





import sys

sys.set_int_max_str_digits(0)  # type: ignore


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			Any            =		[0] * 11
		lowercase__				:			Any            =		[0] * 11

		# mark last 9 numbers
		for _ in range(9 ):
				lowercase__				:			Optional[Any]            =		1
				lowercase__				:			str            =		number // 10
		# flag
		lowercase__				:			Dict            =		True

		# check last 9 numbers for pandigitality

		for x in range(9 ):
				if not check_last[x + 1]:
						lowercase__				:			Optional[int]            =		False
		if not f:
				return f

		# mark first 9 numbers
		lowercase__				:			str            =		int(str(_lowercase )[:9] )

		for _ in range(9 ):
				lowercase__				:			List[str]            =		1
				lowercase__				:			int            =		number // 10

		# check first 9 numbers for pandigitality

		for x in range(9 ):
				if not check_front[x + 1]:
						lowercase__				:			Union[str, Any]            =		False
		return f


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			List[Any]            =		[0] * 11

		# mark last 9 numbers
		for _ in range(9 ):
				lowercase__				:			Optional[Any]            =		1
				lowercase__				:			Dict            =		number // 10
		# flag
		lowercase__				:			List[str]            =		True

		# check last 9 numbers for pandigitality

		for x in range(9 ):
				if not check_last[x + 1]:
						lowercase__				:			Any            =		False
		return f


def 			SCREAMING_SNAKE_CASE__     (					)      ->       int:




		'''simple docstring'''
		lowercase__				:			List[str]            =		1
		lowercase__				:			Tuple            =		1
		lowercase__				:			int            =		2
		# temporary Fibonacci numbers

		lowercase__				:			List[str]            =		1
		lowercase__				:			Tuple            =		1
		lowercase__				:			int            =		2
		# temporary Fibonacci numbers mod 1e9

		# mod m=1e9, done for fast optimisation
		lowercase__				:			Optional[Any]            =		[0] * 1_000_000
		lowercase__				:			Tuple            =		1_000_000_000

		for x in range(1_000_000 ):
				lowercase__				:			str            =		(aa + ba) % m
				lowercase__				:			Tuple            =		ba % m
				lowercase__				:			Optional[int]            =		ca % m
				if checka(_lowercase ):
						lowercase__				:			List[Any]            =		1

		for x in range(1_000_000 ):
				lowercase__				:			int            =		a + b
				lowercase__				:			str            =		b
				lowercase__				:			List[str]            =		c
				# perform check only if in tocheck
				if tocheck[x + 3] and check(_lowercase ):
						return x + 3  # first 2 already done
		return -1


if __name__ == "__main__":
			print(F'{solution() = }')









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


