





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   lowercase__     (				__A	):







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase = 0.93     ,    _lowercase = True     ,    ):
       lowerCAmelCase_       :							Dict			 =		mixing_coeff
       lowerCAmelCase_       :							List[Any]			 =		leapfrog_steps

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

       lowerCAmelCase_       :							int			 =		2 ** (len(self.vae.config.block_out_channels    ) - 1)
       lowerCAmelCase_       :							Optional[Any]			 =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase = None     ,    _lowercase = False    ):
       lowerCAmelCase_       :							str			 =		self.tokenizer(
           _lowercase     ,    padding="""max_length"""     ,    max_length=self.tokenizer.model_max_length     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )

       lowerCAmelCase_       :							Any			 =		self.text_encoder(text_inputs.input_ids.to(self.device    )    ).last_hidden_state

       lowerCAmelCase_       :							Union[str, Any]			 =		prompt_embeds.to(dtype=self.text_encoder.dtype     ,    device=self.device    )

       if do_classifier_free_guidance:
            lowerCAmelCase_       :							List[Any]			 =		"""""" if negative_prompt is None else negative_prompt

            lowerCAmelCase_       :							List[str]			 =		self.tokenizer(
                _lowercase     ,    padding="""max_length"""     ,    max_length=self.tokenizer.model_max_length     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )

            lowerCAmelCase_       :							Dict			 =		self.text_encoder(uncond_input.input_ids.to(self.device    )    ).last_hidden_state

            lowerCAmelCase_       :							Union[str, Any]			 =		torch.cat([negative_prompt_embeds, prompt_embeds]    )

       return prompt_embeds







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							str			 =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       lowerCAmelCase_       :							Tuple			 =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							int			 =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       lowerCAmelCase_       :							Union[str, Any]			 =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       # as self.alphas_cumprod is always in cpu
       lowerCAmelCase_       :							Dict			 =		int(_lowercase    )

       lowerCAmelCase_       :							List[str]			 =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		self._get_alpha_and_beta(_lowercase    )
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		self._get_alpha_and_beta(_lowercase    )

       lowerCAmelCase_       :							List[str]			 =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       lowerCAmelCase_       :							str			 =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       lowerCAmelCase_       :							str			 =		(base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    ):
       lowerCAmelCase_       :							int			 =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[int]			 =		self._get_alpha_and_beta(_lowercase    )
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Union[str, Any]			 =		self._get_alpha_and_beta(_lowercase    )

       lowerCAmelCase_       :							Union[str, Any]			 =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       lowerCAmelCase_       :							str			 =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       lowerCAmelCase_       :							str			 =		a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype    )







  @torch.no_grad()
  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							str			 =		1 / self.vae.config.scaling_factor * latents
       lowerCAmelCase_       :							int			 =		self.vae.decode(_lowercase    ).sample
       lowerCAmelCase_       :							Any			 =		(image / 2 + 0.5).clamp(0     ,    1    )
       return image







  @torch.no_grad()
  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase = None     ,    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		guidance_scale > 1.0

       lowerCAmelCase_       :							int			 =		image.to(device=self.device     ,    dtype=text_embeds.dtype    )
       lowerCAmelCase_       :							Dict			 =		self.vae.encode(_lowercase    ).latent_dist.sample(_lowercase    )

       lowerCAmelCase_       :							Optional[int]			 =		self.vae.config.scaling_factor * latent

       lowerCAmelCase_       :							int			 =		[latent.clone(), latent.clone()]

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

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

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

                 lowerCAmelCase_       :							str			 =		coupled_latents[j]
                 lowerCAmelCase_       :							List[Any]			 =		coupled_latents[k]

                 lowerCAmelCase_       :							Union[str, Any]			 =		torch.cat([model_input] * 2    ) if do_classifier_free_guidance else model_input

                 lowerCAmelCase_       :							Optional[int]			 =		self.unet(_lowercase     ,    _lowercase     ,    encoder_hidden_states=_lowercase    ).sample

                 if do_classifier_free_guidance:
                      lowerCAmelCase_ ,	lowerCAmelCase_       :							int			 =		noise_pred.chunk(2    )
                      lowerCAmelCase_       :							Optional[int]			 =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[int]			 =		self.noise_step(
                     base=_lowercase     ,    model_input=_lowercase     ,    model_output=_lowercase     ,    timestep=_lowercase     ,    )

                 lowerCAmelCase_       :							Optional[int]			 =		model_input

       return coupled_latents







  @torch.no_grad()
  def __call__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase = 3.0     ,    _lowercase = 50     ,    _lowercase = 0.8     ,    _lowercase = None     ,    _lowercase = None     ,    _lowercase = "pil"     ,    ):
       lowerCAmelCase_       :							int			 =		guidance_scale > 1.0

       lowerCAmelCase_       :							Any			 =		self.image_processor.preprocess(_lowercase    )

       lowerCAmelCase_       :							Any			 =		self._encode_prompt(_lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							Any			 =		self._encode_prompt(_lowercase     ,    _lowercase     ,    _lowercase    )

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

       lowerCAmelCase_       :							List[Any]			 =		num_inference_steps - int(num_inference_steps * strength    )
       lowerCAmelCase_       :							Optional[Any]			 =		self.scheduler.timesteps[t_limit:]
       lowerCAmelCase_       :							Tuple			 =		fwd_timesteps.flip(0    )

       lowerCAmelCase_       :							Dict			 =		self.prepare_latents(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )

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

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

                 lowerCAmelCase_       :							Tuple			 =		coupled_latents[j]
                 lowerCAmelCase_       :							List[Any]			 =		coupled_latents[k]

                 lowerCAmelCase_       :							Dict			 =		torch.cat([model_input] * 2    ) if do_classifier_free_guidance else model_input

                 lowerCAmelCase_       :							Optional[int]			 =		self.unet(_lowercase     ,    _lowercase     ,    encoder_hidden_states=_lowercase    ).sample

                 if do_classifier_free_guidance:
                      lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		noise_pred.chunk(2    )
                      lowerCAmelCase_       :							List[str]			 =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 lowerCAmelCase_ ,	lowerCAmelCase_       :							List[Any]			 =		self.denoise_step(
                     base=_lowercase     ,    model_input=_lowercase     ,    model_output=_lowercase     ,    timestep=_lowercase     ,    )

                 lowerCAmelCase_       :							List[Any]			 =		model_input

            lowerCAmelCase_       :							Optional[Any]			 =		self.denoise_mixing_layer(x=coupled_latents[0]     ,    y=coupled_latents[1]    )

       # either one is fine
       lowerCAmelCase_       :							Optional[int]			 =		coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
            lowerCAmelCase_       :							Any			 =		(
                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"""     ,    _lowercase     ,    standard_warn=_lowercase    )
            lowerCAmelCase_       :							Optional[Any]			 =		"""np"""

       if output_type == "latent":
            lowerCAmelCase_       :							List[str]			 =		final_latent
       else:
            lowerCAmelCase_       :							str			 =		self.decode_latents(_lowercase    )
            lowerCAmelCase_       :							Dict			 =		self.image_processor.postprocess(_lowercase     ,    output_type=_lowercase    )

       return image







def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
