


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE       :      Optional[Any]												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Any												= {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	List[str]     =			"""xlm-roberta-xl"""
       def __init__(					self,      lowerCamelCase=25_08_80,      lowerCamelCase=25_60,      lowerCamelCase=36,      lowerCamelCase=32,      lowerCamelCase=1_02_40,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_14,      lowerCamelCase=1,      lowerCamelCase=0.0_2,      lowerCamelCase=1E-05,      lowerCamelCase=1,      lowerCamelCase=0,      lowerCamelCase=2,      lowerCamelCase="absolute",      lowerCamelCase=True,      lowerCamelCase=None,      **lowerCamelCase,      )       ->   Optional[int]:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase,      bos_token_id=lowerCamelCase,      eos_token_id=lowerCamelCase,      **lowerCamelCase)
           _lowercase       :							Dict      		=		vocab_size
           _lowercase       :							Optional[Any]      		=		hidden_size
           _lowercase       :							Optional[Any]      		=		num_hidden_layers
           _lowercase       :							List[Any]      		=		num_attention_heads
           _lowercase       :							Optional[int]      		=		hidden_act
           _lowercase       :							str      		=		intermediate_size
           _lowercase       :							str      		=		hidden_dropout_prob
           _lowercase       :							Tuple      		=		attention_probs_dropout_prob
           _lowercase       :							Optional[Any]      		=		max_position_embeddings
           _lowercase       :							str      		=		type_vocab_size
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							Dict      		=		layer_norm_eps
           _lowercase       :							Dict      		=		position_embedding_type
           _lowercase       :							Optional[int]      		=		use_cache
           _lowercase       :							str      		=		classifier_dropout






class _lowerCamelCase(						_a   ):
       @property
       def 			UpperCamelCase     (					self)       ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""

           if self.task == "multiple-choice":
               _lowercase       :							Tuple      		=		{0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
               _lowercase       :							Any      		=		{0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
               ])






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							List[Any]      		=		[image]

    if isinstance(image[0]							, PIL.Image.Image ):
        _lowercase       :							Tuple      		=		[np.array(i.resize((w, h)							, resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        _lowercase       :							str      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Dict      		=		np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
        _lowercase       :							Optional[int]      		=		image.transpose(0							, 3							, 1							, 2 )
        _lowercase       :							str      		=		2.0 * image - 1.0
        _lowercase       :							Tuple      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(image[0]							, torch.Tensor ):
        _lowercase       :							Any      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return image



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=0.99_95 )    ->						Tuple:
    if not isinstance(lowerCamelCase_							, np.ndarray ):
        _lowercase       :							List[Any]      		=		True
        _lowercase       :							Any      		=		va.device
        _lowercase       :							Union[str, Any]      		=		va.cpu().numpy()
        _lowercase       :							int      		=		va.cpu().numpy()

    _lowercase       :							int      		=		np.sum(va * va / (np.linalg.norm(lowerCamelCase_ ) * np.linalg.norm(lowerCamelCase_ )) )
    if np.abs(lowerCamelCase_ ) > DOT_THRESHOLD:
        _lowercase       :							Any      		=		(1 - t) * va + t * va
    else:
        _lowercase       :							Dict      		=		np.arccos(lowerCamelCase_ )
        _lowercase       :							str      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							int      		=		theta_a * t
        _lowercase       :							Dict      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							Any      		=		np.sin(theta_a - theta_t ) / sin_theta_a
        _lowercase       :							List[Any]      		=		sin_theta_t / sin_theta_a
        _lowercase       :							Dict      		=		sa * va + sa * va

    if inputs_are_torch:
        _lowercase       :							Optional[Any]      		=		torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )

    return va



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    for param in model.parameters():
        _lowercase       :							Any      		=		value






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      )       ->   Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(
               vae=lowerCamelCase,      text_encoder=lowerCamelCase,      clip_model=lowerCamelCase,      tokenizer=lowerCamelCase,      unet=lowerCamelCase,      scheduler=lowerCamelCase,      feature_extractor=lowerCamelCase,      coca_model=lowerCamelCase,      coca_tokenizer=lowerCamelCase,      coca_transform=lowerCamelCase,      )
           _lowercase       :							Tuple      		=		(
               feature_extractor.size
               if isinstance(feature_extractor.size,      lowerCamelCase)
               else feature_extractor.size['shortest_edge']
           )
           _lowercase       :							Union[str, Any]      		=		transforms.Normalize(mean=feature_extractor.image_mean,      std=feature_extractor.image_std)
           set_requires_grad(self.text_encoder,      lowerCamelCase)
           set_requires_grad(self.clip_model,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase = "auto")       ->   Any:




           """simple docstring"""

           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _lowercase       :							Optional[Any]      		=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           self.enable_attention_slicing(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		min(int(num_inference_steps * strength),      lowerCamelCase)

           _lowercase       :							List[Any]      		=		max(num_inference_steps - init_timestep,      0)
           _lowercase       :							int      		=		self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None)       ->   Optional[Any]:




           """simple docstring"""

           if not isinstance(lowerCamelCase,      torch.Tensor):
               raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase)}''')

           _lowercase       :							Any      		=		image.to(device=lowerCamelCase,      dtype=lowerCamelCase)

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Dict      		=		[
                   self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(lowerCamelCase)
               ]
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           else:
               _lowercase       :							int      		=		self.vae.encode(lowerCamelCase).latent_dist.sample(lowerCamelCase)

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							str      		=		0.1_8_2_1_5 * init_latents
           _lowercase       :							List[str]      		=		init_latents.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		randn_tensor(init_latents.shape,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)

           # get latents
           _lowercase       :							Any      		=		self.scheduler.add_noise(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							str      		=		init_latents

           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		self.coca_transform(lowerCamelCase).unsqueeze(0)
           with torch.no_grad(), torch.cuda.amp.autocast():
               _lowercase       :							List[str]      		=		self.coca_model.generate(transformed_image.to(device=self.device,      dtype=self.coca_model.dtype))
           _lowercase       :							int      		=		self.coca_tokenizer.decode(generated[0].cpu().numpy())
           return generated.split('<end_of_text>')[0].replace('<start_of_text>',      '').rstrip(' .,')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.feature_extractor.preprocess(lowerCamelCase)
           _lowercase       :							List[str]      		=		torch.from_numpy(clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
           _lowercase       :							int      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							Dict      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)
           _lowercase       :							int      		=		image_embeddings_clip.repeat_interleave(lowerCamelCase,      dim=0)
           return image_embeddings_clip
       @torch.enable_grad()
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		latents.detach().requires_grad_()

           _lowercase       :							Union[str, Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

           # predict the noise residual
           _lowercase       :							Tuple      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

           if isinstance(self.scheduler,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
               _lowercase       :							Any      		=		self.scheduler.alphas_cumprod[timestep]
               _lowercase       :							Any      		=		1 - alpha_prod_t
               # compute predicted original sample from predicted noise also called
               # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
               _lowercase       :							List[Any]      		=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

               _lowercase       :							List[str]      		=		torch.sqrt(lowerCamelCase)
               _lowercase       :							Dict      		=		pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Dict      		=		self.scheduler.sigmas[index]
               _lowercase       :							List[Any]      		=		latents - sigma * noise_pred
           else:
               raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Dict      		=		1 / 0.1_8_2_1_5 * sample
           _lowercase       :							Optional[Any]      		=		self.vae.decode(lowerCamelCase).sample
           _lowercase       :							int      		=		(image / 2 + 0.5).clamp(0,      1)

           _lowercase       :							Any      		=		transforms.Resize(self.feature_extractor_size)(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.normalize(lowerCamelCase).to(latents.dtype)

           _lowercase       :							List[str]      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		spherical_dist_loss(lowerCamelCase,      lowerCamelCase).mean() * clip_guidance_scale

           _lowercase       :							str      		=		-torch.autograd.grad(lowerCamelCase,      lowerCamelCase)[0]

           if isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Union[str, Any]      		=		latents.detach() + grads * (sigma**2)
               _lowercase       :							List[str]      		=		noise_pred_original
           else:
               _lowercase       :							List[Any]      		=		noise_pred_original - torch.sqrt(lowerCamelCase) * grads
           return noise_pred, latents




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 0.6,      lowerCamelCase = 50,      lowerCamelCase = 7.5,      lowerCamelCase = 1,      lowerCamelCase = 0.0,      lowerCamelCase = 1_00,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      lowerCamelCase = 0.8,      lowerCamelCase = 0.1,      lowerCamelCase = 0.1,      )       ->   int:




           """simple docstring"""

           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase)} generators.''')

           if height % 8 != 0 or width % 8 != 0:
               raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

           if isinstance(lowerCamelCase,      torch.Generator) and batch_size > 1:
               _lowercase       :							Dict      		=		[generator] + [None] * (batch_size - 1)

           _lowercase       :							Optional[int]      		=		[
               ('model', self.coca_model is None),
               ('tokenizer', self.coca_tokenizer is None),
               ('transform', self.coca_transform is None),
           ]
           _lowercase       :							Optional[int]      		=		[x[0] for x in coca_is_none if x[1]]
           _lowercase       :							str      		=		', '.join(lowerCamelCase)
           # generate prompts with coca model if prompt is None
           if content_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							List[Any]      		=		self.get_image_description(lowerCamelCase)
           if style_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							Dict      		=		self.get_image_description(lowerCamelCase)

           # get prompt text embeddings for content and style
           _lowercase       :							Optional[int]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							Optional[int]      		=		self.text_encoder(content_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Union[str, Any]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							List[Any]      		=		self.text_encoder(style_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Any      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # duplicate text embeddings for each generation per prompt
           _lowercase       :							Dict      		=		text_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

           # set timesteps
           _lowercase       :							Dict      		=		'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_offset:
               _lowercase       :							Any      		=		1

           self.scheduler.set_timesteps(lowerCamelCase,      **lowerCamelCase)
           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           self.scheduler.timesteps.to(self.device)

           _lowercase		,				_lowercase       :							List[Any]      		=		self.get_timesteps(lowerCamelCase,      lowerCamelCase,      self.device)
           _lowercase       :							str      		=		timesteps[:1].repeat(lowerCamelCase)

           # Preprocess image
           _lowercase       :							str      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							int      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           if clip_guidance_scale > 0:
               _lowercase       :							Optional[int]      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Dict      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Optional[int]      		=		slerp(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           _lowercase       :							Dict      		=		guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
               _lowercase       :							Tuple      		=		content_text_input.input_ids.shape[-1]
               _lowercase       :							Union[str, Any]      		=		self.tokenizer([''],      padding='max_length',      max_length=lowerCamelCase,      return_tensors='pt')
               _lowercase       :							int      		=		self.text_encoder(uncond_input.input_ids.to(self.device))[0]
               # duplicate unconditional embeddings for each generation per prompt
               _lowercase       :							Union[str, Any]      		=		uncond_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               _lowercase       :							Optional[Any]      		=		torch.cat([uncond_embeddings, text_embeddings])

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _lowercase       :							Tuple      		=		(batch_size, self.unet.config.in_channels, height // 8, width // 8)
           _lowercase       :							Optional[int]      		=		text_embeddings.dtype
           if latents is None:
               if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   _lowercase       :							List[Any]      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device='cpu',      dtype=lowerCamelCase).to(
                       self.device)
               else:
                   _lowercase       :							Any      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=lowerCamelCase)
           else:
               if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
               _lowercase       :							Tuple      		=		latents.to(self.device)

           # scale the initial noise by the standard deviation required by the scheduler
           _lowercase       :							List[Any]      		=		latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _lowercase       :							Dict      		=		'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_eta:
               _lowercase       :							List[Any]      		=		eta

           # check if the scheduler accepts generator
           _lowercase       :							Dict      		=		'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
           if accepts_generator:
               _lowercase       :							str      		=		generator

           with self.progress_bar(total=lowerCamelCase):
               for i, t in enumerate(lowerCamelCase):
                   # expand the latents if we are doing classifier free guidance
                   _lowercase       :							List[str]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                   _lowercase       :							List[Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

                   # predict the noise residual
                   _lowercase       :							Dict      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                       _lowercase		,				_lowercase       :							Optional[Any]      		=		noise_pred.chunk(2)
                       _lowercase       :							Optional[Any]      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                       _lowercase       :							Tuple      		=		(
                           text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                       )
                       _lowercase		,				_lowercase       :							List[Any]      		=		self.cond_fn(
                           lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )

                   # compute the previous noisy sample x_t -> x_t-1
                   _lowercase       :							Optional[Any]      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Any      		=		1 / 0.1_8_2_1_5 * latents
           _lowercase       :							List[str]      		=		self.vae.decode(lowerCamelCase).sample

           _lowercase       :							Tuple      		=		(image / 2 + 0.5).clamp(0,      1)
           _lowercase       :							List[Any]      		=		image.cpu().permute(0,      2,      3,      1).numpy()

           if output_type == "pil":
               _lowercase       :							List[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=lowerCamelCase,      nsfw_content_detected=lowerCamelCase)


