





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							1
	_A       	=							2
	_A       	=							3

	def __repr__(	self:	Any ):
			return F"""{self.__class__.__name__}.{self.name}"""


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   tuple[int, int]      ,						_lowercase    :   int      ,						_lowercase    :   int )      ->       float:




		'''simple docstring'''
		lowercase__      ,							lowercase__				:			Any            =		point
		return degrees(atana(y - miny      ,						x - minx ) )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   tuple[int, int]      ,						_lowercase    :   tuple[int, int]      ,						_lowercase    :   tuple[int, int] )      ->       Direction:




		'''simple docstring'''
		lowercase__      ,							lowercase__				:			Any            =		starting
		lowercase__      ,							lowercase__				:			List[str]            =		via
		lowercase__      ,							lowercase__				:			Any            =		target
		lowercase__				:			Optional[int]            =		degrees(atana(ya - ya      ,						xa - xa ) )
		via_angle %= 360
		lowercase__				:			List[str]            =		degrees(atana(ya - ya      ,						xa - xa ) )
		target_angle %= 360
		# t-
		#  \ \
		#   \ v
		#    \|
		#     s
		# via_angle is always lower than target_angle, if direction is left.
		# If they are same, it means they are on a same line of convex hull.
		if target_angle > via_angle:
				return Direction.left
		elif target_angle == via_angle:
				return Direction.straight
		else:
				return Direction.right





def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list[tuple[int, int]] )      ->       list[tuple[int, int]]:




		'''simple docstring'''

		if len(_lowercase ) <= 2:
				# There is no convex hull
				raise ValueError('graham_scan: argument must contain more than 3 points.' )
		if len(_lowercase ) == 3:
				return points
		# find the lowest and the most left point
		lowercase__				:			Any            =		0
		lowercase__      ,							lowercase__				:			Union[str, Any]            =		maxsize, maxsize
		for i, point in enumerate(_lowercase ):
				lowercase__				:			Tuple            =		point[0]
				lowercase__				:			Optional[int]            =		point[1]
				if y < miny:
						lowercase__				:			Dict            =		y
						lowercase__				:			List[str]            =		x
						lowercase__				:			Union[str, Any]            =		i
				if y == miny and x < minx:
						lowercase__				:			Dict            =		x
						lowercase__				:			Dict            =		i

    # remove the lowest and the most left point from points for preparing for sort
		points.pop(_lowercase )

		lowercase__				:			Tuple            =		sorted(_lowercase      ,						key=lambda _lowercase : angle_comparer(_lowercase      ,						_lowercase      ,						_lowercase ) )
		# This insert actually costs complexity,
		# and you should instead add (minx, miny) into stack later.
		# I'm using insert just for easy understanding.
		sorted_points.insert(0      ,						(minx, miny) )

		lowercase__				:			deque[tuple[int, int]]            =		deque()
		stack.append(sorted_points[0] )
		stack.append(sorted_points[1] )
		stack.append(sorted_points[2] )
		# In any ways, the first 3 points line are towards left.
		# Because we sort them the angle from minx, miny.
		lowercase__				:			Any            =		Direction.left

		for i in range(3      ,						len(_lowercase ) ):
				while True:
						lowercase__				:			Optional[Any]            =		stack[-2]
						lowercase__				:			str            =		stack[-1]
						lowercase__				:			Optional[Any]            =		sorted_points[i]
						lowercase__				:			str            =		check_direction(_lowercase      ,						_lowercase      ,						_lowercase )

						if next_direction == Direction.left:
								lowercase__				:			List[Any]            =		Direction.left
								break
						if next_direction == Direction.straight:
								if current_direction == Direction.left:
										# We keep current_direction as left.
										# Because if the straight line keeps as straight,
										# we want to know if this straight line is towards left.
										break
								elif current_direction == Direction.right:
										# If the straight line is towards right,
										# every previous points on those straigh line is not convex hull.
										stack.pop()
						if next_direction == Direction.right:
								stack.pop()
				stack.append(sorted_points[i] )
		return list(_lowercase )









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


