

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

	if "l" in remove_borders:
		A__							:   str					       =		mask[:, overlap_pixels : mask.shape[1]]
	if "r" in remove_borders:
		A__							:   int					       =		mask[:, 0 : mask.shape[1] - overlap_pixels]
	if "t" in remove_borders:
		A__							:   Optional[Any]					       =		mask[overlap_pixels : mask.shape[0], :]
	if "b" in remove_borders:
		A__							:   str					       =		mask[0 : mask.shape[0] - overlap_pixels, :]
	return mask

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			List[Any]   ,				lowercase_:			Any )					->				List[str]:
	return max(lowercase_   ,				min(lowercase_   ,				lowercase_ ) )

def 						UpperCamelCase (lowercase_:			[int]   ,				lowercase_:			[int]   ,				lowercase_:			[int] )					->				Any:
	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 						UpperCamelCase (lowercase_:			[int]   ,				lowercase_:			int   ,				lowercase_:			[int] )					->				int:
	A__							:   str					       =		list(lowercase_ )
	rect[0] -= overlap
	rect[1] -= overlap
	rect[2] += overlap
	rect[3] += overlap
	A__							:   Union[str, Any]					       =		clamp_rect(lowercase_   ,				[0, 0]   ,				[image_size[0], image_size[1]] )
	return rect

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			str )					->				Union[str, Any]:
	A__							:   Optional[Any]					       =		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(lowercase_   ,				(original_slice, 0) )
	return result

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Optional[Any] )					->				int:
	A__							:   Tuple					       =		(original_image_slice * 4, 0, tile.size[0], tile.size[1])
	A__							:   str					       =		tile.crop(lowercase_ )
	return tile

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple )					->				List[str]:
	A__							:   Tuple					       =		n % d
	return n - divisor



class    _a						(__magic_name__    ):


					'''simple docstring'''






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






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	**A__      ):
						torch.manual_seed(0      )
						A__							:   Optional[Any]					       =		(
						    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      ),
						)
						A__							:   List[Any]					       =		add_overlap_rect(A__   ,	A__   ,	image.size      )
						A__							:   Union[str, Any]					       =		image.crop(A__      )
						A__							:   Optional[int]					       =		((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
						A__							:   int					       =		translated_slice_x - (original_image_slice / 2)
						A__							:   List[str]					       =		max(0   ,	A__      )
						A__							:   Tuple					       =		squeeze_tile(A__   ,	A__   ,	A__   ,	A__      )
						A__							:   Optional[int]					       =		to_input.size
						A__							:   Optional[int]					       =		to_input.resize((tile_size, tile_size)   ,	Image.BICUBIC      )
						A__							:   Tuple					       =		super(A__   ,	self      ).__call__(image=A__   ,	**A__      ).images[0]
						A__							:   Optional[Any]					       =		upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)   ,	Image.BICUBIC      )
						A__							:   Tuple					       =		unsqueeze_tile(A__   ,	A__      )
						A__							:   str					       =		upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)   ,	Image.BICUBIC      )
						A__							:   Any					       =		[]
						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"""      )
						A__							:   List[Any]					       =		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   ,	):
						A__							:   List[str]					       =		Image.new("""RGB"""   ,	(image.size[0] * 4, image.size[1] * 4)      )
						A__							:   Union[str, Any]					       =		math.ceil(image.size[0] / tile_size      )
						A__							:   Tuple					       =		math.ceil(image.size[1] / tile_size      )
						A__							:   Union[str, Any]					       =		tcx * tcy
						A__							:   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 						UpperCamelCase ()					->				Union[str, Any]:
	# Run a demo
	A__							:   Optional[int]					       =		"""stabilityai/stable-diffusion-x4-upscaler"""
	A__							:   List[Any]					       =		StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase_   ,				revision="""fp16"""   ,				torch_dtype=torch.floataa )
	A__							:   Any					       =		pipe.to("""cuda""" )
	A__							:   str					       =		Image.open("""../../docs/source/imgs/diffusers_library.jpg""" )

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

	A__							:   Optional[Any]					       =		pipe(image=lowercase_   ,				prompt="""Black font, white background, vector"""   ,				noise_level=40   ,				callback=lowercase_ )
	final_image.save("""diffusers_library.jpg""" )


if __name__ == "__main__":
	main()






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



