"""simple docstring"""
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     lowercase = False       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	List[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	Tuple     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	str     				=      [Image.fromarray(lowercase) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          a__:	int     				=      [images]
        a__:	Optional[Any]     				=      [np.array(lowercase).astype(np.floataa) / 255.0 for image in images]
        a__:	int     				=      np.stack(lowercase       ,     axis=0)

        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     torch.FloatTensor:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Any     				=      images[..., None]

        a__:	Any     				=      torch.from_numpy(images.transpose(0       ,     3       ,     1       ,     2))
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        a__:	Tuple     				=      images.cpu().permute(0       ,     2       ,     3       ,     1).float().numpy()
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Any:





        '''simple docstring'''





        return 2.0 * images - 1.0



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return (images / 2 + 0.5).clamp(0       ,     1)



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        a__:	List[Any]     				=      image.convert('RGB')
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     PIL.Image.Image:





        '''simple docstring'''





        if height is None:
          a__:	Union[str, Any]     				=      image.height
        if width is None:
          a__:	Optional[Any]     				=      image.width

        a__				,       a__:	int     				=      (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        a__:	Dict     				=      image.resize((width, height)       ,     resample=PIL_INTERPOLATION[self.config.resample])
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     torch.Tensor:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(lowercase       ,     lowercase):
          a__:	str     				=      [image]
        elif not (isinstance(lowercase       ,     lowercase) and all(isinstance(lowercase       ,     lowercase) for i in image)):
          raise ValueError(
              f'Input is in incorrect format: {[type(lowercase) for i in image]}. Currently, we only support {", ".join(lowercase)}')

        if isinstance(image[0]       ,     PIL.Image.Image):
          if self.config.do_convert_rgb:
            a__:	Any     				=      [self.convert_to_rgb(lowercase) for i in image]
          if self.config.do_resize:
            a__:	Any     				=      [self.resize(lowercase       ,     lowercase       ,     lowercase) for i in image]
          a__:	Any     				=      self.pil_to_numpy(lowercase)  # to np
          a__:	Tuple     				=      self.numpy_to_pt(lowercase)  # to pt

        elif isinstance(image[0]       ,     np.ndarray):
          a__:	Any     				=      np.concatenate(lowercase       ,     axis=0) if image[0].ndim == 4 else np.stack(lowercase       ,     axis=0)
          a__:	List[Any]     				=      self.numpy_to_pt(lowercase)
          a__				,       a__				,       a__				,       a__:	Dict     				=      image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        elif isinstance(image[0]       ,     torch.Tensor):
          a__:	int     				=      torch.cat(lowercase       ,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase       ,     axis=0)
          a__				,       a__				,       a__				,       a__:	Tuple     				=      image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        # expected range [0,1], normalize to [-1,1]
        a__:	Optional[Any]     				=      self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'       ,     lowercase       ,     )
          a__:	Union[str, Any]     				=      False

        if do_normalize:
          a__:	Optional[Any]     				=      self.normalize(lowercase)

        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     Dict:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	str     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. 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)
          a__:	int     				=      'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          a__:	Dict     				=      [self.config.do_normalize] * image.shape[0]

        a__:	int     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        if output_type == "pt":
          return image

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(lowercase)







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Tuple     				=      images[None, ...]
        a__:	Optional[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	int     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	Optional[Any]     				=      [Image.fromarray(image[:, :, :3]) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return image[:, :, 1] * 2**8 + image[:, :, 2]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Optional[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
          a__:	List[str]     				=      (images_depth * 2_55).round().astype('uint8')
          a__:	Any     				=      [
              Image.fromarray(self.rgblike_to_depthmap(lowercase)       ,     mode='I;16') for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          a__:	Dict     				=      (images_depth * 65535.0).astype(np.uintaa)
          a__:	Optional[Any]     				=      [Image.fromarray(lowercase       ,     mode='I;16') for image_depth in images_depth]
        else:
          raise Exception('Not supported')

        return pil_images



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     List[str]:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	Optional[Any]     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. 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)
          a__:	List[str]     				=      'np'

        if do_denormalize is None:
          a__:	int     				=      [self.config.do_normalize] * image.shape[0]

        a__:	Optional[Any]     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          if image.shape[-1] == 6:
            a__:	Any     				=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image]       ,     axis=0)
          else:
            a__:	str     				=      image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(lowercase), self.numpy_to_depth(lowercase)
        else:
          raise Exception(f'This type {output_type} is not supported')



"""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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



      def __call__(		self       ,     lowercase       ,     lowercase       ,     lowercase = 0.3       ,     lowercase = 0.6       ,     lowercase = 0.5       ,     lowercase = 42       ,     lowercase = 50       ,     lowercase = 7.5       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      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
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

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

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

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)


