

"""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   _A					(	_a	:						str     ,   _a	:						Dict     ,   _a	:						int=[]				):



      """simple docstring"""






      A				  =		size[0] - overlap_pixels * 2
      A				  =		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
      A				  =		np.ones((size_y, size_x)     ,   dtype=np.uinta				) * 2_5_5
      A				  =		np.pad(_a     ,   mode="""linear_ramp"""     ,   pad_width=_a     ,   end_values=0				)

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



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]     ,   _a	:						Dict				):



      """simple docstring"""






      return max(_a     ,   min(_a     ,   _a				)				)



def   _A					(	_a	:						[int]     ,   _a	:						[int]     ,   _a	:						[int]				):



      """simple docstring"""






      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   _A					(	_a	:						[int]     ,   _a	:						int     ,   _a	:						[int]				):



      """simple docstring"""






      A				  =		list(_a				)
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      A				  =		clamp_rect(_a     ,   [0, 0]     ,   [image_size[0], image_size[1]]				)
      return rect



def   _A					(	_a	:						str     ,   _a	:						Tuple     ,   _a	:						str     ,   _a	:						Any				):



      """simple docstring"""






      A				  =		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(_a     ,   (original_slice, 0)				)
      return result



def   _A					(	_a	:						str     ,   _a	:						int				):



      """simple docstring"""






      A				  =		(original_image_slice * 4, 0, tile.size[0], tile.size[1])
      A				  =		tile.crop(_a				)
      return tile



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Dict				):



      """simple docstring"""






      A				  =		n % d
      return n - divisor





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 3_5_0 ,)    ->      int:
          super().__init__(
              vae=lowerCamelCase_ ,text_encoder=lowerCamelCase_ ,tokenizer=lowerCamelCase_ ,unet=lowerCamelCase_ ,low_res_scheduler=lowerCamelCase_ ,scheduler=lowerCamelCase_ ,max_noise_level=lowerCamelCase_ ,)




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_    )    ->      int:
          torch.manual_seed(0    )
          A				  =		(
              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    ),
          )
          A				  =		add_overlap_rect(lowerCamelCase_ ,lowerCamelCase_ ,image.size    )
          A				  =		image.crop(lowerCamelCase_    )
          A				  =		((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
          A				  =		translated_slice_x - (original_image_slice / 2)
          A				  =		max(0 ,lowerCamelCase_    )
          A				  =		squeeze_tile(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )
          A				  =		to_input.size
          A				  =		to_input.resize((tile_size, tile_size) ,Image.BICUBIC    )
          A				  =		super(lowerCamelCase_ ,self    ).__call__(image=lowerCamelCase_ ,**lowerCamelCase_    ).images[0]
          A				  =		upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,Image.BICUBIC    )
          A				  =		unsqueeze_tile(lowerCamelCase_ ,lowerCamelCase_    )
          A				  =		upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,Image.BICUBIC    )
          A				  =		[]
          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"""    )
          A				  =		Image.fromarray(
              make_transparency_mask(
                  (upscaled_tile.size[0], upscaled_tile.size[1]) ,tile_border * 4 ,remove_borders=lowerCamelCase_    ) ,mode="""L""" ,)
          final_image.paste(
              lowerCamelCase_ ,(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) ,lowerCamelCase_    )




    @torch.no_grad()
    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 7_5 ,lowerCamelCase_ = 9.0 ,lowerCamelCase_ = 5_0 ,lowerCamelCase_ = None ,lowerCamelCase_ = 1 ,lowerCamelCase_ = 0.0 ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = 1 ,lowerCamelCase_ = 1_2_8 ,lowerCamelCase_ = 3_2 ,lowerCamelCase_ = 3_2 ,)    ->      str:
          A				  =		Image.new("""RGB""" ,(image.size[0] * 4, image.size[1] * 4)    )
          A				  =		math.ceil(image.size[0] / tile_size    )
          A				  =		math.ceil(image.size[1] / tile_size    )
          A				  =		tcx * tcy
          A				  =		0
          for y in range(lowerCamelCase_    ):
                for x in range(lowerCamelCase_    ):
                      self._process_tile(
                          lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,prompt=lowerCamelCase_ ,num_inference_steps=lowerCamelCase_ ,guidance_scale=lowerCamelCase_ ,noise_level=lowerCamelCase_ ,negative_prompt=lowerCamelCase_ ,num_images_per_prompt=lowerCamelCase_ ,eta=lowerCamelCase_ ,generator=lowerCamelCase_ ,latents=lowerCamelCase_ ,)
                      current_count += 1
                      if callback is not None:
                            callback({"""progress""": current_count / total_tile_count, """image""": final_image}    )
          return final_image




def   _A					(	):



      """simple docstring"""






      A				  =		"""stabilityai/stable-diffusion-x4-upscaler"""
      A				  =		StableDiffusionTiledUpscalePipeline.from_pretrained(_a     ,   revision="""fp16"""     ,   torch_dtype=torch.floataa				)
      A				  =		pipe.to("""cuda"""				)
      A				  =		Image.open("""../../docs/source/imgs/diffusers_library.jpg"""				)

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

      A				  =		pipe(image=_a     ,   prompt="""Black font, white background, vector"""     ,   noise_level=4_0     ,   callback=_a				)
      final_image.save("""diffusers_library.jpg"""				)


if __name__ == "__main__":
  main()







"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




