
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 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=[]			)		->					Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase         =						size[0] - overlap_pixels * 2
		_UpperCamelCase         =						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
		_UpperCamelCase         =						np.ones((size_y, size_x)    ,	dtype=np.uinta			) * 2_5_5
		_UpperCamelCase         =						np.pad(_a    ,	mode="""linear_ramp"""    ,	pad_width=_a    ,	end_values=0			)

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					str:

		"""simple docstring"""






		return max(_a    ,	min(_a    ,	_a			)			)


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					str:

		"""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 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					List[str]:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase         =						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(_a    ,	(original_slice, 0)			)
		return result


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					int:

		"""simple docstring"""






		_UpperCamelCase         =						n % d
		return n - divisor







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a							,				a							,				a							,				a							,				a							,				a = 3_50							,				)       ->   int:


								'''simple docstring'''

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

						def 			A_					(      self							,				a							,				a							,				a							,				a							,				a							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								torch.manual_seed(0					)
								_UpperCamelCase         =						(
								    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					),
								)
								_UpperCamelCase         =						add_overlap_rect(lowerCamelCase_							,				lowerCamelCase_							,				image.size					)
								_UpperCamelCase         =						image.crop(lowerCamelCase_					)
								_UpperCamelCase         =						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
								_UpperCamelCase         =						translated_slice_x - (original_image_slice / 2)
								_UpperCamelCase         =						max(0							,				lowerCamelCase_					)
								_UpperCamelCase         =						squeeze_tile(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_					)
								_UpperCamelCase         =						to_input.size
								_UpperCamelCase         =						to_input.resize((tile_size, tile_size)							,				Image.BICUBIC					)
								_UpperCamelCase         =						super(lowerCamelCase_							,				self					).__call__(image=lowerCamelCase_							,				**lowerCamelCase_					).images[0]
								_UpperCamelCase         =						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)							,				Image.BICUBIC					)
								_UpperCamelCase         =						unsqueeze_tile(lowerCamelCase_							,				lowerCamelCase_					)
								_UpperCamelCase         =						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)							,				Image.BICUBIC					)
								_UpperCamelCase         =						[]
								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"""					)
								_UpperCamelCase         =						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							,				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 = 1_28							,				a = 32							,				a = 32							,				)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						Image.new("""RGB"""							,				(image.size[0] * 4, image.size[1] * 4)					)
								_UpperCamelCase         =						math.ceil(image.size[0] / tile_size					)
								_UpperCamelCase         =						math.ceil(image.size[1] / tile_size					)
								_UpperCamelCase         =						tcx * tcy
								_UpperCamelCase         =						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 				__A()		->					Dict:

		"""simple docstring"""






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

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

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


if __name__ == "__main__":
		main()






"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




