
"""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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=[]						):
			lowerCamelCase_					=  size[0] - overlap_pixels * 2
			lowerCamelCase_					=  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_					=  np.ones((size_y, size_x)	,dtype=np.uinta						) * 255
			lowerCamelCase_					=  np.pad(lowerCAmelCase__	,mode='''linear_ramp'''	,pad_width=lowerCAmelCase__	,end_values=0						)

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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			return max(lowerCAmelCase__	,min(lowerCAmelCase__	,lowerCAmelCase__						)						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			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						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  list(lowerCAmelCase__						)
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			lowerCamelCase_					=  clamp_rect(lowerCAmelCase__	,[0, 0]	,[image_size[0], image_size[1]]						)
			return rect






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  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(lowerCAmelCase__	,(original_slice, 0)						)
			return result






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
			lowerCamelCase_					=  tile.crop(lowerCAmelCase__						)
			return tile






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  n % d
			return n - divisor




class  __lowerCamelCase  ( lowerCAmelCase			):




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




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





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






def  lowercase						(	):
			# Run a demo
			lowerCamelCase_					=  '''stabilityai/stable-diffusion-x4-upscaler'''
			lowerCamelCase_					=  StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCAmelCase__	,revision='''fp16'''	,torch_dtype=torch.floataa						)
			lowerCamelCase_					=  pipe.to('''cuda'''						)
			lowerCamelCase_					=  Image.open('''../../docs/source/imgs/diffusers_library.jpg'''						)

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

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


if __name__ == "__main__":
						main()










def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    return int((input_a, input_a).count(0				) != 0				)





def 				__lowerCAmelCase(       )							-> None:




    """simple docstring"""




    assert nand_gate(0							,     0				) == 1
    assert nand_gate(0							,     1				) == 1
    assert nand_gate(1							,     0				) == 1
    assert nand_gate(1							,     1				) == 0


if __name__ == "__main__":
 print(nand_gate(0, 0))
 print(nand_gate(0, 1))
 print(nand_gate(1, 0))
 print(nand_gate(1, 1))





