from functools import partial
from typing import Optional, Callable, List, Any

import torch
import torch.nn.functional as F
from torch import nn, Tensor

from ..ops.stochastic_depth import StochasticDepth
from ..transforms._presets import ImageClassification, InterpolationMode
from ..utils import _log_api_usage_once
from ._api import WeightsEnum, Weights
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_named_param
from .convnext import Permute
from .vision_transformer import MLPBlock


__all__ = [
    "SwinTransformer",
    "Swin_T_Weights",
    "swin_t",
]


class PatchMerging(nn.Module):
    """Patch Merging Layer.
    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
    """

    def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x: Tensor):
        B, H, W, C = x.shape

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)
        x = x.view(B, H // 2, W // 2, 2 * C)
        return x


def shifted_window_attention(
    input: Tensor,
    qkv_weight: Tensor,
    proj_weight: Tensor,
    relative_position_bias: Tensor,
    window_size: int,
    num_heads: int,
    shift_size: int = 0,
    attention_dropout: float = 0.0,
    dropout: float = 0.0,
    qkv_bias: Optional[Tensor] = None,
    proj_bias: Optional[Tensor] = None,
):
    """
    Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        input (Tensor[N, H, W, C]): The input tensor or 4-dimensions.
        qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value.
        proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection.
        relative_position_bias (Tensor): The learned relative position bias added to attention.
        window_size (int): Window size.
        num_heads (int): Number of attention heads.
        shift_size (int): Shift size for shifted window attention. Default: 0.
        attention_dropout (float): Dropout ratio of attention weight. Default: 0.0.
        dropout (float): Dropout ratio of output. Default: 0.0.
        qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None.
        proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None.
    Returns:
        Tensor[N, H, W, C]: The output tensor after shifted window attention.
    """
    B, H, W, C = input.shape
    # pad feature maps to multiples of window size
    pad_r = (window_size - W % window_size) % window_size
    pad_b = (window_size - H % window_size) % window_size
    x = F.pad(input, (0, 0, 0, pad_r, 0, pad_b))
    _, pad_H, pad_W, _ = x.shape

    # If window size is larger than feature size, there is no need to shift window.
    if window_size == min(pad_H, pad_W):
        shift_size = 0

    # cyclic shift
    if shift_size > 0:
        x = torch.roll(x, shifts=(-shift_size, -shift_size), dims=(1, 2))

    # partition windows
    num_windows = (pad_H // window_size) * (pad_W // window_size)
    x = x.view(B, pad_H // window_size, window_size, pad_W // window_size, window_size, C)
    x = x.permute(0, 1, 3, 2, 4, 5).reshape(B * num_windows, window_size * window_size, C)  # B*nW, Ws*Ws, C

    # multi-head attention
    qkv = F.linear(x, qkv_weight, qkv_bias)
    qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, C // num_heads).permute(2, 0, 3, 1, 4)
    q, k, v = qkv[0], qkv[1], qkv[2]
    q = q * (C // num_heads) ** -0.5
    attn = q.matmul(k.transpose(-2, -1))
    # add relative position bias
    attn = attn + relative_position_bias

    if shift_size > 0:
        # generate attention mask
        attn_mask = x.new_zeros((pad_H, pad_W))
        slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, None))
        count = 0
        for h in slices:
            for w in slices:
                attn_mask[h[0] : h[1], w[0] : w[1]] = count
                count += 1
        attn_mask = attn_mask.view(pad_H // window_size, window_size, pad_W // window_size, window_size)
        attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(num_windows, window_size * window_size)
        attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1))
        attn = attn + attn_mask.unsqueeze(1).unsqueeze(0)
        attn = attn.view(-1, num_heads, x.size(1), x.size(1))

    attn = F.softmax(attn, dim=-1)
    attn = F.dropout(attn, p=attention_dropout)

    x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), C)
    x = F.linear(x, proj_weight, proj_bias)
    x = F.dropout(x, p=dropout)

    # reverse windows
    x = x.view(B, pad_H // window_size, pad_W // window_size, window_size, window_size, C)
    x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, pad_H, pad_W, C)

    # reverse cyclic shift
    if shift_size > 0:
        x = torch.roll(x, shifts=(shift_size, shift_size), dims=(1, 2))

    # unpad features
    x = x[:, :H, :W, :].contiguous()
    return x


torch.fx.wrap("shifted_window_attention")


class ShiftedWindowAttention(nn.Module):
    """
    See :func:`shifted_window_attention`.
    """

    def __init__(
        self,
        dim: int,
        window_size: int,
        shift_size: int,
        num_heads: int,
        qkv_bias: bool = True,
        proj_bias: bool = True,
        attention_dropout: float = 0.0,
        dropout: float = 0.0,
    ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = shift_size
        self.num_heads = num_heads
        self.attention_dropout = attention_dropout
        self.dropout = dropout

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.proj = nn.Linear(dim, dim, bias=proj_bias)

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads)
        )  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size)
        coords_w = torch.arange(self.window_size)
        coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size - 1
        relative_coords[:, :, 0] *= 2 * self.window_size - 1
        relative_position_index = relative_coords.sum(-1).view(-1)  # Wh*Ww*Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)

    def forward(self, x: Tensor):
        relative_position_bias = self.relative_position_bias_table[self.relative_position_index]  # type: ignore[index]
        relative_position_bias = relative_position_bias.view(
            self.window_size * self.window_size, self.window_size * self.window_size, -1
        )
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0)

        return shifted_window_attention(
            x,
            self.qkv.weight,
            self.proj.weight,
            relative_position_bias,
            self.window_size,
            self.num_heads,
            shift_size=self.shift_size,
            attention_dropout=self.attention_dropout,
            dropout=self.dropout,
            qkv_bias=self.qkv.bias,
            proj_bias=self.proj.bias,
        )


class SwinTransformerBlock(nn.Module):
    """
    Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): Window size. Default: 7.
        shift_size (int): Shift size for shifted window attention. Default: 0.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
        dropout (float): Dropout rate. Default: 0.0.
        attention_dropout (float): Attention dropout rate. Default: 0.0.
        stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0.
        norm_layer (nn.Module): Normalization layer.  Default: nn.LayerNorm.
    """

    def __init__(
        self,
        dim: int,
        num_heads: int,
        window_size: int = 7,
        shift_size: int = 0,
        mlp_ratio: float = 4.0,
        dropout: float = 0.0,
        attention_dropout: float = 0.0,
        stochastic_depth_prob: float = 0.0,
        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
    ):
        super().__init__()

        self.norm1 = norm_layer(dim)
        self.attn = ShiftedWindowAttention(
            dim,
            window_size,
            shift_size,
            num_heads,
            attention_dropout=attention_dropout,
            dropout=dropout,
        )
        self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row")
        self.norm2 = norm_layer(dim)
        self.mlp = MLPBlock(dim, int(dim * mlp_ratio), dropout)

    def forward(self, x: Tensor):
        x = x + self.stochastic_depth(self.attn(self.norm1(x)))
        x = x + self.stochastic_depth(self.mlp(self.norm2(x)))
        return x


class SwinTransformer(nn.Module):
    """
    Implements Swin Transformer from the `"Swin Transformer: Hierarchical Vision Transformer using
    Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_ paper.
    Args:
        patch_size (int): Patch size.
        embed_dim (int): Patch embedding dimension.
        depths (List(int)): Depth of each Swin Transformer layer.
        num_heads (List(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
        dropout (float): Dropout rate. Default: 0.0.
        attention_dropout (float): Attention dropout rate. Default: 0.0.
        stochastic_depth_prob (float): Stochastic depth rate. Default: 0.0.
        num_classes (int): Number of classes for classification head. Default: 1000.
        block (nn.Module, optional): SwinTransformer Block. Default: None.
        norm_layer (nn.Module, optional): Normalization layer. Default: None.
    """

    def __init__(
        self,
        patch_size: int,
        embed_dim: int,
        depths: List[int],
        num_heads: List[int],
        window_size: int = 7,
        mlp_ratio: float = 4.0,
        dropout: float = 0.0,
        attention_dropout: float = 0.0,
        stochastic_depth_prob: float = 0.0,
        num_classes: int = 1000,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
        block: Optional[Callable[..., nn.Module]] = None,
    ):
        super().__init__()
        _log_api_usage_once(self)
        self.num_classes = num_classes

        if block is None:
            block = SwinTransformerBlock

        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-5)

        layers: List[nn.Module] = []
        # split image into non-overlapping patches
        layers.append(
            nn.Sequential(
                nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size),
                Permute([0, 2, 3, 1]),
                norm_layer(embed_dim),
            )
        )

        total_stage_blocks = sum(depths)
        stage_block_id = 0
        # build SwinTransformer blocks
        for i_stage in range(len(depths)):
            stage: List[nn.Module] = []
            dim = embed_dim * 2 ** i_stage
            for i_layer in range(depths[i_stage]):
                # adjust stochastic depth probability based on the depth of the stage block
                sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1)
                stage.append(
                    block(
                        dim,
                        num_heads[i_stage],
                        window_size=window_size,
                        shift_size=0 if i_layer % 2 == 0 else window_size // 2,
                        mlp_ratio=mlp_ratio,
                        dropout=dropout,
                        attention_dropout=attention_dropout,
                        stochastic_depth_prob=sd_prob,
                        norm_layer=norm_layer,
                    )
                )
                stage_block_id += 1
            layers.append(nn.Sequential(*stage))
            # add patch merging layer
            if i_stage < (len(depths) - 1):
                layers.append(PatchMerging(dim, norm_layer))
        self.features = nn.Sequential(*layers)

        num_features = embed_dim * 2 ** (len(depths) - 1)
        self.norm = norm_layer(num_features)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.head = nn.Linear(num_features, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

    def forward(self, x):
        x = self.features(x)
        x = self.norm(x)
        x = x.permute(0, 3, 1, 2)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.head(x)
        return x


def _swin_transformer(
    patch_size: int,
    embed_dim: int,
    depths: List[int],
    num_heads: List[int],
    window_size: int,
    stochastic_depth_prob: float,
    weights: Optional[WeightsEnum],
    progress: bool,
    **kwargs: Any,
) -> SwinTransformer:
    if weights is not None:
        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))

    model = SwinTransformer(
        patch_size=patch_size,
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=window_size,
        stochastic_depth_prob=stochastic_depth_prob,
        **kwargs,
    )

    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))

    return model


_COMMON_META = {
    "categories": _IMAGENET_CATEGORIES,
}


class Swin_T_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/swin_t-81486767.pth",
        transforms=partial(
            ImageClassification, crop_size=224, resize_size=238, interpolation=InterpolationMode.BICUBIC
        ),
        meta={
            **_COMMON_META,
            "num_params": 28288354,
            "min_size": (224, 224),
            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swin_t",
            "metrics": {
                "acc@1": 81.358,
                "acc@5": 95.526,
            },
        },
    )
    DEFAULT = IMAGENET1K_V1


def swin_t(*, weights: Optional[Swin_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
    """
    Constructs a swin_tiny architecture from
    `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows <https://arxiv.org/pdf/2103.14030>`_.

    Args:
        weights (:class:`~torchvision.models.Swin_T_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.Swin_T_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.Swin_T_Weights
        :members:
    """
    weights = Swin_T_Weights.verify(weights)

    return _swin_transformer(
        patch_size=4,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        stochastic_depth_prob=0.2,
        weights=weights,
        progress=progress,
        **kwargs,
    )
