import torch
import copy
from torch import nn, Tensor
import os

import math
import torch.nn.functional as F
from torch import nn


class MLP(nn.Module):
    """ Very simple multi-layer perceptron (also called FFN)"""

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x


def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1/x2)


def gen_encoder_output_proposals(memory:Tensor, memory_padding_mask:Tensor, spatial_shapes:Tensor):
    """
    Input:
        - memory: bs, \sum{hw}, d_model
        - memory_padding_mask: bs, \sum{hw}
        - spatial_shapes: nlevel, 2
    Output:
        - output_memory: bs, \sum{hw}, d_model
        - output_proposals: bs, \sum{hw}, 4
    """
    N_, S_, C_ = memory.shape
    base_scale = 4.0
    proposals = []
    _cur = 0
    for lvl, (H_, W_) in enumerate(spatial_shapes):
        mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)

        grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
                                        torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
        wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
        proposals.append(proposal)
        _cur += (H_ * W_)
    output_proposals = torch.cat(proposals, 1)
    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
    output_proposals = torch.log(output_proposals / (1 - output_proposals))
    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))

    output_memory = memory
    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
    return output_memory, output_proposals


def gen_sineembed_for_position(pos_tensor):
    # n_query, bs, _ = pos_tensor.size()
    # sineembed_tensor = torch.zeros(n_query, bs, 256)
    scale = 2 * math.pi
    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
    dim_t = 10000 ** (2 * (dim_t // 2) / 128)
    x_embed = pos_tensor[:, :, 0] * scale
    y_embed = pos_tensor[:, :, 1] * scale
    pos_x = x_embed[:, :, None] / dim_t
    pos_y = y_embed[:, :, None] / dim_t
    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
    if pos_tensor.size(-1) == 2:
        pos = torch.cat((pos_y, pos_x), dim=2)
    elif pos_tensor.size(-1) == 4:
        w_embed = pos_tensor[:, :, 2] * scale
        pos_w = w_embed[:, :, None] / dim_t
        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)

        h_embed = pos_tensor[:, :, 3] * scale
        pos_h = h_embed[:, :, None] / dim_t
        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)

        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
    else:
        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
    return pos


def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    if activation == "prelu":
        return nn.PReLU()
    if activation == "selu":
        return F.selu
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")


def _get_clones(module, N, layer_share=False):

    if layer_share:
        return nn.ModuleList([module for i in range(N)])
    else:
        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])

def _get_clones_advanced(module, N, N_valid):
    assert N_valid <= N
    layers = []
    for i in range(N):
        if i < N_valid:
            layers.append(copy.deepcopy(module))
        else:
            layers.append(nn.Identity())
    return nn.ModuleList(layers)