

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		:	List[str]		,				_UpperCamelCase		:	str						)		->				int:
 '''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						) / 2_55.0
  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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	str=0.99_95						)		->				str:
 '''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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple						)		->				List[str]:
 '''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 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	List[str]						)		->				Optional[int]:
 '''simple docstring'''


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




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def __init__(      self			:   Dict				,     snake_case__			:   AutoencoderKL				,     snake_case__			:   CLIPTextModel				,     snake_case__			:   CLIPModel				,     snake_case__			:   CLIPTokenizer				,     snake_case__			:   UNetaDConditionModel				,     snake_case__			:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]				,     snake_case__			:   CLIPFeatureExtractor				,     snake_case__			:   Any=None				,     snake_case__			:   List[str]=None				,     snake_case__			:   int=None				,     ):

     """simple docstring"""






     super().__init__()
     self.register_modules(
         vae=snake_case__				,     text_encoder=snake_case__				,     clip_model=snake_case__				,     tokenizer=snake_case__				,     unet=snake_case__				,     scheduler=snake_case__				,     feature_extractor=snake_case__				,     coca_model=snake_case__				,     coca_tokenizer=snake_case__				,     coca_transform=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      (
         feature_extractor.size
         if isinstance(feature_extractor.size				,     snake_case__     )
         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				,     snake_case__     )
     set_requires_grad(self.clip_model				,     snake_case__     )

    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   Optional[Union[str, int]] = "auto"     ):

     """simple docstring"""






     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(snake_case__     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     self.enable_attention_slicing(snake_case__     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     set_requires_grad(self.vae				,     snake_case__     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     set_requires_grad(self.vae				,     snake_case__     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     set_requires_grad(self.unet				,     snake_case__     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     set_requires_grad(self.unet				,     snake_case__     )

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Tuple				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      min(int(num_inference_steps * strength     )				,     snake_case__     )

     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 UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Optional[int]				,     snake_case__			:   Optional[Any]				,     snake_case__			:   str				,     snake_case__			:   List[Any]				,     snake_case__			:   List[str]				,     snake_case__			:   Union[str, Any]=None     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      image.to(device=snake_case__				,     dtype=snake_case__     )

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

     # 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(snake_case__				,     dim=0     )

     SCREAMING_SNAKE_CASE		    =      randn_tensor(init_latents.shape				,     generator=snake_case__				,     device=snake_case__				,     dtype=snake_case__     )

     # get latents
     SCREAMING_SNAKE_CASE		    =      self.scheduler.add_noise(snake_case__				,     snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      init_latents

     return latents

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.coca_transform(snake_case__     ).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 UpperCamelCase      (      self			:   Any				,     snake_case__			:   List[Any]				,     snake_case__			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.feature_extractor.preprocess(snake_case__     )
     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(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      image_embeddings_clip / image_embeddings_clip.norm(p=2				,     dim=-1				,     keepdim=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      image_embeddings_clip.repeat_interleave(snake_case__				,     dim=0     )
     return image_embeddings_clip

    @torch.enable_grad()
    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Optional[Any]				,     snake_case__			:   List[Any]				,     snake_case__			:   Any				,     snake_case__			:   str				,     snake_case__			:   Dict				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      latents.detach().requires_grad_()

     SCREAMING_SNAKE_CASE		    =      self.scheduler.scale_model_input(snake_case__				,     snake_case__     )

     # predict the noise residual
     SCREAMING_SNAKE_CASE		    =      self.unet(snake_case__				,     snake_case__				,     encoder_hidden_states=snake_case__     ).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(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      pred_original_sample * (fac) + latents * (1 - fac)
     elif isinstance(self.scheduler				,     snake_case__     ):
      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(snake_case__     ).sample
     SCREAMING_SNAKE_CASE		    =      (image / 2 + 0.5).clamp(0				,     1     )

     SCREAMING_SNAKE_CASE		    =      transforms.Resize(self.feature_extractor_size     )(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.normalize(snake_case__     ).to(latents.dtype     )

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

     SCREAMING_SNAKE_CASE		    =      spherical_dist_loss(snake_case__				,     snake_case__     ).mean() * clip_guidance_scale

     SCREAMING_SNAKE_CASE		    =      -torch.autograd.grad(snake_case__				,     snake_case__     )[0]

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




    @torch.no_grad()
    def __call__(      self			:   Tuple				,     snake_case__			:   Union[torch.FloatTensor, PIL.Image.Image]				,     snake_case__			:   Union[torch.FloatTensor, PIL.Image.Image]				,     snake_case__			:   Optional[str] = None				,     snake_case__			:   Optional[str] = None				,     snake_case__			:   Optional[int] = 5_1_2				,     snake_case__			:   Optional[int] = 5_1_2				,     snake_case__			:   float = 0.6				,     snake_case__			:   Optional[int] = 5_0				,     snake_case__			:   Optional[float] = 7.5				,     snake_case__			:   Optional[int] = 1				,     snake_case__			:   float = 0.0				,     snake_case__			:   Optional[float] = 1_0_0				,     snake_case__			:   Optional[torch.Generator] = None				,     snake_case__			:   Optional[str] = "pil"				,     snake_case__			:   bool = True				,     snake_case__			:   float = 0.8				,     snake_case__			:   float = 0.1				,     snake_case__			:   float = 0.1				,     ):

     """simple docstring"""






     if isinstance(snake_case__				,     snake_case__     ) and len(snake_case__     ) != batch_size:
      raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(snake_case__     )} 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(snake_case__				,     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(snake_case__     )
     # generate prompts with coca model if prompt is None
     if content_prompt is None:
      if len(snake_case__     ):
       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(snake_case__     )
     if style_prompt is None:
      if len(snake_case__     ):
       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(snake_case__     )

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

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

     SCREAMING_SNAKE_CASE		    =      slerp(snake_case__				,     snake_case__				,     snake_case__     )

     # duplicate text embeddings for each generation per prompt
     SCREAMING_SNAKE_CASE		    =      text_embeddings.repeat_interleave(snake_case__				,     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(snake_case__				,     **snake_case__     )
     # 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(snake_case__				,     snake_case__				,     self.device     )
     SCREAMING_SNAKE_CASE		    =      timesteps[:1].repeat(snake_case__     )

     # Preprocess image
     SCREAMING_SNAKE_CASE		    =      preprocess(snake_case__				,     snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.prepare_latents(
         snake_case__				,     snake_case__				,     snake_case__				,     text_embeddings.dtype				,     self.device				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      preprocess(snake_case__				,     snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.prepare_latents(
         snake_case__				,     snake_case__				,     snake_case__				,     text_embeddings.dtype				,     self.device				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      slerp(snake_case__				,     snake_case__				,     snake_case__     )

     if clip_guidance_scale > 0:
      SCREAMING_SNAKE_CASE		    =      self.get_clip_image_embeddings(snake_case__				,     snake_case__     )
      SCREAMING_SNAKE_CASE		    =      self.get_clip_image_embeddings(snake_case__				,     snake_case__     )
      SCREAMING_SNAKE_CASE		    =      slerp(
          snake_case__				,     snake_case__				,     snake_case__     )

     # 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=snake_case__				,     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(snake_case__				,     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(snake_case__				,     generator=snake_case__				,     device='cpu'				,     dtype=snake_case__     ).to(
           self.device     )
      else:
       SCREAMING_SNAKE_CASE		    =      torch.randn(snake_case__				,     generator=snake_case__				,     device=self.device				,     dtype=snake_case__     )
     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=snake_case__     ):
      for i, t in enumerate(snake_case__     ):
       # 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(snake_case__				,     snake_case__     )

       # predict the noise residual
       SCREAMING_SNAKE_CASE		    =      self.unet(snake_case__				,     snake_case__				,     encoder_hidden_states=snake_case__     ).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(
            snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     )

       # compute the previous noisy sample x_t -> x_t-1
       SCREAMING_SNAKE_CASE		    =      self.scheduler.step(snake_case__				,     snake_case__				,     snake_case__				,     **snake_case__     ).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(snake_case__     ).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(snake_case__     )

     if not return_dict:
      return (image, None)

     return StableDiffusionPipelineOutput(images=snake_case__				,     nsfw_content_detected=snake_case__     )




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [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 UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

     if os.path.abspath(self.vocab_file     ) != os.path.abspath(snake_case__     ):
      copyfile(self.vocab_file				,     snake_case__     )

     return (out_vocab_file,)

