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__       :							Optional[int] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Optional[int]=[]					):
					SCREAMING_SNAKE_CASE           =    size[0] - overlap_pixels * 2
					SCREAMING_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
					SCREAMING_SNAKE_CASE           =    np.ones((size_y, size_x) ,	dtype=np.uinta					) * 2_5_5
					SCREAMING_SNAKE_CASE           =    np.pad(__lowerCAmelCase ,	mode="linear_ramp" ,	pad_width=__lowerCAmelCase ,	end_values=0					)

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any]					):
					return max(__lowerCAmelCase ,	min(__lowerCAmelCase ,	__lowerCAmelCase					)					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							[int] ,	UpperCAmelCase__       :							[int] ,	UpperCAmelCase__       :							[int]					):
					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__       :							[int] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							[int]					):
					SCREAMING_SNAKE_CASE           =    list(__lowerCAmelCase					)
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					SCREAMING_SNAKE_CASE           =    clamp_rect(__lowerCAmelCase ,	[0, 0] ,	[image_size[0], image_size[1]]					)
					return rect





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any					):
					SCREAMING_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(__lowerCAmelCase ,	(original_slice, 0)					)
					return result





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					SCREAMING_SNAKE_CASE           =    tile.crop(__lowerCAmelCase					)
					return tile





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    n % d
					return n - divisor


class   lowercase      (       A__       ):




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



												'''simple docstring'''

												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 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str]		,							**_UpperCamelCase				:   Optional[Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_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 ),
												)
												SCREAMING_SNAKE_CASE           =    add_overlap_rect(__snake_case		,							__snake_case		,							image.size )
												SCREAMING_SNAKE_CASE           =    image.crop(__snake_case )
												SCREAMING_SNAKE_CASE           =    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
												SCREAMING_SNAKE_CASE           =    translated_slice_x - (original_image_slice / 2)
												SCREAMING_SNAKE_CASE           =    max(0		,							__snake_case )
												SCREAMING_SNAKE_CASE           =    squeeze_tile(__snake_case		,							__snake_case		,							__snake_case		,							__snake_case )
												SCREAMING_SNAKE_CASE           =    to_input.size
												SCREAMING_SNAKE_CASE           =    to_input.resize((tile_size, tile_size)		,							Image.BICUBIC )
												SCREAMING_SNAKE_CASE           =    super(__snake_case		,							self ).__call__(image=__snake_case		,							**__snake_case ).images[0]
												SCREAMING_SNAKE_CASE           =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,							Image.BICUBIC )
												SCREAMING_SNAKE_CASE           =    unsqueeze_tile(__snake_case		,							__snake_case )
												SCREAMING_SNAKE_CASE           =    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,							Image.BICUBIC )
												SCREAMING_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" )
												SCREAMING_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				:   List[Any]		,							_UpperCamelCase				:   Union[str, List[str]]		,							_UpperCamelCase				:   Union[PIL.Image.Image, List[PIL.Image.Image]]		,							_UpperCamelCase				:   int = 75		,							_UpperCamelCase				:   float = 9.0		,							_UpperCamelCase				:   int = 50		,							_UpperCamelCase				:   Optional[Union[str, List[str]]] = None		,							_UpperCamelCase				:   Optional[int] = 1		,							_UpperCamelCase				:   float = 0.0		,							_UpperCamelCase				:   Optional[torch.Generator] = None		,							_UpperCamelCase				:   Optional[torch.FloatTensor] = None		,							_UpperCamelCase				:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,							_UpperCamelCase				:   int = 1		,							_UpperCamelCase				:   int = 128		,							_UpperCamelCase				:   int = 32		,							_UpperCamelCase				:   int = 32		,							)   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    Image.new("RGB"		,							(image.size[0] * 4, image.size[1] * 4) )
												SCREAMING_SNAKE_CASE           =    math.ceil(image.size[0] / tile_size )
												SCREAMING_SNAKE_CASE           =    math.ceil(image.size[1] / tile_size )
												SCREAMING_SNAKE_CASE           =    tcx * tcy
												SCREAMING_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							():
					# Run a demo
					SCREAMING_SNAKE_CASE           =    "stabilityai/stable-diffusion-x4-upscaler"
					SCREAMING_SNAKE_CASE           =    StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase ,	revision="fp16" ,	torch_dtype=torch.floataa					)
					SCREAMING_SNAKE_CASE           =    pipe.to("cuda"					)
					SCREAMING_SNAKE_CASE           =    Image.open("../../docs/source/imgs/diffusers_library.jpg"					)

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

					SCREAMING_SNAKE_CASE           =    pipe(image=__lowerCAmelCase ,	prompt="Black font, white background, vector" ,	noise_level=4_0 ,	callback=__lowerCAmelCase					)
					final_image.save("diffusers_library.jpg"					)


if __name__ == "__main__":
	main()





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




