'''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    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = 0.93					,      lowerCAmelCase__ = True					,      )				->     int:
        """simple docstring"""





        _UpperCamelCase  :int       =mixing_coeff
        _UpperCamelCase  :Tuple       =leapfrog_steps

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

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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = False     )				->     Tuple:
        """simple docstring"""





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

        _UpperCamelCase  :Tuple       =self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

        _UpperCamelCase  :List[str]       =prompt_embeds.to(dtype=self.text_encoder.dtype					,      device=self.device     )

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

             _UpperCamelCase  :Any       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )

             _UpperCamelCase  :Optional[Any]       =self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

             _UpperCamelCase  :int       =torch.cat([negative_prompt_embeds, prompt_embeds]     )

        return prompt_embeds



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        _UpperCamelCase  :int       =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        _UpperCamelCase  :Optional[int]       =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =int(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

        return alpha_prod, 1 - alpha_prod



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self._get_alpha_and_beta(lowerCAmelCase__     )
        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self._get_alpha_and_beta(lowerCAmelCase__     )

        _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  :Union[str, Any]       =(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self._get_alpha_and_beta(lowerCAmelCase__     )
        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._get_alpha_and_beta(lowerCAmelCase__     )

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

        return model_input, next_model_input.to(base.dtype     )



   @torch.no_grad()
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =1 / self.vae.config.scaling_factor * latents
        _UpperCamelCase  :str       =self.vae.decode(lowerCAmelCase__     ).sample
        _UpperCamelCase  :Optional[Any]       =(image / 2 + 0.5).clamp(0					,      1     )
        return image



   @torch.no_grad()
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =guidance_scale > 1.0

        _UpperCamelCase  :str       =image.to(device=self.device					,      dtype=text_embeds.dtype     )
        _UpperCamelCase  :List[str]       =self.vae.encode(lowerCAmelCase__     ).latent_dist.sample(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =self.vae.config.scaling_factor * latent

        _UpperCamelCase  :Union[str, Any]       =[latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(lowerCAmelCase__     )					,      total=len(lowerCAmelCase__     )     ):
             _UpperCamelCase  :str       =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  :Union[str, Any]       =j ^ 1

                  if self.leapfrog_steps:
                       if i % 2 == 0:
                            _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =j, k

                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[j]
                  _UpperCamelCase  :List[Any]       =coupled_latents[k]

                  _UpperCamelCase  :int       =torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                  _UpperCamelCase  :Tuple       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

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

                  _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.noise_step(
                      base=lowerCAmelCase__					,      model_input=lowerCAmelCase__					,      model_output=lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      )

                  _UpperCamelCase  :Any       =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"					,      )				->     int:
        """simple docstring"""





        _UpperCamelCase  :str       =guidance_scale > 1.0

        _UpperCamelCase  :Dict       =self.image_processor.preprocess(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =self._encode_prompt(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =self._encode_prompt(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

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

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

        _UpperCamelCase  :Any       =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     ):
                  _UpperCamelCase  :Union[str, Any]       =k ^ 1

                  if self.leapfrog_steps:
                       if i % 2 == 1:
                            _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =j, k

                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[j]
                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[k]

                  _UpperCamelCase  :int       =torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                  _UpperCamelCase  :str       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

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

                  _UpperCamelCase       ,      _UpperCamelCase  :str       =self.denoise_step(
                      base=lowerCAmelCase__					,      model_input=lowerCAmelCase__					,      model_output=lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      )

                  _UpperCamelCase  :List[str]       =model_input

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

        # either one is fine
        _UpperCamelCase  :Optional[int]       =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"""					,      lowerCAmelCase__					,      standard_warn=lowerCAmelCase__     )
             _UpperCamelCase  :Dict       ="""np"""

        if output_type == "latent":
             _UpperCamelCase  :int       =final_latent
        else:
             _UpperCamelCase  :str       =self.decode_latents(lowerCAmelCase__     )
             _UpperCamelCase  :Tuple       =self.image_processor.postprocess(lowerCAmelCase__					,      output_type=lowerCAmelCase__     )

        return image






"""simple docstring"""






from __future__ import annotations

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       _UpperCAmelCase			     =  [None] * (len(SCREAMING_SNAKE_CASE				) + len(SCREAMING_SNAKE_CASE				))

       _UpperCAmelCase			     =  0  # pointer to current index for left Half
       _UpperCAmelCase			     =  0  # pointer to current index for the right Half
       _UpperCAmelCase			     =  0  # pointer to current index for the sorted array Half

       while pointera < len(SCREAMING_SNAKE_CASE				) and pointera < len(SCREAMING_SNAKE_CASE				):
              if left_half[pointera] < right_half[pointera]:
                     _UpperCAmelCase			     =  left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     _UpperCAmelCase			     =  right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE				) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       _UpperCAmelCase			     =  0 + (len(SCREAMING_SNAKE_CASE				) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       _UpperCAmelCase			     =  array[:middle]
       _UpperCAmelCase			     =  array[middle:]

       return merge(merge_sort(SCREAMING_SNAKE_CASE				),merge_sort(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






