





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:   Optional[Any]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   str=[] )	->						int:







      '''simple docstring'''






      __A          =				size[0] - overlap_pixels * 2
      __A          =				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
      __A          =				np.ones((size_y, size_x)					,				dtype=np.uinta ) * 2_55
      __A          =				np.pad(SCREAMING_SNAKE_CASE__					,				mode='''linear_ramp'''					,				pad_width=SCREAMING_SNAKE_CASE__					,				end_values=0 )

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



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   List[str] )	->						Optional[Any]:







      '''simple docstring'''






      return max(SCREAMING_SNAKE_CASE__					,				min(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ ) )



def _a				(	lowerCamelCase:   [int]					,				lowerCamelCase:   [int]					,				lowerCamelCase:   [int] )	->						Any:







      '''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] )	->						Tuple:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Any					,				lowerCamelCase:   Any					,				lowerCamelCase:   Optional[int] )	->						Any:







      '''simple docstring'''






      __A          =				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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Dict )	->						Tuple:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Union[str, Any] )	->						List[Any]:







      '''simple docstring'''






      __A          =				n % d
      return n - divisor





class A_					(			A_   ):




   def __init__(self	:List[Any]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:List[Any] = 350    ,   )->					Union[str, Any]:
         super().__init__(
             vae=A_    ,   text_encoder=A_    ,   tokenizer=A_    ,   unet=A_    ,   low_res_scheduler=A_    ,   scheduler=A_    ,   max_noise_level=A_    ,   )




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






   @torch.no_grad()
   def __call__(self	:Union[str, Any]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:str = 75    ,   _UpperCamelCase	:Tuple = 9.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Union[str, Any] = None    ,   _UpperCamelCase	:Tuple = 1    ,   _UpperCamelCase	:int = 0.0    ,   _UpperCamelCase	:List[str] = None    ,   _UpperCamelCase	:List[str] = None    ,   _UpperCamelCase	:Union[str, Any] = None    ,   _UpperCamelCase	:str = 1    ,   _UpperCamelCase	:Union[str, Any] = 128    ,   _UpperCamelCase	:int = 32    ,   _UpperCamelCase	:Optional[Any] = 32    ,   )->					Tuple:
         __A          =				Image.new('''RGB'''    ,   (image.size[0] * 4, image.size[1] * 4) )
         __A          =				math.ceil(image.size[0] / tile_size )
         __A          =				math.ceil(image.size[1] / tile_size )
         __A          =				tcx * tcy
         __A          =				0
         for y in range(A_ ):
               for x in range(A_ ):
                     self._process_tile(
                         A_    ,   A_    ,   A_    ,   A_    ,   A_    ,   A_    ,   A_    ,   prompt=A_    ,   num_inference_steps=A_    ,   guidance_scale=A_    ,   noise_level=A_    ,   negative_prompt=A_    ,   num_images_per_prompt=A_    ,   eta=A_    ,   generator=A_    ,   latents=A_    ,   )
                     current_count += 1
                     if callback is not None:
                           callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
         return final_image





def _a				(	)	->						int:







      '''simple docstring'''






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

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

      __A          =				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()









from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_ = None    )  ->       None:
         if components is None:
              __UpperCamelCase    							=[]
         __UpperCamelCase    							=list(A_    )






    def __len__(			self    )  ->       int:
         return len(self.__components    )






    def __str__(			self    )  ->       str:
         return "(" + ",".join(map(A_					,   self.__components    )    ) + ")"






    def __add__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] + other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:
              raise Exception('must have the same size'    )






    def __sub__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] - other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:  # error case
              raise Exception('must have the same size'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       float:
         ...






    def __mul__(			self					,   A_    )  ->       float | Vector:
         if isinstance(A_					,   (float, int)    ):
              __UpperCamelCase    							=[c * other for c in self.__components]
              return Vector(A_    )
         elif isinstance(A_					,   A_    ) and len(self    ) == len(A_    ):
              __UpperCamelCase    							=len(self    )
              __UpperCamelCase    							=[self.__components[i] * other.component(A_    ) for i in range(A_    )]
              return sum(A_    )
         else:  # error case
              raise Exception('invalid operand!'    )






    def _a					(			self    )  ->       Vector:
         return Vector(self.__components    )






    def _a					(			self					,   A_    )  ->       float:
         if isinstance(A_					,   A_    ) and -len(self.__components    ) <= i < len(self.__components    ):
              return self.__components[i]
         else:
              raise Exception('index out of range'    )






    def _a					(			self					,   A_					,   A_    )  ->       None:
         assert -len(self.__components    ) <= pos < len(self.__components    )
         __UpperCamelCase    							=value






    def _a					(			self    )  ->       float:
         if len(self.__components    ) == 0:
              raise Exception('Vector is empty'    )
         __UpperCamelCase    							=[c**2 for c in self.__components]
         return math.sqrt(sum(A_    )    )






    def _a					(			self					,   A_					,   A_ = False    )  ->       float:
         __UpperCamelCase    							=self * other
         __UpperCamelCase    							=self.euclidean_length() * other.euclidean_length()
         if deg:
              return math.degrees(math.acos(num / den    )    )
         else:
              return math.acos(num / den    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     return Vector([0] * dimension				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) and (isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				))
     __UpperCamelCase    							=[0] * dimension
     __UpperCamelCase    							=1
     return Vector(SCREAMING_SNAKE_CASE__				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					float			,     SCREAMING_SNAKE_CASE__			:					Vector			,     SCREAMING_SNAKE_CASE__			:					Vector				):
     assert (
         isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and (isinstance(SCREAMING_SNAKE_CASE__			,     (int, float)				))
     )
     return x * scalar + y

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Vector(SCREAMING_SNAKE_CASE__				)






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_    )  ->       None:
         __UpperCamelCase    							=matrix
         __UpperCamelCase    							=w
         __UpperCamelCase    							=h






    def __str__(			self    )  ->       str:
         __UpperCamelCase    							=''
         for i in range(self.__height    ):
              ans += "|"
              for j in range(self.__width    ):
                   if j < self.__width - 1:
                        ans += str(self.__matrix[i][j]    ) + ","
                   else:
                        ans += str(self.__matrix[i][j]    ) + "|\n"
         return ans






    def __add__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] + other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrix must have the same dimension!'    )






    def __sub__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] - other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrices must have the same dimension!'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Matrix:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    def __mul__(			self					,   A_    )  ->       Vector | Matrix:
         if isinstance(A_					,   A_    ):  # matrix-vector
              if len(A_    ) == self.__width:
                   __UpperCamelCase    							=zero_vector(self.__height    )
                   for i in range(self.__height    ):
                        __UpperCamelCase    							=[
                            self.__matrix[i][j] * other.component(A_    )
                            for j in range(self.__width    )
                        ]
                        ans.change_component(A_					,   sum(A_    )    )
                   return ans
              else:
                   raise Exception(
                       'vector must have the same size as the '
                       'number of columns of the matrix!'    )
         elif isinstance(A_					,   (int, float)    ):  # matrix-scalar
              __UpperCamelCase    							=[
                  [self.__matrix[i][j] * other for j in range(self.__width    )]
                  for i in range(self.__height    )
              ]
              return Matrix(A_					,   self.__width					,   self.__height    )
         return None






    def _a					(			self    )  ->       int:
         return self.__height






    def _a					(			self    )  ->       int:
         return self.__width






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return self.__matrix[x][y]
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       None:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              __UpperCamelCase    							=value
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         __UpperCamelCase    							=self.__matrix[:x] + self.__matrix[x + 1 :]
         for i in range(len(A_    )    ):
              __UpperCamelCase    							=minor[i][:y] + minor[i][y + 1 :]
         return Matrix(A_					,   self.__width - 1					,   self.__height - 1    ).determinant()






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return (-1) ** (x + y) * self.minor(A_					,   A_    )
         else:
              raise Exception('Indices out of bounds'    )






    def _a					(			self    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if self.__height < 1:
              raise Exception('Matrix has no element'    )
         elif self.__height == 1:
              return self.__matrix[0][0]
         elif self.__height == 2:
              return (
                  self.__matrix[0][0] * self.__matrix[1][1]
                  - self.__matrix[0][1] * self.__matrix[1][0]
              )
         else:
              __UpperCamelCase    							=[
                  self.__matrix[0][y] * self.cofactor(0					,   A_    ) for y in range(self.__width    )
              ]
              return sum(A_    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=[[0] * n for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[
         [random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)] for _ in range(SCREAMING_SNAKE_CASE__				)
     ]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





