

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 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







						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     ,						)->							Optional[Any]:
													lowerCamelCase_								=mixing_coeff
													lowerCamelCase_								=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_								=2 ** (len(self.vae.config.block_out_channels							) - 1)
													lowerCamelCase_								=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = False							)->							Dict:
													lowerCamelCase_								=self.tokenizer(
													    _SCREAMING_SNAKE_CASE     ,						padding="""max_length"""     ,						max_length=self.tokenizer.model_max_length     ,						truncation=_SCREAMING_SNAKE_CASE     ,						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(
																				    _SCREAMING_SNAKE_CASE     ,						padding="""max_length"""     ,						max_length=self.tokenizer.model_max_length     ,						truncation=_SCREAMING_SNAKE_CASE     ,						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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Optional[int]:
													lowerCamelCase_								=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
													lowerCamelCase_								=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

													return [x, y]







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Dict:
													lowerCamelCase_								=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
													lowerCamelCase_								=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

													return [x, y]







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Dict:
													# as self.alphas_cumprod is always in cpu
													lowerCamelCase_								=int(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=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_								=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													lowerCamelCase_	,   lowerCamelCase_								=self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_	,   lowerCamelCase_								=self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE							)

													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						)->							Union[str, Any]:
													lowerCamelCase_								=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													lowerCamelCase_	,   lowerCamelCase_								=self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_	,   lowerCamelCase_								=self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE							)

													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Any:
													lowerCamelCase_								=1 / self.vae.config.scaling_factor * latents
													lowerCamelCase_								=self.vae.decode(_SCREAMING_SNAKE_CASE							).sample
													lowerCamelCase_								=(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_								=guidance_scale > 1.0

													lowerCamelCase_								=image.to(device=self.device     ,						dtype=text_embeds.dtype							)
													lowerCamelCase_								=self.vae.encode(_SCREAMING_SNAKE_CASE							).latent_dist.sample(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=self.vae.config.scaling_factor * latent

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

													for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE							)     ,						total=len(_SCREAMING_SNAKE_CASE							)							):
																				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(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE							).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=_SCREAMING_SNAKE_CASE     ,						model_input=_SCREAMING_SNAKE_CASE     ,						model_output=_SCREAMING_SNAKE_CASE     ,						timestep=_SCREAMING_SNAKE_CASE     ,						)

																											lowerCamelCase_								=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"     ,						)->							int:
													lowerCamelCase_								=guidance_scale > 1.0

													lowerCamelCase_								=self.image_processor.preprocess(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=self._encode_prompt(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=self._encode_prompt(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

													self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE     ,						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(_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_								=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(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE							).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=_SCREAMING_SNAKE_CASE     ,						model_input=_SCREAMING_SNAKE_CASE     ,						model_output=_SCREAMING_SNAKE_CASE     ,						timestep=_SCREAMING_SNAKE_CASE     ,						)

																											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"""     ,						_SCREAMING_SNAKE_CASE     ,						standard_warn=_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								="""np"""

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

													return image



def        __UpperCamelCase     (      _A					:							str		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=lowerCamelCase_								=0
							for letter in data:
														lowerCamelCase_								=(hash_ << 4) + ord(_A		)
														lowerCamelCase_								=hash_ & 0XF0000000
														if x != 0:
																					hash_ ^= x >> 24
														hash_ &= ~x
							return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()
