
'''simple docstring'''
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				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=[]				):
							_snake_case       =  size[0] - overlap_pixels * 2
							_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
							_snake_case       =  np.ones((size_y, size_x)							, dtype=np.uinta				) * 255
							_snake_case       =  np.pad(_SCREAMING_SNAKE_CASE							, mode="""linear_ramp"""							, pad_width=_SCREAMING_SNAKE_CASE							, end_values=0				)

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							return max(_SCREAMING_SNAKE_CASE							, min(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							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				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  list(_SCREAMING_SNAKE_CASE				)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							_snake_case       =  clamp_rect(_SCREAMING_SNAKE_CASE							, [0, 0]							, [image_size[0], image_size[1]]				)
							return rect






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_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(_SCREAMING_SNAKE_CASE							, (original_slice, 0)				)
							return result






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							_snake_case       =  tile.crop(_SCREAMING_SNAKE_CASE				)
							return tile






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  n % d
							return n - divisor


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 350  ,  )					->       Tuple:
														super().__init__(
														    vae=UpperCAmelCase  ,  text_encoder=UpperCAmelCase  ,  tokenizer=UpperCAmelCase  ,  unet=UpperCAmelCase  ,  low_res_scheduler=UpperCAmelCase  ,  scheduler=UpperCAmelCase  ,  max_noise_level=UpperCAmelCase  ,  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       List[str]:
														torch.manual_seed(0  )
														_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  ),
														)
														_snake_case       =  add_overlap_rect(UpperCAmelCase  ,  UpperCAmelCase  ,  image.size  )
														_snake_case       =  image.crop(UpperCAmelCase  )
														_snake_case       =  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
														_snake_case       =  translated_slice_x - (original_image_slice / 2)
														_snake_case       =  max(0  ,  UpperCAmelCase  )
														_snake_case       =  squeeze_tile(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )
														_snake_case       =  to_input.size
														_snake_case       =  to_input.resize((tile_size, tile_size)  ,  Image.BICUBIC  )
														_snake_case       =  super(UpperCAmelCase  ,  self  ).__call__(image=UpperCAmelCase  ,  **UpperCAmelCase  ).images[0]
														_snake_case       =  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)  ,  Image.BICUBIC  )
														_snake_case       =  unsqueeze_tile(UpperCAmelCase  ,  UpperCAmelCase  )
														_snake_case       =  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)  ,  Image.BICUBIC  )
														_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"""  )
														_snake_case       =  Image.fromarray(
														    make_transparency_mask(
														        (upscaled_tile.size[0], upscaled_tile.size[1])  ,  tile_border * 4  ,  remove_borders=UpperCAmelCase  )  ,  mode="""L"""  ,  )
														final_image.paste(
														    UpperCAmelCase  ,  (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)  ,  UpperCAmelCase  )





							@torch.no_grad()
							def __call__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 75  ,  UpperCAmelCase = 9.0  ,  UpperCAmelCase = 50  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 1  ,  UpperCAmelCase = 0.0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 1  ,  UpperCAmelCase = 128  ,  UpperCAmelCase = 32  ,  UpperCAmelCase = 32  ,  )					->       Tuple:
														_snake_case       =  Image.new("""RGB"""  ,  (image.size[0] * 4, image.size[1] * 4)  )
														_snake_case       =  math.ceil(image.size[0] / tile_size  )
														_snake_case       =  math.ceil(image.size[1] / tile_size  )
														_snake_case       =  tcx * tcy
														_snake_case       =  0
														for y in range(UpperCAmelCase  ):
																					for x in range(UpperCAmelCase  ):
																												self._process_tile(
																												    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  prompt=UpperCAmelCase  ,  num_inference_steps=UpperCAmelCase  ,  guidance_scale=UpperCAmelCase  ,  noise_level=UpperCAmelCase  ,  negative_prompt=UpperCAmelCase  ,  num_images_per_prompt=UpperCAmelCase  ,  eta=UpperCAmelCase  ,  generator=UpperCAmelCase  ,  latents=UpperCAmelCase  ,  )
																												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				(  ):
							# Run a demo
							_snake_case       =  """stabilityai/stable-diffusion-x4-upscaler"""
							_snake_case       =  StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE							, revision="""fp16"""							, torch_dtype=torch.floataa				)
							_snake_case       =  pipe.to("""cuda"""				)
							_snake_case       =  Image.open("""../../docs/source/imgs/diffusers_library.jpg"""				)

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

							_snake_case       =  pipe(image=_SCREAMING_SNAKE_CASE							, prompt="""Black font, white background, vector"""							, noise_level=40							, callback=_SCREAMING_SNAKE_CASE				)
							final_image.save("""diffusers_library.jpg"""				)


if __name__ == "__main__":
				main()

'''simple docstring'''
from __future__ import annotations






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# We need to create solution object to save path.
							_snake_case       =  [[0 for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]
							_snake_case       =  run_maze(_SCREAMING_SNAKE_CASE							, 0							, 0							, _SCREAMING_SNAKE_CASE				)
							if solved:
														print("""\n""".join(str(_SCREAMING_SNAKE_CASE				) for row in solutions				)				)
							else:
														print("""No solution exists!"""				)
							return solved






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Final check point.
							if i == j == (size - 1):
														_snake_case       =  1
														return True

							_snake_case       =  (not i < 0) and (not j < 0)  # Check lower bounds
							_snake_case       =  (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														_snake_case       =  (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					_snake_case       =  1

																					# check for directions
																					if (
																					    run_maze(_SCREAMING_SNAKE_CASE							, i + 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j + 1							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, i - 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j - 1							, _SCREAMING_SNAKE_CASE				)
																					):
																												return True

																					_snake_case       =  0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()