


'''simple docstring'''



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    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Any   ,     A     :    List[str]  ):




						'''simple docstring'''






						if isinstance(A   ,     torch.Tensor  ):
												return image
						elif isinstance(A   ,     PIL.Image.Image  ):
												UpperCAmelCase         =			[image]

						if isinstance(image[0]   ,     PIL.Image.Image  ):
												UpperCAmelCase         =			[np.array(i.resize((w, h)   ,     resample=PIL_INTERPOLATION['''lanczos''']  )  )[None, :] for i in image]
												UpperCAmelCase         =			np.concatenate(A   ,     axis=0  )
												UpperCAmelCase         =			np.array(A  ).astype(np.floataa  ) / 255.0
												UpperCAmelCase         =			image.transpose(0   ,     3   ,     1   ,     2  )
												UpperCAmelCase         =			2.0 * image - 1.0
												UpperCAmelCase         =			torch.from_numpy(A  )
						elif isinstance(image[0]   ,     torch.Tensor  ):
												UpperCAmelCase         =			torch.cat(A   ,     dim=0  )
						return image


def    lowerCamelCase__   ( A     :    str   ,     A     :    str   ,     A     :    List[Any]   ,     A     :    Optional[int]=0.9_995  ):




						'''simple docstring'''






						if not isinstance(A   ,     np.ndarray  ):
												UpperCAmelCase         =			True
												UpperCAmelCase         =			va.device
												UpperCAmelCase         =			va.cpu().numpy()
												UpperCAmelCase         =			va.cpu().numpy()

						UpperCAmelCase         =			np.sum(va * va / (np.linalg.norm(A  ) * np.linalg.norm(A  ))  )
						if np.abs(A  ) > DOT_THRESHOLD:
												UpperCAmelCase         =			(1 - t) * va + t * va
						else:
												UpperCAmelCase         =			np.arccos(A  )
												UpperCAmelCase         =			np.sin(A  )
												UpperCAmelCase         =			theta_a * t
												UpperCAmelCase         =			np.sin(A  )
												UpperCAmelCase         =			np.sin(theta_a - theta_t  ) / sin_theta_a
												UpperCAmelCase         =			sin_theta_t / sin_theta_a
												UpperCAmelCase         =			sa * va + sa * va

						if inputs_are_torch:
												UpperCAmelCase         =			torch.from_numpy(A  ).to(A  )

						return va


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Optional[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			F.normalize(A   ,     dim=-1  )
						UpperCAmelCase         =			F.normalize(A   ,     dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Any  ):




						'''simple docstring'''






						for param in model.parameters():
												UpperCAmelCase         =			value






class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				AutoencoderKL   ,					lowerCAmelCase    :				CLIPTextModel   ,					lowerCAmelCase    :				CLIPModel   ,					lowerCAmelCase    :				CLIPTokenizer   ,					lowerCAmelCase    :				UNetaDConditionModel   ,					lowerCAmelCase    :				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]   ,					lowerCAmelCase    :				CLIPFeatureExtractor   ,					lowerCAmelCase    :				List[str]=None   ,					lowerCAmelCase    :				Tuple=None   ,					lowerCAmelCase    :				Tuple=None   ,					)->  Optional[Any]:

										"""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   ,					)
										UpperCAmelCase         =			(
										    feature_extractor.size
										    if isinstance(feature_extractor.size   ,					lowerCAmelCase   )
										    else feature_extractor.size['''shortest_edge''']
										)
										UpperCAmelCase         =			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   a__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[Union[str, int]] = "auto"   )->  Any:

										"""simple docstring"""




										if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																UpperCAmelCase         =			self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCAmelCase   )




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

										"""simple docstring"""




										self.enable_attention_slicing(lowerCAmelCase   )




				def   a__(	self    :				str   )->  str:

										"""simple docstring"""




										set_requires_grad(self.vae   ,					lowerCAmelCase   )




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										set_requires_grad(self.vae   ,					lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										set_requires_grad(self.unet   ,					lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   )->  Optional[int]:

										"""simple docstring"""




										set_requires_grad(self.unet   ,					lowerCAmelCase   )




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			min(int(num_inference_steps * strength   )   ,					lowerCAmelCase   )

										UpperCAmelCase         =			max(num_inference_steps - init_timestep   ,					0   )
										UpperCAmelCase         =			self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[str]=None   )->  List[str]:

										"""simple docstring"""




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

										UpperCAmelCase         =			image.to(device=lowerCAmelCase   ,					dtype=lowerCAmelCase   )

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

										# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
										UpperCAmelCase         =			0.18215 * init_latents
										UpperCAmelCase         =			init_latents.repeat_interleave(lowerCAmelCase   ,					dim=0   )

										UpperCAmelCase         =			randn_tensor(init_latents.shape   ,					generator=lowerCAmelCase   ,					device=lowerCAmelCase   ,					dtype=lowerCAmelCase   )

										# get latents
										UpperCAmelCase         =			self.scheduler.add_noise(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			init_latents

										return latents




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.coca_transform(lowerCAmelCase   ).unsqueeze(0   )
										with torch.no_grad(), torch.cuda.amp.autocast():
																UpperCAmelCase         =			self.coca_model.generate(transformed_image.to(device=self.device   ,					dtype=self.coca_model.dtype   )   )
										UpperCAmelCase         =			self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
										return generated.split('''<end_of_text>'''   )[0].replace('''<start_of_text>'''   ,					''''''   ).rstrip(''' .,'''   )




				def   a__(	self    :				int   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Any   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.feature_extractor.preprocess(lowerCAmelCase   )
										UpperCAmelCase         =			torch.from_numpy(clip_image_input['''pixel_values'''][0]   ).unsqueeze(0   ).to(self.device   ).half()
										UpperCAmelCase         =			self.clip_model.get_image_features(lowerCAmelCase   )
										UpperCAmelCase         =			image_embeddings_clip / image_embeddings_clip.norm(p=2   ,					dim=-1   ,					keepdim=lowerCAmelCase   )
										UpperCAmelCase         =			image_embeddings_clip.repeat_interleave(lowerCAmelCase   ,					dim=0   )
										return image_embeddings_clip




				@torch.enable_grad()
				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Tuple   ,					)->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			latents.detach().requires_grad_()

										UpperCAmelCase         =			self.scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

										# predict the noise residual
										UpperCAmelCase         =			self.unet(lowerCAmelCase   ,					lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ).sample

										if isinstance(self.scheduler   ,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
																UpperCAmelCase         =			self.scheduler.alphas_cumprod[timestep]
																UpperCAmelCase         =			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
																UpperCAmelCase         =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																UpperCAmelCase         =			torch.sqrt(lowerCAmelCase   )
																UpperCAmelCase         =			pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler   ,					lowerCAmelCase   ):
																UpperCAmelCase         =			self.scheduler.sigmas[index]
																UpperCAmelCase         =			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
										UpperCAmelCase         =			1 / 0.18215 * sample
										UpperCAmelCase         =			self.vae.decode(lowerCAmelCase   ).sample
										UpperCAmelCase         =			(image / 2 + 0.5).clamp(0   ,					1   )

										UpperCAmelCase         =			transforms.Resize(self.feature_extractor_size   )(lowerCAmelCase   )
										UpperCAmelCase         =			self.normalize(lowerCAmelCase   ).to(latents.dtype   )

										UpperCAmelCase         =			self.clip_model.get_image_features(lowerCAmelCase   )
										UpperCAmelCase         =			image_embeddings_clip / image_embeddings_clip.norm(p=2   ,					dim=-1   ,					keepdim=lowerCAmelCase   )

										UpperCAmelCase         =			spherical_dist_loss(lowerCAmelCase   ,					lowerCAmelCase   ).mean() * clip_guidance_scale

										UpperCAmelCase         =			-torch.autograd.grad(lowerCAmelCase   ,					lowerCAmelCase   )[0]

										if isinstance(self.scheduler   ,					lowerCAmelCase   ):
																UpperCAmelCase         =			latents.detach() + grads * (sigma**2)
																UpperCAmelCase         =			noise_pred_original
										else:
																UpperCAmelCase         =			noise_pred_original - torch.sqrt(lowerCAmelCase   ) * grads
										return noise_pred, latents





				@torch.no_grad()
				def __call__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Union[torch.FloatTensor, PIL.Image.Image]   ,					lowerCAmelCase    :				Union[torch.FloatTensor, PIL.Image.Image]   ,					lowerCAmelCase    :				Optional[str] = None   ,					lowerCAmelCase    :				Optional[str] = None   ,					lowerCAmelCase    :				Optional[int] = 512   ,					lowerCAmelCase    :				Optional[int] = 512   ,					lowerCAmelCase    :				float = 0.6   ,					lowerCAmelCase    :				Optional[int] = 50   ,					lowerCAmelCase    :				Optional[float] = 7.5   ,					lowerCAmelCase    :				Optional[int] = 1   ,					lowerCAmelCase    :				float = 0.0   ,					lowerCAmelCase    :				Optional[float] = 100   ,					lowerCAmelCase    :				Optional[torch.Generator] = None   ,					lowerCAmelCase    :				Optional[str] = "pil"   ,					lowerCAmelCase    :				bool = True   ,					lowerCAmelCase    :				float = 0.8   ,					lowerCAmelCase    :				float = 0.1   ,					lowerCAmelCase    :				float = 0.1   ,					)->  Optional[Any]:

										"""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:
																UpperCAmelCase         =			[generator] + [None] * (batch_size - 1)

										UpperCAmelCase         =			[
										    ('''model''', self.coca_model is None),
										    ('''tokenizer''', self.coca_tokenizer is None),
										    ('''transform''', self.coca_transform is None),
										]
										UpperCAmelCase         =			[x[0] for x in coca_is_none if x[1]]
										UpperCAmelCase         =			''', '''.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."""   )
																UpperCAmelCase         =			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."""   )
																UpperCAmelCase         =			self.get_image_description(lowerCAmelCase   )

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

										UpperCAmelCase         =			self.tokenizer(
										    lowerCAmelCase   ,					padding='''max_length'''   ,					max_length=self.tokenizer.model_max_length   ,					truncation=lowerCAmelCase   ,					return_tensors='''pt'''   ,					)
										UpperCAmelCase         =			self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

										UpperCAmelCase         =			slerp(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

										# duplicate text embeddings for each generation per prompt
										UpperCAmelCase         =			text_embeddings.repeat_interleave(lowerCAmelCase   ,					dim=0   )

										# set timesteps
										UpperCAmelCase         =			'''offset''' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
										UpperCAmelCase         =			{}
										if accepts_offset:
																UpperCAmelCase         =			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   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.get_timesteps(lowerCAmelCase   ,					lowerCAmelCase   ,					self.device   )
										UpperCAmelCase         =			timesteps[:1].repeat(lowerCAmelCase   )

										# Preprocess image
										UpperCAmelCase         =			preprocess(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			self.prepare_latents(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					text_embeddings.dtype   ,					self.device   ,					lowerCAmelCase   )

										UpperCAmelCase         =			preprocess(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			self.prepare_latents(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					text_embeddings.dtype   ,					self.device   ,					lowerCAmelCase   )

										UpperCAmelCase         =			slerp(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

										if clip_guidance_scale > 0:
																UpperCAmelCase         =			self.get_clip_image_embeddings(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			self.get_clip_image_embeddings(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			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.
										UpperCAmelCase         =			guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																UpperCAmelCase         =			content_text_input.input_ids.shape[-1]
																UpperCAmelCase         =			self.tokenizer(['''''']   ,					padding='''max_length'''   ,					max_length=lowerCAmelCase   ,					return_tensors='''pt'''   )
																UpperCAmelCase         =			self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																# duplicate unconditional embeddings for each generation per prompt
																UpperCAmelCase         =			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
																UpperCAmelCase         =			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`.
										UpperCAmelCase         =			(batch_size, self.unet.config.in_channels, height // 8, width // 8)
										UpperCAmelCase         =			text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not work reproducibly on mps
																						UpperCAmelCase         =			torch.randn(lowerCAmelCase   ,					generator=lowerCAmelCase   ,					device='''cpu'''   ,					dtype=lowerCAmelCase   ).to(
																						    self.device   )
																else:
																						UpperCAmelCase         =			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}"""   )
																UpperCAmelCase         =			latents.to(self.device   )

										# scale the initial noise by the standard deviation required by the scheduler
										UpperCAmelCase         =			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]
										UpperCAmelCase         =			'''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
										UpperCAmelCase         =			{}
										if accepts_eta:
																UpperCAmelCase         =			eta

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

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

																						# predict the noise residual
																						UpperCAmelCase         =			self.unet(lowerCAmelCase   ,					lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ).sample

																						# perform classifier free guidance
																						if do_classifier_free_guidance:
																												UpperCAmelCase       ,       UpperCAmelCase         =			noise_pred.chunk(2   )
																												UpperCAmelCase         =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						# perform clip guidance
																						if clip_guidance_scale > 0:
																												UpperCAmelCase         =			(
																												    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																												)
																												UpperCAmelCase       ,       UpperCAmelCase         =			self.cond_fn(
																												    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					)

																						# compute the previous noisy sample x_t -> x_t-1
																						UpperCAmelCase         =			self.scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
										UpperCAmelCase         =			1 / 0.18215 * latents
										UpperCAmelCase         =			self.vae.decode(lowerCAmelCase   ).sample

										UpperCAmelCase         =			(image / 2 + 0.5).clamp(0   ,					1   )
										UpperCAmelCase         =			image.cpu().permute(0   ,					2   ,					3   ,					1   ).numpy()

										if output_type == "pil":
																UpperCAmelCase         =			self.numpy_to_pil(lowerCAmelCase   )

										if not return_dict:
																return (image, None)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase   ,					nsfw_content_detected=lowerCAmelCase   )









'''simple docstring'''



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    :    int			     =  {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    str			     =  [
	    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XmodForCausalLM""",
	    """XmodForMaskedLM""",
	    """XmodForMultipleChoice""",
	    """XmodForQuestionAnswering""",
	    """XmodForSequenceClassification""",
	    """XmodForTokenClassification""",
	    """XmodModel""",
	    """XmodPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





