




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_					: List[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: List[str]=[]      ):
     """simple docstring"""

     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 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     return max(lowerCAmelCase_       ,    min(lowerCAmelCase_       ,    lowerCAmelCase_      )      )



def 							_A       (						lowerCAmelCase_					: [int]       ,    lowerCAmelCase_					: [int]       ,    lowerCAmelCase_					: [int]      ):
     """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_					: [int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: [int]      ):
     """simple docstring"""

     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 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     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 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			n % d
     return n - divisor





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					AutoencoderKL       ,     SCREAMING_SNAKE_CASE__					:					CLIPTextModel       ,     SCREAMING_SNAKE_CASE__					:					CLIPTokenizer       ,     SCREAMING_SNAKE_CASE__					:					UNetaDConditionModel       ,     SCREAMING_SNAKE_CASE__					:					DDPMScheduler       ,     SCREAMING_SNAKE_CASE__					:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,     SCREAMING_SNAKE_CASE__					:					int = 350       ,     )				->	List[Any]:
         super().__init__(
             vae=SCREAMING_SNAKE_CASE__       ,     text_encoder=SCREAMING_SNAKE_CASE__       ,     tokenizer=SCREAMING_SNAKE_CASE__       ,     unet=SCREAMING_SNAKE_CASE__       ,     low_res_scheduler=SCREAMING_SNAKE_CASE__       ,     scheduler=SCREAMING_SNAKE_CASE__       ,     max_noise_level=SCREAMING_SNAKE_CASE__       ,     )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     image.size       )
         lowerCAmelCase__          =			image.crop(SCREAMING_SNAKE_CASE__       )
         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       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			squeeze_tile(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			to_input.size
         lowerCAmelCase__          =			to_input.resize((tile_size, tile_size)       ,     Image.BICUBIC       )
         lowerCAmelCase__          =			super(SCREAMING_SNAKE_CASE__       ,     self       ).__call__(image=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).images[0]
         lowerCAmelCase__          =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)       ,     Image.BICUBIC       )
         lowerCAmelCase__          =			unsqueeze_tile(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         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=SCREAMING_SNAKE_CASE__       )       ,     mode="L"       ,     )
         final_image.paste(
             SCREAMING_SNAKE_CASE__       ,     (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)       ,     SCREAMING_SNAKE_CASE__       )


    @torch.no_grad()
    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, List[str]]       ,     SCREAMING_SNAKE_CASE__					:					Union[PIL.Image.Image, List[PIL.Image.Image]]       ,     SCREAMING_SNAKE_CASE__					:					int = 75       ,     SCREAMING_SNAKE_CASE__					:					float = 9.0       ,     SCREAMING_SNAKE_CASE__					:					int = 50       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, List[str]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = 1       ,     SCREAMING_SNAKE_CASE__					:					float = 0.0       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     SCREAMING_SNAKE_CASE__					:					int = 128       ,     SCREAMING_SNAKE_CASE__					:					int = 32       ,     SCREAMING_SNAKE_CASE__					:					int = 32       ,     )				->	Optional[Any]:
         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(SCREAMING_SNAKE_CASE__       ):
              for x in range(SCREAMING_SNAKE_CASE__       ):
                   self._process_tile(
                       SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     prompt=SCREAMING_SNAKE_CASE__       ,     num_inference_steps=SCREAMING_SNAKE_CASE__       ,     guidance_scale=SCREAMING_SNAKE_CASE__       ,     noise_level=SCREAMING_SNAKE_CASE__       ,     negative_prompt=SCREAMING_SNAKE_CASE__       ,     num_images_per_prompt=SCREAMING_SNAKE_CASE__       ,     eta=SCREAMING_SNAKE_CASE__       ,     generator=SCREAMING_SNAKE_CASE__       ,     latents=SCREAMING_SNAKE_CASE__       ,     )
                   current_count += 1
                   if callback is not None:
                        callback({"progress": current_count / total_tile_count, "image": final_image}       )
         return final_image






def 							_A       (						):
     """simple docstring"""

     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_					: int      ):
          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()






from __future__ import annotations

UpperCamelCase								=					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def 							_A       (						lowerCAmelCase_					: list[list[int]]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: list[list[int]]       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the reference grid
     lowerCAmelCase__          =			1
     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the action grid

     lowerCAmelCase__          =			init[0]
     lowerCAmelCase__          =			init[1]
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			g + heuristic[x][y]  # cost from starting cell to destination cell
     lowerCAmelCase__          =			[[f, g, x, y]]

     lowerCAmelCase__          =			False  # flag that is set when search is complete
     lowerCAmelCase__          =			False  # flag set if we can't find expand

     while not found and not resign:
          if len(lowerCAmelCase_      ) == 0:
               raise ValueError("Algorithm is unable to find solution"      )
          else:  # to choose the least costliest action so as to move closer to the goal
               cell.sort()
               cell.reverse()
               lowerCAmelCase__          =			cell.pop()
               lowerCAmelCase__          =			next_cell[2]
               lowerCAmelCase__          =			next_cell[3]
               lowerCAmelCase__          =			next_cell[1]

               if x == goal[0] and y == goal[1]:
                    lowerCAmelCase__          =			True
               else:
                    for i in range(len(lowerCAmelCase_      )      ):  # to try out different valid actions
                         lowerCAmelCase__          =			x + DIRECTIONS[i][0]
                         lowerCAmelCase__          =			y + DIRECTIONS[i][1]
                         if xa >= 0 and xa < len(lowerCAmelCase_      ) and ya >= 0 and ya < len(grid[0]      ):
                              if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                   lowerCAmelCase__          =			g + cost
                                   lowerCAmelCase__          =			ga + heuristic[xa][ya]
                                   cell.append([fa, ga, xa, ya]      )
                                   lowerCAmelCase__          =			1
                                   lowerCAmelCase__          =			i
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			goal[0]
     lowerCAmelCase__          =			goal[1]
     invpath.append([x, y]      )  # we get the reverse path from here
     while x != init[0] or y != init[1]:
          lowerCAmelCase__          =			x - DIRECTIONS[action[x][y]][0]
          lowerCAmelCase__          =			y - DIRECTIONS[action[x][y]][1]
          lowerCAmelCase__          =			xa
          lowerCAmelCase__          =			ya
          invpath.append([x, y]      )

     lowerCAmelCase__          =			[]
     for i in range(len(lowerCAmelCase_      )      ):
          path.append(invpath[len(lowerCAmelCase_      ) - 1 - i]      )
     return path, action


if __name__ == "__main__":
     UpperCamelCase								=					[
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 1, 0],
         [0, 0, 0, 0, 1, 0],
     ]

     UpperCamelCase								=					[0, 0]
     # all coordinates are given in format [y,x]
     UpperCamelCase								=					[len(grid) - 1, len(grid[0]) - 1]
     UpperCamelCase								=					1

     # the cost map which pushes the path closer to the goal
     UpperCamelCase								=					[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
     for i in range(len(grid)):
          for j in range(len(grid[0])):
               UpperCamelCase								=					abs(i - goal[0]) + abs(j - goal[1])
               if grid[i][j] == 1:
                    # added extra penalty in the heuristic map
                    UpperCamelCase								=					99

     UpperCamelCase						,  UpperCamelCase								=					search(grid, init, goal, cost, heuristic)

     print('ACTION MAP')
     for i in range(len(action)):
          print(action[i])

     for i in range(len(path)):
          print(path[i])
