





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 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A,							A,							A,							A,							A,							):





            """simple docstring"""

            super().__init__()

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

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            with torch.no_grad():
                   _UpperCamelCase														=    self.vae.encode(tfms.ToTensor()(A   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
                   _UpperCamelCase														=    0.18_215 * latent.latent_dist.sample()
            return latent

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    (1 / 0.18_215) * latent
            with torch.no_grad():
                   _UpperCamelCase														=    self.vae.decode(A   ).sample
            _UpperCamelCase														=    (img / 2 + 0.5).clamp(0,							1   )
            _UpperCamelCase														=    img.detach().cpu().permute(0,							2,							3,							1   ).numpy()
            _UpperCamelCase														=    (img * 255).round().astype('''uint8'''   )
            return Image.fromarray(img[0]   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

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

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

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

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

            return torch.cat([uncond_embedding, text_embedding]   )



     def __call__(   self,							A,							A,							A = 0.3,							A = 0.6,							A = 0.5,							A = 42,							A = 50,							A = 7.5,							):





            """simple docstring"""

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

            _UpperCamelCase														=    self.prep_text(A   )

            self.scheduler.set_timesteps(A   )

            _UpperCamelCase							,		_UpperCamelCase														=    img.size
            _UpperCamelCase														=    self.encode(A   )

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

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

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

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

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

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

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

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

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

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

                          _UpperCamelCase														=    self.scheduler.scale_model_input(A,							A   )

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

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

                          _UpperCamelCase														=    self.scheduler.step(A,							A,							A   ).prev_sample

            return self.decode(A   )













import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            warnings.warn(
                '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MobileNetV2ImageProcessor instead.''',							A,							)
            super().__init__(*A,							**A   )






