






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_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = 0.93			,			lowercase_ = True			,			)					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     mixing_coeff
														lowerCAmelCase_										=     leapfrog_steps

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

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = False   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     self.tokenizer(
														    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			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(
																					    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			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      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
														lowerCAmelCase_										=     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

														return [x, y]

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
														lowerCAmelCase_										=     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

														return [x, y]

							def      _lowercase       (		self			,			lowercase_   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     int(lowercase_   )

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

														return alpha_prod, 1 - alpha_prod

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

														lowerCAmelCase_						,   lowerCAmelCase_										=     self._get_alpha_and_beta(lowercase_   )
														lowerCAmelCase_						,   lowerCAmelCase_										=     self._get_alpha_and_beta(lowercase_   )

														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      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

														lowerCAmelCase_						,   lowerCAmelCase_										=     self._get_alpha_and_beta(lowercase_   )
														lowerCAmelCase_						,   lowerCAmelCase_										=     self._get_alpha_and_beta(lowercase_   )

														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      _lowercase       (		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * latents
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample
														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )
														return image

							@torch.no_grad()
							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			)					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     guidance_scale > 1.0

														lowerCAmelCase_										=     image.to(device=self.device			,			dtype=text_embeds.dtype   )
														lowerCAmelCase_										=     self.vae.encode(lowercase_   ).latent_dist.sample(lowercase_   )

														lowerCAmelCase_										=     self.vae.config.scaling_factor * latent

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

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

																												lowerCAmelCase_										=     model_input

														return coupled_latents






							@torch.no_grad()
							def __call__(		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = 3.0			,			lowercase_ = 5_0			,			lowercase_ = 0.8			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = "pil"			,			)					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     guidance_scale > 1.0

														lowerCAmelCase_										=     self.image_processor.preprocess(lowercase_   )

														lowerCAmelCase_										=     self._encode_prompt(lowercase_			,			lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     self._encode_prompt(lowercase_			,			lowercase_			,			lowercase_   )

														self.scheduler.set_timesteps(lowercase_			,			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(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   ):
																												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(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).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=lowercase_			,			model_input=lowercase_			,			model_output=lowercase_			,			timestep=lowercase_			,			)

																												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'			,			lowercase_			,			standard_warn=lowercase_   )
																					lowerCAmelCase_										=     'np'

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

														return image











from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



