

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase = 0.93							,       lowerCAmelCase = True							,       )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=mixing_coeff
        _lowercase     	=leapfrog_steps

        super().__init__()
        self.register_modules(
            vae=lowerCAmelCase							,       text_encoder=lowerCAmelCase							,       tokenizer=lowerCAmelCase							,       unet=lowerCAmelCase							,       scheduler=lowerCAmelCase							,       )

        _lowercase     	=2 ** (len(self.vae.config.block_out_channels ) - 1)
        _lowercase     	=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase = None							,       lowerCAmelCase = False )  ->			Any:
        '''simple docstring'''






        _lowercase     	=self.tokenizer(
            lowerCAmelCase							,       padding='max_length'							,       max_length=self.tokenizer.model_max_length							,       truncation=lowerCAmelCase							,       return_tensors='pt'							,       )

        _lowercase     	=self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

        _lowercase     	=prompt_embeds.to(dtype=self.text_encoder.dtype							,       device=self.device )

        if do_classifier_free_guidance:
              _lowercase     	='' if negative_prompt is None else negative_prompt

              _lowercase     	=self.tokenizer(
                  lowerCAmelCase							,       padding='max_length'							,       max_length=self.tokenizer.model_max_length							,       truncation=lowerCAmelCase							,       return_tensors='pt'							,       )

              _lowercase     	=self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

              _lowercase     	=torch.cat([negative_prompt_embeds, prompt_embeds] )

        return prompt_embeds




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        _lowercase     	=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        _lowercase     	=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]




  def   A__					(    self							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=int(lowerCAmelCase )

        _lowercase     	=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

        return alpha_prod, 1 - alpha_prod




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _lowercase	,	_lowercase     	=self._get_alpha_and_beta(lowerCAmelCase )
        _lowercase	,	_lowercase     	=self._get_alpha_and_beta(lowerCAmelCase )

        _lowercase     	=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        _lowercase     	=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        _lowercase     	=(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       )  ->			int:
        '''simple docstring'''






        _lowercase     	=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _lowercase	,	_lowercase     	=self._get_alpha_and_beta(lowerCAmelCase )
        _lowercase	,	_lowercase     	=self._get_alpha_and_beta(lowerCAmelCase )

        _lowercase     	=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        _lowercase     	=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
        _lowercase     	=a_t * base + b_t * model_output

        return model_input, next_model_input.to(base.dtype )




  @torch.no_grad()
  def   A__					(    self							,       lowerCAmelCase )  ->			int:
        '''simple docstring'''






        _lowercase     	=1 / self.vae.config.scaling_factor * latents
        _lowercase     	=self.vae.decode(lowerCAmelCase ).sample
        _lowercase     	=(image / 2 + 0.5).clamp(0							,       1 )
        return image




  @torch.no_grad()
  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase = None							,       )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=guidance_scale > 1.0

        _lowercase     	=image.to(device=self.device							,       dtype=text_embeds.dtype )
        _lowercase     	=self.vae.encode(lowerCAmelCase ).latent_dist.sample(lowerCAmelCase )

        _lowercase     	=self.vae.config.scaling_factor * latent

        _lowercase     	=[latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(lowerCAmelCase )							,       total=len(lowerCAmelCase ) ):
              _lowercase     	=self.noise_mixing_layer(x=coupled_latents[0]							,       y=coupled_latents[1] )

              # j - model_input index, k - base index
              for j in range(2 ):
                    _lowercase     	=j ^ 1

                    if self.leapfrog_steps:
                          if i % 2 == 0:
                                _lowercase	,	_lowercase     	=j, k

                    _lowercase     	=coupled_latents[j]
                    _lowercase     	=coupled_latents[k]

                    _lowercase     	=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                    _lowercase     	=self.unet(lowerCAmelCase							,       lowerCAmelCase							,       encoder_hidden_states=lowerCAmelCase ).sample

                    if do_classifier_free_guidance:
                          _lowercase	,	_lowercase     	=noise_pred.chunk(2 )
                          _lowercase     	=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    _lowercase	,	_lowercase     	=self.noise_step(
                        base=lowerCAmelCase							,       model_input=lowerCAmelCase							,       model_output=lowerCAmelCase							,       timestep=lowerCAmelCase							,       )

                    _lowercase     	=model_input

        return coupled_latents




  @torch.no_grad()
  def __call__(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase = 3.0							,       lowerCAmelCase = 50							,       lowerCAmelCase = 0.8							,       lowerCAmelCase = None							,       lowerCAmelCase = None							,       lowerCAmelCase = "pil"							,       )  ->			Any:
        '''simple docstring'''






        _lowercase     	=guidance_scale > 1.0

        _lowercase     	=self.image_processor.preprocess(lowerCAmelCase )

        _lowercase     	=self._encode_prompt(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )
        _lowercase     	=self._encode_prompt(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )

        self.scheduler.set_timesteps(lowerCAmelCase							,       self.device )

        _lowercase     	=num_inference_steps - int(num_inference_steps * strength )
        _lowercase     	=self.scheduler.timesteps[t_limit:]
        _lowercase     	=fwd_timesteps.flip(0 )

        _lowercase     	=self.prepare_latents(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )

        for i, t in tqdm(enumerate(lowerCAmelCase )							,       total=len(lowerCAmelCase ) ):
              # j - model_input index, k - base index
              for k in range(2 ):
                    _lowercase     	=k ^ 1

                    if self.leapfrog_steps:
                          if i % 2 == 1:
                                _lowercase	,	_lowercase     	=j, k

                    _lowercase     	=coupled_latents[j]
                    _lowercase     	=coupled_latents[k]

                    _lowercase     	=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                    _lowercase     	=self.unet(lowerCAmelCase							,       lowerCAmelCase							,       encoder_hidden_states=lowerCAmelCase ).sample

                    if do_classifier_free_guidance:
                          _lowercase	,	_lowercase     	=noise_pred.chunk(2 )
                          _lowercase     	=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    _lowercase	,	_lowercase     	=self.denoise_step(
                        base=lowerCAmelCase							,       model_input=lowerCAmelCase							,       model_output=lowerCAmelCase							,       timestep=lowerCAmelCase							,       )

                    _lowercase     	=model_input

              _lowercase     	=self.denoise_mixing_layer(x=coupled_latents[0]							,       y=coupled_latents[1] )

        # either one is fine
        _lowercase     	=coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
              _lowercase     	=(
                  F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                  '`pil`, `np`, `pt`, `latent`'
              )
              deprecate('Unsupported output_type'							,       '1.0.0'							,       lowerCAmelCase							,       standard_warn=lowerCAmelCase )
              _lowercase     	='np'

        if output_type == "latent":
              _lowercase     	=final_latent
        else:
              _lowercase     	=self.decode_latents(lowerCAmelCase )
              _lowercase     	=self.image_processor.postprocess(lowerCAmelCase							,       output_type=lowerCAmelCase )

        return image





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use LayoutLMv3ImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


