

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      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=[] ):







							'''simple docstring'''






							snake_case_     							=       size[0] - overlap_pixels * 2
							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
							snake_case_     							=       np.ones((size_y, size_x)  ,				dtype=np.uinta ) * 255
							snake_case_     							=       np.pad(UpperCamelCase__  ,				mode='linear_ramp'  ,				pad_width=UpperCamelCase__  ,				end_values=0 )

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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							return max(UpperCamelCase__  ,				min(UpperCamelCase__  ,				UpperCamelCase__ ) )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							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(UpperCamelCase__  ,				(original_slice, 0) )
							return result





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       n % d
							return n - divisor







class 					lowercase							(			lowercase_     ):




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 350 ,  ):
											super().__init__(
											    vae=snake_case ,  text_encoder=snake_case ,  tokenizer=snake_case ,  unet=snake_case ,  low_res_scheduler=snake_case ,  scheduler=snake_case ,  max_noise_level=snake_case ,  )




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




				@torch.no_grad()
				def __call__(   self ,  snake_case ,  snake_case ,  snake_case = 75 ,  snake_case = 9.0 ,  snake_case = 50 ,  snake_case = None ,  snake_case = 1 ,  snake_case = 0.0 ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = 1 ,  snake_case = 128 ,  snake_case = 32 ,  snake_case = 32 ,  ):
											snake_case_     							=       Image.new('RGB' ,  (image.size[0] * 4, image.size[1] * 4)			)
											snake_case_     							=       math.ceil(image.size[0] / tile_size			)
											snake_case_     							=       math.ceil(image.size[1] / tile_size			)
											snake_case_     							=       tcx * tcy
											snake_case_     							=       0
											for y in range(snake_case			):
																		for x in range(snake_case			):
																									self._process_tile(
																									    snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  prompt=snake_case ,  num_inference_steps=snake_case ,  guidance_scale=snake_case ,  noise_level=snake_case ,  negative_prompt=snake_case ,  num_images_per_prompt=snake_case ,  eta=snake_case ,  generator=snake_case ,  latents=snake_case ,  )
																									current_count += 1
																									if callback is not None:
																																callback({'progress': current_count / total_tile_count, 'image': final_image}			)
											return final_image





def    __lowerCamelCase      (					):







							'''simple docstring'''






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

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

							snake_case_     							=       pipe(image=UpperCamelCase__  ,				prompt='Black font, white background, vector'  ,				noise_level=40  ,				callback=UpperCamelCase__ )
							final_image.save('diffusers_library.jpg' )


if __name__ == "__main__":
					main()







import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {' ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}' ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  int									=			argparse.ArgumentParser()
					parser.add_argument("""--version""", help="""Release version.""")
					_UpperCAmelCase						:  str									=			parser.parse_args()
					update_custom_js(args.version)




