





import os

_UpperCAmelCase				:       int	   =				{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    lowerCamelCase__    :      List[str]					 =		0

    lowerCamelCase__    :      Optional[Any]					 =		0
    while index < len(_UpperCAmelCase ) - 1:
        lowerCamelCase__    :      Optional[int]					 =		SYMBOLS[numerals[index]]
        lowerCamelCase__    :      Optional[Any]					 =		SYMBOLS[numerals[index + 1]]
        if current_value < next_value:
            total_value -= current_value
        else:
            total_value += current_value
        index += 1
    total_value += SYMBOLS[numerals[index]]

    return total_value


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      int					 =		''

    lowerCamelCase__    :      Any					 =		num // 1000
    numerals += m_count * "M"
    num %= 1000

    lowerCamelCase__    :      str					 =		num // 100
    if c_count == 9:
        numerals += "CM"
        c_count -= 9
    elif c_count == 4:
        numerals += "CD"
        c_count -= 4
    if c_count >= 5:
        numerals += "D"
        c_count -= 5
    numerals += c_count * "C"
    num %= 100

    lowerCamelCase__    :      List[str]					 =		num // 10
    if x_count == 9:
        numerals += "XC"
        x_count -= 9
    elif x_count == 4:
        numerals += "XL"
        x_count -= 4
    if x_count >= 5:
        numerals += "L"
        x_count -= 5
    numerals += x_count * "X"
    num %= 10

    if num == 9:
        numerals += "IX"
        num -= 9
    elif num == 4:
        numerals += "IV"
        num -= 4
    if num >= 5:
        numerals += "V"
        num -= 5
    numerals += num * "I"

    return numerals


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = "/p089_roman.txt" )			->     int:
    lowerCamelCase__    :      Tuple					 =		0

    with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
        lowerCamelCase__    :      List[str]					 =		filea.readlines()

    for line in lines:
        lowerCamelCase__    :      Optional[int]					 =		line.strip()
        lowerCamelCase__    :      Union[str, Any]					 =		parse_roman_numerals(_UpperCAmelCase )
        lowerCamelCase__    :      List[Any]					 =		generate_roman_numerals(_UpperCAmelCase )
        savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )

    return savings


if __name__ == "__main__":
    print(F"""{solution() = }""")









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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=[]	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				size[0] - overlap_pixels * 2
  lowerCamelCase :					int    =				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
  lowerCamelCase :					Tuple    =				np.ones((size_y, size_x)      ,						dtype=np.uinta	) * 255
  lowerCamelCase :					List[Any]    =				np.pad(SCREAMING_SNAKE_CASE_      ,						mode="linear_ramp"      ,						pad_width=SCREAMING_SNAKE_CASE_      ,						end_values=0	)

  if "l" in remove_borders:
    lowerCamelCase :					Optional[Any]    =				mask[:, overlap_pixels : mask.shape[1]]
  if "r" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[:, 0 : mask.shape[1] - overlap_pixels]
  if "t" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[overlap_pixels : mask.shape[0], :]
  if "b" in remove_borders:
    lowerCamelCase :					Tuple    =				mask[0 : mask.shape[0] - overlap_pixels, :]
  return mask






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return max(SCREAMING_SNAKE_CASE_      ,						min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				list(SCREAMING_SNAKE_CASE_	)
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  lowerCamelCase :					Any    =				clamp_rect(SCREAMING_SNAKE_CASE_      ,						[0, 0]      ,						[image_size[0], image_size[1]]	)
  return rect






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				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(SCREAMING_SNAKE_CASE_      ,						(original_slice, 0)	)
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  lowerCamelCase :					int    =				tile.crop(SCREAMING_SNAKE_CASE_	)
  return tile






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				n % d
  return n - divisor
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A = 350						,				):






     """simple docstring"""





     super().__init__(
         vae=__A						,				text_encoder=__A						,				tokenizer=__A						,				unet=__A						,				low_res_scheduler=__A						,				scheduler=__A						,				max_noise_level=__A						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					Tuple    =				(
         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 ),
     )
     lowerCamelCase :					Union[str, Any]    =				add_overlap_rect(__A						,				__A						,				image.size )
     lowerCamelCase :					List[str]    =				image.crop(__A )
     lowerCamelCase :					Optional[int]    =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     lowerCamelCase :					int    =				translated_slice_x - (original_image_slice / 2)
     lowerCamelCase :					Optional[Any]    =				max(0						,				__A )
     lowerCamelCase :					Tuple    =				squeeze_tile(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					Dict    =				to_input.size
     lowerCamelCase :					Optional[int]    =				to_input.resize((tile_size, tile_size)						,				Image.BICUBIC )
     lowerCamelCase :					Dict    =				super(__A						,				self ).__call__(image=__A						,				**__A ).images[0]
     lowerCamelCase :					Tuple    =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					Optional[Any]    =				unsqueeze_tile(__A						,				__A )
     lowerCamelCase :					Optional[Any]    =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					int    =				[]
     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" )
     lowerCamelCase :					int    =				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						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				Image.new("RGB"						,				(image.size[0] * 4, image.size[1] * 4) )
     lowerCamelCase :					Union[str, Any]    =				math.ceil(image.size[0] / tile_size )
     lowerCamelCase :					Dict    =				math.ceil(image.size[1] / tile_size )
     lowerCamelCase :					str    =				tcx * tcy
     lowerCamelCase :					int    =				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 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"stabilityai/stable-diffusion-x4-upscaler"
  lowerCamelCase :					Union[str, Any]    =				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,						revision="fp16"      ,						torch_dtype=torch.floataa	)
  lowerCamelCase :					Optional[Any]    =				pipe.to("cuda"	)
  lowerCamelCase :					List[str]    =				Image.open("../../docs/source/imgs/diffusers_library.jpg"	)

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

  lowerCamelCase :					int    =				pipe(image=SCREAMING_SNAKE_CASE_      ,						prompt="Black font, white background, vector"      ,						noise_level=40      ,						callback=SCREAMING_SNAKE_CASE_	)
  final_image.save("diffusers_library.jpg"	)


if __name__ == "__main__":
   main()



