



"""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_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






						def __init__(      self				:Tuple     ,						lowercase_				:AutoencoderKL     ,						lowercase_				:CLIPTextModel     ,						lowercase_				:CLIPTokenizer     ,						lowercase_				:UNetaDConditionModel     ,						lowercase_				:DDIMScheduler     ,						lowercase_				:float = 0.93     ,						lowercase_				:bool = True     ,						)       ->    List[str]:
												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 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:str     ,						lowercase_				:Optional[str] = None     ,						lowercase_				:bool = False      )       ->    List[str]:
												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 			UpperCAmelCase__  (      self				:str     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor      )       ->    int:
												UpperCAmelCase    =				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
												UpperCAmelCase    =				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

												return [x, y]






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor      )       ->    Any:
												UpperCAmelCase    =				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
												UpperCAmelCase    =				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

												return [x, y]






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:torch.Tensor      )       ->    Optional[int]:
												# 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 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						)       ->    Any:
												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 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						)       ->    Optional[int]:
												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 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.Tensor      )       ->    List[str]:
												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 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:Image.Image     ,						lowercase_				:torch.Tensor     ,						lowercase_				:torch.Tensor     ,						lowercase_				:float     ,						lowercase_				:Optional[torch.Generator] = None     ,						)       ->    Tuple:
												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				:Any     ,						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"     ,						)       ->    Optional[int]:
												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"""


from math import exp

def 				_lowerCAmelCase   (     lowercase_		):
						return 8 * x - 2 * exp(-x		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				lower_bound
						UpperCAmelCase    =				upper_bound
						for _ in range(0	,			lowercase_		):
												UpperCAmelCase   ,					UpperCAmelCase    =				xa, xa - (f(lowercase_		) * (xa - xa)) / (f(lowercase_		) - f(lowercase_		))
						return xa


if __name__ == "__main__":
				print(f'''Example: {secant_method(1, 3, 2)}''')





