





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

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[str]  )   ->    Dict:
  return max(snake_case__      ,				min(snake_case__      ,				snake_case__  )  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:[int]      ,				snake_case__	:[int]      ,				snake_case__	:[int]  )   ->    Optional[Any]:
  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__							(	snake_case__	:[int]      ,				snake_case__	:int      ,				snake_case__	:[int]  )   ->    Dict:
  _lowercase						    =			list(snake_case__  )
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  _lowercase						    =			clamp_rect(snake_case__      ,				[0, 0]      ,				[image_size[0], image_size[1]]  )
  return rect

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:str  )   ->    Tuple:
  _lowercase						    =			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(snake_case__      ,				(original_slice, 0)  )
  return result

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Tuple  )   ->    List[str]:
  _lowercase						    =			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  _lowercase						    =			tile.crop(snake_case__  )
  return tile

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





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						AutoencoderKL    ,__A						:						CLIPTextModel    ,__A						:						CLIPTokenizer    ,__A						:						UNetaDConditionModel    ,__A						:						DDPMScheduler    ,__A						:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,__A						:						int = 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  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Dict    ,__A						:						Tuple    ,__A						:						List[Any]    ,__A						:						str    ,__A						:						Optional[Any]    ,__A						:						Union[str, Any]    ,__A						:						Optional[int]    ,**__A						:						List[Any]	)    ->       int:
         torch.manual_seed(0	)
         _lowercase						    =			(
             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	),
         )
         _lowercase						    =			add_overlap_rect(__A    ,__A    ,image.size	)
         _lowercase						    =			image.crop(__A	)
         _lowercase						    =			((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         _lowercase						    =			translated_slice_x - (original_image_slice / 2)
         _lowercase						    =			max(0    ,__A	)
         _lowercase						    =			squeeze_tile(__A    ,__A    ,__A    ,__A	)
         _lowercase						    =			to_input.size
         _lowercase						    =			to_input.resize((tile_size, tile_size)    ,Image.BICUBIC	)
         _lowercase						    =			super(__A    ,self	).__call__(image=__A    ,**__A	).images[0]
         _lowercase						    =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)    ,Image.BICUBIC	)
         _lowercase						    =			unsqueeze_tile(__A    ,__A	)
         _lowercase						    =			upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)    ,Image.BICUBIC	)
         _lowercase						    =			[]
         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'	)
         _lowercase						    =			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						:						List[Any]    ,__A						:						Union[str, List[str]]    ,__A						:						Union[PIL.Image.Image, List[PIL.Image.Image]]    ,__A						:						int = 75    ,__A						:						float = 9.0    ,__A						:						int = 50    ,__A						:						Optional[Union[str, List[str]]] = None    ,__A						:						Optional[int] = 1    ,__A						:						float = 0.0    ,__A						:						Optional[torch.Generator] = None    ,__A						:						Optional[torch.FloatTensor] = None    ,__A						:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,__A						:						int = 1    ,__A						:						int = 128    ,__A						:						int = 32    ,__A						:						int = 32    ,)    ->       int:
         _lowercase						    =			Image.new('RGB'    ,(image.size[0] * 4, image.size[1] * 4)	)
         _lowercase						    =			math.ceil(image.size[0] / tile_size	)
         _lowercase						    =			math.ceil(image.size[1] / tile_size	)
         _lowercase						    =			tcx * tcy
         _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    List[Any]:
  # Run a demo
  _lowercase						    =			'stabilityai/stable-diffusion-x4-upscaler'
  _lowercase						    =			StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case__      ,				revision='fp16'      ,				torch_dtype=torch.floataa  )
  _lowercase						    =			pipe.to('cuda'  )
  _lowercase						    =			Image.open('../../docs/source/imgs/diffusers_library.jpg'  )

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

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


if __name__ == "__main__":
      main()


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





