import torch
import torch.nn as nn

# https://github.com/facebookresearch/DiT

from typing import Union

import torch
from einops import rearrange
from torch import Tensor

# Ref: https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
# Ref: https://github.com/lucidrains/rotary-embedding-torch


def compute_rope_rotations(length: int,
                           dim: int,
                           theta: int,
                           *,
                           freq_scaling: float = 1.0,
                           device: Union[torch.device, str] = 'cpu') -> Tensor:
    assert dim % 2 == 0

    with torch.amp.autocast(device_type='cuda', enabled=False):
        pos = torch.arange(length, dtype=torch.float32, device=device)
        freqs = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
        freqs *= freq_scaling

        rot = torch.einsum('..., f -> ... f', pos, freqs)
        rot = torch.stack([torch.cos(rot), -torch.sin(rot), torch.sin(rot), torch.cos(rot)], dim=-1)
        rot = rearrange(rot, 'n d (i j) -> 1 n d i j', i=2, j=2)
        return rot


def apply_rope(x: Tensor, rot: Tensor) -> tuple[Tensor, Tensor]:
    with torch.amp.autocast(device_type='cuda', enabled=False):
        _x = x.float()
        _x = _x.view(*_x.shape[:-1], -1, 1, 2)
        x_out = rot[..., 0] * _x[..., 0] + rot[..., 1] * _x[..., 1]
        return x_out.reshape(*x.shape).to(dtype=x.dtype)


class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """

    def __init__(self, dim, frequency_embedding_size, max_period):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, dim),
            nn.SiLU(),
            nn.Linear(dim, dim),
        )
        self.dim = dim
        self.max_period = max_period
        assert dim % 2 == 0, 'dim must be even.'

        with torch.autocast('cuda', enabled=False):
            self.register_buffer("freqs",
                1.0 / (10000**(torch.arange(0, frequency_embedding_size, 2, dtype=torch.float32) /
                               frequency_embedding_size)),
                persistent=False)
            freq_scale = 10000 / max_period
            self.freqs = freq_scale * self.freqs

    def timestep_embedding(self, t):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py

        args = t[:, None].float() * self.freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t).to(t.dtype)
        t_emb = self.mlp(t_freq)
        return t_emb