





'''simple docstring'''




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_A:					List[str]							=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def _lowerCAmelCase     (      _lowerCAmelCase    )->     str:
 warnings.warn(
     'The preprocess method is deprecated and will be removed in a future version. Please'
     ' use VaeImageProcessor.preprocess instead'       ,    _lowerCAmelCase       ,    )
 if isinstance(_lowerCAmelCase       ,    torch.Tensor    ):
  return image
 elif isinstance(_lowerCAmelCase       ,    PIL.Image.Image    ):
  __UpperCAmelCase					=   [image]

 if isinstance(image[0]       ,    PIL.Image.Image    ):
  __UpperCAmelCase					,    __UpperCAmelCase					=   image[0].size
  __UpperCAmelCase					,    __UpperCAmelCase					=   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  __UpperCAmelCase					=   [np.array(i.resize((w, h)       ,    resample=PIL_INTERPOLATION['lanczos']    )    )[None, :] for i in image]
  __UpperCAmelCase					=   np.concatenate(_lowerCAmelCase       ,    axis=0    )
  __UpperCAmelCase					=   np.array(_lowerCAmelCase    ).astype(np.floataa    ) / 255.0
  __UpperCAmelCase					=   image.transpose(0       ,    3       ,    1       ,    2    )
  __UpperCAmelCase					=   2.0 * image - 1.0
  __UpperCAmelCase					=   torch.from_numpy(_lowerCAmelCase    )
 elif isinstance(image[0]       ,    torch.Tensor    ):
  __UpperCAmelCase					=   torch.cat(_lowerCAmelCase       ,    dim=0    )
 return image







def _lowerCAmelCase     (      _lowerCAmelCase    )->     int:
 if isinstance(_lowerCAmelCase       ,    torch.Tensor    ):
  return mask
 elif isinstance(_lowerCAmelCase       ,    PIL.Image.Image    ):
  __UpperCAmelCase					=   [mask]

 if isinstance(mask[0]       ,    PIL.Image.Image    ):
  __UpperCAmelCase					,    __UpperCAmelCase					=   mask[0].size
  __UpperCAmelCase					,    __UpperCAmelCase					=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __UpperCAmelCase					=   [np.array(m.convert('L'    ).resize((w, h)       ,    resample=PIL_INTERPOLATION['nearest']    )    )[None, :] for m in mask]
  __UpperCAmelCase					=   np.concatenate(_lowerCAmelCase       ,    axis=0    )
  __UpperCAmelCase					=   mask.astype(np.floataa    ) / 255.0
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   1
  __UpperCAmelCase					=   torch.from_numpy(_lowerCAmelCase    )
 elif isinstance(mask[0]       ,    torch.Tensor    ):
  __UpperCAmelCase					=   torch.cat(_lowerCAmelCase       ,    dim=0    )
 return mask






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			UNetaDModel
    _A :			RePaintScheduler



    def __init__(  self     ,			__A     ,			__A					):
     super().__init__()
     self.register_modules(unet=__A     ,			scheduler=__A					)




    @torch.no_grad()
    def __call__(  self     ,			__A     ,			__A     ,			__A = 250     ,			__A = 0.0     ,			__A = 10     ,			__A = 10     ,			__A = None     ,			__A = "pil"     ,			__A = True     ,			):
     __UpperCAmelCase					=   image

     __UpperCAmelCase					=   _preprocess_image(__A					)
     __UpperCAmelCase					=   original_image.to(device=self.device     ,			dtype=self.unet.dtype					)
     __UpperCAmelCase					=   _preprocess_mask(__A					)
     __UpperCAmelCase					=   mask_image.to(device=self.device     ,			dtype=self.unet.dtype					)

     __UpperCAmelCase					=   original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(__A     ,			__A					) and len(__A					) != batch_size:
      raise ValueError(
          f'You have passed a list of generators of length {len(__A					)}, but requested an effective batch'
          f' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

     __UpperCAmelCase					=   original_image.shape
     __UpperCAmelCase					=   randn_tensor(__A     ,			generator=__A     ,			device=self.device     ,			dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(__A     ,			__A     ,			__A     ,			self.device					)
     __UpperCAmelCase					=   eta

     __UpperCAmelCase					=   self.scheduler.timesteps[0] + 1
     __UpperCAmelCase					=   generator[0] if isinstance(__A     ,			__A					) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
      if t < t_last:
       # predict the noise residual
       __UpperCAmelCase					=   self.unet(__A     ,			__A					).sample
       # compute previous image: x_t -> x_t-1
       __UpperCAmelCase					=   self.scheduler.step(__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					).prev_sample

      else:
       # compute the reverse: x_t-1 -> x_t
       __UpperCAmelCase					=   self.scheduler.undo_step(__A     ,			__A     ,			__A					)
      __UpperCAmelCase					=   t

     __UpperCAmelCase					=   (image / 2 + 0.5).clamp(0     ,			1					)
     __UpperCAmelCase					=   image.cpu().permute(0     ,			2     ,			3     ,			1					).numpy()
     if output_type == "pil":
      __UpperCAmelCase					=   self.numpy_to_pil(__A					)

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=__A					)












'''simple docstring'''




import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=[]    )->     Any:
 __UpperCAmelCase					=   size[0] - overlap_pixels * 2
 __UpperCAmelCase					=   size[1] - overlap_pixels * 2
 for letter in ["l", "r"]:
  if letter in remove_borders:
   size_x += overlap_pixels
 for letter in ["t", "b"]:
  if letter in remove_borders:
   size_y += overlap_pixels
 __UpperCAmelCase					=   np.ones((size_y, size_x)       ,    dtype=np.uinta    ) * 2_55
 __UpperCAmelCase					=   np.pad(_lowerCAmelCase       ,    mode='linear_ramp'       ,    pad_width=_lowerCAmelCase       ,    end_values=0    )

 if "l" in remove_borders:
  __UpperCAmelCase					=   mask[:, overlap_pixels : mask.shape[1]]
 if "r" in remove_borders:
  __UpperCAmelCase					=   mask[:, 0 : mask.shape[1] - overlap_pixels]
 if "t" in remove_borders:
  __UpperCAmelCase					=   mask[overlap_pixels : mask.shape[0], :]
 if "b" in remove_borders:
  __UpperCAmelCase					=   mask[0 : mask.shape[0] - overlap_pixels, :]
 return mask







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
 return max(_lowerCAmelCase       ,    min(_lowerCAmelCase       ,    _lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 return (
     clamp(rect[0]       ,    min[0]       ,    max[0]    ),
     clamp(rect[1]       ,    min[1]       ,    max[1]    ),
     clamp(rect[2]       ,    min[0]       ,    max[0]    ),
     clamp(rect[3]       ,    min[1]       ,    max[1]    ),
 )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 __UpperCAmelCase					=   clamp_rect(_lowerCAmelCase       ,    [0, 0]       ,    [image_size[0], image_size[1]]    )
 return rect







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   Image.new('RGB'       ,    (tile.size[0] + original_slice, tile.size[1])    )
 result.paste(
     original_image.resize((tile.size[0], tile.size[1])       ,    Image.BICUBIC    ).crop(
         (slice_x, 0, slice_x + original_slice, tile.size[1])    )       ,    (0, 0)       ,    )
 result.paste(_lowerCAmelCase       ,    (original_slice, 0)    )
 return result







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					=   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 __UpperCAmelCase					=   tile.crop(_lowerCAmelCase    )
 return tile







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					=   n % d
 return n - divisor






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A = 350     ,			):
     super().__init__(
         vae=__A     ,			text_encoder=__A     ,			tokenizer=__A     ,			unet=__A     ,			low_res_scheduler=__A     ,			scheduler=__A     ,			max_noise_level=__A     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			**__A					):
     torch.manual_seed(0					)
     __UpperCAmelCase					=   (
         min(image.size[0] - (tile_size + original_image_slice)     ,			x * tile_size					),
         min(image.size[1] - (tile_size + original_image_slice)     ,			y * tile_size					),
         min(image.size[0]     ,			(x + 1) * tile_size					),
         min(image.size[1]     ,			(y + 1) * tile_size					),
     )
     __UpperCAmelCase					=   add_overlap_rect(__A     ,			__A     ,			image.size					)
     __UpperCAmelCase					=   image.crop(__A					)
     __UpperCAmelCase					=   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     __UpperCAmelCase					=   translated_slice_x - (original_image_slice / 2)
     __UpperCAmelCase					=   max(0     ,			__A					)
     __UpperCAmelCase					=   squeeze_tile(__A     ,			__A     ,			__A     ,			__A					)
     __UpperCAmelCase					=   to_input.size
     __UpperCAmelCase					=   to_input.resize((tile_size, tile_size)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   super(__A     ,			self					).__call__(image=__A     ,			**__A					).images[0]
     __UpperCAmelCase					=   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   unsqueeze_tile(__A     ,			__A					)
     __UpperCAmelCase					=   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   []
     if x == 0:
      remove_borders.append('l'					)
     elif crop_rect[2] == image.size[0]:
      remove_borders.append('r'					)
     if y == 0:
      remove_borders.append('t'					)
     elif crop_rect[3] == image.size[1]:
      remove_borders.append('b'					)
     __UpperCAmelCase					=   Image.fromarray(
         make_transparency_mask(
             (upscaled_tile.size[0], upscaled_tile.size[1])     ,			tile_border * 4     ,			remove_borders=__A					)     ,			mode='L'     ,			)
     final_image.paste(
         __A     ,			(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)     ,			__A					)




    @torch.no_grad()
    def __call__(  self     ,			__A     ,			__A     ,			__A = 75     ,			__A = 9.0     ,			__A = 50     ,			__A = None     ,			__A = 1     ,			__A = 0.0     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = 1     ,			__A = 128     ,			__A = 32     ,			__A = 32     ,			):
     __UpperCAmelCase					=   Image.new('RGB'     ,			(image.size[0] * 4, image.size[1] * 4)					)
     __UpperCAmelCase					=   math.ceil(image.size[0] / tile_size					)
     __UpperCAmelCase					=   math.ceil(image.size[1] / tile_size					)
     __UpperCAmelCase					=   tcx * tcy
     __UpperCAmelCase					=   0
     for y in range(__A					):
      for x in range(__A					):
       self._process_tile(
           __A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			prompt=__A     ,			num_inference_steps=__A     ,			guidance_scale=__A     ,			noise_level=__A     ,			negative_prompt=__A     ,			num_images_per_prompt=__A     ,			eta=__A     ,			generator=__A     ,			latents=__A     ,			)
       current_count += 1
       if callback is not None:
        callback({'progress': current_count / total_tile_count, 'image': final_image}					)
     return final_image







def _lowerCAmelCase     (      )->     str:
 # Run a demo
 __UpperCAmelCase					=   'stabilityai/stable-diffusion-x4-upscaler'
 __UpperCAmelCase					=   StableDiffusionTiledUpscalePipeline.from_pretrained(_lowerCAmelCase       ,    revision='fp16'       ,    torch_dtype=torch.floataa    )
 __UpperCAmelCase					=   pipe.to('cuda'    )
 __UpperCAmelCase					=   Image.open('../../docs/source/imgs/diffusers_library.jpg'    )

 def callback(_lowerCAmelCase    ):
  print(F'progress: {obj["progress"]:.4f}'    )
  obj["image"].save('diffusers_library_progress.jpg'    )

 __UpperCAmelCase					=   pipe(image=_lowerCAmelCase       ,    prompt='Black font, white background, vector'       ,    noise_level=40       ,    callback=_lowerCAmelCase    )
 final_image.save('diffusers_library.jpg'    )


if __name__ == "__main__":
    main()





