
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						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Any:
							if isinstance(UpperCAmelCase__			,				torch.Tensor       ):
														return image
							elif isinstance(UpperCAmelCase__			,				PIL.Image.Image       ):
														lowerCamelCase_							  =					[image]

							if isinstance(image[0]			,				PIL.Image.Image       ):
														lowerCamelCase_							  =					[np.array(i.resize((w, h)			,				resample=PIL_INTERPOLATION["""lanczos"""]       )       )[None, :] for i in image]
														lowerCamelCase_							  =					np.concatenate(UpperCAmelCase__			,				axis=0       )
														lowerCamelCase_							  =					np.array(UpperCAmelCase__       ).astype(np.floataa       ) / 2_5_5.0
														lowerCamelCase_							  =					image.transpose(0			,				3			,				1			,				2       )
														lowerCamelCase_							  =					2.0 * image - 1.0
														lowerCamelCase_							  =					torch.from_numpy(UpperCAmelCase__       )
							elif isinstance(image[0]			,				torch.Tensor       ):
														lowerCamelCase_							  =					torch.cat(UpperCAmelCase__			,				dim=0       )
							return image



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Tuple=0.9_9_9_5       )  ->    List[str]:
							if not isinstance(UpperCAmelCase__			,				np.ndarray       ):
														lowerCamelCase_							  =					True
														lowerCamelCase_							  =					va.device
														lowerCamelCase_							  =					va.cpu().numpy()
														lowerCamelCase_							  =					va.cpu().numpy()

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

							if inputs_are_torch:
														lowerCamelCase_							  =					torch.from_numpy(UpperCAmelCase__       ).to(UpperCAmelCase__       )

							return va



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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      List[Any]       )  ->    Dict:
							for param in model.parameters():
														lowerCamelCase_							  =					value





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     AutoencoderKL   ,					__UpperCamelCase					:     CLIPTextModel   ,					__UpperCamelCase					:     CLIPModel   ,					__UpperCamelCase					:     CLIPTokenizer   ,					__UpperCamelCase					:     UNetaDConditionModel   ,					__UpperCamelCase					:     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]   ,					__UpperCamelCase					:     CLIPFeatureExtractor   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					):
												super().__init__()
												self.register_modules(
												    vae=__UpperCamelCase   ,					text_encoder=__UpperCamelCase   ,					clip_model=__UpperCamelCase   ,					tokenizer=__UpperCamelCase   ,					unet=__UpperCamelCase   ,					scheduler=__UpperCamelCase   ,					feature_extractor=__UpperCamelCase   ,					coca_model=__UpperCamelCase   ,					coca_tokenizer=__UpperCamelCase   ,					coca_transform=__UpperCamelCase   ,					)
												lowerCamelCase_							  =					(
												    feature_extractor.size
												    if isinstance(feature_extractor.size   ,					__UpperCamelCase   )
												    else feature_extractor.size["""shortest_edge"""]
												)
												lowerCamelCase_							  =					transforms.Normalize(mean=feature_extractor.image_mean   ,					std=feature_extractor.image_std   )
												set_requires_grad(self.text_encoder   ,					__UpperCamelCase   )
												set_requires_grad(self.clip_model   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Optional[Union[str, int]] = "auto"   ):
												if slice_size == "auto":
																			# half the attention head size is usually a good trade-off between
																			# speed and memory
																			lowerCamelCase_							  =					self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												self.enable_attention_slicing(__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												set_requires_grad(self.vae   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												set_requires_grad(self.vae   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Tuple   ):
												set_requires_grad(self.unet   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												set_requires_grad(self.unet   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# get the original timestep using init_timestep
												lowerCamelCase_							  =					min(int(num_inference_steps * strength   )   ,					__UpperCamelCase   )

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

												return timesteps, num_inference_steps - t_start

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     str=None   ):
												if not isinstance(__UpperCamelCase   ,					torch.Tensor   ):
																			raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__UpperCamelCase   )}'''   )

												lowerCamelCase_							  =					image.to(device=__UpperCamelCase   ,					dtype=__UpperCamelCase   )

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

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

												lowerCamelCase_							  =					randn_tensor(init_latents.shape   ,					generator=__UpperCamelCase   ,					device=__UpperCamelCase   ,					dtype=__UpperCamelCase   )

												# get latents
												lowerCamelCase_							  =					self.scheduler.add_noise(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					init_latents

												return latents

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.coca_transform(__UpperCamelCase   ).unsqueeze(0   )
												with torch.no_grad(), torch.cuda.amp.autocast():
																			lowerCamelCase_							  =					self.coca_model.generate(transformed_image.to(device=self.device   ,					dtype=self.coca_model.dtype   )   )
												lowerCamelCase_							  =					self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
												return generated.split("""<end_of_text>"""   )[0].replace("""<start_of_text>"""   ,					""""""   ).rstrip(""" .,"""   )

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					self.feature_extractor.preprocess(__UpperCamelCase   )
												lowerCamelCase_							  =					torch.from_numpy(clip_image_input["""pixel_values"""][0]   ).unsqueeze(0   ).to(self.device   ).half()
												lowerCamelCase_							  =					self.clip_model.get_image_features(__UpperCamelCase   )
												lowerCamelCase_							  =					image_embeddings_clip / image_embeddings_clip.norm(p=2   ,					dim=-1   ,					keepdim=__UpperCamelCase   )
												lowerCamelCase_							  =					image_embeddings_clip.repeat_interleave(__UpperCamelCase   ,					dim=0   )
												return image_embeddings_clip

					@torch.enable_grad()
					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					latents.detach().requires_grad_()

												lowerCamelCase_							  =					self.scheduler.scale_model_input(__UpperCamelCase   ,					__UpperCamelCase   )

												# predict the noise residual
												lowerCamelCase_							  =					self.unet(__UpperCamelCase   ,					__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ).sample

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

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

												lowerCamelCase_							  =					transforms.Resize(self.feature_extractor_size   )(__UpperCamelCase   )
												lowerCamelCase_							  =					self.normalize(__UpperCamelCase   ).to(latents.dtype   )

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

												lowerCamelCase_							  =					spherical_dist_loss(__UpperCamelCase   ,					__UpperCamelCase   ).mean() * clip_guidance_scale

												lowerCamelCase_							  =					-torch.autograd.grad(__UpperCamelCase   ,					__UpperCamelCase   )[0]

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



					@torch.no_grad()
					def __call__(	self					:     List[Any]   ,					__UpperCamelCase					:     Union[torch.FloatTensor, PIL.Image.Image]   ,					__UpperCamelCase					:     Union[torch.FloatTensor, PIL.Image.Image]   ,					__UpperCamelCase					:     Optional[str] = None   ,					__UpperCamelCase					:     Optional[str] = None   ,					__UpperCamelCase					:     Optional[int] = 5_1_2   ,					__UpperCamelCase					:     Optional[int] = 5_1_2   ,					__UpperCamelCase					:     float = 0.6   ,					__UpperCamelCase					:     Optional[int] = 5_0   ,					__UpperCamelCase					:     Optional[float] = 7.5   ,					__UpperCamelCase					:     Optional[int] = 1   ,					__UpperCamelCase					:     float = 0.0   ,					__UpperCamelCase					:     Optional[float] = 1_0_0   ,					__UpperCamelCase					:     Optional[torch.Generator] = None   ,					__UpperCamelCase					:     Optional[str] = "pil"   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     float = 0.8   ,					__UpperCamelCase					:     float = 0.1   ,					__UpperCamelCase					:     float = 0.1   ,					):
												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) and len(__UpperCamelCase   ) != batch_size:
																			raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__UpperCamelCase   )} 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(__UpperCamelCase   ,					torch.Generator   ) and batch_size > 1:
																			lowerCamelCase_							  =					[generator] + [None] * (batch_size - 1)

												lowerCamelCase_							  =					[
												    ("""model""", self.coca_model is None),
												    ("""tokenizer""", self.coca_tokenizer is None),
												    ("""transform""", self.coca_transform is None),
												]
												lowerCamelCase_							  =					[x[0] for x in coca_is_none if x[1]]
												lowerCamelCase_							  =					""", """.join(__UpperCamelCase   )
												# generate prompts with coca model if prompt is None
												if content_prompt is None:
																			if len(__UpperCamelCase   ):
																										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.'''   )
																			lowerCamelCase_							  =					self.get_image_description(__UpperCamelCase   )
												if style_prompt is None:
																			if len(__UpperCamelCase   ):
																										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.'''   )
																			lowerCamelCase_							  =					self.get_image_description(__UpperCamelCase   )

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

												lowerCamelCase_							  =					self.tokenizer(
												    __UpperCamelCase   ,					padding="""max_length"""   ,					max_length=self.tokenizer.model_max_length   ,					truncation=__UpperCamelCase   ,					return_tensors="""pt"""   ,					)
												lowerCamelCase_							  =					self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

												lowerCamelCase_							  =					slerp(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

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

												# set timesteps
												lowerCamelCase_							  =					"""offset""" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
												lowerCamelCase_							  =					{}
												if accepts_offset:
																			lowerCamelCase_							  =					1

												self.scheduler.set_timesteps(__UpperCamelCase   ,					**__UpperCamelCase   )
												# 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   )

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.get_timesteps(__UpperCamelCase   ,					__UpperCamelCase   ,					self.device   )
												lowerCamelCase_							  =					timesteps[:1].repeat(__UpperCamelCase   )

												# Preprocess image
												lowerCamelCase_							  =					preprocess(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.prepare_latents(
												    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					text_embeddings.dtype   ,					self.device   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					preprocess(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.prepare_latents(
												    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					text_embeddings.dtype   ,					self.device   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					slerp(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

												if clip_guidance_scale > 0:
																			lowerCamelCase_							  =					self.get_clip_image_embeddings(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self.get_clip_image_embeddings(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					slerp(
																			    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

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

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

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

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

																										# predict the noise residual
																										lowerCamelCase_							  =					self.unet(__UpperCamelCase   ,					__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ).sample

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

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

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

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

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

												if output_type == "pil":
																			lowerCamelCase_							  =					self.numpy_to_pil(__UpperCamelCase   )

												if not return_dict:
																			return (image, None)

												return StableDiffusionPipelineOutput(images=__UpperCamelCase   ,					nsfw_content_detected=__UpperCamelCase   )







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase        =  {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

lowercase        =  {'''mobilebert-uncased''': 5_1_2}


lowercase        =  {}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    MobileBertTokenizer

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Any="[UNK]"   ,					__UpperCamelCase					:     str="[SEP]"   ,					__UpperCamelCase					:     Dict="[PAD]"   ,					__UpperCamelCase					:     List[str]="[CLS]"   ,					__UpperCamelCase					:     Any="[MASK]"   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     int=None   ,					**__UpperCamelCase					:     Dict   ,					):
												super().__init__(
												    __UpperCamelCase   ,					tokenizer_file=__UpperCamelCase   ,					do_lower_case=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					sep_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					cls_token=__UpperCamelCase   ,					mask_token=__UpperCamelCase   ,					tokenize_chinese_chars=__UpperCamelCase   ,					strip_accents=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
												if (
												    normalizer_state.get("""lowercase"""   ,					__UpperCamelCase   ) != do_lower_case
												    or normalizer_state.get("""strip_accents"""   ,					__UpperCamelCase   ) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""   ,					__UpperCamelCase   ) != tokenize_chinese_chars
												):
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					normalizer_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					do_lower_case
																			lowerCamelCase_							  =					strip_accents
																			lowerCamelCase_							  =					tokenize_chinese_chars
																			lowerCamelCase_							  =					normalizer_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					do_lower_case

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[self.cls_token_id]
												if token_ids_a is None:
																			return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )





