

'''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__      ,    a__      ,    a__=[]						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      size[0] - overlap_pixels * 2
     __SCREAMING_SNAKE_CASE     =      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
     __SCREAMING_SNAKE_CASE     =      np.ones((size_y, size_x)      ,    dtype=np.uinta						) * 2_55
     __SCREAMING_SNAKE_CASE     =      np.pad(a__      ,    mode="""linear_ramp"""      ,    pad_width=a__      ,    end_values=0						)

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







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     return max(a__      ,    min(a__      ,    a__						)						)







def    a__       (  a__      ,    a__      ,    a__						):




     """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__      ,    a__      ,    a__						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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__      ,    a__						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      n % d
     return n - divisor







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       AutoencoderKL					,							__SCREAMING_SNAKE_CASE					:       CLIPTextModel					,							__SCREAMING_SNAKE_CASE					:       CLIPTokenizer					,							__SCREAMING_SNAKE_CASE					:       UNetaDConditionModel					,							__SCREAMING_SNAKE_CASE					:       DDPMScheduler					,							__SCREAMING_SNAKE_CASE					:       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,							__SCREAMING_SNAKE_CASE					:       int = 350					,							)  -> int:


      """simple docstring"""




      super().__init__(
          vae=__SCREAMING_SNAKE_CASE					,							text_encoder=__SCREAMING_SNAKE_CASE					,							tokenizer=__SCREAMING_SNAKE_CASE					,							unet=__SCREAMING_SNAKE_CASE					,							low_res_scheduler=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							max_noise_level=__SCREAMING_SNAKE_CASE					,							)
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> List[Any]:


      """simple docstring"""




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


 @torch.no_grad()
 def __call__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Union[str, List[str]]					,							__SCREAMING_SNAKE_CASE					:       Union[PIL.Image.Image, List[PIL.Image.Image]]					,							__SCREAMING_SNAKE_CASE					:       int = 75					,							__SCREAMING_SNAKE_CASE					:       float = 9.0					,							__SCREAMING_SNAKE_CASE					:       int = 50					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[str, List[str]]] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = 1					,							__SCREAMING_SNAKE_CASE					:       float = 0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.Generator] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[torch.FloatTensor] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,							__SCREAMING_SNAKE_CASE					:       int = 1					,							__SCREAMING_SNAKE_CASE					:       int = 128					,							__SCREAMING_SNAKE_CASE					:       int = 32					,							__SCREAMING_SNAKE_CASE					:       int = 32					,							)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      Image.new("""RGB"""					,							(image.size[0] * 4, image.size[1] * 4)			)
      __SCREAMING_SNAKE_CASE     =      math.ceil(image.size[0] / tile_size			)
      __SCREAMING_SNAKE_CASE     =      math.ceil(image.size[1] / tile_size			)
      __SCREAMING_SNAKE_CASE     =      tcx * tcy
      __SCREAMING_SNAKE_CASE     =      0
      for y in range(__SCREAMING_SNAKE_CASE			):
           for x in range(__SCREAMING_SNAKE_CASE			):
                self._process_tile(
                    __SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							prompt=__SCREAMING_SNAKE_CASE					,							num_inference_steps=__SCREAMING_SNAKE_CASE					,							guidance_scale=__SCREAMING_SNAKE_CASE					,							noise_level=__SCREAMING_SNAKE_CASE					,							negative_prompt=__SCREAMING_SNAKE_CASE					,							num_images_per_prompt=__SCREAMING_SNAKE_CASE					,							eta=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							latents=__SCREAMING_SNAKE_CASE					,							)
                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"""


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

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

     __SCREAMING_SNAKE_CASE     =      pipe(image=a__      ,    prompt="""Black font, white background, vector"""      ,    noise_level=40      ,    callback=a__						)
     final_image.save("""diffusers_library.jpg"""						)


if __name__ == "__main__":
  main()









'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)

     # Initialize Result
     __SCREAMING_SNAKE_CASE     =      []

     # Traverse through all denomination
     for denomination in reversed(a__						):
          # Find denominations
          while int(a__						) >= int(a__						):
               total_value -= int(a__						)
               answer.append(a__						)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     []
  UpperCAmelCase :       List[str]			   =     '0'

  if (
      input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
      == "y"
  ):
    UpperCAmelCase :       List[str]			   =     int(input('Enter the number of denominations you want to add: ').strip())

    for i in range(0, n):
      denominations.append(int(input(f"""Denomination {i}: """).strip()))
    UpperCAmelCase :       str			   =     input('Enter the change you want to make in Indian Currency: ').strip()
  else:
    # All denominations of Indian Currency if user does not enter
    UpperCAmelCase :       int			   =     [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
    UpperCAmelCase :       Any			   =     input('Enter the change you want to make: ').strip()

  if int(value) == 0 or int(value) < 0:
    print('The total value cannot be zero or negative.')

  else:
    print(f"""Following is minimal change for {value}: """)
    UpperCAmelCase :       Any			   =     find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=' ')






