import logging
from math import pi

import torch
from einops import rearrange, repeat
from torch import nn


def broadcat(tensors, dim=-1):
    num_tensors = len(tensors)
    shape_lens = set(list(map(lambda t: len(t.shape), tensors)))
    assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
    shape_len = list(shape_lens)[0]
    dim = (dim + shape_len) if dim < 0 else dim
    dims = list(zip(*map(lambda t: list(t.shape), tensors)))
    expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
    assert all(
        [*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]
    ), "invalid dimensions for broadcastable concatentation"
    max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
    expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
    expanded_dims.insert(dim, (dim, dims[dim]))
    expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
    tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
    return torch.cat(tensors, dim=dim)


def rotate_half(x):
    x = rearrange(x, "... (d r) -> ... d r", r=2)
    x1, x2 = x.unbind(dim=-1)
    x = torch.stack((-x2, x1), dim=-1)
    return rearrange(x, "... d r -> ... (d r)")


class VisionRotaryEmbeddingFast(nn.Module):
    def __init__(
        self,
        dim,
        pt_seq_len,
        ft_seq_len=None,
        custom_freqs=None,
        freqs_for="lang",
        theta=10000,
        max_freq=10,
        num_freqs=1,
        patch_dropout=0.0,
    ):
        super().__init__()
        if custom_freqs:
            freqs = custom_freqs
        elif freqs_for == "lang":
            freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
        elif freqs_for == "pixel":
            freqs = torch.linspace(1.0, max_freq / 2, dim // 2) * pi
        elif freqs_for == "constant":
            freqs = torch.ones(num_freqs).float()
        else:
            raise ValueError(f"unknown modality {freqs_for}")

        if ft_seq_len is None:
            ft_seq_len = pt_seq_len
        t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len

        freqs = torch.einsum("..., f -> ... f", t, freqs)
        freqs = repeat(freqs, "... n -> ... (n r)", r=2)
        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim=-1)

        freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
        freqs_sin = freqs.sin().view(-1, freqs.shape[-1])

        self.patch_dropout = patch_dropout

        self.register_buffer("freqs_cos", freqs_cos)
        self.register_buffer("freqs_sin", freqs_sin)

        logging.info(f"Shape of rope freq: {self.freqs_cos.shape}")

    def forward(self, t, patch_indices_keep=None):
        if patch_indices_keep is not None:
            batch = t.size()[0]
            batch_indices = torch.arange(batch)
            batch_indices = batch_indices[..., None]

            freqs_cos = repeat(self.freqs_cos, "i j -> n i m j", n=t.shape[0], m=t.shape[1])
            freqs_sin = repeat(self.freqs_sin, "i j -> n i m j", n=t.shape[0], m=t.shape[1])

            freqs_cos = freqs_cos[batch_indices, patch_indices_keep]
            freqs_cos = rearrange(freqs_cos, "n i m j -> n m i j")
            freqs_sin = freqs_sin[batch_indices, patch_indices_keep]
            freqs_sin = rearrange(freqs_sin, "n i m j -> n m i j")

            return t * freqs_cos + rotate_half(t) * freqs_sin

        return t * self.freqs_cos + rotate_half(t) * self.freqs_sin
