import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from craftsman.utils.typing import *
from craftsman.utils.checkpoint import checkpoint

from .utils import init_linear, MLP
from timm.models.vision_transformer import Attention

def scaled_dot_product_gqa(
    query: Tensor,
    key: Tensor,
    value: Tensor,
    dropout: float = 0.0,
    scale: Optional[float] = None,
    mask: Optional[Tensor] = None,
    is_causal: Optional[bool] = None,
    need_weights: bool = False,
    average_attn_weights: bool = False,
    force_grouped: bool = False,
):
    """Scaled dot product attention with support for grouped queries.

    Einstein notation:
    - b: batch size
    - n / s: sequence length
    - h: number of heads
    - g: number of groups
    - d: dimension of query/key/value

    Args:
        query: Query tensor of shape (b, n, h, d)
        key: Key tensor of shape (b, s, h, d)
        value: Value tensor of shape (b, s, h, d)
        dropout: Dropout probability (default: 0.0)
        scale: Scale factor for query (default: d_query ** 0.5)
        mask: Mask tensor of shape (b, n, s) or (b, s). If 'ndim == 2', the mask is
            applied to all 'n' rows of the attention matrix. (default: None)
        force_grouped: If True, apply grouped-query attention even if the number of
            heads is equal for query, key, and value. (default: False)

    Returns:
        2-tuple of:
        - Attention output with shape (b, n, h, d)
        - (Optional) Attention weights with shape (b, h, n, s). Only returned if
          'need_weights' is True.
    """
    if (mask is not None) and (is_causal is not None):
        raise ValueError(
            "Only one of 'mask' and 'is_causal' should be provided, but got both."
        )
    elif not query.ndim == key.ndim == value.ndim == 4:
        raise ValueError(
            f"Expected query, key, and value to be 4-dimensional, but got shapes "
            f"{query.shape}, {key.shape}, and {value.shape}."
        )

    # Move sequence length dimension to axis 2.
    # This makes the attention operations below *much* faster.
    query = rearrange(query, "b n h d -> b h n d")
    key = rearrange(key, "b s h d -> b h s d")
    value = rearrange(value, "b s h d -> b h s d")

    bq, hq, nq, dq = query.shape
    bk, hk, nk, dk = key.shape
    bv, hv, nv, dv = value.shape
    if not (bq == bk == bv and dq == dk == dv):
        raise ValueError(
            "Expected query, key, and value to have the same batch size (dim=0) and "
            f"embedding dimension (dim=3), but got query: {query.shape}, "
            f"key: {key.shape}, and value: {value.shape}."
        )
    elif (hk != hv) or (nk != nv):
        raise ValueError(
            "Expected key and value to have the same size in dimensions 1 and 2, but "
            f"got key: {key.shape} and value: {value.shape}."
        )
    elif hq % hk != 0:
        raise ValueError(
            "Expected query heads to be a multiple of key/value heads, but got "
            f"query: {query.shape} and key/value: {key.shape}."
        )

    if scale is None:
        scale = query.size(-1) ** 0.5
    query = query / scale

    num_head_groups = hq // hk
    query = rearrange(query, "b (h g) n d -> b g h n d", g=num_head_groups)
    similarity = einsum(query, key, "b g h n d, b h s d -> b g h n s")

    if is_causal:
        # Mask out the upper triangular portion of the attention matrix. This prevents
        # the model from attending to tokens in the future.
        mask = torch.ones((bq, nq, nk), device=query.device, dtype=torch.bool).tril_()

    if mask is not None:
        # Expand mask to match the shape of the attention matrix.
        # If mask is 2D, assume that it is applied to the key/value sequence dimension.
        # Else if mask is 3D, assume that it is applied to the query/key/value sequence
        # dimension for all attention heads.
        #
        if mask.ndim == 2:
            mask = rearrange(mask, "b s -> b () () () s")
        elif mask.ndim == 3:
            mask = rearrange(mask, "b n s -> b () () n s")
        # Mask similarity values by setting them to negative infinity.  This guarantees
        # that they will not contribute to the softmax computation below.
        similarity.masked_fill_(~mask, torch.finfo(similarity.dtype).min)

    attention = F.softmax(similarity, dim=-1)
    if dropout > 0.0:
        attention = F.dropout(attention, p=dropout)

    # Apply attention matrix to the value Tensor.
    out = einsum(attention, value, "b g h n s, b h s d -> b g h n d")
    # Move head dimension back to axis 2
    out = rearrange(out, "b g h n d -> b n (h g) d")

    attn_weights: Optional[Tensor] = None
    if need_weights:
        # Move the sequence dimensions back to positions 1, 2.  Move the head dimension
        # to position 3.  This more closely matches the return shape of the attention
        # output: (b, n, h, d).
        attn_weights = rearrange(attention, "b g h n s -> b n s (h g)")
        if average_attn_weights:
            attn_weights = attn_weights.mean(dim=1)

    return out, attn_weights

class MultiheadAttention(nn.Module):
    def __init__(
        self,
        *,
        n_ctx: int,
        width: int,
        heads: int,
        init_scale: float,
        qkv_bias: bool,
        use_flash: bool = False
    ):
        super().__init__()
        self.n_ctx = n_ctx
        self.width = width
        self.heads = heads
        self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias)
        self.c_proj = nn.Linear(width, width)
        self.attention = QKVMultiheadAttention(heads=heads, n_ctx=n_ctx, use_flash=use_flash)
        init_linear(self.c_qkv, init_scale)
        init_linear(self.c_proj, init_scale)

    def forward(self, x):
        x = self.c_qkv(x)
        x = checkpoint(self.attention, (x,), (), True)
        x = self.c_proj(x)
        return x


class QKVMultiheadAttention(nn.Module):
    def __init__(self, *, heads: int, n_ctx: int, use_flash: bool = False):
        super().__init__()
        self.heads = heads
        self.n_ctx = n_ctx
        self.use_flash = use_flash

    def forward(self, qkv):
        bs, n_ctx, width = qkv.shape
        attn_ch = width // self.heads // 3
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        qkv = qkv.view(bs, n_ctx, self.heads, -1)
        q, k, v = torch.split(qkv, attn_ch, dim=-1)

        if self.use_flash:
            q = q.permute(0, 2, 1, 3)
            k = k.permute(0, 2, 1, 3)
            v = v.permute(0, 2, 1, 3)
            out = F.scaled_dot_product_attention(q, k, v).permute(0, 2, 1, 3).reshape(bs, n_ctx, -1)
        else:
            weight = torch.einsum(
                "bthc,bshc->bhts", q * scale, k * scale
            )  # More stable with f16 than dividing afterwards
            wdtype = weight.dtype
            weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
            out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)

        return out

class ResidualAttentionBlock(nn.Module):
    def __init__(
        self,
        *,
        n_ctx: int,
        width: int,
        heads: int,
        init_scale: float = 1.0,
        qkv_bias: bool = True,
        use_flash: bool = False,
        use_checkpoint: bool = False
    ):
        super().__init__()

        self.use_checkpoint = use_checkpoint

        self.attn = MultiheadAttention(
            n_ctx=n_ctx,
            width=width,
            heads=heads,
            init_scale=init_scale,
            qkv_bias=qkv_bias,
            use_flash=use_flash
        )
        self.ln_1 = nn.LayerNorm(width)
        self.mlp = MLP(width=width, init_scale=init_scale)
        self.ln_2 = nn.LayerNorm(width)

    def _forward(self, x: torch.Tensor):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

    def forward(self, x: torch.Tensor):
        return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)


class MultiheadCrossAttention(nn.Module):
    def __init__(
        self,
        *,
        width: int,
        heads: int,
        init_scale: float,
        qkv_bias: bool = True,
        use_flash: bool = False,
        n_data: Optional[int] = None,
        data_width: Optional[int] = None,
    ):
        super().__init__()
        self.n_data = n_data
        self.width = width
        self.heads = heads
        self.data_width = width if data_width is None else data_width
        self.c_q = nn.Linear(width, width, bias=qkv_bias)
        self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias)
        self.c_proj = nn.Linear(width, width)
        self.attention = QKVMultiheadCrossAttention(
            heads=heads, n_data=n_data, use_flash=use_flash
        )
        init_linear(self.c_q, init_scale)
        init_linear(self.c_kv, init_scale)
        init_linear(self.c_proj, init_scale)

    def forward(self, x, data):
        x = self.c_q(x)
        data = self.c_kv(data)
        x = checkpoint(self.attention, (x, data), (), True)
        x = self.c_proj(x)
        return x


class QKVMultiheadCrossAttention(nn.Module):
    def __init__(self, *, heads: int, use_flash: bool = False, n_data: Optional[int] = None):

        super().__init__()
        self.heads = heads
        self.n_data = n_data
        self.use_flash = use_flash

    def forward(self, q, kv):
        _, n_ctx, _ = q.shape
        bs, n_data, width = kv.shape
        attn_ch = width // self.heads // 2
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        q = q.view(bs, n_ctx, self.heads, -1)
        kv = kv.view(bs, n_data, self.heads, -1)
        k, v = torch.split(kv, attn_ch, dim=-1)

        if self.use_flash:
            
            q = q.permute(0, 2, 1, 3)
            k = k.permute(0, 2, 1, 3)
            v = v.permute(0, 2, 1, 3)
            out = F.scaled_dot_product_attention(q, k, v).permute(0, 2, 1, 3).reshape(bs, n_ctx, -1)
        else:
            weight = torch.einsum(
                "bthc,bshc->bhts", q * scale, k * scale
            )  # More stable with f16 than dividing afterwards
            wdtype = weight.dtype
            weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
            out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)

        return out


class ResidualCrossAttentionBlock(nn.Module):
    def __init__(
        self,
        *,
        n_data: Optional[int] = None,
        width: int,
        heads: int,
        data_width: Optional[int] = None,
        init_scale: float = 0.25,
        qkv_bias: bool = True,
        use_flash: bool = False
    ):
        super().__init__()

        if data_width is None:
            data_width = width

        self.attn = MultiheadCrossAttention(
            n_data=n_data,
            width=width,
            heads=heads,
            data_width=data_width,
            init_scale=init_scale,
            qkv_bias=qkv_bias,
            use_flash=use_flash,
        )
        self.ln_1 = nn.LayerNorm(width)
        self.ln_2 = nn.LayerNorm(data_width)
        self.mlp = MLP(width=width, init_scale=init_scale)
        self.ln_3 = nn.LayerNorm(width)

    def forward(self, x: torch.Tensor, data: torch.Tensor):
        x = x + self.attn(self.ln_1(x), self.ln_2(data))
        x = x + self.mlp(self.ln_3(x))
        return x