






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       ( a					:Union[str, Any]      ,				a					:Union[str, Any]      ,				a					:str=[]					)				->     str:
 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					) * 255
 a						    = np.pad(a      ,				mode='''linear_ramp'''      ,				pad_width=a      ,				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       ( a					:List[str]      ,				a					:List[Any]      ,				a					:Optional[int]					)				->     Optional[int]:
 return max(a      ,				min(a      ,				a					)					)
def 		_a       ( a					:[int]      ,				a					:[int]      ,				a					:[int]					)				->     int:
 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       ( a					:[int]      ,				a					:int      ,				a					:[int]					)				->     Optional[Any]:
 a						    = list(a					)
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 a						    = clamp_rect(a      ,				[0, 0]      ,				[image_size[0], image_size[1]]					)
 return rect
def 		_a       ( a					:Union[str, Any]      ,				a					:List[str]      ,				a					:Optional[int]      ,				a					:List[str]					)				->     str:
 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(a      ,				(original_slice, 0)					)
 return result
def 		_a       ( a					:Union[str, Any]      ,				a					:Optional[Any]					)				->     List[Any]:
 a						    = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 a						    = tile.crop(a					)
 return tile
def 		_a       ( a					:Any      ,				a					:int					)				->     Optional[int]:
 a						    = n % d
 return n - divisor






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		AutoencoderKL					,   __UpperCAmelCase :		CLIPTextModel					,   __UpperCAmelCase :		CLIPTokenizer					,   __UpperCAmelCase :		UNetaDConditionModel					,   __UpperCAmelCase :		DDPMScheduler					,   __UpperCAmelCase :		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,   __UpperCAmelCase :		int = 350					,   )       ->Optional[Any]:




       """simple docstring"""


       super().__init__(
           vae=__UpperCAmelCase					,   text_encoder=__UpperCAmelCase					,   tokenizer=__UpperCAmelCase					,   unet=__UpperCAmelCase					,   low_res_scheduler=__UpperCAmelCase					,   scheduler=__UpperCAmelCase					,   max_noise_level=__UpperCAmelCase					,   )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Optional[int]					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Dict					,   **__UpperCAmelCase :		Any    )       ->int:




       """simple docstring"""


       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(__UpperCAmelCase					,   __UpperCAmelCase					,   image.size    )
       a						    = image.crop(__UpperCAmelCase    )
       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					,   __UpperCAmelCase    )
       a						    = squeeze_tile(__UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase    )
       a						    = to_input.size
       a						    = to_input.resize((tile_size, tile_size)					,   Image.BICUBIC    )
       a						    = super(__UpperCAmelCase					,   self    ).__call__(image=__UpperCAmelCase					,   **__UpperCAmelCase    ).images[0]
       a						    = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)					,   Image.BICUBIC    )
       a						    = unsqueeze_tile(__UpperCAmelCase					,   __UpperCAmelCase    )
       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=__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 :		List[str]					,   __UpperCAmelCase :		Union[str, List[str]]					,   __UpperCAmelCase :		Union[PIL.Image.Image, List[PIL.Image.Image]]					,   __UpperCAmelCase :		int = 75					,   __UpperCAmelCase :		float = 9.0					,   __UpperCAmelCase :		int = 50					,   __UpperCAmelCase :		Optional[Union[str, List[str]]] = None					,   __UpperCAmelCase :		Optional[int] = 1					,   __UpperCAmelCase :		float = 0.0					,   __UpperCAmelCase :		Optional[torch.Generator] = None					,   __UpperCAmelCase :		Optional[torch.FloatTensor] = None					,   __UpperCAmelCase :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,   __UpperCAmelCase :		int = 1					,   __UpperCAmelCase :		int = 128					,   __UpperCAmelCase :		int = 32					,   __UpperCAmelCase :		int = 32					,   )       ->Any:




       """simple docstring"""


       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(__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 		_a       ( )				->     Optional[int]:
 # Run a demo
 a						    = '''stabilityai/stable-diffusion-x4-upscaler'''
 a						    = StableDiffusionTiledUpscalePipeline.from_pretrained(a      ,				revision='''fp16'''      ,				torch_dtype=torch.floataa					)
 a						    = pipe.to('''cuda'''					)
 a						    = Image.open('''../../docs/source/imgs/diffusers_library.jpg'''					)

 def callback(a					:Union[str, Any]					):
  print(F"""progress: {obj['progress']:.4f}"""					)
  obj["image"].save('''diffusers_library_progress.jpg'''					)

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


if __name__ == "__main__":
     main()










from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


