import torch
import torch.nn as nn
import numpy as np
import torch
from torch import nn
import torch

from typing import List, Dict, Optional
from torch import Tensor

class TimestepEmbedderMDM(nn.Module):
    def __init__(self, latent_dim):
        super().__init__()
        self.latent_dim = latent_dim

        time_embed_dim = self.latent_dim
        self.sequence_pos_encoder = PositionalEncoding(d_model=self.latent_dim)
        # TODO add time embedding learnable
        self.time_embed = nn.Sequential(
            nn.Linear(self.latent_dim, time_embed_dim),
            nn.SiLU(),
            nn.Linear(time_embed_dim, time_embed_dim),
        ).to('cuda')

    def forward(self, timesteps):
        return self.time_embed(self.sequence_pos_encoder.pe[timesteps]).permute(1, 0, 2)


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1,
                 max_len=5000, batch_first=False, negative=False):
        super().__init__()
        self.batch_first = batch_first

        self.dropout = nn.Dropout(p=dropout)
        self.max_len = max_len
        
        self.negative = negative
        
        if negative:
            pe = torch.zeros(2*max_len, d_model,device='cuda')
            position = torch.arange(-max_len, max_len, dtype=torch.float).unsqueeze(1)
        else:
            pe = torch.zeros(max_len, d_model,device='cuda')
            position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)

        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)

        self.register_buffer('pe', pe, persistent=False)

    def forward(self, x, hist_frames=0):
        if not self.negative:
            center = 0
            assert hist_frames == 0
            first = 0
        else:
            center = self.max_len
            first = center-hist_frames
        if self.batch_first:
            last = first + x.shape[1]
            x = x + self.pe.permute(1, 0, 2)[:, first:last, :]
        else:
            last = first + x.shape[0]
            x = x + self.pe[first:last, :]
        return self.dropout(x)

def collate_tensor_with_padding(batch: List[Tensor]) -> Tensor:
    dims = batch[0].dim()
    max_size = [max([b.size(i) for b in batch]) for i in range(dims)]
    size = (len(batch),) + tuple(max_size)
    canvas = batch[0].new_zeros(size=size)
    for i, b in enumerate(batch):
        sub_tensor = canvas[i]
        for d in range(dims):
            sub_tensor = sub_tensor.narrow(d, 0, b.size(d))
        sub_tensor.add_(b)
    return canvas


def collate_x_dict(lst_x_dict: List, *, device: Optional[str] = 'cuda') -> Dict:
    x = collate_tensor_with_padding([x_dict["x"] for x_dict in lst_x_dict])
    if device is not None:
        x = x.to(device)
    length = [x_dict["length"] for x_dict in lst_x_dict]

    if isinstance(length, list):
        length = torch.tensor(length, device=device)

    max_len = max(length)
    mask = torch.arange(max_len, device=device).expand(
        len(length), max_len
    ) < length.unsqueeze(1)

    batch = {"x": x, "length": length, "mask": mask}
    return batch