# encoding: utf-8
"""
The actual nodes defined by the package.
"""

import typing as _t

from inspect import cleandoc as _cleandoc
from math import sqrt as _sqrt
import sys as _sys

from frozendict import deepfreeze

from comfy.comfy_types.node_typing import IO as _IO

from .node_types import *
from ._funcs import (
	aspect_ratios_sorted as _aspect_ratios_sorted,
	number_to_int as _number_to_int,
	simple_result_from_approx_wh as _simple_result_from_approx_wh
)


# Tiny optimization by reusing the same immutable dict:
_input_types_simple = deepfreeze({
	"required":  {
		"width": (_IO.INT, dict(type_dict_res, **{"tooltip": "Approximate width"})),
		"height": (_IO.INT, dict(type_dict_res, **{"tooltip": "Approximate height"})),
		"step": (_IO.INT, dict(type_dict_step_init, **{"tooltip": (
			"Both width and height will be divisible by this value - by rounding them "
			"to the closest appropriate resolution.\n\n"
			"The default 48 is (8 * 3 * 2), so it's a safe choice because:\n"
			"- it's compatible with SD1.5/XL downsampling factor (divisible by 8),\n"
			"- it can be upscaled by x1.5 or x1.333 at the first iteration, which is optimal for latent-upscale,\n"
			"- after x1.5 upscale, if you only do x2 later (it's OK for already high resolutions) - it will be divisible "
			"by 3 AND 9, which might become handy at that point, where you'll probably use UltimateSDUpscale.\n\n"
			"Other values worth trying first: 64, 96, 128."
		)})),
	},
	"hidden": {
		"unique_id": "UNIQUE_ID",  # used for text display at the bottom of the node
	},
	# "optional": {},
})

# _return_types_simple = (_IO.INT, _IO.INT, _IO.STRING)
# _return_names_simple = ("width", "height", "report")
_return_types_simple = (_IO.INT, _IO.INT)
_return_names_simple = ("width", "height")


class BestResolutionSimple:
	"""
	The simplest node for easier selection of an optimal resolution:
	only rounding performed, desired image size specified directly.
	"""
	NODE_NAME = "BestResolutionSimple"
	CATEGORY = "utils/resolution"
	DESCRIPTION = _cleandoc(__doc__)

	OUTPUT_NODE = True

	FUNCTION = "main"
	RETURN_TYPES = _return_types_simple
	# RETURN_TYPES_TOOLTIPS = {}
	RETURN_NAMES = _return_names_simple

	@classmethod
	def INPUT_TYPES(cls):
		return _input_types_simple

	def main(self, width: int, height: int, step: int, unique_id: str = None):
		return _simple_result_from_approx_wh(float(width), height, step, unique_id, _sqrt(width * height))


_tooltip_aspect = (
	"Two aspects together define an aspect ratio (16:9, 4:3, etc).\n"
	"Order doesn't matter: image orientation is defined by the 'landscape' toggle.\n\n"
	"The specified aspect ratio is APPROXIMATE: step parameter has priority over the exact image proportions."
)

_input_types_orient = deepfreeze({
	"required":  {
		"size": (_IO.INT, dict(type_dict_res, **{"tooltip": (
			"Approximate size of one of the image sides.\nWhich one - see the 'size_is_big' and 'landscape' toggles."
		)})),
		"step": _input_types_simple["required"]["step"],
		"size_is_big": (_IO.BOOLEAN, {
			"default": True, "label_on": "bigger", "label_off": "smaller",
			"tooltip": "When ON, size parameter represents the bigger image side.\nWhen OFF, it specifies the smaller one."
		}),
		"landscape": (_IO.BOOLEAN, {
			"default": True, "label_on": "landscape", "label_off": "portrait",
			"tooltip": (
				"Specifies image orientation:\n\n"
				"When ON, width is bigger (image is horizontal).\n"
				"When OFF, height is bigger (image is vertical)."
			)
		}),
		"aspect_a": (_IO.FLOAT, {
			"default": 16.0, "min": 1.0, "max": _sys.float_info.max, "step": 1.0, "round": 0.001,
			"tooltip": _tooltip_aspect
		}),
		"aspect_b": (_IO.FLOAT, {
			"default": 9.0, "min": 1.0, "max": _sys.float_info.max, "step": 1.0, "round": 0.001,
			"tooltip": _tooltip_aspect
		}),
	},
	"hidden": {
		"unique_id": "UNIQUE_ID",
	},
	# "optional": {},
})


class BestResolutionFromAspectRatio:
	"""
	A more advanced node for easier selection of an optimal resolution:
	image size selected indirectly - by one of the sides + aspect ratio.
	"""
	NODE_NAME = "BestResolutionFromAspectRatio"
	CATEGORY = "utils/resolution"
	DESCRIPTION = _cleandoc(__doc__)

	OUTPUT_NODE = True

	FUNCTION = "main"
	RETURN_TYPES = _return_types_simple
	RETURN_NAMES = _return_names_simple

	@classmethod
	def INPUT_TYPES(cls):
		return _input_types_orient

	def main(self, size: int, step: int, size_is_big: bool, landscape: bool, aspect_a: float, aspect_b: float, unique_id: str = None):
		aspect_big, aspect_small = _aspect_ratios_sorted(aspect_a, aspect_b)

		side_main_f = float(size)
		side_other_f = side_main_f * (
			(aspect_small / aspect_big) if size_is_big else (aspect_big / aspect_small)
		)

		width_f, height_f = (side_other_f, side_main_f) if side_other_f > side_main_f else (side_main_f, side_other_f)
		if not landscape:
			# The opposite: height is bigger
			width_f, height_f = height_f, width_f

		return _simple_result_from_approx_wh(width_f, height_f, step, unique_id, _sqrt(width_f * height_f))


_input_types_area = deepfreeze({
	"required":  {
		"square_size": (_IO.INT, dict(type_dict_res, **{"tooltip": (
			"The total resolution of the image would be the same as of a square with this side.\n"
			"The width and height would be such to respect aspect ratio, but also be as close as possible to the "
			"total number of pixels as in this square image.\n\n"
			"- 512x512 square (SD 1.5): ~0.25 megapixels\n"
			"- 1024x1024 square (SDXL): ~1 megapixel"
		)})),
		"step": _input_types_simple["required"]["step"],
		"landscape": _input_types_orient["required"]["landscape"],
		"aspect_a": _input_types_orient["required"]["aspect_a"],
		"aspect_b": _input_types_orient["required"]["aspect_b"],
	},
	"hidden": {
		"unique_id": "UNIQUE_ID",
	},
	# "optional": {},
})


class BestResolutionFromArea:
	"""
	The most efficient way of selecting an optimal resolution:
	image size selected indirectly - by the total desired resolution (area) + aspect ratio.

	Desired resolution (aka image area/megapixels/pixel count) is specified with a side of a square image. This isn't
	accidental: most models disclose what image resolution they're trained on, and usually they're square:

	- SD 1.5 - 512x512 pixels
	- SDXL - 1024x1024 pixels

	By simply providing this single number and setting your aspect ratio/orientation, you get the width and height to
	produce the closest total resolution to the training set, while also respecting image proportions and step-rounding.
	"""
	NODE_NAME = "BestResolutionFromArea"
	CATEGORY = "utils/resolution"
	DESCRIPTION = _cleandoc(__doc__)

	OUTPUT_NODE = True

	FUNCTION = "main"
	RETURN_TYPES = _return_types_simple
	RETURN_NAMES = _return_names_simple

	@classmethod
	def INPUT_TYPES(cls):
		return _input_types_area

	def main(self, square_size: int, step: int, landscape: bool, aspect_a: float, aspect_b: float, unique_id: str = None):
		# square_size = 1024; step = 48; landscape = True; aspect_a = 9.0; aspect_b = 16.0
		aspect_big, aspect_small = _aspect_ratios_sorted(aspect_a, aspect_b)
		aspect_x, aspect_y = (aspect_big, aspect_small) if landscape else (aspect_small, aspect_big)

		aspect_area = aspect_x * aspect_y
		aspect_norm_scale = 1.0 / _sqrt(aspect_area)
		aspect_x *= aspect_norm_scale
		aspect_y *= aspect_norm_scale
		# Now the two aspects produce a normalized rectangle - i.e., it's area is 1

		square_size = _number_to_int(square_size)
		width_f = aspect_x * square_size
		height_f = aspect_y * square_size

		return _simple_result_from_approx_wh(width_f, height_f, step, unique_id, square_size)