# Copyright (c) OpenMMLab. All rights reserved.
import copy
import math
from typing import Dict, Optional, Sequence, Union

import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import BaseTransformerLayer
from mmengine.model import ModuleList

from mmocr.models.common.modules import PositionalEncoding
from mmocr.models.textrecog.dictionary import Dictionary
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
from .base_decoder import BaseDecoder


def clones(module: nn.Module, N: int) -> nn.ModuleList:
    """Produce N identical layers.

    Args:
        module (nn.Module): A pytorch nn.module.
        N (int): Number of copies.

    Returns:
        nn.ModuleList: A pytorch nn.ModuleList with the copies.
    """
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])


class Embeddings(nn.Module):
    """Construct the word embeddings given vocab size and embed dim.

    Args:
        d_model (int): The embedding dimension.
        vocab (int): Vocablury size.
    """

    def __init__(self, d_model: int, vocab: int):
        super().__init__()
        self.lut = nn.Embedding(vocab, d_model)
        self.d_model = d_model

    def forward(self, *input: torch.Tensor) -> torch.Tensor:
        """Forward the embeddings.

        Args:
            input (torch.Tensor): The input tensors.

        Returns:
            torch.Tensor: The embeddings.
        """
        x = input[0]
        return self.lut(x) * math.sqrt(self.d_model)


@MODELS.register_module()
class MasterDecoder(BaseDecoder):
    """Decoder module in `MASTER <https://arxiv.org/abs/1910.02562>`_.

    Code is partially modified from https://github.com/wenwenyu/MASTER-pytorch.

    Args:
        n_layers (int): Number of attention layers. Defaults to 3.
        n_head (int): Number of parallel attention heads. Defaults to 8.
        d_model (int): Dimension :math:`E` of the input from previous model.
            Defaults to 512.
        feat_size (int): The size of the input feature from previous model,
            usually :math:`H * W`. Defaults to 6 * 40.
        d_inner (int): Hidden dimension of feedforward layers.
            Defaults to 2048.
        attn_drop (float): Dropout rate of the attention layer. Defaults to 0.
        ffn_drop (float): Dropout rate of the feedforward layer. Defaults to 0.
        feat_pe_drop (float): Dropout rate of the feature positional encoding
            layer. Defaults to 0.2.
        dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
            the instance of `Dictionary`. Defaults to None.
        module_loss (dict, optional): Config to build module_loss. Defaults
            to None.
        postprocessor (dict, optional): Config to build postprocessor.
            Defaults to None.
        max_seq_len (int): Maximum output sequence length :math:`T`. Defaults
            to 30.
        init_cfg (dict or list[dict], optional): Initialization configs.
    """

    def __init__(
        self,
        n_layers: int = 3,
        n_head: int = 8,
        d_model: int = 512,
        feat_size: int = 6 * 40,
        d_inner: int = 2048,
        attn_drop: float = 0.,
        ffn_drop: float = 0.,
        feat_pe_drop: float = 0.2,
        module_loss: Optional[Dict] = None,
        postprocessor: Optional[Dict] = None,
        dictionary: Optional[Union[Dict, Dictionary]] = None,
        max_seq_len: int = 30,
        init_cfg: Optional[Union[Dict, Sequence[Dict]]] = None,
    ):
        super().__init__(
            module_loss=module_loss,
            postprocessor=postprocessor,
            dictionary=dictionary,
            init_cfg=init_cfg,
            max_seq_len=max_seq_len)
        operation_order = ('norm', 'self_attn', 'norm', 'cross_attn', 'norm',
                           'ffn')
        decoder_layer = BaseTransformerLayer(
            operation_order=operation_order,
            attn_cfgs=dict(
                type='MultiheadAttention',
                embed_dims=d_model,
                num_heads=n_head,
                attn_drop=attn_drop,
                dropout_layer=dict(type='Dropout', drop_prob=attn_drop),
            ),
            ffn_cfgs=dict(
                type='FFN',
                embed_dims=d_model,
                feedforward_channels=d_inner,
                ffn_drop=ffn_drop,
                dropout_layer=dict(type='Dropout', drop_prob=ffn_drop),
            ),
            norm_cfg=dict(type='LN'),
            batch_first=True,
        )
        self.decoder_layers = ModuleList(
            [copy.deepcopy(decoder_layer) for _ in range(n_layers)])

        self.cls = nn.Linear(d_model, self.dictionary.num_classes)

        self.SOS = self.dictionary.start_idx
        self.PAD = self.dictionary.padding_idx
        self.max_seq_len = max_seq_len
        self.feat_size = feat_size
        self.n_head = n_head

        self.embedding = Embeddings(
            d_model=d_model, vocab=self.dictionary.num_classes)

        # TODO:
        self.positional_encoding = PositionalEncoding(
            d_hid=d_model, n_position=self.max_seq_len + 1)
        self.feat_positional_encoding = PositionalEncoding(
            d_hid=d_model, n_position=self.feat_size, dropout=feat_pe_drop)
        self.norm = nn.LayerNorm(d_model)
        self.softmax = nn.Softmax(dim=-1)

    def make_target_mask(self, tgt: torch.Tensor,
                         device: torch.device) -> torch.Tensor:
        """Make target mask for self attention.

        Args:
            tgt (Tensor): Shape [N, l_tgt]
            device (torch.device): Mask device.

        Returns:
            Tensor: Mask of shape [N * self.n_head, l_tgt, l_tgt]
        """

        trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3).bool()
        tgt_len = tgt.size(1)
        trg_sub_mask = torch.tril(
            torch.ones((tgt_len, tgt_len), dtype=torch.bool, device=device))
        tgt_mask = trg_pad_mask & trg_sub_mask

        # inverse for mmcv's BaseTransformerLayer
        tril_mask = tgt_mask.clone()
        tgt_mask = tgt_mask.float().masked_fill_(tril_mask == 0, -1e9)
        tgt_mask = tgt_mask.masked_fill_(tril_mask, 0)
        tgt_mask = tgt_mask.repeat(1, self.n_head, 1, 1)
        tgt_mask = tgt_mask.view(-1, tgt_len, tgt_len)
        return tgt_mask

    def decode(self, tgt_seq: torch.Tensor, feature: torch.Tensor,
               src_mask: torch.BoolTensor,
               tgt_mask: torch.BoolTensor) -> torch.Tensor:
        """Decode the input sequence.

        Args:
            tgt_seq (Tensor): Target sequence of shape: math: `(N, T, C)`.
            feature (Tensor): Input feature map from encoder of
                shape: math: `(N, C, H, W)`
            src_mask (BoolTensor): The source mask of shape: math: `(N, H*W)`.
            tgt_mask (BoolTensor): The target mask of shape: math: `(N, T, T)`.

        Return:
            Tensor: The decoded sequence.
        """
        tgt_seq = self.embedding(tgt_seq)
        x = self.positional_encoding(tgt_seq)
        attn_masks = [tgt_mask, src_mask]
        for layer in self.decoder_layers:
            x = layer(
                query=x, key=feature, value=feature, attn_masks=attn_masks)
        x = self.norm(x)
        return self.cls(x)

    def forward_train(self,
                      feat: Optional[torch.Tensor] = None,
                      out_enc: torch.Tensor = None,
                      data_samples: Sequence[TextRecogDataSample] = None
                      ) -> torch.Tensor:
        """Forward for training. Source mask will not be used here.

        Args:
            feat (Tensor, optional): Input feature map from backbone.
            out_enc (Tensor): Unused.
            data_samples (list[TextRecogDataSample]): Batch of
                TextRecogDataSample, containing gt_text and valid_ratio
                information.

        Returns:
            Tensor: The raw logit tensor. Shape :math:`(N, T, C)` where
            :math:`C` is ``num_classes``.
        """

        # flatten 2D feature map
        if len(feat.shape) > 3:
            b, c, h, w = feat.shape
            feat = feat.view(b, c, h * w)
            feat = feat.permute((0, 2, 1))
        feat = self.feat_positional_encoding(feat)

        trg_seq = []
        for target in data_samples:
            trg_seq.append(target.gt_text.padded_indexes.to(feat.device))

        trg_seq = torch.stack(trg_seq, dim=0)

        src_mask = None
        tgt_mask = self.make_target_mask(trg_seq, device=feat.device)
        return self.decode(trg_seq, feat, src_mask, tgt_mask)

    def forward_test(self,
                     feat: Optional[torch.Tensor] = None,
                     out_enc: torch.Tensor = None,
                     data_samples: Sequence[TextRecogDataSample] = None
                     ) -> torch.Tensor:
        """Forward for testing.

        Args:
            feat (Tensor, optional): Input feature map from backbone.
            out_enc (Tensor): Unused.
            data_samples (list[TextRecogDataSample]): Unused.

        Returns:
            Tensor: Character probabilities. of shape
            :math:`(N, self.max_seq_len, C)` where :math:`C` is
            ``num_classes``.
        """

        # flatten 2D feature map
        if len(feat.shape) > 3:
            b, c, h, w = feat.shape
            feat = feat.view(b, c, h * w)
            feat = feat.permute((0, 2, 1))
        feat = self.feat_positional_encoding(feat)

        N = feat.shape[0]
        input = torch.full((N, 1),
                           self.SOS,
                           device=feat.device,
                           dtype=torch.long)
        output = None
        for _ in range(self.max_seq_len):
            target_mask = self.make_target_mask(input, device=feat.device)
            out = self.decode(input, feat, None, target_mask)
            output = out
            _, next_word = torch.max(out, dim=-1)
            input = torch.cat([input, next_word[:, -1].unsqueeze(-1)], dim=1)
        return self.softmax(output)
