import math
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from typing import Optional







class ShortCut_CrossAttention(nn.Module):

    def __init__(self, d_model, nhead, panoptic_on = False):
        super().__init__()
        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
        self.norm = nn.LayerNorm(d_model)
        self.activation = F.relu

        self._reset_parameters()

        self.MLP = nn.Linear(d_model, d_model)
        self.panoptic_on = panoptic_on
        if panoptic_on:
            nn.init.constant(self.MLP.weight, 0.0)
            nn.init.constant(self.MLP.bias, 0.0)


    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward(self, tgt, memory,
                memory_mask: Optional[Tensor] = None,
                memory_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None,
                query_pos: Optional[Tensor] = None):
        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
                                   key=self.with_pos_embed(memory, pos),
                                   value=memory, attn_mask=memory_mask,
                                   key_padding_mask=memory_key_padding_mask)[0]
        
        if self.panoptic_on:
            tgt = tgt + self.norm(self.MLP(tgt2))
        else:
            tgt = self.norm(tgt + self.MLP(tgt2))

        return tgt
    


class ContentDependentTransfer(nn.Module):

    def __init__(self, d_model, nhead, panoptic_on):
        super().__init__()
        self.pe_layer = PositionEmbeddingSine(d_model//2, normalize=True)
        self.cross_atten = ShortCut_CrossAttention(d_model = d_model, nhead = nhead, panoptic_on = panoptic_on)

    def visual_prediction_forward_convnext(self, x):
        batch, channel, h, w = x.shape
        x = x.reshape(batch*h*w, channel).unsqueeze(-1).unsqueeze(-1) # fake 2D input
        x = self.truck_head(x)
        x = self.head(x)
        return x.reshape(batch, h, w, x.shape[-1]).permute(0,3,1,2) # B x num_queries x 640
    

    def forward(self, img_feat, text_classifier, ):
        text_classifier = text_classifier.unsqueeze(0).repeat(img_feat.shape[0],1,1)

        pos = self.pe_layer(img_feat, None).flatten(2).permute(2, 0, 1)  # hw * b * c
        img_feat = img_feat.flatten(2).permute(2, 0, 1)  # hw * b * c

        bias = self.cross_atten(text_classifier.permute(1, 0, 2), img_feat, memory_mask=None, memory_key_padding_mask=None, pos=pos, query_pos=None)

        return bias.permute(1, 0, 2) 

class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """

    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, x, mask=None):
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
        not_mask = ~mask
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

        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=4
        ).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos
    
    def __repr__(self, _repr_indent=4):
        head = "Positional encoding " + self.__class__.__name__
        body = [
            "num_pos_feats: {}".format(self.num_pos_feats),
            "temperature: {}".format(self.temperature),
            "normalize: {}".format(self.normalize),
            "scale: {}".format(self.scale),
        ]
        # _repr_indent = 4
        lines = [head] + [" " * _repr_indent + line for line in body]
        return "\n".join(lines)