


from __future__ import annotations






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


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				[]
  lowerCamelCase    ,	lowerCamelCase :					Tuple    =				input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0	)	)
  lowerCamelCase :					Optional[Any]    =				result + left + right
  return input_list






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if len(SCREAMING_SNAKE_CASE_	) <= 1:
    return input_list
  lowerCamelCase :					Union[str, Any]    =				list(SCREAMING_SNAKE_CASE_	)

  # iteration for two-way merging
  lowerCamelCase :					List[Any]    =				2
  while p <= len(SCREAMING_SNAKE_CASE_	):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0      ,						len(SCREAMING_SNAKE_CASE_	)      ,						SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					Union[str, Any]    =				i
      lowerCamelCase :					Any    =				i + p - 1
      lowerCamelCase :					Optional[int]    =				(low + high + 1) // 2
      lowerCamelCase :					Union[str, Any]    =				merge(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
    # final merge of last two parts
    if p * 2 >= len(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					List[str]    =				i
      lowerCamelCase :					List[str]    =				merge(SCREAMING_SNAKE_CASE_      ,						0      ,						SCREAMING_SNAKE_CASE_      ,						len(SCREAMING_SNAKE_CASE_	) - 1	)
      break
    p *= 2

  return input_list


if __name__ == "__main__":
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   if user_input == "":
      _snake_case             =     []
   else:
      _snake_case             =     [int(item.strip()) for item in user_input.split(''',''')]
   print(iter_merge_sort(unsorted))







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()



