





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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=[]							)		->    str:
		__lowercase											=     size[0] - overlap_pixels * 2
		__lowercase											=     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
		__lowercase											=     np.ones((size_y, size_x)						,   dtype=np.uinta							) * 255
		__lowercase											=     np.pad(snake_case						,   mode='linear_ramp'						,   pad_width=snake_case						,   end_values=0							)

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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		return max(snake_case						,   min(snake_case						,   snake_case							)							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Tuple:
		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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    List[Any]:
		__lowercase											=     list(snake_case							)
		rect[0] -= overlap
		rect[1] -= overlap
		rect[2] += overlap
		rect[3] += overlap
		__lowercase											=     clamp_rect(snake_case						,   [0, 0]						,   [image_size[0], image_size[1]]							)
		return rect



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Optional[int]:
		__lowercase											=     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(snake_case						,   (original_slice, 0)							)
		return result



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[Any]:
		__lowercase											=     (original_image_slice * 4, 0, tile.size[0], tile.size[1])
		__lowercase											=     tile.crop(snake_case							)
		return tile



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Tuple:
		__lowercase											=     n % d
		return n - divisor

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      __lowerCamelCase							:     AutoencoderKL					,      __lowerCamelCase							:     CLIPTextModel					,      __lowerCamelCase							:     CLIPTokenizer					,      __lowerCamelCase							:     UNetaDConditionModel					,      __lowerCamelCase							:     DDPMScheduler					,      __lowerCamelCase							:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,      __lowerCamelCase							:     int = 350					,      )       -> str:

			'''simple docstring'''




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




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Tuple					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     List[str]					,      **__lowerCamelCase							:     Dict   )       -> int:

			'''simple docstring'''




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






	@torch.no_grad()
	def __call__(						self							:     Dict					,      __lowerCamelCase							:     Union[str, List[str]]					,      __lowerCamelCase							:     Union[PIL.Image.Image, List[PIL.Image.Image]]					,      __lowerCamelCase							:     int = 75					,      __lowerCamelCase							:     float = 9.0					,      __lowerCamelCase							:     int = 50					,      __lowerCamelCase							:     Optional[Union[str, List[str]]] = None					,      __lowerCamelCase							:     Optional[int] = 1					,      __lowerCamelCase							:     float = 0.0					,      __lowerCamelCase							:     Optional[torch.Generator] = None					,      __lowerCamelCase							:     Optional[torch.FloatTensor] = None					,      __lowerCamelCase							:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      __lowerCamelCase							:     int = 1					,      __lowerCamelCase							:     int = 128					,      __lowerCamelCase							:     int = 32					,      __lowerCamelCase							:     int = 32					,      )       -> Dict:

			'''simple docstring'''




			__lowercase											=     Image.new('RGB'					,      (image.size[0] * 4, image.size[1] * 4)   )
			__lowercase											=     math.ceil(image.size[0] / tile_size   )
			__lowercase											=     math.ceil(image.size[1] / tile_size   )
			__lowercase											=     tcx * tcy
			__lowercase											=     0
			for y in range(__lowerCamelCase   ):
					for x in range(__lowerCamelCase   ):
							self._process_tile(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      prompt=__lowerCamelCase					,      num_inference_steps=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      noise_level=__lowerCamelCase					,      negative_prompt=__lowerCamelCase					,      num_images_per_prompt=__lowerCamelCase					,      eta=__lowerCamelCase					,      generator=__lowerCamelCase					,      latents=__lowerCamelCase					,      )
							current_count += 1
							if callback is not None:
									callback({'progress': current_count / total_tile_count, 'image': final_image}   )
			return final_image





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

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

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


if __name__ == "__main__":
		main()











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (position - 1) // 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 1



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 2

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




	def __repr__(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			return str(self.heap   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> bool:

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(__lowerCamelCase   )
					else:
							self._bubble_down(__lowerCamelCase   )
			else:
					self._bubble_down(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			if child_left_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			else:
					return None
			if child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			return None






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




	def __repr__(						self							:     Union[str, Any]   )       -> str:

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





def  SCREAMING_SNAKE_CASE				(      snake_case						,   )		->    tuple[dict[T, int], dict[T, T | None]]:
		__lowercase											=     {node: maxsize for node in graph.connections}
		__lowercase											=     {node: None for node in graph.connections}

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase											=     priority_queue.extract_min()
		__lowercase											=     0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase											=     dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(snake_case						,   dist[neighbour]							)
						__lowercase											=     node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase											=     priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase											=     dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(snake_case						,   dist[neighbour]							)
								__lowercase											=     node
		return dist, parent




