


"""simple docstring"""




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 __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:DDIMScheduler				,  UpperCamelCase__							:float = 0.93				,  UpperCamelCase__							:bool = True				,  ):
          _a          =				mixing_coeff
          _a          =				leapfrog_steps

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

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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:Optional[str] = None				,  UpperCamelCase__							:bool = False       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

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

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

          if do_classifier_free_guidance:
                 _a          =				"" if negative_prompt is None else negative_prompt

                 _a          =				self.tokenizer(
                     UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

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

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

          return prompt_embeds




   def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor       ):
          _a          =				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
          _a          =				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

          return [x, y]




   def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor       ):
          _a          =				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
          _a          =				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

          return [x, y]




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:torch.Tensor       ):
          # as self.alphas_cumprod is always in cpu
          _a          =				int(UpperCamelCase__       )

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

          return alpha_prod, 1 - alpha_prod




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  ):
          _a          =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

          _a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
          _a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )

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

          _a          =				(base - b_t * model_output) / a_t

          return model_input, next_model_input.to(base.dtype       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  ):
          _a          =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

          _a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
          _a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )

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

          return model_input, next_model_input.to(base.dtype       )




   @torch.no_grad()
   def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:torch.Tensor       ):
          _a          =				1 / self.vae.config.scaling_factor * latents
          _a          =				self.vae.decode(UpperCamelCase__       ).sample
          _a          =				(image / 2 + 0.5).clamp(0				,  1       )
          return image




   @torch.no_grad()
   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:float				,  UpperCamelCase__							:Optional[torch.Generator] = None				,  ):
          _a          =				guidance_scale > 1.0

          _a          =				image.to(device=self.device				,  dtype=text_embeds.dtype       )
          _a          =				self.vae.encode(UpperCamelCase__       ).latent_dist.sample(UpperCamelCase__       )

          _a          =				self.vae.config.scaling_factor * latent

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

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

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

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

                        _a          =				coupled_latents[j]
                        _a          =				coupled_latents[k]

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

                        _a          =				self.unet(UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__       ).sample

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

                        _a   , _a          =				self.noise_step(
                            base=UpperCamelCase__				,  model_input=UpperCamelCase__				,  model_output=UpperCamelCase__				,  timestep=UpperCamelCase__				,  )

                        _a          =				model_input

          return coupled_latents




   @torch.no_grad()
   def __call__(		self							:List[str]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:str				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:float = 3.0				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 0.8				,  UpperCamelCase__							:Optional[str] = None				,  UpperCamelCase__							:Optional[torch.Generator] = None				,  UpperCamelCase__							:Optional[str] = "pil"				,  ):
          _a          =				guidance_scale > 1.0

          _a          =				self.image_processor.preprocess(UpperCamelCase__       )

          _a          =				self._encode_prompt(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				self._encode_prompt(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )

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

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

          _a          =				self.prepare_latents(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )

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

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

                        _a          =				coupled_latents[j]
                        _a          =				coupled_latents[k]

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

                        _a          =				self.unet(UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__       ).sample

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

                        _a   , _a          =				self.denoise_step(
                            base=UpperCamelCase__				,  model_input=UpperCamelCase__				,  model_output=UpperCamelCase__				,  timestep=UpperCamelCase__				,  )

                        _a          =				model_input

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

          # either one is fine
          _a          =				coupled_latents[0]

          if output_type not in ["latent", "pt", "np", "pil"]:
                 _a          =				(
                     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"				,  UpperCamelCase__				,  standard_warn=UpperCamelCase__       )
                 _a          =				"np"

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

          return image




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:Tuple				,  *UpperCamelCase__							:Tuple				,  **UpperCamelCase__							:Any       ):
          warnings.warn(
              "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use ViTImageProcessor instead."				,  UpperCamelCase__				,  )
          super().__init__(*UpperCamelCase__				,  **UpperCamelCase__       )
