





'''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 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = 0.9_3     , SCREAMING_SNAKE_CASE = True     , )				->  Any:
        __lowerCAmelCase		:    Any			     =  mixing_coeff
        __lowerCAmelCase		:    int			     =  leapfrog_steps

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

        __lowerCAmelCase		:    int			     =  2 ** (len(self.vae.config.block_out_channels ) - 1)
        __lowerCAmelCase		:    List[str]			     =  VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = False )				->  Optional[int]:
        __lowerCAmelCase		:    int			     =  self.tokenizer(
            SCREAMING_SNAKE_CASE     , padding='max_length'     , max_length=self.tokenizer.model_max_length     , truncation=SCREAMING_SNAKE_CASE     , return_tensors='pt'     , )

        __lowerCAmelCase		:    Union[str, Any]			     =  self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

        __lowerCAmelCase		:    List[Any]			     =  prompt_embeds.to(dtype=self.text_encoder.dtype     , device=self.device )

        if do_classifier_free_guidance:
          __lowerCAmelCase		:    Any			     =  '' if negative_prompt is None else negative_prompt

          __lowerCAmelCase		:    Tuple			     =  self.tokenizer(
              SCREAMING_SNAKE_CASE     , padding='max_length'     , max_length=self.tokenizer.model_max_length     , truncation=SCREAMING_SNAKE_CASE     , return_tensors='pt'     , )

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

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

        return prompt_embeds




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  str:
        __lowerCAmelCase		:    str			     =  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        __lowerCAmelCase		:    Union[str, Any]			     =  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Dict:
        __lowerCAmelCase		:    List[str]			     =  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        __lowerCAmelCase		:    Optional[Any]			     =  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Optional[Any]:
        # as self.alphas_cumprod is always in cpu
        __lowerCAmelCase		:    int			     =  int(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    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     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )				->  Union[str, Any]:
        __lowerCAmelCase		:    List[str]			     =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        __lowerCAmelCase     ,					__lowerCAmelCase		:    List[str]			     =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE )

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

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

        return model_input, next_model_input.to(base.dtype )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )				->  Optional[Any]:
        __lowerCAmelCase		:    Any			     =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        __lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[Any]			     =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Tuple			     =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE )

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

        return model_input, next_model_input.to(base.dtype )




      @torch.no_grad()
      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  int:
        __lowerCAmelCase		:    List[str]			     =  1 / self.vae.config.scaling_factor * latents
        __lowerCAmelCase		:    Dict			     =  self.vae.decode(SCREAMING_SNAKE_CASE ).sample
        __lowerCAmelCase		:    str			     =  (image / 2 + 0.5).clamp(0     , 1 )
        return image




      @torch.no_grad()
      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , )				->  Any:
        __lowerCAmelCase		:    Optional[Any]			     =  guidance_scale > 1.0

        __lowerCAmelCase		:    List[Any]			     =  image.to(device=self.device     , dtype=text_embeds.dtype )
        __lowerCAmelCase		:    List[Any]			     =  self.vae.encode(SCREAMING_SNAKE_CASE ).latent_dist.sample(SCREAMING_SNAKE_CASE )

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

        __lowerCAmelCase		:    Any			     =  [latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE )     , total=len(SCREAMING_SNAKE_CASE ) ):
          __lowerCAmelCase		:    Optional[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		:    Dict			     =  j ^ 1

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

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

            __lowerCAmelCase		:    str			     =  torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

            __lowerCAmelCase		:    str			     =  self.unet(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , encoder_hidden_states=SCREAMING_SNAKE_CASE ).sample

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

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

            __lowerCAmelCase		:    Optional[Any]			     =  model_input

        return coupled_latents







      @torch.no_grad()
      def __call__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = 3.0     , SCREAMING_SNAKE_CASE = 50     , SCREAMING_SNAKE_CASE = 0.8     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = "pil"     , )				->  Any:
        __lowerCAmelCase		:    List[Any]			     =  guidance_scale > 1.0

        __lowerCAmelCase		:    Tuple			     =  self.image_processor.preprocess(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Optional[int]			     =  self._encode_prompt(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Any			     =  self._encode_prompt(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

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

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

        __lowerCAmelCase		:    Dict			     =  self.prepare_latents(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE )     , total=len(SCREAMING_SNAKE_CASE ) ):
          # j - model_input index, k - base index
          for k in range(2 ):
            __lowerCAmelCase		:    Union[str, Any]			     =  k ^ 1

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

            __lowerCAmelCase		:    Optional[int]			     =  coupled_latents[j]
            __lowerCAmelCase		:    List[Any]			     =  coupled_latents[k]

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

            __lowerCAmelCase		:    str			     =  self.unet(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , encoder_hidden_states=SCREAMING_SNAKE_CASE ).sample

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

            __lowerCAmelCase     ,					__lowerCAmelCase		:    str			     =  self.denoise_step(
                base=SCREAMING_SNAKE_CASE     , model_input=SCREAMING_SNAKE_CASE     , model_output=SCREAMING_SNAKE_CASE     , timestep=SCREAMING_SNAKE_CASE     , )

            __lowerCAmelCase		:    Union[str, Any]			     =  model_input

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

        # either one is fine
        __lowerCAmelCase		:    str			     =  coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
          __lowerCAmelCase		:    Optional[int]			     =  (
              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'     , SCREAMING_SNAKE_CASE     , standard_warn=SCREAMING_SNAKE_CASE )
          __lowerCAmelCase		:    Any			     =  'np'

        if output_type == "latent":
          __lowerCAmelCase		:    Union[str, Any]			     =  final_latent
        else:
          __lowerCAmelCase		:    Optional[Any]			     =  self.decode_latents(SCREAMING_SNAKE_CASE )
          __lowerCAmelCase		:    Optional[Any]			     =  self.image_processor.postprocess(SCREAMING_SNAKE_CASE     , output_type=SCREAMING_SNAKE_CASE )

        return image












'''simple docstring'''


from __future__ import annotations




def       A							(      _UpperCAmelCase   :				list[int]						,_UpperCAmelCase   :				int					)  ->				int:


  '''simple docstring'''




  __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  0, len(_UpperCAmelCase					) - 1
  while start_ind <= end_ind:
    if array[start_ind] == search_item:
      return start_ind
    elif array[end_ind] == search_item:
      return end_ind
    else:
      start_ind += 1
      end_ind -= 1
    # returns -1 if search_item is not found in array
  return -1


if __name__ == "__main__":
    print(double_linear_search(list(range(1_00)), 40))





