

"""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     __a   (      snake_case__    ):

			"""simple docstring"""

			def __init__(							self     :					Tuple						,  lowercase_     :					AutoencoderKL						,  lowercase_     :					CLIPTextModel						,  lowercase_     :					CLIPTokenizer						,  lowercase_     :					UNetaDConditionModel						,  lowercase_     :					DDIMScheduler						,  lowercase_     :					float = 0.9_3						,  lowercase_     :					bool = True						,  ):
								UpperCamelCase__		:    List[Any]         =mixing_coeff
								UpperCamelCase__		:    Union[str, Any]         =leapfrog_steps

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

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






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					str						,  lowercase_     :					Optional[str] = None						,  lowercase_     :					bool = False						):
								UpperCamelCase__		:    List[Any]         =self.tokenizer(
								    lowercase_						,  padding='''max_length'''						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''pt'''						,  )

								UpperCamelCase__		:    List[str]         =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__		:    List[Any]         ='''''' if negative_prompt is None else negative_prompt

													UpperCamelCase__		:    str         =self.tokenizer(
													    lowercase_						,  padding='''max_length'''						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''pt'''						,  )

													UpperCamelCase__		:    List[Any]         =self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

													UpperCamelCase__		:    Any         =torch.cat([negative_prompt_embeds, prompt_embeds]						)

								return prompt_embeds






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						):
								UpperCamelCase__		:    Optional[Any]         =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								UpperCamelCase__		:    int         =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						):
								UpperCamelCase__		:    Optional[int]         =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCamelCase__		:    List[Any]         =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					torch.Tensor						):
								# as self.alphas_cumprod is always in cpu
								UpperCamelCase__		:    int         =int(lowercase_						)

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

								return alpha_prod, 1 - alpha_prod






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  ):
								UpperCamelCase__		:    str         =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =self._get_alpha_and_beta(lowercase_						)
								UpperCamelCase__  ,			UpperCamelCase__		:    Optional[Any]         =self._get_alpha_and_beta(lowercase_						)

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

								UpperCamelCase__		:    List[str]         =(base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  ):
								UpperCamelCase__		:    Optional[Any]         =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCamelCase__  ,			UpperCamelCase__		:    List[Any]         =self._get_alpha_and_beta(lowercase_						)
								UpperCamelCase__  ,			UpperCamelCase__		:    Optional[Any]         =self._get_alpha_and_beta(lowercase_						)

								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    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					torch.Tensor						):
								UpperCamelCase__		:    Union[str, Any]         =1 / self.vae.config.scaling_factor * latents
								UpperCamelCase__		:    int         =self.vae.decode(lowercase_						).sample
								UpperCamelCase__		:    Dict         =(image / 2 + 0.5).clamp(0						,  1						)
								return image






			@torch.no_grad()
			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					Image.Image						,  lowercase_     :					torch.Tensor						,  lowercase_     :					torch.Tensor						,  lowercase_     :					float						,  lowercase_     :					Optional[torch.Generator] = None						,  ):
								UpperCamelCase__		:    Tuple         =guidance_scale > 1.0

								UpperCamelCase__		:    Union[str, Any]         =image.to(device=self.device						,  dtype=text_embeds.dtype						)
								UpperCamelCase__		:    int         =self.vae.encode(lowercase_						).latent_dist.sample(lowercase_						)

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

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

								for i, t in tqdm(enumerate(lowercase_						)						,  total=len(lowercase_						)						):
													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__		:    List[Any]         =j ^ 1

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

																		UpperCamelCase__		:    List[str]         =coupled_latents[j]
																		UpperCamelCase__		:    Optional[int]         =coupled_latents[k]

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

																		UpperCamelCase__		:    str         =self.unet(lowercase_						,  lowercase_						,  encoder_hidden_states=lowercase_						).sample

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

																		UpperCamelCase__  ,			UpperCamelCase__		:    Optional[Any]         =self.noise_step(
																		    base=lowercase_						,  model_input=lowercase_						,  model_output=lowercase_						,  timestep=lowercase_						,  )

																		UpperCamelCase__		:    str         =model_input

								return coupled_latents






			@torch.no_grad()
			def __call__(							self     :					Dict						,  lowercase_     :					str						,  lowercase_     :					str						,  lowercase_     :					Image.Image						,  lowercase_     :					float = 3.0						,  lowercase_     :					int = 50						,  lowercase_     :					float = 0.8						,  lowercase_     :					Optional[str] = None						,  lowercase_     :					Optional[torch.Generator] = None						,  lowercase_     :					Optional[str] = "pil"						,  ):
								UpperCamelCase__		:    List[Any]         =guidance_scale > 1.0

								UpperCamelCase__		:    List[Any]         =self.image_processor.preprocess(lowercase_						)

								UpperCamelCase__		:    int         =self._encode_prompt(lowercase_						,  lowercase_						,  lowercase_						)
								UpperCamelCase__		:    Dict         =self._encode_prompt(lowercase_						,  lowercase_						,  lowercase_						)

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

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

								UpperCamelCase__		:    int         =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						):
																		UpperCamelCase__		:    str         =k ^ 1

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

																		UpperCamelCase__		:    Optional[int]         =coupled_latents[j]
																		UpperCamelCase__		:    Tuple         =coupled_latents[k]

																		UpperCamelCase__		:    Tuple         =torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

																		UpperCamelCase__		:    Tuple         =self.unet(lowercase_						,  lowercase_						,  encoder_hidden_states=lowercase_						).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__		:    Dict         =self.denoise_step(
																		    base=lowercase_						,  model_input=lowercase_						,  model_output=lowercase_						,  timestep=lowercase_						,  )

																		UpperCamelCase__		:    Dict         =model_input

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

								# either one is fine
								UpperCamelCase__		:    Union[str, Any]         =coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
													UpperCamelCase__		:    List[str]         =(
													    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_						)
													UpperCamelCase__		:    List[Any]         ='''np'''

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

								return image





"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						('_front', '_back', '_len')

			@dataclass
			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None



			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						('_cur',)






						def __init__(							self     :					Optional[int]						,  lowercase_     :					Deque._Node | None						):
											UpperCamelCase__		:    List[str]         =cur






						def __iter__(							self     :					List[str]						):
											return self






						def    _lowerCAmelCase			(							self     :					List[Any]						):
											if self._cur is None:
																# finished iterating
																raise StopIteration
											UpperCamelCase__		:    str         =self._cur.val
											UpperCamelCase__		:    Any         =self._cur.next_node

											return val






			def __init__(							self     :					str						,  lowercase_     :					Iterable[Any] | None = None						):
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    int         =0

								if iterable is not None:
													# append every value to the deque
													for val in iterable:
																		self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Any						):
								UpperCamelCase__		:    Union[str, Any]         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    List[Any]         =1
								else:
													# connect nodes
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    Optional[Any]         =self._back
													UpperCamelCase__		:    str         =node  # assign new back to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Any						):
								UpperCamelCase__		:    str         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    str         =node
													UpperCamelCase__		:    Any         =1
								else:
													# connect nodes
													UpperCamelCase__		:    int         =self._front
													UpperCamelCase__		:    int         =node
													UpperCamelCase__		:    Any         =node  # assign new front to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.appendleft(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Optional[int]						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[Any]         =self._back
								UpperCamelCase__		:    Any         =self._back.prev_node  # set new back
								# drop the last node - python will deallocate memory automatically
								UpperCamelCase__		:    List[str]         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					str						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[str]         =self._front
								UpperCamelCase__		:    List[Any]         =self._front.next_node  # set new front and drop the first node
								UpperCamelCase__		:    Dict         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					Dict						):
								return self._front is None






			def __len__(							self     :					Any						):
								return self._len






			def __eq__(							self     :					Dict						,  lowercase_     :					object						):

								if not isinstance(lowercase_						,  lowercase_						):
													return NotImplemented

								UpperCamelCase__		:    Union[str, Any]         =self._front
								UpperCamelCase__		:    Union[str, Any]         =other._front

								# if the length of the dequeues are not the same, they are not equal
								if len(self						) != len(lowercase_						):
													return False

								while me is not None and oth is not None:
													# compare every value
													if me.val != oth.val:
																		return False
													UpperCamelCase__		:    Union[str, Any]         =me.next_node
													UpperCamelCase__		:    Union[str, Any]         =oth.next_node

								return True






			def __iter__(							self     :					Optional[int]						):
								return Deque._Iterator(self._front						)






			def __repr__(							self     :					Tuple						):
								UpperCamelCase__		:    List[Any]         =[]
								UpperCamelCase__		:    Optional[int]         =self._front
								while aux is not None:
													# append the values in a list to display
													values_list.append(aux.val						)
													UpperCamelCase__		:    Optional[Any]         =aux.next_node

								return f'''[{', '.join(repr(lowercase_						) for val in values_list						)}]'''


if __name__ == "__main__":
				import doctest

				doctest.testmod()


