# -*- coding: utf-8 -*-

import torch
from torch import nn, Tensor
from torch import isnan,tensor
from torch import sum as summer
from torchnorms.tnorms.base import BaseTNorm

from typing import Optional


class YagerTNorm(BaseTNorm):
	def __init__(self,
				 p: Optional[Tensor] = None,
				 default_p: float = 2.0) -> None:
		super().__init__()
		self.p = p
		if self.p is None:
			self.p = nn.Parameter(torch.tensor(default_p))
		assert len(self.p.shape) == 0

		self.relu = torch.nn.ReLU()
		self.eps = 0.001

	def __call__(self,
				 a: Tensor,
				 b: Tensor) -> Tensor:
		res: Optional[Tensor] = None

		self.adjust_nan_param()

		self.p.data = self.relu(self.p.data)
		if self.p == 0:
			self.p.data += self.eps

		p_1 = torch.pow(1.0 - a, self.p)
		p_2 = torch.pow(1.0 - b, self.p)
		p_3 = torch.pow(p_1 + p_2, 1.0 / self.p)
		res = torch.minimum(torch.tensor(1.0) , p_3)
		res = 1.0 - res

		return res

	def adjust_nan_param(self):
		for param in self.parameters():
			if summer(isnan(param)):
				param.data = tensor(0.1, device = param.device)
