from typing import Optional

import torch
from torch import Tensor
import torch.nn as nn

from fairseq.modules import (
    LayerNorm,
    MultiheadAttention,
    RelPositionMultiheadAttention,
    RelativeMultiheadAttention,
    ConvolutionModule,
    ESPNETMultiHeadedAttention,
    RelPositionMultiHeadedAttention,
    ReducedRelPositionMultiHeadedAttention,
    LegacyRelPositionMultiHeadedAttention,
    LocalMultiheadAttention,
    ReducedMultiheadAttention,
    RotaryPositionMultiHeadedAttention,
    MultiHeadSelfAttentionModule,
)
from fairseq.modules.s2t_transformer_layer import FeedForwardModule
from fairseq.modules.fairseq_dropout import FairseqDropout
from .utils import Transpose, Permute3D


class PDSTransformerEncoderLayer(nn.Module):
    """Encoder layer block for progressive down-sampling method.

    In the original paper each operation (multi-head attention or FFN) is
    post-processed with: `dropout -> add residual -> layernorm`. In the
    tensor2tensor code they suggest that learning is more robust when
    preprocessing each layer with layernorm and postprocessing with:
    `dropout -> add residual`. We default to the approach in the paper, but the
    tensor2tensor approach can be enabled by setting
    *args.encoder_normalize_before* to ``True``.

    Args:
        args (argparse.Namespace): parsed command-line arguments
    """

    def __init__(self, args,
                 embed_dim,
                 ffn_ratio,
                 num_head,
                 attn_sample_ratio=1,
                 attn_stride=1,
                 conv_stride=1,
                 expand_embed_dim=None,
                 cnn_kernel_size=None):
        super().__init__()
        self.args = args

        embed_dim = embed_dim
        dropout = args.dropout

        if expand_embed_dim is None:
            expand_embed_dim = embed_dim

        self.dropout_module = FairseqDropout(
            dropout, module_name=self.__class__.__name__
        )
        self.quant_noise = getattr(args, 'quant_noise_pq', 0)
        self.quant_noise_block_size = getattr(args, 'quant_noise_pq_block_size', 8) or 8

        self.normalize_before = args.encoder_normalize_before
        activation = getattr(args, 'encoder_activation_fn', 'relu')

        # attention
        self.attn_type = getattr(args, "encoder_attention_type", "selfattn")
        self.self_attn = self.build_self_attention(args, embed_dim, num_head, attn_sample_ratio)
        self.self_attn_layer_norm = LayerNorm(embed_dim)

        # Attention Residual
        self.attn_res = nn.Sequential(
            Permute3D(1, 2, 0),
            nn.MaxPool1d(kernel_size=1, stride=attn_stride),
            Permute3D(2, 0, 1)
        ) if attn_stride > 1 else nn.Identity()

        if args.macaron_style:
            self.macaron_ffn = FeedForwardModule(
                embed_dim,
                embed_dim * ffn_ratio,
                dropout,
                dropout,
                activation
            )
            self.macaron_norm = LayerNorm(embed_dim)
            self.ffn_scale = 0.5
        else:
            self.macaron_ffn = None
            self.macaron_norm = None
            self.ffn_scale = 1.0

        self.conv_stride = conv_stride
        if args.use_cnn_module:
            self.conv_norm = LayerNorm(embed_dim)
            self.conv_module = ConvolutionModule(
                embed_dim,
                expand_embed_dim,
                depthwise_kernel_size=args.cnn_module_kernel if cnn_kernel_size is None else cnn_kernel_size,
                dropout=args.dropout,
                activation_fn=activation,
                stride=conv_stride
            )
            self.final_norm = LayerNorm(expand_embed_dim)

            # Convolution Residual
            self.conv_res = nn.Sequential(
                Permute3D(1, 2, 0),
                nn.Conv1d(embed_dim, expand_embed_dim, kernel_size=1, stride=conv_stride),
                Permute3D(2, 0, 1)
            ) if embed_dim != expand_embed_dim else nn.Sequential(
                Permute3D(1, 2, 0),
                nn.MaxPool1d(kernel_size=1, stride=conv_stride),
                Permute3D(2, 0, 1)
            ) if conv_stride > 1 else nn.Identity()
        else:
            self.conv_norm = None
            self.conv_module = None
            self.final_norm = None

        self.ffn = FeedForwardModule(
                expand_embed_dim,
                expand_embed_dim * ffn_ratio,
                dropout,
                dropout,
                activation
            )
        self.ffn_norm = LayerNorm(expand_embed_dim)

    def build_self_attention(self, args, embed_dim, num_head, sample_ratio=1):
        attention_heads = num_head
        dropout = args.dropout

        if self.attn_type == "selfattn":
            attn_func = MultiheadAttention
        elif self.attn_type == "rel_selfattn":
            attn_func = RelPositionMultiheadAttention
        elif self.attn_type == "relative":
            max_relative_length = max(getattr(args, "max_encoder_relative_length", -1),
                                      getattr(args, "max_relative_length", -1))
            if max_relative_length != -1:
                return RelativeMultiheadAttention(
                    embed_dim,
                    attention_heads,
                    dropout=dropout,
                    self_attention=True,
                    q_noise=self.quant_noise,
                    qn_block_size=self.quant_noise_block_size,
                    max_relative_length=max_relative_length,
                )
            else:
                print("The maximum encoder relative length %d can not be -1!" % max_relative_length)
                exit(1)
        elif self.attn_type == "local":
            hard_mask_window = getattr(args, "hard_mask_window", 0)
            gauss_mask_sigma = getattr(args, "gauss_mask_sigma", 0)
            init_mask_weight = getattr(args, "init_mask_weight", 0)
            return LocalMultiheadAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
                self_attention=True,
                q_noise=self.quant_noise,
                qn_block_size=self.quant_noise_block_size,
                hard_mask_window=hard_mask_window,
                gauss_mask_sigma=gauss_mask_sigma,
                init_mask_weight=init_mask_weight
            )
        elif self.attn_type == "rel_pos":
            return RelPositionMultiHeadedAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
            )
        elif self.attn_type == "reduced_rel_pos":
            return ReducedRelPositionMultiHeadedAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
                sample_ratio=sample_ratio,
                reduced_method=getattr(args, "attention_reduced_method", "conv"),
                reduced_q=getattr(args, "attention_reduced_q", False)
            )
        elif self.attn_type == "rel_pos_legacy":
            return LegacyRelPositionMultiHeadedAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
            )
        elif self.attn_type == "rope":
            return RotaryPositionMultiHeadedAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
                precision=args.fp16
            )
        elif self.attn_type == "abs":
            return ESPNETMultiHeadedAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
            )
        elif self.attn_type == "reduced":
            return ReducedMultiheadAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
                self_attention=True,
                q_noise=self.quant_noise,
                qn_block_size=self.quant_noise_block_size,
                sample_ratio=sample_ratio,
                reduced_method=getattr(args, "attention_reduced_method", "conv"),
                reduced_q=getattr(args, "attention_reduced_q", False)
            )
        elif self.attn_type == "transfer":
            return MultiHeadSelfAttentionModule(
                embed_dim,
                attention_heads,
                dropout,
                max_pos_encoding=args.max_source_positions,
                relative_pos_enc=getattr(args, "relative_pos_enc", False),
                linear_att=getattr(args, "linear_att", False),
            )
        else:
            print("The encoder attention type %s is not supported!" % self.attn_type)
            exit(1)

        return attn_func(
            embed_dim,
            attention_heads,
            dropout=dropout,
            self_attention=True,
            q_noise=self.quant_noise,
            qn_block_size=self.quant_noise_block_size,
        )

    def residual_connection(self, x, residual):
        return residual + x

    def upgrade_state_dict_named(self, state_dict, name):
        """
        Rename layer norm states from `...layer_norms.0.weight` to
        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
        `...final_layer_norm.weight`
        """
        layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"}
        for old, new in layer_norm_map.items():
            for m in ("weight", "bias"):
                k = "{}.layer_norms.{}.{}".format(name, old, m)
                if k in state_dict:
                    state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k]
                    del state_dict[k]

    def dump(self, fstream, info=""):
        self.self_attn.dump(fstream, "%s Self Attn" % (info)) if hasattr(self.self_attn, "dump") else None

    def set_flag(self, **kwargs):
        self.self_attn.set_flag(**kwargs)

    def forward(self, x,
                encoder_padding_mask: Optional[Tensor],
                attn_mask: Optional[Tensor] = None,
                pos_emb: Optional[Tensor] = None):
        """
        Args:
            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_padding_mask (ByteTensor): binary ByteTensor of shape
                `(batch, seq_len)` where padding elements are indicated by ``1``.
            attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`,
                where `tgt_len` is the length of output and `src_len` is the
                length of input, though here both are equal to `seq_len`.
                `attn_mask[tgt_i, src_j] = 1` means that when calculating the
                embedding for `tgt_i`, we exclude (mask out) `src_j`. This is
                useful for strided self-attention.
            pos_emb (Tensor): the position embedding for relative position encoding

        Returns:
            encoded output of shape `(seq_len, batch, embed_dim)`
        """
        # anything in original attn_mask = 1, becomes -1e8
        # anything in original attn_mask = 0, becomes 0
        # Note that we cannot use -inf here, because at some edge cases,
        # the attention weight (before softmax) for some padded element in query
        # will become -inf, which results in NaN in model parameters
        if attn_mask is not None:
            attn_mask = attn_mask.masked_fill(attn_mask.to(torch.bool), -1e8)

        # whether to use macaron style
        if self.macaron_norm is not None:
            residual = x
            if self.normalize_before:
                x = self.macaron_norm(x)
            x = self.macaron_ffn(x)
            x = residual + self.ffn_scale * x
            if not self.normalize_before:
                x = self.macaron_norm(x)

        residual = x
        if self.normalize_before:
            x = self.self_attn_layer_norm(x)
        if self.attn_type in ["rel_pos", "rel_pos_legacy", "rel_selfattn", "reduced_rel_pos"]:
            assert pos_emb is not None, "Positions is necessary for RPE!"
            x, _ = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=encoder_padding_mask,
                need_weights=False,
                attn_mask=attn_mask,
                pos_emb=pos_emb
            )
        elif self.attn_type == "transfer":
            x, _ = self.self_attn(
                x, encoder_padding_mask
            )
        else:
            x, _ = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=encoder_padding_mask,
                need_weights=False,
                attn_mask=attn_mask,
            )
        x = self.dropout_module(x)
        x = self.residual_connection(self.attn_res(x), residual)
        if not self.normalize_before:
            x = self.self_attn_layer_norm(x)

        # convolution module
        if self.conv_module is not None:
            residual = x

            x = x.transpose(0, 1)
            if self.normalize_before:
                x = self.conv_norm(x)

            x = self.conv_module(x, encoder_padding_mask)
            x = x.transpose(0, 1)
            x = self.conv_res(residual) + x

            if not self.normalize_before:
                x = self.conv_norm(x)

        residual = x
        if self.normalize_before:
            x = self.ffn_norm(x)
        x = self.ffn(x)
        x = self.residual_connection(self.ffn_scale * x, residual)
        if not self.normalize_before:
            x = self.ffn_norm(x)

        if self.conv_module is not None:
            x = self.final_norm(x)

        return x
