

"""simple docstring"""





from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class     __a   (      A__    ):

			"""simple docstring"""

			def __init__(							self     :					Optional[int]						,  lowercase_     :					AutoencoderKL						,  lowercase_     :					CLIPTextModel						,  lowercase_     :					CLIPTokenizer						,  lowercase_     :					UNetaDConditionModel						,  lowercase_     :					Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]						,  ):
								super().__init__()

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






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Dict						):






								with torch.no_grad():
													UpperCamelCase__		:    Any         =self.vae.encode(tfms.ToTensor()(lowercase_						).unsqueeze(0						).to(self.device						) * 2 - 1						)
													UpperCamelCase__		:    Tuple         =0.1_8_2_1_5 * latent.latent_dist.sample()
								return latent






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					Any						):
								UpperCamelCase__		:    List[Any]         =(1 / 0.1_8_2_1_5) * latent
								with torch.no_grad():
													UpperCamelCase__		:    Tuple         =self.vae.decode(lowercase_						).sample
								UpperCamelCase__		:    List[Any]         =(img / 2 + 0.5).clamp(0						,  1						)
								UpperCamelCase__		:    Optional[Any]         =img.detach().cpu().permute(0						,  2						,  3						,  1						).numpy()
								UpperCamelCase__		:    Tuple         =(img * 255).round().astype('''uint8'''						)
								return Image.fromarray(img[0]						)






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					str						):
								UpperCamelCase__		:    List[Any]         =self.tokenizer(
								    lowercase_						,  padding='''max_length'''						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''pt'''						,  )

								UpperCamelCase__		:    List[Any]         =self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

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

								UpperCamelCase__		:    Dict         =self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

								return torch.cat([uncond_embedding, text_embedding]						)






			def __call__(							self     :					List[Any]						,  lowercase_     :					Image.Image						,  lowercase_     :					str						,  lowercase_     :					float = 0.3						,  lowercase_     :					float = 0.6						,  lowercase_     :					float = 0.5						,  lowercase_     :					int = 42						,  lowercase_     :					int = 50						,  lowercase_     :					float = 7.5						,  ):
								UpperCamelCase__		:    Dict         =steps - int(kmin * steps						)
								UpperCamelCase__		:    Optional[int]         =steps - int(kmax * steps						)

								UpperCamelCase__		:    List[str]         =self.prep_text(lowercase_						)

								self.scheduler.set_timesteps(lowercase_						)

								UpperCamelCase__		:    Optional[int]         =img.size
								UpperCamelCase__		:    str         =self.encode(lowercase_						)

								torch.manual_seed(lowercase_						)
								UpperCamelCase__		:    Optional[int]         =torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)						,  ).to(self.device						)

								UpperCamelCase__		:    List[Any]         =self.scheduler.add_noise(
								    lowercase_						,  lowercase_						,  timesteps=self.scheduler.timesteps[tmax]						,  )

								UpperCamelCase__		:    Tuple         =torch.cat([latents] * 2						)

								UpperCamelCase__		:    Optional[int]         =self.scheduler.scale_model_input(lowercase_						,  self.scheduler.timesteps[tmax]						)

								with torch.no_grad():
													UpperCamelCase__		:    str         =self.unet(
													    lowercase_						,  self.scheduler.timesteps[tmax]						,  encoder_hidden_states=lowercase_						,  ).sample

								UpperCamelCase__		:    int         =pred.chunk(2						)
								UpperCamelCase__		:    Optional[Any]         =pred_uncond + guidance_scale * (pred_text - pred_uncond)

								UpperCamelCase__		:    List[str]         =self.scheduler.step(lowercase_						,  self.scheduler.timesteps[tmax]						,  lowercase_						).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
													if i > tmax:
																		if i < tmin:  # layout generation phase
																							UpperCamelCase__		:    Any         =self.scheduler.add_noise(
																							    lowercase_						,  lowercase_						,  timesteps=lowercase_						,  )

																							UpperCamelCase__		:    List[Any]         =(mix_factor * latents) + (
																							    1 - mix_factor
																							) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																							UpperCamelCase__		:    Optional[int]         =torch.cat([input] * 2						)

																		else:  # content generation phase
																							UpperCamelCase__		:    Dict         =torch.cat([latents] * 2						)

																		UpperCamelCase__		:    Union[str, Any]         =self.scheduler.scale_model_input(lowercase_						,  lowercase_						)

																		with torch.no_grad():
																							UpperCamelCase__		:    Optional[int]         =self.unet(
																							    lowercase_						,  lowercase_						,  encoder_hidden_states=lowercase_						,  ).sample

																		UpperCamelCase__		:    Optional[int]         =pred.chunk(2						)
																		UpperCamelCase__		:    Optional[Any]         =pred_uncond + guidance_scale * (pred_text - pred_uncond)

																		UpperCamelCase__		:    List[str]         =self.scheduler.step(lowercase_						,  lowercase_						,  lowercase_						).prev_sample

								return self.decode(lowercase_						)





"""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()


