





'''simple docstring'''




from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase = None    )->     str:
 if version.parse(hfh.__version__    ).release < version.parse('0.11.0'    ).release:
  # old versions of hfh don't url-encode the file path
  __UpperCAmelCase					=   quote(_lowerCAmelCase    )
 return hfh.hf_hub_url(_lowerCAmelCase       ,    _lowerCAmelCase       ,    repo_type='dataset'       ,    revision=_lowerCAmelCase    )












'''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()





