class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[str]	)			->				None:
        # numbermap keys are the number and its values are lists of indexes sorted
        # in ascending order
        _UpperCamelCase  :							dict[int, list[int]]	    =	{}
        # indexmap keys are an index and it's values are the number at that index
        _UpperCamelCase  :							dict[int, int]	    =	{}

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		list | str | range							,      lowerCamelCase							:		int	)			->				list[int]:
        if isinstance(lowerCamelCase							,      (range, str)	):
               _UpperCamelCase  :							int	    =	list(lowerCamelCase	)
        elif not isinstance(lowerCamelCase							,      lowerCamelCase	):
               raise TypeError(
                   'binary_search_delete() only accepts either a list, range or str'	)

        _UpperCamelCase  :							Optional[int]	    =	0
        _UpperCamelCase  :							List[str]	    =	len(lowerCamelCase	) - 1

        while low <= high:
               _UpperCamelCase  :							List[Any]	    =	(low + high) // 2
               if array[mid] == item:
                      array.pop(lowerCamelCase	)
                      return array
               elif array[mid] < item:
                      _UpperCamelCase  :							Any	    =	mid + 1
               else:
                      _UpperCamelCase  :							List[str]	    =	mid - 1
        raise ValueError(
            'Either the item is not in the array or the array was unsorted'	)

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		list | str | range							,      lowerCamelCase							:		int	)			->				list[int]:
        if isinstance(lowerCamelCase							,      (range, str)	):
               _UpperCamelCase  :							Tuple	    =	list(lowerCamelCase	)
        elif not isinstance(lowerCamelCase							,      lowerCamelCase	):
               raise TypeError(
                   'binary_search_insert() only accepts either a list, range or str'	)

        _UpperCamelCase  :							Optional[Any]	    =	0
        _UpperCamelCase  :							Any	    =	len(lowerCamelCase	) - 1

        while low <= high:
               _UpperCamelCase  :							Optional[int]	    =	(low + high) // 2
               if array[mid] == index:
                      # If the item already exists in the array,
                      # insert it after the existing item
                      array.insert(mid + 1							,      lowerCamelCase	)
                      return array
               elif array[mid] < index:
                      _UpperCamelCase  :							int	    =	mid + 1
               else:
                      _UpperCamelCase  :							int	    =	mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
        array.insert(lowerCamelCase							,      lowerCamelCase	)
        return array

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				None:
        # Remove previous index
        if index in self.indexmap:
               _UpperCamelCase  :							List[Any]	    =	self.indexmap[index]
               if len(self.numbermap[n]	) == 1:
                      del self.numbermap[n]
               else:
                      _UpperCamelCase  :							Optional[Any]	    =	self.binary_search_delete(self.numbermap[n]							,      lowerCamelCase	)

        # Set new index
        _UpperCamelCase  :							int	    =	number

        # Number not seen before or empty so insert number value
        if number not in self.numbermap:
               _UpperCamelCase  :							Optional[Any]	    =	[index]

        # Here we need to perform a binary search insertion in order to insert
        # The item in the correct place
        else:
               _UpperCamelCase  :							Dict	    =	self.binary_search_insert(
                   self.numbermap[number]							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		int	)			->				int:
        return self.numbermap.get(lowerCamelCase							,      [-1]	)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()








'''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__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  AutoencoderKL   ,		__A		:  CLIPTextModel   ,		__A		:  CLIPTokenizer   ,		__A		:  UNetaDConditionModel   ,		__A		:  DDIMScheduler   ,		__A		:  float = 0.9_3   ,		__A		:  bool = True   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   mixing_coeff
								lowerCAmelCase__  =   leapfrog_steps

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

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




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  str   ,		__A		:  Optional[str] = None   ,		__A		:  bool = False					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer(
								    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

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

															lowerCAmelCase__  =   self.tokenizer(
															    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

								return prompt_embeds




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								lowerCAmelCase__  =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




	def      lowercase__							(  self		:  Dict   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								lowerCAmelCase__  =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




	def      lowercase__							(  self		:  List[str]   ,		__A		:  torch.Tensor					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   int(__A					)

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

								return alpha_prod, 1 - alpha_prod




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								lowerCAmelCase__  =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								return model_input, next_model_input.to(base.dtype					)




	@torch.no_grad()
	def      lowercase__							(  self		:  int   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   1 / self.vae.config.scaling_factor * latents
								lowerCAmelCase__  =   self.vae.decode(__A					).sample
								lowerCAmelCase__  =   (image / 2 + 0.5).clamp(0   ,		1					)
								return image




	@torch.no_grad()
	def      lowercase__							(  self		:  List[str]   ,		__A		:  Image.Image   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  float   ,		__A		:  Optional[torch.Generator] = None   ,		)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   image.to(device=self.device   ,		dtype=text_embeds.dtype					)
								lowerCAmelCase__  =   self.vae.encode(__A					).latent_dist.sample(__A					)

								lowerCAmelCase__  =   self.vae.config.scaling_factor * latent

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

								for i, t in tqdm(enumerate(__A					)   ,		total=len(__A					)					):
															lowerCAmelCase__  =   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__  =   j ^ 1

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.noise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

								return coupled_latents




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




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   self.image_processor.preprocess(__A					)

								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)
								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)

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

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

								lowerCAmelCase__  =   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					):
																						lowerCAmelCase__  =   k ^ 1

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.denoise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

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

								# either one is fine
								lowerCAmelCase__  =   coupled_latents[0]

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

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

								return image





