




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__    ):





	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			,    )     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	mixing_coeff
				SCREAMING_SNAKE_CASE_			=	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			,    )

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.tokenizer(
				    SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=self.tokenizer.model_max_length			,    truncation=SCREAMING_SNAKE_CASE			,    return_tensors='pt'			,    )

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

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

				if do_classifier_free_guidance:
							SCREAMING_SNAKE_CASE_			=	'' if negative_prompt is None else negative_prompt

							SCREAMING_SNAKE_CASE_			=	self.tokenizer(
							    SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=self.tokenizer.model_max_length			,    truncation=SCREAMING_SNAKE_CASE			,    return_tensors='pt'			,    )

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

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

				return prompt_embeds





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Any:




				"""simple docstring"""



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

				return [x, y]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:




				"""simple docstring"""



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

				return [x, y]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	int(SCREAMING_SNAKE_CASE			)

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

				return alpha_prod, 1 - alpha_prod





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )     ->				Dict:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self._get_alpha_and_beta(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self._get_alpha_and_beta(SCREAMING_SNAKE_CASE			)

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

				SCREAMING_SNAKE_CASE_			=	(base - b_t * model_output) / a_t

				return model_input, next_model_input.to(base.dtype			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )     ->				Tuple:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self._get_alpha_and_beta(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self._get_alpha_and_beta(SCREAMING_SNAKE_CASE			)

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

				return model_input, next_model_input.to(base.dtype			)





	@torch.no_grad()
	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Any:




				"""simple docstring"""



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





	@torch.no_grad()
	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    )     ->				List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	guidance_scale > 1.0

				SCREAMING_SNAKE_CASE_			=	image.to(device=self.device			,    dtype=text_embeds.dtype			)
				SCREAMING_SNAKE_CASE_			=	self.vae.encode(SCREAMING_SNAKE_CASE			).latent_dist.sample(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	self.vae.config.scaling_factor * latent

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

				for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE			)			,    total=len(SCREAMING_SNAKE_CASE			)			):
							SCREAMING_SNAKE_CASE_			=	self.noise_mixing_layer(x=coupled_latents[0]			,    y=coupled_latents[1]			)

							# j - model_input index, k - base index
							for j in range(2			):
										SCREAMING_SNAKE_CASE_			=	j ^ 1

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

										SCREAMING_SNAKE_CASE_			=	coupled_latents[j]
										SCREAMING_SNAKE_CASE_			=	coupled_latents[k]

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

										SCREAMING_SNAKE_CASE_			=	self.unet(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    encoder_hidden_states=SCREAMING_SNAKE_CASE			).sample

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

										SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.noise_step(
										    base=SCREAMING_SNAKE_CASE			,    model_input=SCREAMING_SNAKE_CASE			,    model_output=SCREAMING_SNAKE_CASE			,    timestep=SCREAMING_SNAKE_CASE			,    )

										SCREAMING_SNAKE_CASE_			=	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"			,    )     ->				Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	guidance_scale > 1.0

				SCREAMING_SNAKE_CASE_			=	self.image_processor.preprocess(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	self._encode_prompt(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	self._encode_prompt(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)

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

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

				SCREAMING_SNAKE_CASE_			=	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			):
										SCREAMING_SNAKE_CASE_			=	k ^ 1

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

										SCREAMING_SNAKE_CASE_			=	coupled_latents[j]
										SCREAMING_SNAKE_CASE_			=	coupled_latents[k]

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

										SCREAMING_SNAKE_CASE_			=	self.unet(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    encoder_hidden_states=SCREAMING_SNAKE_CASE			).sample

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

										SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.denoise_step(
										    base=SCREAMING_SNAKE_CASE			,    model_input=SCREAMING_SNAKE_CASE			,    model_output=SCREAMING_SNAKE_CASE			,    timestep=SCREAMING_SNAKE_CASE			,    )

										SCREAMING_SNAKE_CASE_			=	model_input

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

				# either one is fine
				SCREAMING_SNAKE_CASE_			=	coupled_latents[0]

				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    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			)
							SCREAMING_SNAKE_CASE_			=	'np'

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

				return image






SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
