# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Dict, Optional, Tuple
import torch
from fairseq import utils
from fairseq.modules.quant_noise import quant_noise
from torch import Tensor, nn
from torch.nn import Parameter

from fairseq.modules.multihead_attention import MultiheadAttention
from ..modules.multihead_functional import multi_head_attention_forward


class MultiheadAttentionSelection(MultiheadAttention):

    def __init__(
        self,
        embed_dim,
        total_num_heads,
        num_heads,
        kdim=None,
        vdim=None,
        dropout=0.0,
        bias=True,
        add_bias_kv=False,
        add_zero_attn=False,
        self_attention=False,
        encoder_decoder_attention=False,
        q_noise=0.0,
        qn_block_size=8,
        layer_idx=0,
        attn_head_selector=None
    ):
        super().__init__(
            embed_dim,
            num_heads,
            kdim=kdim,
            vdim=vdim,
            dropout=dropout,
            bias=bias,
            add_bias_kv=add_bias_kv,
            add_zero_attn=add_zero_attn,
            self_attention=self_attention,
            encoder_decoder_attention=encoder_decoder_attention,
            q_noise=q_noise,
            qn_block_size=qn_block_size,
        )
        self.layer_idx = layer_idx
        self.attn_head_selector = attn_head_selector
        self.total_num_heads = total_num_heads
        self.total_embed_dim = self.head_dim * total_num_heads
        self.k_proj = quant_noise(
            nn.Linear(self.kdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size
        )
        self.v_proj = quant_noise(
            nn.Linear(self.vdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size
        )
        self.q_proj = quant_noise(
            nn.Linear(embed_dim, self.total_embed_dim, bias=bias), q_noise, qn_block_size
        )
        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, self.total_embed_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, self.total_embed_dim))
        else:
            self.bias_k = self.bias_v = None
        self.reset_parameters()

    def forward(
        self,
        query,
        key: Optional[Tensor],
        value: Optional[Tensor],
        key_padding_mask: Optional[Tensor] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        need_weights: bool = True,
        static_kv: bool = False,
        attn_mask: Optional[Tensor] = None,
        before_softmax: bool = False,
        need_head_weights: bool = False,
        # subset_heads: Optional[Tensor] = None,
        # subset_weights: Optional[Tensor] = None
    ) -> Tuple[Tensor, Optional[Tensor]]:
        if need_head_weights:
            need_weights = True

        is_tpu = query.device.type == "xla"

        subset_heads, subset_weights = self.attn_head_selector(self.layer_idx)

        tgt_len, bsz, embed_dim = query.size()
        src_len = tgt_len
        assert list(query.size()) == [tgt_len, bsz, self.embed_dim]
        if key is not None:
            src_len, key_bsz, _ = key.size()
            if not torch.jit.is_scripting():
                assert key_bsz == bsz
                assert value is not None
                assert src_len, bsz == value.shape[:2]

        if (
            not self.onnx_trace
            and not is_tpu  # don't use PyTorch version on TPUs
            and incremental_state is None
            and not static_kv
            # A workaround for quantization to work. Otherwise JIT compilation
            # treats bias in linear module as method.
            and not torch.jit.is_scripting()
        ):
            assert key is not None and value is not None
            return multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.total_num_heads,
                self.num_heads,
                torch.empty([0]),
                torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout_module.p,
                self.out_proj.weight,
                self.out_proj.bias,
                self.training or self.dropout_module.apply_during_inference,
                key_padding_mask,
                need_weights,
                attn_mask,
                use_separate_proj_weight=True,
                q_proj_weight=self.q_proj.weight,
                k_proj_weight=self.k_proj.weight,
                v_proj_weight=self.v_proj.weight,
                subset_heads=subset_heads,
                subset_weights=subset_weights
            )

        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if saved_state is not None and "prev_key" in saved_state:
                # previous time steps are cached - no need to recompute
                # key and value if they are static
                if static_kv:
                    assert self.encoder_decoder_attention and not self.self_attention
                    key = value = None
        else:
            saved_state = None

        if self.self_attention:
            q = self.q_proj(query)
            k = self.k_proj(query)
            v = self.v_proj(query)
        elif self.encoder_decoder_attention:
            # encoder-decoder attention
            q = self.q_proj(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)

        else:
            assert key is not None and value is not None
            q = self.q_proj(query)
            k = self.k_proj(key)
            v = self.v_proj(value)
        q *= self.scaling

        if self.bias_k is not None:
            assert self.bias_v is not None
            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = torch.cat(
                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
                )
            if key_padding_mask is not None:
                key_padding_mask = torch.cat(
                    [
                        key_padding_mask,
                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
                    ],
                    dim=1,
                )

        q = (
            q.contiguous()
            .view(tgt_len, bsz * self.total_num_heads, self.head_dim)
            .transpose(0, 1)
        )
        if k is not None:
            k = (
                k.contiguous()
                .view(-1, bsz * self.total_num_heads, self.head_dim)
                .transpose(0, 1)
            )
        if v is not None:
            v = (
                v.contiguous()
                .view(-1, bsz * self.total_num_heads, self.head_dim)
                .transpose(0, 1)
            )

        if saved_state is not None:
            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
            if "prev_key" in saved_state:
                _prev_key = saved_state["prev_key"]
                assert _prev_key is not None
                prev_key = _prev_key.view(bsz * self.total_num_heads, -1, self.head_dim)
                if static_kv:
                    k = prev_key
                else:
                    assert k is not None
                    k = torch.cat([prev_key, k], dim=1)
                src_len = k.size(1)
            if "prev_value" in saved_state:
                _prev_value = saved_state["prev_value"]
                assert _prev_value is not None
                prev_value = _prev_value.view(bsz * self.total_num_heads, -1, self.head_dim)
                if static_kv:
                    v = prev_value
                else:
                    assert v is not None
                    v = torch.cat([prev_value, v], dim=1)
            prev_key_padding_mask: Optional[Tensor] = None
            if "prev_key_padding_mask" in saved_state:
                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
            assert k is not None and v is not None
            key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
                key_padding_mask=key_padding_mask,
                prev_key_padding_mask=prev_key_padding_mask,
                batch_size=bsz,
                src_len=k.size(1),
                static_kv=static_kv,
            )

            saved_state["prev_key"] = k.view(bsz, self.total_num_heads, -1, self.head_dim)
            saved_state["prev_value"] = v.view(bsz, self.total_num_heads, -1, self.head_dim)
            saved_state["prev_key_padding_mask"] = key_padding_mask
            # In this branch incremental_state is never None
            assert incremental_state is not None
            incremental_state = self._set_input_buffer(incremental_state, saved_state)
        assert k is not None
        assert k.size(1) == src_len

        # This is part of a workaround to get around fork/join parallelism
        # not supporting Optional types.
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None

        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz
            assert key_padding_mask.size(1) == src_len

        if self.add_zero_attn:
            assert v is not None
            src_len += 1
            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
            if attn_mask is not None:
                attn_mask = torch.cat(
                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
                )
            if key_padding_mask is not None:
                key_padding_mask = torch.cat(
                    [
                        key_padding_mask,
                        torch.zeros(key_padding_mask.size(0), 1).type_as(
                            key_padding_mask
                        ),
                    ],
                    dim=1,
                )

        attn_weights = torch.bmm(q, k.transpose(1, 2))
        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)

        assert list(attn_weights.size()) == [bsz * self.total_num_heads, tgt_len, src_len]

        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(0)
            if self.onnx_trace:
                attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
            attn_weights += attn_mask

        if key_padding_mask is not None:
            # don't attend to padding symbols
            attn_weights = attn_weights.view(bsz, self.total_num_heads, tgt_len, src_len)
            if not is_tpu:
                attn_weights = attn_weights.masked_fill(
                    key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
                    float("-inf"),
                )
            else:
                attn_weights = attn_weights.transpose(0, 2)
                attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
                attn_weights = attn_weights.transpose(0, 2)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        if before_softmax:
            return attn_weights, v

        attn_weights_float = utils.softmax(
            attn_weights, dim=-1, onnx_trace=self.onnx_trace
        )
        attn_weights = attn_weights_float.type_as(attn_weights)
        attn_probs = self.dropout_module(attn_weights)

        assert v is not None

        # evaluation
        if subset_heads is not None and subset_heads.numel() == 1:
            subset_heads = subset_heads.repeat(bsz)
            subset_weights = subset_weights.repeat(bsz)

        if subset_heads is None:
            attn = torch.bmm(attn_probs, v)
        else:
            # training with head selection
            mixed_attn = torch.bmm(attn_probs, v).contiguous().view(bsz, self.total_num_heads, tgt_len, self.head_dim)
            attn = torch.stack(
                [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1
            )
            attn = attn * subset_weights.unsqueeze(2).unsqueeze(3)
            attn = attn.contiguous().view(bsz * self.num_heads, tgt_len, self.head_dim)

        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
        if self.onnx_trace and attn.size(1) == 1:
            # when ONNX tracing a single decoder step (sequence length == 1)
            # the transpose is a no-op copy before view, thus unnecessary
            attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
        else:
            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn = self.out_proj(attn)
        attn_weights: Optional[Tensor] = None
        if need_weights:
            if subset_heads is None:
                attn_weights = attn_weights_float.view(
                    bsz, self.num_heads, tgt_len, src_len
                ).transpose(1, 0)
            else:
                mixed_attn_weights = attn_weights_float.view(
                    bsz, self.total_num_heads, tgt_len, src_len
                )
                attn_weights = torch.stack(
                    [mixed_attn_weights[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1
                ).transpose(1, 0)
            if not need_head_weights:
                # average attention weights over heads
                attn_weights = attn_weights.mean(dim=0)

        return attn, attn_weights