import torch
import torch.nn as nn
import numpy as np
from typing import Tuple, Union, Optional

def get_1d_rotary_pos_embed(
    dim: int,
    pos: Union[np.ndarray, int],
    theta: float = 10000.0,
    use_real=False,
    linear_factor=1.0,
    ntk_factor=1.0,
    repeat_interleave_real=True,
    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
):
    """
    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.

    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
    data type.

    Args:
        dim (`int`): Dimension of the frequency tensor.
        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
        theta (`float`, *optional*, defaults to 10000.0):
            Scaling factor for frequency computation. Defaults to 10000.0.
        use_real (`bool`, *optional*):
            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
        linear_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the context extrapolation. Defaults to 1.0.
        ntk_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
            Otherwise, they are concateanted with themselves.
        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
            the dtype of the frequency tensor.
    Returns:
        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
    """
    assert dim % 2 == 0

    if isinstance(pos, int):
        pos = torch.arange(pos)
    if isinstance(pos, np.ndarray):
        pos = torch.from_numpy(pos)  # type: ignore  # [S]

    theta = theta * ntk_factor
    freqs = (
        1.0
        / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
        / linear_factor
    )  # [D/2]
    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
    if use_real and repeat_interleave_real:
        # flux, hunyuan-dit, cogvideox
        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
        return freqs_cos, freqs_sin
    elif use_real:
        # stable audio
        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
        return freqs_cos, freqs_sin
    else:
        # lumina
        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
        return freqs_cis
    
def get_3d_rotary_pos_embed(
    embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    RoPE for video tokens with 3D structure.

    Args:
    embed_dim: (`int`):
        The embedding dimension size, corresponding to hidden_size_head.
    crops_coords (`Tuple[int]`):
        The top-left and bottom-right coordinates of the crop.
    grid_size (`Tuple[int]`):
        The grid size of the spatial positional embedding (height, width).
    temporal_size (`int`):
        The size of the temporal dimension.
    theta (`float`):
        Scaling factor for frequency computation.

    Returns:
        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
    """
    if use_real is not True:
        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
    start, stop = crops_coords
    grid_size_h, grid_size_w = grid_size
    grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
    grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
    grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)

    # Compute dimensions for each axis
    dim_t = embed_dim // 4
    dim_h = embed_dim // 8 * 3
    dim_w = embed_dim // 8 * 3

    # Temporal frequencies
    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
    # Spatial frequencies for height and width
    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)

    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
        freqs_t = freqs_t[:, None, None, :].expand(
            -1, grid_size_h, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_w, dim_t
        freqs_h = freqs_h[None, :, None, :].expand(
            temporal_size, -1, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_h
        freqs_w = freqs_w[None, None, :, :].expand(
            temporal_size, grid_size_h, -1, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_w

        freqs = torch.cat(
            [freqs_t, freqs_h, freqs_w], dim=-1
        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
        freqs = freqs.view(
            temporal_size * grid_size_h * grid_size_w, -1
        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
        return freqs

    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
    cos = combine_time_height_width(t_cos, h_cos, w_cos)
    sin = combine_time_height_width(t_sin, h_sin, w_sin)
    return cos, sin

def get_3d_sincos_pos_embed(
    embed_dim: int,
    spatial_size: Union[int, Tuple[int, int]],
    temporal_size: int,
    spatial_interpolation_scale: float = 1.0,
    temporal_interpolation_scale: float = 1.0,
) -> np.ndarray:
    r"""
    Args:
        embed_dim (`int`):
        spatial_size (`int` or `Tuple[int, int]`):
        temporal_size (`int`):
        spatial_interpolation_scale (`float`, defaults to 1.0):
        temporal_interpolation_scale (`float`, defaults to 1.0):
    """
    if embed_dim % 4 != 0:
        raise ValueError("`embed_dim` must be divisible by 4")
    if isinstance(spatial_size, int):
        spatial_size = (spatial_size, spatial_size)

    embed_dim_spatial = 3 * embed_dim // 4
    embed_dim_temporal = embed_dim // 4

    # 1. Spatial
    grid_h = np.arange(spatial_size[1], dtype=np.float32) / spatial_interpolation_scale
    grid_w = np.arange(spatial_size[0], dtype=np.float32) / spatial_interpolation_scale
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
    pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid)

    # 2. Temporal
    grid_t = np.arange(temporal_size, dtype=np.float32) / temporal_interpolation_scale
    pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t)

    # 3. Concat
    pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
    pos_embed_spatial = np.repeat(pos_embed_spatial, temporal_size, axis=0)  # [T, H*W, D // 4 * 3]

    pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
    pos_embed_temporal = np.repeat(pos_embed_temporal, spatial_size[0] * spatial_size[1], axis=1)  # [T, H*W, D // 4]

    pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1)  # [T, H*W, D]
    return pos_embed


def get_2d_sincos_pos_embed(
    embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
):
    """
    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    if isinstance(grid_size, int):
        grid_size = (grid_size, grid_size)

    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
    """
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb

class CogVideoXPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 2,
        patch_size_t: Optional[int] = None,
        in_channels: int = 16,
        embed_dim: int = 1920,
        text_embed_dim: int = 4096,
        bias: bool = True,
        sample_width: int = 90,
        sample_height: int = 60,
        sample_frames: int = 49,
        temporal_compression_ratio: int = 4,
        max_text_seq_length: int = 226,
        spatial_interpolation_scale: float = 1.875,
        temporal_interpolation_scale: float = 1.0,
        use_positional_embeddings: bool = True,
        use_learned_positional_embeddings: bool = True,
    ) -> None:
        super().__init__()

        self.patch_size = patch_size
        self.patch_size_t = patch_size_t
        self.embed_dim = embed_dim
        self.sample_height = sample_height
        self.sample_width = sample_width
        self.sample_frames = sample_frames
        self.temporal_compression_ratio = temporal_compression_ratio
        self.max_text_seq_length = max_text_seq_length
        self.spatial_interpolation_scale = spatial_interpolation_scale
        self.temporal_interpolation_scale = temporal_interpolation_scale
        self.use_positional_embeddings = use_positional_embeddings
        self.use_learned_positional_embeddings = use_learned_positional_embeddings

        if patch_size_t is None:
            # CogVideoX 1.0 checkpoints
            self.proj = nn.Conv2d(
                in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
            )
        else:
            # CogVideoX 1.5 checkpoints
            self.proj = nn.Linear(in_channels * patch_size * patch_size * patch_size_t, embed_dim)

        self.text_proj = nn.Linear(text_embed_dim, embed_dim)

        if use_positional_embeddings or use_learned_positional_embeddings:
            persistent = use_learned_positional_embeddings
            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)

    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
        post_patch_height = sample_height // self.patch_size
        post_patch_width = sample_width // self.patch_size
        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
        num_patches = post_patch_height * post_patch_width * post_time_compression_frames

        pos_embedding = get_3d_sincos_pos_embed(
            self.embed_dim,
            (post_patch_width, post_patch_height),
            post_time_compression_frames,
            self.spatial_interpolation_scale,
            self.temporal_interpolation_scale,
        )
        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
        joint_pos_embedding = torch.zeros(
            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
        )
        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)

        return joint_pos_embedding

    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
        r"""
        Args:
            text_embeds (`torch.Tensor`):
                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
            image_embeds (`torch.Tensor`):
                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
        """
        text_embeds = self.text_proj(text_embeds)

        batch_size, num_frames, channels, height, width = image_embeds.shape

        if self.patch_size_t is None:
            image_embeds = image_embeds.reshape(-1, channels, height, width)
            image_embeds = self.proj(image_embeds)
            image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
            image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
            image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
        else:
            p = self.patch_size
            p_t = self.patch_size_t

            image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
            image_embeds = image_embeds.reshape(
                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
            )
            image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
            image_embeds = self.proj(image_embeds)

        embeds = torch.cat(
            [text_embeds, image_embeds], dim=1
        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]

        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
            if self.use_learned_positional_embeddings and (self.sample_width != width or self.sample_height != height):
                raise ValueError(
                    "It is currently not possible to generate videos at a different resolution that the defaults. This should only be the case with 'THUDM/CogVideoX-5b-I2V'."
                    "If you think this is incorrect, please open an issue at https://github.com/huggingface/diffusers/issues."
                )

            pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1

            if (
                self.sample_height != height
                or self.sample_width != width
                or self.sample_frames != pre_time_compression_frames
            ):
                pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
            else:
                pos_embedding = self.pos_embedding

            embeds = embeds + pos_embedding

        return embeds