

'''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 					lowerCAmelCase       ( A				):







       def __init__(							self     :	Optional[int]  , __lowercase     :	AutoencoderKL  , __lowercase     :	CLIPTextModel  , __lowercase     :	CLIPTokenizer  , __lowercase     :	UNetaDConditionModel  , __lowercase     :	DDIMScheduler  , __lowercase     :	float = 0.9_3  , __lowercase     :	bool = True  , ):







            """simple docstring"""







            __lowercase     						=mixing_coeff
            __lowercase     						=leapfrog_steps

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

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







       def      snake_case				(							self     :	Dict  , __lowercase     :	str  , __lowercase     :	Optional[str] = None  , __lowercase     :	bool = False ):







            """simple docstring"""







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

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

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

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

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

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

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

            return prompt_embeds







       def      snake_case				(							self     :	Optional[int]  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







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

            return [x, y]







       def      snake_case				(							self     :	int  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







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

            return [x, y]







       def      snake_case				(							self     :	Dict  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







            __lowercase     						=int(__lowercase )

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

            return alpha_prod, 1 - alpha_prod







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )
            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )

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

            __lowercase     						=(base - b_t * model_output) / a_t

            return model_input, next_model_input.to(base.dtype )







       def      snake_case				(							self     :	Dict  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )
            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )

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

            return model_input, next_model_input.to(base.dtype )







       @torch.no_grad()
       def      snake_case				(							self     :	List[str]  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







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







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







            """simple docstring"""







            __lowercase     						=guidance_scale > 1.0

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

            __lowercase     						=self.vae.config.scaling_factor * latent

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

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

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

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

                      __lowercase     						=coupled_latents[j]
                      __lowercase     						=coupled_latents[k]

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

                      __lowercase     						=self.unet(__lowercase  , __lowercase  , encoder_hidden_states=__lowercase ).sample

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

                      __lowercase						,       __lowercase     						=self.noise_step(
                          base=__lowercase  , model_input=__lowercase  , model_output=__lowercase  , timestep=__lowercase  , )

                      __lowercase     						=model_input

            return coupled_latents







       @torch.no_grad()
       def __call__(							self     :	int  , __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"  , ):







            """simple docstring"""







            __lowercase     						=guidance_scale > 1.0

            __lowercase     						=self.image_processor.preprocess(__lowercase )

            __lowercase     						=self._encode_prompt(__lowercase  , __lowercase  , __lowercase )
            __lowercase     						=self._encode_prompt(__lowercase  , __lowercase  , __lowercase )

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

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

            __lowercase     						=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 ):
                      __lowercase     						=k ^ 1

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

                      __lowercase     						=coupled_latents[j]
                      __lowercase     						=coupled_latents[k]

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

                      __lowercase     						=self.unet(__lowercase  , __lowercase  , encoder_hidden_states=__lowercase ).sample

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

                      __lowercase						,       __lowercase     						=self.denoise_step(
                          base=__lowercase  , model_input=__lowercase  , model_output=__lowercase  , timestep=__lowercase  , )

                      __lowercase     						=model_input

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

            # either one is fine
            __lowercase     						=coupled_latents[0]

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

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

            return image





'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	Union[str, Any]  , __lowercase     :	AutoencoderKL  , __lowercase     :	CLIPTextModel  , __lowercase     :	CLIPTokenizer  , __lowercase     :	UNetaDConditionModel  , __lowercase     :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]  , ):







            """simple docstring"""







            super().__init__()

            self.register_modules(vae=__lowercase  , text_encoder=__lowercase  , tokenizer=__lowercase  , unet=__lowercase  , scheduler=__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            with torch.no_grad():
                 __lowercase     						=self.vae.encode(tfms.ToTensor()(__lowercase ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
                 __lowercase     						=0.1_8_2_1_5 * latent.latent_dist.sample()
            return latent







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Tuple ):







            """simple docstring"""







            __lowercase     						=(1 / 0.1_8_2_1_5) * latent
            with torch.no_grad():
                 __lowercase     						=self.vae.decode(__lowercase ).sample
            __lowercase     						=(img / 2 + 0.5).clamp(0  , 1 )
            __lowercase     						=img.detach().cpu().permute(0  , 2  , 3  , 1 ).numpy()
            __lowercase     						=(img * 255).round().astype('uint8' )
            return Image.fromarray(img[0] )







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[int] ):







            """simple docstring"""







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

            __lowercase     						=self.text_encoder(text_input.input_ids.to(self.device ) )[0]

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

            __lowercase     						=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

            return torch.cat([uncond_embedding, text_embedding] )







       def __call__(							self     :	Any  , __lowercase     :	Image.Image  , __lowercase     :	str  , __lowercase     :	float = 0.3  , __lowercase     :	float = 0.6  , __lowercase     :	float = 0.5  , __lowercase     :	int = 42  , __lowercase     :	int = 50  , __lowercase     :	float = 7.5  , ):







            """simple docstring"""







            __lowercase     						=steps - int(kmin * steps )
            __lowercase     						=steps - int(kmax * steps )

            __lowercase     						=self.prep_text(__lowercase )

            self.scheduler.set_timesteps(__lowercase )

            __lowercase						,       __lowercase     						=img.size
            __lowercase     						=self.encode(__lowercase )

            torch.manual_seed(__lowercase )
            __lowercase     						=torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)  , ).to(self.device )

            __lowercase     						=self.scheduler.add_noise(
                __lowercase  , __lowercase  , timesteps=self.scheduler.timesteps[tmax]  , )

            __lowercase     						=torch.cat([latents] * 2 )

            __lowercase     						=self.scheduler.scale_model_input(__lowercase  , self.scheduler.timesteps[tmax] )

            with torch.no_grad():
                 __lowercase     						=self.unet(
                     __lowercase  , self.scheduler.timesteps[tmax]  , encoder_hidden_states=__lowercase  , ).sample

            __lowercase						,       __lowercase     						=pred.chunk(2 )
            __lowercase     						=pred_uncond + guidance_scale * (pred_text - pred_uncond)

            __lowercase     						=self.scheduler.step(__lowercase  , self.scheduler.timesteps[tmax]  , __lowercase ).prev_sample

            for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
                 if i > tmax:
                      if i < tmin:  # layout generation phase
                           __lowercase     						=self.scheduler.add_noise(
                               __lowercase  , __lowercase  , timesteps=__lowercase  , )

                           __lowercase     						=(mix_factor * latents) + (
                               1 - mix_factor
                           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                           __lowercase     						=torch.cat([input] * 2 )

                      else:  # content generation phase
                           __lowercase     						=torch.cat([latents] * 2 )

                      __lowercase     						=self.scheduler.scale_model_input(__lowercase  , __lowercase )

                      with torch.no_grad():
                           __lowercase     						=self.unet(
                               __lowercase  , __lowercase  , encoder_hidden_states=__lowercase  , ).sample

                      __lowercase						,       __lowercase     						=pred.chunk(2 )
                      __lowercase     						=pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      __lowercase     						=self.scheduler.step(__lowercase  , __lowercase  , __lowercase ).prev_sample

            return self.decode(__lowercase )


