




'''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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=[]		)    ->   Tuple:
      snake_case__       :   List[Any]	      =    size[0] - overlap_pixels * 2
      snake_case__       :   Dict	      =    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__       :   Tuple	      =    np.ones((size_y, size_x)				,      dtype=np.uinta		) * 255
      snake_case__       :   Any	      =    np.pad(__lowerCAmelCase				,      mode='linear_ramp'				,      pad_width=__lowerCAmelCase				,      end_values=0		)

      if "l" in remove_borders:
            snake_case__       :   str	      =    mask[:, overlap_pixels : mask.shape[1]]
      if "r" in remove_borders:
            snake_case__       :   Any	      =    mask[:, 0 : mask.shape[1] - overlap_pixels]
      if "t" in remove_borders:
            snake_case__       :   Dict	      =    mask[overlap_pixels : mask.shape[0], :]
      if "b" in remove_borders:
            snake_case__       :   Optional[int]	      =    mask[0 : mask.shape[0] - overlap_pixels, :]
      return mask






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      return max(__lowerCAmelCase				,      min(__lowerCAmelCase				,      __lowerCAmelCase		)		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Tuple	      =    list(__lowerCAmelCase		)
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      snake_case__       :   int	      =    clamp_rect(__lowerCAmelCase				,      [0, 0]				,      [image_size[0], image_size[1]]		)
      return rect






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   List[Any]	      =    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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      snake_case__       :   Optional[Any]	      =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
      snake_case__       :   str	      =    tile.crop(__lowerCAmelCase		)
      return tile






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      snake_case__       :   Optional[int]	      =    n % d
      return n - divisor






class        lowercase_      (						SCREAMING_SNAKE_CASE_							):





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





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Optional[Any]			):
           torch.manual_seed(0			)
           snake_case__       :   List[Any]	      =    (
               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__       :   Union[str, Any]	      =    add_overlap_rect(__lowerCamelCase     ,							__lowerCamelCase     ,							image.size			)
           snake_case__       :   List[Any]	      =    image.crop(__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
           snake_case__       :   Union[str, Any]	      =    translated_slice_x - (original_image_slice / 2)
           snake_case__       :   Any	      =    max(0     ,							__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    squeeze_tile(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   int	      =    to_input.size
           snake_case__       :   str	      =    to_input.resize((tile_size, tile_size)     ,							Image.BICUBIC			)
           snake_case__       :   Union[str, Any]	      =    super(__lowerCamelCase     ,							self			).__call__(image=__lowerCamelCase     ,							**__lowerCamelCase			).images[0]
           snake_case__       :   List[Any]	      =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,							Image.BICUBIC			)
           snake_case__       :   Optional[Any]	      =    unsqueeze_tile(__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,							Image.BICUBIC			)
           snake_case__       :   Dict	      =    []
           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__       :   List[Any]	      =    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     :					List[str]     ,							__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     ,							):
           snake_case__       :   Optional[int]	      =    Image.new('RGB'     ,							(image.size[0] * 4, image.size[1] * 4)			)
           snake_case__       :   Dict	      =    math.ceil(image.size[0] / tile_size			)
           snake_case__       :   Optional[Any]	      =    math.ceil(image.size[1] / tile_size			)
           snake_case__       :   str	      =    tcx * tcy
           snake_case__       :   Union[str, Any]	      =    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 		UpperCamelCase__      (		)    ->   Optional[int]:
      # Run a demo
      snake_case__       :   Optional[int]	      =    'stabilityai/stable-diffusion-x4-upscaler'
      snake_case__       :   int	      =    StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase				,      revision='fp16'				,      torch_dtype=torch.floataa		)
      snake_case__       :   Any	      =    pipe.to('cuda'		)
      snake_case__       :   str	      =    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__       :   Any	      =    pipe(image=__lowerCAmelCase				,      prompt='Black font, white background, vector'				,      noise_level=40				,      callback=__lowerCAmelCase		)
      final_image.save('diffusers_library.jpg'		)


if __name__ == "__main__":
      main()





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()