# -*- coding: utf-8 -*-
from abc import abstractmethod
from typing import Tuple, Union

from ..tensor import Parameter, Tensor
from .module import Module

def __init__(self, oshp: Union[Tuple[int, int], int, Tensor], **kwargs):
self.oshp = oshp

@abstractmethod
def forward(self, inp):
pass

r"""Applies a 2D max adaptive pooling over an input.

For instance, given an input of the size :math:(N, C, H, W) and
an output shape :math:(OH, OW), this layer generates the output of
the size :math:(N, C, OH, OW) through a process described as:

.. math::
\begin{aligned}
out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1}
\text{input}(N_i, C_j, \text{stride[0]} \times h + m,
\text{stride[1]} \times w + n)
\end{aligned}

kernel_size and stride can be inferred from input shape and out shape:

* stride: (floor(IH / OH), floor(IW / OW))
* kernel_size: (IH - (OH - 1) * stride_h, IW - (OW - 1) * stride_w)

Examples:
>>> import numpy as np
>>> inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
>>> oup = m(inp)
>>> oup.numpy()
array([[[[ 5.,  7.],
[13., 15.]]]], dtype=float32)
"""

def forward(self, inp):

r"""Applies a 2D average pooling over an input.

For instance, given an input of the size :math:(N, C, H, W) and
an output shape :math:(OH, OW), this layer generates the output of
the size :math:(N, C, OH, OW) through a process described as:

.. math::

out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)

kernel_size and stride can be inferred from input shape and out shape:

* stride: (floor(IH / OH), floor(IW / OW))
* kernel_size: (IH - (OH - 1) * stride_h, IW - (OW - 1) * stride_w)

Examples:
>>> import numpy as np