"""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       _lowercase		( __lowerCamelCase	):

				def __init__(  self							:     Dict							,     _lowercase							:     AutoencoderKL							,     _lowercase							:     CLIPTextModel							,     _lowercase							:     CLIPTokenizer							,     _lowercase							:     UNetaDConditionModel							,     _lowercase							:     DDIMScheduler							,     _lowercase							:     float = 0.93							,     _lowercase							:     bool = True							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		mixing_coeff
										UpperCAmelCase__       						=		leapfrog_steps

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

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

				def 							A			(  self							:     Tuple							,     _lowercase							:     str							,     _lowercase							:     Optional[str] = None							,     _lowercase							:     bool = False  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.tokenizer(
										    _lowercase							,     padding="""max_length"""							,     max_length=self.tokenizer.model_max_length							,     truncation=_lowercase							,     return_tensors="""pt"""							,     )

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

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

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

																UpperCAmelCase__       						=		self.tokenizer(
																    _lowercase							,     padding="""max_length"""							,     max_length=self.tokenizer.model_max_length							,     truncation=_lowercase							,     return_tensors="""pt"""							,     )

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

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

										return prompt_embeds

				def 							A			(  self							:     List[Any]							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										UpperCAmelCase__       						=		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]

				def 							A			(  self							:     Optional[Any]							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										UpperCAmelCase__       						=		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]

				def 							A			(  self							:     Tuple							,     _lowercase							:     torch.Tensor  ):


										'''simple docstring'''
										# as self.alphas_cumprod is always in cpu
										UpperCAmelCase__       						=		int(_lowercase  )

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

										return alpha_prod, 1 - alpha_prod

				def 							A			(  self							:     Any							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_alpha_and_beta(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_alpha_and_beta(_lowercase  )

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

										UpperCAmelCase__       						=		(base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype  )

				def 							A			(  self							:     Optional[Any]							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_alpha_and_beta(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_alpha_and_beta(_lowercase  )

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

										return model_input, next_model_input.to(base.dtype  )

				@torch.no_grad()
				def 							A			(  self							:     Tuple							,     _lowercase							:     torch.Tensor  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		1 / self.vae.config.scaling_factor * latents
										UpperCAmelCase__       						=		self.vae.decode(_lowercase  ).sample
										UpperCAmelCase__       						=		(image / 2 + 0.5).clamp(0							,     1  )
										return image

				@torch.no_grad()
				def 							A			(  self							:     Optional[Any]							,     _lowercase							:     Image.Image							,     _lowercase							:     torch.Tensor							,     _lowercase							:     torch.Tensor							,     _lowercase							:     float							,     _lowercase							:     Optional[torch.Generator] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		guidance_scale > 1.0

										UpperCAmelCase__       						=		image.to(device=self.device							,     dtype=text_embeds.dtype  )
										UpperCAmelCase__       						=		self.vae.encode(_lowercase  ).latent_dist.sample(_lowercase  )

										UpperCAmelCase__       						=		self.vae.config.scaling_factor * latent

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

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

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

																						UpperCAmelCase__       						=		coupled_latents[j]
																						UpperCAmelCase__       						=		coupled_latents[k]

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

																						UpperCAmelCase__       						=		self.unet(_lowercase							,     _lowercase							,     encoder_hidden_states=_lowercase  ).sample

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

																						UpperCAmelCase__						,   UpperCAmelCase__       						=		self.noise_step(
																						    base=_lowercase							,     model_input=_lowercase							,     model_output=_lowercase							,     timestep=_lowercase							,     )

																						UpperCAmelCase__       						=		model_input

										return coupled_latents

				@torch.no_grad()
				def __call__(  self							:     Optional[int]							,     _lowercase							:     str							,     _lowercase							:     str							,     _lowercase							:     Image.Image							,     _lowercase							:     float = 3.0							,     _lowercase							:     int = 5_0							,     _lowercase							:     float = 0.8							,     _lowercase							:     Optional[str] = None							,     _lowercase							:     Optional[torch.Generator] = None							,     _lowercase							:     Optional[str] = "pil"							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		guidance_scale > 1.0

										UpperCAmelCase__       						=		self.image_processor.preprocess(_lowercase  )

										UpperCAmelCase__       						=		self._encode_prompt(_lowercase							,     _lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		self._encode_prompt(_lowercase							,     _lowercase							,     _lowercase  )

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

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

										UpperCAmelCase__       						=		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__       						=		k ^ 1

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

																						UpperCAmelCase__       						=		coupled_latents[j]
																						UpperCAmelCase__       						=		coupled_latents[k]

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

																						UpperCAmelCase__       						=		self.unet(_lowercase							,     _lowercase							,     encoder_hidden_states=_lowercase  ).sample

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

																						UpperCAmelCase__						,   UpperCAmelCase__       						=		self.denoise_step(
																						    base=_lowercase							,     model_input=_lowercase							,     model_output=_lowercase							,     timestep=_lowercase							,     )

																						UpperCAmelCase__       						=		model_input

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

										# either one is fine
										UpperCAmelCase__       						=		coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
																UpperCAmelCase__       						=		(
																    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__       						=		"""np"""

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

										return image


"""simple docstring"""







def 	a__   (		UpperCAmelCase__							:      list[int]      )->						int:
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__      )      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

