	sigma_max ** (1 / rho)
  				SCREAMING_SNAKE_CASE_			=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  				return sigmas
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 0.0			,    SCREAMING_SNAKE_CASE = 0.0			,    SCREAMING_SNAKE_CASE = float('inf'			)			,    SCREAMING_SNAKE_CASE = 1.0			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = True			,    )     ->				Union[EulerDiscreteSchedulerOutput, Tuple]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if (
  				    isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				    or isinstance(SCREAMING_SNAKE_CASE			,    torch.IntTensor			)
  				    or isinstance(SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							SCREAMING_SNAKE_CASE_			=	timestep.to(self.timesteps.device			)
  
  				SCREAMING_SNAKE_CASE_			=	(self.timesteps == timestep).nonzero().item()
  				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index]
  
  				SCREAMING_SNAKE_CASE_			=	min(s_churn / (len(self.sigmas			) - 1)			,    2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0
  
  				SCREAMING_SNAKE_CASE_			=	randn_tensor(
  				    model_output.shape			,    dtype=model_output.dtype			,    device=model_output.device			,    generator=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	noise * s_noise
  				SCREAMING_SNAKE_CASE_			=	sigma * (gamma + 1)
  
  				if gamma > 0:
  							SCREAMING_SNAKE_CASE_			=	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":
  							SCREAMING_SNAKE_CASE_			=	model_output
  				elif self.config.prediction_type == "epsilon":
  							SCREAMING_SNAKE_CASE_			=	sample - sigma_hat * model_output
  				elif self.config.prediction_type == "v_prediction":
  							# * c_out + input * c_skip
  							SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	(sample - pred_original_sample) / sigma_hat
  
  				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index + 1] - sigma_hat
  
  				SCREAMING_SNAKE_CASE_			=	sample + derivative * dt
  
  				if not return_dict:
  							return (prev_sample,)
  
  				return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE			,    pred_original_sample=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )     ->				torch.FloatTensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.sigmas.to(device=original_samples.device			,    dtype=original_samples.dtype			)
  				if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE			):
  							# mps does not support float64
  							SCREAMING_SNAKE_CASE_			=	self.timesteps.to(original_samples.device			,    dtype=torch.floataa			)
  							SCREAMING_SNAKE_CASE_			=	timesteps.to(original_samples.device			,    dtype=torch.floataa			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	self.timesteps.to(original_samples.device			)
  							SCREAMING_SNAKE_CASE_			=	timesteps.to(original_samples.device			)
  
  				SCREAMING_SNAKE_CASE_			=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  				SCREAMING_SNAKE_CASE_			=	sigmas[step_indices].flatten()
  				while len(sigma.shape			) < len(original_samples.shape			):
  							SCREAMING_SNAKE_CASE_			=	sigma.unsqueeze(-1			)
  
  				SCREAMING_SNAKE_CASE_			=	original_samples + noise * sigma
  				return noisy_samples
  
  
  
  
  
  	def __len__(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.config.num_train_timesteps
  
style_context: 
  
  
  
  
  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						(     SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			for name in unet.attn_processors.keys():
  						SCREAMING_SNAKE_CASE_			=	None if name.endswith('attn1.processor'   ) else unet.config.cross_attention_dim
  						if name.startswith('mid_block'   ):
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[-1]
  						elif name.startswith('up_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('up_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	list(reversed(unet.config.block_out_channels   )   )[block_id]
  						elif name.startswith('down_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('down_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[block_id]
  						SCREAMING_SNAKE_CASE_			=	(
  						    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  						)
  						SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  						    hidden_size=SCREAMING_SNAKE_CASE					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return lora_attn_procs, unet_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			SCREAMING_SNAKE_CASE_			=	(
  			    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  			)
  			for name, module in text_encoder.named_modules():
  						if name.endswith(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  									    hidden_size=module.out_proj.out_features					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			return text_lora_attn_procs
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return text_encoder_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=False   ) ->       Dict:
  
  
  			'''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'   ):
  												SCREAMING_SNAKE_CASE_			=	(
  												    torch.randn_like(layer_module.up.weight   )
  												    if randn_weight
  												    else torch.zeros_like(layer_module.up.weight   )
  												)
  												SCREAMING_SNAKE_CASE_			=	torch.nn.Parameter(SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	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			,    )
  				SCREAMING_SNAKE_CASE_			=	DDIMScheduler(
  				    beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='scaled_linear'			,    clip_sample=SCREAMING_SNAKE_CASE			,    set_alpha_to_one=SCREAMING_SNAKE_CASE			,    steps_offset=1			,    )
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	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			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextConfig(
  				    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextModel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_unet_lora_layers(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet': unet,
  				    'scheduler': scheduler,
  				    'vae': vae,
  				    'text_encoder': text_encoder,
  				    'tokenizer': tokenizer,
  				    'safety_checker': None,
  				    'feature_extractor': None,
  				}
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet_lora_layers': unet_lora_layers,
  				    'text_encoder_lora_layers': text_encoder_lora_layers,
  				    'unet_lora_attn_procs': unet_lora_attn_procs,
  				}
  				return pipeline_components, lora_components
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1
  				SCREAMING_SNAKE_CASE_			=	10
  				SCREAMING_SNAKE_CASE_			=	4
  				SCREAMING_SNAKE_CASE_			=	(32, 32)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	floats_tensor((batch_size, num_channels) + sizes			)
  				SCREAMING_SNAKE_CASE_			=	torch.randint(1			,    SCREAMING_SNAKE_CASE			,    size=(batch_size, sequence_length)			,    generator=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    '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			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	77
  
  				SCREAMING_SNAKE_CASE_			=	torch.randint(2			,    56			,    size=(1, max_seq_length)			,    generator=torch.manual_seed(0			)			)
  
  				SCREAMING_SNAKE_CASE_			=	{}
  				SCREAMING_SNAKE_CASE_			=	inputs
  				return prepared_inputs
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				LoraLoaderMixin.save_lora_weights(
  				    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    safe_serialization=SCREAMING_SNAKE_CASE			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.safetensors'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	lora_components['unet_lora_attn_procs']
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.unet
  							unet.set_attn_processor(SCREAMING_SNAKE_CASE			)
  							unet.save_attn_procs(SCREAMING_SNAKE_CASE			)
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with zeroed out up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "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
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora outputs should be different to without lora outputs"
  
  				# remove monkey patch
  				pipe._remove_text_encoder_monkey_patch()
  
  				# inference with removed lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora_removed.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			,    cross_attention_kwargs={'scale': 0.5}			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images_with_scale[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(
  				    torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# check if vanilla attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    (AttnProcessor, AttnProcessor2_0)			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													SCREAMING_SNAKE_CASE_			=	(
  													    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,    'scaled_dot_product_attention'			) else LoRAAttnProcessor
  													)
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# 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(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				# enable XFormers
  				sd_pipe.enable_xformers_memory_efficient_attention()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  