# ignore_header_test
# ruff: noqa: E402
""""""

"""
Transolver model. This code was modified from, https://github.com/thuml/Transolver

The following license is provided from their source,

MIT License

Copyright (c) 2024 THUML @ Tsinghua University

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import math

import torch
import torch.nn as nn
from einops import rearrange


class RotaryEmbedding(nn.Module):
    "ROPE: Rotary Position Embedding"

    def __init__(self, dim, min_freq=1 / 2, scale=1.0):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.min_freq = min_freq
        self.scale = scale
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, coordinates, device):
        # coordinates [b, n]
        t = coordinates.to(device).type_as(self.inv_freq)
        t = t * (self.scale / self.min_freq)
        freqs = torch.einsum("... i , j -> ... i j", t, self.inv_freq)  # [b, n, d//2]
        return torch.cat((freqs, freqs), dim=-1)  # [b, n, d]


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


def apply_rotary_pos_emb(t, freqs):
    return (t * freqs.cos()) + (rotate_half(t) * freqs.sin())


def apply_2d_rotary_pos_emb(t, freqs_x, freqs_y):
    # split t into first half and second half
    # t: [b, h, n, d]
    # freq_x/y: [b, n, d]
    d = t.shape[-1]
    t_x, t_y = t[..., : d // 2], t[..., d // 2 :]

    return torch.cat(
        (apply_rotary_pos_emb(t_x, freqs_x), apply_rotary_pos_emb(t_y, freqs_y)), dim=-1
    )


class PositionalEncoding(nn.Module):
    "Implement the PE function."

    def __init__(self, d_model, dropout, max_len=421 * 421):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2) * -(math.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)
        self.register_buffer("pe", pe)

    def forward(self, x):
        x = x + self.pe[:, : x.size(1)].requires_grad_(False)
        return self.dropout(x)


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: 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 x dim] Tensor of positional embeddings.
    """

    half = dim // 2
    freqs = torch.exp(
        -math.log(max_period)
        * torch.arange(start=0, end=half, dtype=torch.float32)
        / half
    ).to(device=timesteps.device)
    args = timesteps[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    return embedding