





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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			Optional[Any]      )      ->	str:
    return max(lowercase__		,   min(lowercase__		,   lowercase__      )      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Any      )      ->	Tuple:
    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:			Any		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	Union[str, Any]:
    UpperCamelCase__							:				Any          =      list(lowercase__      )
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    UpperCamelCase__							:				str          =      clamp_rect(lowercase__		,   [0, 0]		,   [image_size[0], image_size[1]]      )
    return rect




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Tuple      )      ->	Optional[Any]:
    UpperCamelCase__							:				Tuple          =      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(lowercase__		,   (original_slice, 0)      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			Union[str, Any]      )      ->	Dict:
    UpperCamelCase__							:				Optional[int]          =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
    UpperCamelCase__							:				Optional[int]          =      tile.crop(lowercase__      )
    return tile




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple      )      ->	List[str]:
    UpperCamelCase__							:				Tuple          =      n % d
    return n - divisor






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = 350,		) ->      List[str]:






       """simple docstring"""
       super().__init__(
           vae=__magic_name__,		text_encoder=__magic_name__,		tokenizer=__magic_name__,		unet=__magic_name__,		low_res_scheduler=__magic_name__,		scheduler=__magic_name__,		max_noise_level=__magic_name__,		)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							) ->      str:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Optional[int]          =      (
           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__							:				str          =      add_overlap_rect(__magic_name__,		__magic_name__,		image.size							)
       UpperCamelCase__							:				Dict          =      image.crop(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       UpperCamelCase__							:				List[str]          =      translated_slice_x - (original_image_slice / 2)
       UpperCamelCase__							:				List[Any]          =      max(0,		__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      squeeze_tile(__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)
       UpperCamelCase__							:				int          =      to_input.size
       UpperCamelCase__							:				Union[str, Any]          =      to_input.resize((tile_size, tile_size),		Image.BICUBIC							)
       UpperCamelCase__							:				Union[str, Any]          =      super(__magic_name__,		self							).__call__(image=__magic_name__,		**__magic_name__							).images[0]
       UpperCamelCase__							:				Tuple          =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4),		Image.BICUBIC							)
       UpperCamelCase__							:				List[Any]          =      unsqueeze_tile(__magic_name__,		__magic_name__							)
       UpperCamelCase__							:				Dict          =      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4),		Image.BICUBIC							)
       UpperCamelCase__							:				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'''							)
       UpperCamelCase__							:				str          =      Image.fromarray(
           make_transparency_mask(
               (upscaled_tile.size[0], upscaled_tile.size[1]),		tile_border * 4,		remove_borders=__magic_name__							),		mode='''L''',		)
       final_image.paste(
           __magic_name__,		(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4),		__magic_name__							)




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = 75,		__magic_name__ = 9.0,		__magic_name__ = 50,		__magic_name__ = None,		__magic_name__ = 1,		__magic_name__ = 0.0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 1,		__magic_name__ = 128,		__magic_name__ = 32,		__magic_name__ = 32,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      Image.new('''RGB''',		(image.size[0] * 4, image.size[1] * 4)							)
       UpperCamelCase__							:				Optional[int]          =      math.ceil(image.size[0] / tile_size							)
       UpperCamelCase__							:				Optional[Any]          =      math.ceil(image.size[1] / tile_size							)
       UpperCamelCase__							:				Dict          =      tcx * tcy
       UpperCamelCase__							:				List[str]          =      0
       for y in range(__magic_name__							):
           for x in range(__magic_name__							):
               self._process_tile(
                   __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		prompt=__magic_name__,		num_inference_steps=__magic_name__,		guidance_scale=__magic_name__,		noise_level=__magic_name__,		negative_prompt=__magic_name__,		num_images_per_prompt=__magic_name__,		eta=__magic_name__,		generator=__magic_name__,		latents=__magic_name__,		)
               current_count += 1
               if callback is not None:
                   callback({'''progress''': current_count / total_tile_count, '''image''': final_image}							)
       return final_image




def 		lowerCAmelCase_    (  )      ->	Dict:
    # Run a demo
    UpperCamelCase__							:				int          =      'stabilityai/stable-diffusion-x4-upscaler'
    UpperCamelCase__							:				Optional[Any]          =      StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase__		,   revision='''fp16'''		,   torch_dtype=torch.floataa      )
    UpperCamelCase__							:				Optional[int]          =      pipe.to('''cuda'''      )
    UpperCamelCase__							:				List[Any]          =      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'''      )

    UpperCamelCase__							:				str          =      pipe(image=lowercase__		,   prompt='''Black font, white background, vector'''		,   noise_level=40		,   callback=lowercase__      )
    final_image.save('''diffusers_library.jpg'''      )


if __name__ == "__main__":
  main()







"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()