
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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=0.9_995		)		->		Dict:
					'''simple docstring'''


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

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

					if inputs_are_torch:
										SCREAMING_SNAKE_CASE__													= torch.from_numpy(UpperCamelCase_		).to(UpperCamelCase_		)

					return va






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


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




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   AutoencoderKL       ,   UpperCAmelCase_							:   CLIPTextModel       ,   UpperCAmelCase_							:   CLIPModel       ,   UpperCAmelCase_							:   CLIPTokenizer       ,   UpperCAmelCase_							:   UNetaDConditionModel       ,   UpperCAmelCase_							:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]       ,   UpperCAmelCase_							:   CLIPFeatureExtractor       ,   UpperCAmelCase_							:   str=None       ,   UpperCAmelCase_							:   List[Any]=None       ,   UpperCAmelCase_							:   List[str]=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_       ,   )
						SCREAMING_SNAKE_CASE__													= (
						    feature_extractor.size
						    if isinstance(feature_extractor.size       ,   UpperCAmelCase_							)
						    else feature_extractor.size['shortest_edge']
						)
						SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   Union[str, Any]       ,   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
											SCREAMING_SNAKE_CASE__													= self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						self.enable_attention_slicing(UpperCAmelCase_							)
	def      A_				(			self							:   List[str]							):
						set_requires_grad(self.vae       ,   UpperCAmelCase_							)
	def      A_				(			self							:   str							):
						set_requires_grad(self.vae       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						set_requires_grad(self.unet       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						set_requires_grad(self.unet       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]							):
						# get the original timestep using init_timestep
						SCREAMING_SNAKE_CASE__													= min(int(num_inference_steps * strength							)       ,   UpperCAmelCase_							)

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

						return timesteps, num_inference_steps - t_start
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Dict=None							):
						if not isinstance(UpperCAmelCase_       ,   torch.Tensor							):
											raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase_							)}'							)

						SCREAMING_SNAKE_CASE__													= image.to(device=UpperCAmelCase_       ,   dtype=UpperCAmelCase_							)

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

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						SCREAMING_SNAKE_CASE__													= 0.18_215 * init_latents
						SCREAMING_SNAKE_CASE__													= init_latents.repeat_interleave(UpperCAmelCase_       ,   dim=0							)

						SCREAMING_SNAKE_CASE__													= randn_tensor(init_latents.shape       ,   generator=UpperCAmelCase_       ,   device=UpperCAmelCase_       ,   dtype=UpperCAmelCase_							)

						# get latents
						SCREAMING_SNAKE_CASE__													= self.scheduler.add_noise(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= init_latents

						return latents
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__													= self.coca_transform(UpperCAmelCase_							).unsqueeze(0							)
						with torch.no_grad(), torch.cuda.amp.autocast():
											SCREAMING_SNAKE_CASE__													= self.coca_model.generate(transformed_image.to(device=self.device       ,   dtype=self.coca_model.dtype							)							)
						SCREAMING_SNAKE_CASE__													= self.coca_tokenizer.decode(generated[0].cpu().numpy()							)
						return generated.split('<end_of_text>'							)[0].replace('<start_of_text>'       ,   ''							).rstrip(' .,'							)
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= self.feature_extractor.preprocess(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.from_numpy(clip_image_input['pixel_values'][0]							).unsqueeze(0							).to(self.device							).half()
						SCREAMING_SNAKE_CASE__													= self.clip_model.get_image_features(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= image_embeddings_clip / image_embeddings_clip.norm(p=2       ,   dim=-1       ,   keepdim=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= image_embeddings_clip.repeat_interleave(UpperCAmelCase_       ,   dim=0							)
						return image_embeddings_clip
	@torch.enable_grad()
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Optional[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= latents.detach().requires_grad_()

						SCREAMING_SNAKE_CASE__													= self.scheduler.scale_model_input(UpperCAmelCase_       ,   UpperCAmelCase_							)

						# predict the noise residual
						SCREAMING_SNAKE_CASE__													= self.unet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   encoder_hidden_states=UpperCAmelCase_							).sample

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

											SCREAMING_SNAKE_CASE__													= torch.sqrt(UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= self.scheduler.sigmas[index]
											SCREAMING_SNAKE_CASE__													= 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
						SCREAMING_SNAKE_CASE__													= 1 / 0.18_215 * sample
						SCREAMING_SNAKE_CASE__													= self.vae.decode(UpperCAmelCase_							).sample
						SCREAMING_SNAKE_CASE__													= (image / 2 + 0.5).clamp(0       ,   1							)

						SCREAMING_SNAKE_CASE__													= transforms.Resize(self.feature_extractor_size							)(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.normalize(UpperCAmelCase_							).to(latents.dtype							)

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

						SCREAMING_SNAKE_CASE__													= spherical_dist_loss(UpperCAmelCase_       ,   UpperCAmelCase_							).mean() * clip_guidance_scale

						SCREAMING_SNAKE_CASE__													= -torch.autograd.grad(UpperCAmelCase_       ,   UpperCAmelCase_							)[0]

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





	@torch.no_grad()
	def __call__(			self							:   Optional[int]       ,   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] = 512       ,   UpperCAmelCase_							:   Optional[int] = 512       ,   UpperCAmelCase_							:   float = 0.6       ,   UpperCAmelCase_							:   Optional[int] = 50       ,   UpperCAmelCase_							:   Optional[float] = 7.5       ,   UpperCAmelCase_							:   Optional[int] = 1       ,   UpperCAmelCase_							:   float = 0.0       ,   UpperCAmelCase_							:   Optional[float] = 100       ,   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:
											SCREAMING_SNAKE_CASE__													= [generator] + [None] * (batch_size - 1)

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

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

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

						SCREAMING_SNAKE_CASE__													= slerp(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

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

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

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.get_timesteps(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.device							)
						SCREAMING_SNAKE_CASE__													= timesteps[:1].repeat(UpperCAmelCase_							)

						# Preprocess image
						SCREAMING_SNAKE_CASE__													= preprocess(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.prepare_latents(
						    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   text_embeddings.dtype       ,   self.device       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= preprocess(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.prepare_latents(
						    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   text_embeddings.dtype       ,   self.device       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= slerp(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

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

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

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

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

																# predict the noise residual
																SCREAMING_SNAKE_CASE__													= self.unet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   encoder_hidden_states=UpperCAmelCase_							).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						SCREAMING_SNAKE_CASE__													= 1 / 0.18_215 * latents
						SCREAMING_SNAKE_CASE__													= self.vae.decode(UpperCAmelCase_							).sample

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

						if output_type == "pil":
											SCREAMING_SNAKE_CASE__													= self.numpy_to_pil(UpperCAmelCase_							)

						if not return_dict:
											return (image, None)

						return StableDiffusionPipelineOutput(images=UpperCAmelCase_       ,   nsfw_content_detected=UpperCAmelCase_							)







import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.eos_token_id]  # No sep token in ESM vocabulary
						if token_ids_a is None:
											if self.eos_token_id is None:
																return cls + token_ids_a
											else:
																return cls + token_ids_a + sep
						elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'							)
						return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'							)

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





