


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 		_SCREAMING_SNAKE_CASE    (	SCREAMING_SNAKE_CASE__    ):


     def __init__(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_ = 0.9_3       ,				A_ = True       ,				)     ->   Dict:
       _UpperCAmelCase		:       Union[str, Any]       =    mixing_coeff
       _UpperCAmelCase		:       Union[str, Any]       =    leapfrog_steps

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

       _UpperCAmelCase		:       Any       =    2 ** (len(self.vae.config.block_out_channels						) - 1)
       _UpperCAmelCase		:       List[Any]       =    VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)


     def 					__snake_case(     self       ,				A_       ,				A_ = None       ,				A_ = False						)     ->   Any:
       _UpperCAmelCase		:       List[Any]       =    self.tokenizer(
           A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)

       _UpperCAmelCase		:       int       =    self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

       _UpperCAmelCase		:       Any       =    prompt_embeds.to(dtype=self.text_encoder.dtype       ,				device=self.device						)

       if do_classifier_free_guidance:
         _UpperCAmelCase		:       Optional[Any]       =    """""" if negative_prompt is None else negative_prompt

         _UpperCAmelCase		:       List[Any]       =    self.tokenizer(
             A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)

         _UpperCAmelCase		:       Any       =    self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

         _UpperCAmelCase		:       Dict       =    torch.cat([negative_prompt_embeds, prompt_embeds]						)

       return prompt_embeds


     def 					__snake_case(     self       ,				A_       ,				A_						)     ->   Any:
       _UpperCAmelCase		:       int       =    self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       _UpperCAmelCase		:       Tuple       =    self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]


     def 					__snake_case(     self       ,				A_       ,				A_						)     ->   Union[str, Any]:
       _UpperCAmelCase		:       Dict       =    (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       _UpperCAmelCase		:       Any       =    (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]


     def 					__snake_case(     self       ,				A_						)     ->   Union[str, Any]:
       _UpperCAmelCase		:       str       =    int(A_						)

       _UpperCAmelCase		:       Tuple       =    self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				)     ->   Dict:
       _UpperCAmelCase		:       List[str]       =    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    self._get_alpha_and_beta(A_						)
       _UpperCAmelCase,_UpperCAmelCase		:       int       =    self._get_alpha_and_beta(A_						)

       _UpperCAmelCase		:       Dict       =    (alpha_prod_t_prev / alpha_prod_t) ** 0.5
       _UpperCAmelCase		:       Any       =    -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       _UpperCAmelCase		:       List[Any]       =    (base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype						)


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				)     ->   Tuple:
       _UpperCAmelCase		:       int       =    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    self._get_alpha_and_beta(A_						)
       _UpperCAmelCase,_UpperCAmelCase		:       Any       =    self._get_alpha_and_beta(A_						)

       _UpperCAmelCase		:       str       =    (alpha_prod_t_prev / alpha_prod_t) ** 0.5
       _UpperCAmelCase		:       Optional[Any]       =    -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       _UpperCAmelCase		:       Dict       =    a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype						)


     @torch.no_grad()
     def 					__snake_case(     self       ,				A_						)     ->   Any:
       _UpperCAmelCase		:       str       =    1 / self.vae.config.scaling_factor * latents
       _UpperCAmelCase		:       Union[str, Any]       =    self.vae.decode(A_						).sample
       _UpperCAmelCase		:       Optional[int]       =    (image / 2 + 0.5).clamp(0       ,				1						)
       return image


     @torch.no_grad()
     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_ = None       ,				)     ->   List[Any]:
       _UpperCAmelCase		:       Optional[Any]       =    guidance_scale > 1.0

       _UpperCAmelCase		:       Tuple       =    image.to(device=self.device       ,				dtype=text_embeds.dtype						)
       _UpperCAmelCase		:       int       =    self.vae.encode(A_						).latent_dist.sample(A_						)

       _UpperCAmelCase		:       Optional[Any]       =    self.vae.config.scaling_factor * latent

       _UpperCAmelCase		:       str       =    [latent.clone(), latent.clone()]

       for i, t in tqdm(enumerate(A_						)       ,				total=len(A_						)						):
         _UpperCAmelCase		:       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						):
           _UpperCAmelCase		:       List[str]       =    j ^ 1

           if self.leapfrog_steps:
             if i % 2 == 0:
               _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    j, k

           _UpperCAmelCase		:       str       =    coupled_latents[j]
           _UpperCAmelCase		:       Tuple       =    coupled_latents[k]

           _UpperCAmelCase		:       Any       =    torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

           _UpperCAmelCase		:       List[Any]       =    self.unet(A_       ,				A_       ,				encoder_hidden_states=A_						).sample

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

           _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    self.noise_step(
               base=A_       ,				model_input=A_       ,				model_output=A_       ,				timestep=A_       ,				)

           _UpperCAmelCase		:       Optional[Any]       =    model_input

       return coupled_latents




     @torch.no_grad()
     def __call__(     self       ,				A_       ,				A_       ,				A_       ,				A_ = 3.0       ,				A_ = 50       ,				A_ = 0.8       ,				A_ = None       ,				A_ = None       ,				A_ = "pil"       ,				)     ->   Optional[int]:
       _UpperCAmelCase		:       Any       =    guidance_scale > 1.0

       _UpperCAmelCase		:       str       =    self.image_processor.preprocess(A_						)

       _UpperCAmelCase		:       Union[str, Any]       =    self._encode_prompt(A_       ,				A_       ,				A_						)
       _UpperCAmelCase		:       Tuple       =    self._encode_prompt(A_       ,				A_       ,				A_						)

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

       _UpperCAmelCase		:       Union[str, Any]       =    num_inference_steps - int(num_inference_steps * strength						)
       _UpperCAmelCase		:       Any       =    self.scheduler.timesteps[t_limit:]
       _UpperCAmelCase		:       Dict       =    fwd_timesteps.flip(0						)

       _UpperCAmelCase		:       Any       =    self.prepare_latents(A_       ,				A_       ,				A_       ,				A_       ,				A_						)

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

           if self.leapfrog_steps:
             if i % 2 == 1:
               _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    j, k

           _UpperCAmelCase		:       Tuple       =    coupled_latents[j]
           _UpperCAmelCase		:       Any       =    coupled_latents[k]

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

           _UpperCAmelCase		:       int       =    self.unet(A_       ,				A_       ,				encoder_hidden_states=A_						).sample

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

           _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    self.denoise_step(
               base=A_       ,				model_input=A_       ,				model_output=A_       ,				timestep=A_       ,				)

           _UpperCAmelCase		:       Dict       =    model_input

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

       # either one is fine
       _UpperCAmelCase		:       Dict       =    coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
         _UpperCAmelCase		:       Tuple       =    (
             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"""       ,				A_       ,				standard_warn=A_						)
         _UpperCAmelCase		:       Optional[int]       =    """np"""

       if output_type == "latent":
         _UpperCAmelCase		:       Any       =    final_latent
       else:
         _UpperCAmelCase		:       Dict       =    self.decode_latents(A_						)
         _UpperCAmelCase		:       Optional[int]       =    self.image_processor.postprocess(A_       ,				output_type=A_						)

       return image









from typing import Any


class 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    []
       _UpperCAmelCase		:       Dict       =    0


     def __str__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
       return printed


     def 					__snake_case(     self       ,				A_						):
       self.stack.append(A_						)
       _UpperCAmelCase		:       Optional[Any]       =    self.length + 1


     def 					__snake_case(     self						):
       self.rotate(1						)
       _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
       _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
       self.rotate(self.length - 1						)
       _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
       return dequeued


     def 					__snake_case(     self       ,				A_						):
       for _ in range(A_						):
         _UpperCAmelCase		:       Dict       =    self.stack[0]
         _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
         self.put(A_						)
         _UpperCAmelCase		:       Dict       =    self.length - 1


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    self.get()
       self.put(A_						)
       self.rotate(self.length - 1						)
       return front


     def 					__snake_case(     self						):
       return self.length





