# Adapted from: https://github.com/zhu-xlab/SSL4EO-S12/tree/main

import math
from functools import partial
from logging import Logger
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import drop_path, to_2tuple, trunc_normal_
from timm.layers import trunc_normal_ as __call_trunc_normal_

from pangaea.encoders.base import Encoder


def trunc_normal_(tensor, mean=0.0, std=1.0):
    __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return "p={}".format(self.drop_prob)


class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        # x = self.drop(x)
        # commit this for the orignal BERT implement
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        window_size=None,
        attn_head_dim=None,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        if attn_head_dim is not None:
            head_dim = attn_head_dim
        all_head_dim = head_dim * self.num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
        else:
            self.q_bias = None
            self.v_bias = None

        if window_size:
            self.window_size = window_size
            self.num_relative_distance = (2 * window_size[0] - 1) * (
                2 * window_size[1] - 1
            ) + 3
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros(self.num_relative_distance, num_heads)
            )  # 2*Wh-1 * 2*Ww-1, nH
            # cls to token & token 2 cls & cls to cls

            # get pair-wise relative position index for each token inside the window
            coords_h = torch.arange(window_size[0])
            coords_w = torch.arange(window_size[1])
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 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] += window_size[0] - 1  # shift to start from 0
            relative_coords[:, :, 1] += window_size[1] - 1
            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
            relative_position_index = torch.zeros(
                size=(window_size[0] * window_size[1] + 1,) * 2,
                dtype=relative_coords.dtype,
            )
            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            relative_position_index[0, 0:] = self.num_relative_distance - 3
            relative_position_index[0:, 0] = self.num_relative_distance - 2
            relative_position_index[0, 0] = self.num_relative_distance - 1

            self.register_buffer("relative_position_index", relative_position_index)
        else:
            self.window_size = None
            self.relative_position_bias_table = None
            self.relative_position_index = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, rel_pos_bias=None):
        B, N, C = x.shape
        qkv_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat(
                (
                    self.q_bias,
                    torch.zeros_like(self.v_bias, requires_grad=False),
                    self.v_bias,
                )
            )
        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = (
            qkv[0],
            qkv[1],
            qkv[2],
        )  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        if self.relative_position_bias_table is not None:
            relative_position_bias = self.relative_position_bias_table[
                self.relative_position_index.view(-1)
            ].view(
                self.window_size[0] * self.window_size[1] + 1,
                self.window_size[0] * self.window_size[1] + 1,
                -1,
            )  # Wh*Ww,Wh*Ww,nH
            relative_position_bias = relative_position_bias.permute(
                2, 0, 1
            ).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

        if rel_pos_bias is not None:
            attn = attn + rel_pos_bias

        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        init_values=None,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        window_size=None,
        attn_head_dim=None,
    ):
        super().__init__()

        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            window_size=window_size,
            attn_head_dim=attn_head_dim,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

        if init_values:
            self.gamma_1 = nn.Parameter(
                init_values * torch.ones((dim)), requires_grad=True
            )
            self.gamma_2 = nn.Parameter(
                init_values * torch.ones((dim)), requires_grad=True
            )
        else:
            self.gamma_1, self.gamma_2 = None, None

    def forward(self, x, rel_pos_bias=None):
        if self.gamma_1 is None:
            x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
            fc_feature = self.drop_path(self.mlp(self.norm2(x)))
            x = x + fc_feature
        else:
            x = x + self.drop_path(
                self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)
            )
            fc_feature = self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
            x = x + fc_feature
        return x, fc_feature


class PatchEmbed(nn.Module):
    """Image to Patch Embedding"""

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
        )

    def forward(self, x, **kwargs):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert (
            H == self.img_size[0] and W == self.img_size[1]
        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class RelativePositionBias(nn.Module):
    def __init__(self, window_size, num_heads):
        super().__init__()
        self.window_size = window_size
        self.num_relative_distance = (2 * window_size[0] - 1) * (
            2 * window_size[1] - 1
        ) + 3
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(self.num_relative_distance, num_heads)
        )  # 2*Wh-1 * 2*Ww-1, nH
        # cls to token & token 2 cls & cls to cls

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(window_size[0])
        coords_w = torch.arange(window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 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] += window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
        relative_position_index = torch.zeros(
            size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype
        )
        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        relative_position_index[0, 0:] = self.num_relative_distance - 3
        relative_position_index[0:, 0] = self.num_relative_distance - 2
        relative_position_index[0, 0] = self.num_relative_distance - 1

        self.register_buffer("relative_position_index", relative_position_index)

        # trunc_normal_(self.relative_position_bias_table, std=.02)

    def forward(self):
        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.view(-1)
        ].view(
            self.window_size[0] * self.window_size[1] + 1,
            self.window_size[0] * self.window_size[1] + 1,
            -1,
        )  # Wh*Ww,Wh*Ww,nH
        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww


class SSL4EO_Data2Vec_Encoder(Encoder):
    """
    Paper: https://arxiv.org/abs/2211.07044
    SSL4EO_Data2Vec_Encoder is a class that implements an encoder for the Data2Vec model.
    Attributes:
        output_layers (int | list[int]): The layers from which to extract the output features.
        num_features (int): The number of features in the embedding dimension.
        embed_dim (int): The embedding dimension.
        patch_embed (PatchEmbed): The patch embedding layer.
        img_size (int): The size of the input image.
        patch_size (int): The size of each patch.
        cls_token (nn.Parameter): The class token parameter.
        mask_token (nn.Parameter): The mask token parameter.
        pos_embed (nn.Parameter): The positional embedding parameter.
        pos_drop (nn.Dropout): The dropout layer for positional embeddings.
        rel_pos_bias (RelativePositionBias): The relative position bias layer.
        blocks (nn.ModuleList): The list of transformer blocks.
        norm (nn.LayerNorm): The normalization layer.
        init_std (float): The standard deviation for weight initialization.
    Methods:
        __init__(self, encoder_weights: str | Path, input_size: int, input_bands: dict[str, list[str]], output_layers: int | list[int], in_chans: int = 3, patch_size: int = 16, embed_dim: int = 768, depth: int = 12, num_heads: int = 12, mlp_ratio: float = 4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=None, attn_head_dim=None, use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, init_std=0.02):
            Initializes the SSL4EO_Data2Vec_Encoder class.
        fix_init_weight(self):
            Fixes the initialization weights by rescaling them.
        _init_weights(self, m):
            Initializes the weights of the model.
        no_weight_decay(self):
            Specifies the parameters that should not have weight decay.
        forward(self, images):
            Defines the forward pass of the encoder.
        load_encoder_weights(self, logger: Logger) -> None:
            Loads the encoder weights from a checkpoint.
    """

    def __init__(
        self,
        encoder_weights: str | Path,
        input_size: int,
        input_bands: dict[str, list[str]],
        output_layers: int | list[int],
        output_dim: int | list[int],
        download_url: str,
        in_chans: int = 3,
        patch_size: int = 16,
        # num_frames=3,
        embed_dim: int = 768,
        depth: int = 12,
        num_heads: int = 12,
        mlp_ratio: float = 4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        init_values=None,
        attn_head_dim=None,
        use_abs_pos_emb=True,
        use_rel_pos_bias=False,
        use_shared_rel_pos_bias=False,
        init_std=0.02,
    ):
        super().__init__(
            model_name="ssl4eo_data2vec",
            encoder_weights=encoder_weights,
            input_bands=input_bands,
            input_size=input_size,
            embed_dim=embed_dim,
            output_dim=output_dim,
            output_layers=output_layers,
            multi_temporal=False,
            multi_temporal_output=False,
            pyramid_output=False,
            download_url=download_url,
        )

        self.output_layers = output_layers
        self.num_features = self.embed_dim = (
            embed_dim  # num_features for consistency with other models
        )

        self.patch_embed = PatchEmbed(
            img_size=self.input_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )
        num_patches = self.patch_embed.num_patches

        self.img_size = self.input_size
        self.patch_size = patch_size

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        if use_abs_pos_emb:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        else:
            self.pos_embed = None
        self.pos_drop = nn.Dropout(p=drop_rate)

        if use_shared_rel_pos_bias:
            self.rel_pos_bias = RelativePositionBias(
                window_size=self.patch_embed.patch_shape, num_heads=num_heads
            )
        else:
            self.rel_pos_bias = None

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, depth)
        ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    init_values=init_values,
                    window_size=self.patch_embed.patch_shape
                    if use_rel_pos_bias
                    else None,
                    attn_head_dim=attn_head_dim,
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim)

        self.init_std = init_std

        if self.pos_embed is not None:
            trunc_normal_(self.pos_embed, std=self.init_std)
        trunc_normal_(self.cls_token, std=self.init_std)
        trunc_normal_(self.mask_token, std=self.init_std)
        self.apply(self._init_weights)
        self.fix_init_weight()

    def fix_init_weight(self):
        def rescale(param, layer_id):
            param.div_(math.sqrt(2.0 * layer_id))

        for layer_id, layer in enumerate(self.blocks):
            rescale(layer.attn.proj.weight.data, layer_id + 1)
            rescale(layer.mlp.fc2.weight.data, layer_id + 1)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=self.init_std)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=self.init_std)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"pos_embed", "cls_token"}

    def forward(self, images):
        x = images["optical"].squeeze(2)
        x = self.patch_embed(x)
        batch_size, seq_len, _ = x.size()

        cls_tokens = self.cls_token.expand(
            batch_size, -1, -1
        )  # stole cls_tokens impl from Phil Wang, thanks

        x = torch.cat((cls_tokens, x), dim=1)
        if self.pos_embed is not None:
            x = x + self.pos_embed
        x = self.pos_drop(x)

        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None

        output = []
        for i, blk in enumerate(self.blocks):
            x, fc_feature = blk(x, rel_pos_bias=rel_pos_bias)
            if i in self.output_layers:
                out = (
                    x[:, 1:]
                    .permute(0, 2, 1)
                    .view(
                        x.shape[0],
                        -1,
                        self.img_size // self.patch_size,
                        self.img_size // self.patch_size,
                    )
                    .contiguous()
                )
                output.append(out)

        return output

    def load_encoder_weights(self, logger: Logger) -> None:
        checkpoint = torch.load(self.encoder_weights, map_location="cpu", weights_only=True)
        pretrained_model = checkpoint["model"]

        k = pretrained_model.keys()
        pretrained_encoder = {}
        incompatible_shape = {}
        missing = {}
        for name, param in self.named_parameters():
            if name not in k:
                missing[name] = param.shape
            elif pretrained_model[name].shape != param.shape:
                incompatible_shape[name] = (param.shape, pretrained_model[name].shape)
            else:
                pretrained_encoder[name] = pretrained_model[name]

        self.load_state_dict(pretrained_encoder, strict=False)
        self.parameters_warning(missing, incompatible_shape, logger)
