# -*- coding: utf-8 -*-
import logging
import math
from collections import Counter
from typing import List

import numpy
import torch

from ...nn.init import reinit_embedding_, reinit_layer_
from ...nn.modules import CombinedDropout
from ...wrapper import Batch
from ..encoder import EncoderConfig
from .base import DecoderBase, SingleDecoderConfigBase
from .boundaries import MAX_SIZE_ID_COV_RATE
from .span_attr_classification import ChunkSinglesDecoderMixin

logger = logging.getLogger(__name__)


class MaskedSpanAttrClsDecoderConfig(SingleDecoderConfigBase, ChunkSinglesDecoderMixin):
    def __init__(self, **kwargs):
        self.size_emb_dim = kwargs.pop("size_emb_dim", 0)
        self.label_emb_dim = kwargs.pop("label_emb_dim", 0)
        self.reduction = kwargs.pop(
            "reduction",
            EncoderConfig(
                arch="FFN",
                hid_dim=150,
                num_layers=1,
                in_drop_rates=(0.0, 0.0, 0.0),
                hid_drop_rate=0.0,
            ),
        )

        self.in_drop_rates = kwargs.pop("in_drop_rates", (0.4, 0.0, 0.0))
        self.hid_drop_rates = kwargs.pop("hid_drop_rates", (0.2, 0.0, 0.0))

        self.neg_sampling_rate = kwargs.pop("neg_sampling_rate", 1.0)

        self.ck_loss_weight = kwargs.pop("ck_loss_weight", 0)

        self.check_ac_labels = kwargs.pop("check_ac_labels", False)
        self.none_label = kwargs.pop("none_label", "<none>")
        self.idx2label = kwargs.pop("idx2label", None)
        self.ck_none_label = kwargs.pop("ck_none_label", "<none>")
        self.idx2ck_label = kwargs.pop("idx2ck_label", None)

        # Change the default as multi-label classification
        kwargs["multilabel"] = kwargs.pop("multilabel", True)
        super().__init__(**kwargs)

    @property
    def name(self):
        return self._name_sep.join([self.criterion])

    def __repr__(self):
        repr_attr_dict = {
            key: getattr(self, key)
            for key in ["in_dim", "in_drop_rates", "hid_drop_rates", "criterion"]
        }
        return self._repr_non_config_attrs(repr_attr_dict)

    @property
    def in_dim(self):
        return self._in_dim

    @in_dim.setter
    def in_dim(self, dim: int):
        if dim is not None:
            self._in_dim = dim
            self.reduction.in_dim = dim + self.size_emb_dim + self.label_emb_dim

    def build_vocab(self, *partitions):
        counter = Counter(
            label
            for data in partitions
            for entry in data
            for label, start, end in entry["chunks"]
        )
        self.idx2ck_label = [self.ck_none_label] + list(counter.keys())

        span_sizes = [
            end - start
            for data in partitions
            for entry in data
            for label, start, end in entry["chunks"]
        ]
        self.max_size_id = (
            math.ceil(numpy.quantile(span_sizes, MAX_SIZE_ID_COV_RATE)) - 1
        )

        counter = Counter(
            label
            for data in partitions
            for entry in data
            for label, chunk in entry["attributes"]
        )
        self.idx2label = [self.none_label] + list(counter.keys())

        counter = Counter(
            (label, chunk[0])
            for data in partitions
            for entry in data
            for label, chunk in entry["attributes"]
        )
        self.existing_ac_labels = set(list(counter.keys()))

    def exemplify(self, entry: dict, training: bool = True):
        example = super().exemplify(entry, training)

        # Attention mask for each example
        cs_obj = example["cs_obj"]
        num_tokens, num_chunks = cs_obj.num_tokens, len(cs_obj.chunks)
        example["masked_span_bert_like"] = {"span_size_ids": cs_obj.span_size_ids}

        ck2tok_mask = torch.ones(num_chunks, num_tokens, dtype=torch.bool)
        for i, (label, start, end) in enumerate(cs_obj.chunks):
            for j in range(start, end):
                ck2tok_mask[i, j] = False
        example["masked_span_bert_like"].update({"ck2tok_mask": ck2tok_mask})

        return example

    def batchify(self, batch_examples: List[dict], batch_sub_mask: torch.Tensor):
        batch = super().batchify(batch_examples)

        # Batchify chunk-to-token attention mask
        # Remove `[CLS]` and `[SEP]`
        batch_sub_mask = batch_sub_mask[:, 2:]
        batch["masked_span_bert_like"] = {}
        for tensor_name in batch_examples[0]["masked_span_bert_like"].keys():
            if tensor_name.endswith("_mask"):
                max_num_items = max(
                    ex["masked_span_bert_like"][tensor_name].size(0)
                    for ex in batch_examples
                )
                batch_item2tok_mask = batch_sub_mask.unsqueeze(1).repeat(
                    1, max_num_items, 1
                )

                for i, ex in enumerate(batch_examples):
                    item2tok_mask = ex["masked_span_bert_like"][tensor_name]
                    num_items, num_tokens = item2tok_mask.size()
                    batch_item2tok_mask[i, :num_items, :num_tokens].logical_or_(
                        item2tok_mask
                    )

                batch["masked_span_bert_like"].update(
                    {tensor_name: batch_item2tok_mask}
                )

            elif tensor_name.endswith("_ids"):
                batch_ids = [
                    ex["masked_span_bert_like"][tensor_name] for ex in batch_examples
                ]
                batch_ids = torch.nn.utils.rnn.pad_sequence(
                    batch_ids, batch_first=True, padding_value=0
                )
                batch["masked_span_bert_like"].update({tensor_name: batch_ids})

        return batch

    def instantiate(self):
        return MaskedSpanAttrClsDecoder(self)


class MaskedSpanAttrClsDecoder(DecoderBase, ChunkSinglesDecoderMixin):
    def __init__(self, config: MaskedSpanAttrClsDecoderConfig):
        super().__init__()
        self.max_size_id = config.max_size_id
        self.neg_sampling_rate = config.neg_sampling_rate
        self.multilabel = config.multilabel
        self.conf_thresh = config.conf_thresh
        self.ck_loss_weight = config.ck_loss_weight
        self.none_label = config.none_label
        self.idx2label = config.idx2label
        self.ck_none_label = config.ck_none_label
        self.idx2ck_label = config.idx2ck_label
        self.check_ac_labels = config.check_ac_labels
        self.existing_ac_labels = config.existing_ac_labels

        if config.size_emb_dim > 0:
            self.size_embedding = torch.nn.Embedding(
                config.max_size_id + 1, config.size_emb_dim
            )
            reinit_embedding_(self.size_embedding)

        if config.label_emb_dim > 0:
            self.label_embedding = torch.nn.Embedding(
                config.ck_voc_dim, config.label_emb_dim
            )
            reinit_embedding_(self.label_embedding)

        self.in_dropout = CombinedDropout(*config.in_drop_rates)
        self.hid_dropout = CombinedDropout(*config.hid_drop_rates)

        if config.reduction.num_layers > 0 and config.reduction.out_dim > 0:
            self.reduction = config.reduction.instantiate()
            self.hid2logit = torch.nn.Linear(config.reduction.out_dim, config.voc_dim)
        else:
            self.hid2logit = torch.nn.Linear(config.reduction.in_dim, config.voc_dim)
        reinit_layer_(self.hid2logit, "sigmoid")

        if config.ck_loss_weight > 0:
            self.ck_hid2logit = torch.nn.Linear(
                config.in_dim + config.size_emb_dim + config.label_emb_dim,
                config.ck_voc_dim,
            )
            reinit_layer_(self.ck_hid2logit, "sigmoid")

        self.criterion = config.instantiate_criterion(reduction="sum")

    def assign_chunks_pred(self, batch: Batch, batch_chunks_pred: List[List[tuple]]):
        """This method should be called on-the-fly for joint modeling."""
        for cs_obj, chunks_pred in zip(batch.cs_objs, batch_chunks_pred):
            if cs_obj.chunks_pred is None:
                cs_obj.chunks_pred = chunks_pred
                cs_obj.build(self)
                cs_obj.to(self.hid2logit.weight.device)

    def get_logits(
        self, batch: Batch, full_hidden: torch.Tensor, span_query_hidden: torch.Tensor
    ):
        # full_hidden: (batch, step, hid_dim)
        # span_query_hidden: (batch, num_chunks, hid_dim)
        batch_logits = []
        for i, cs_obj in enumerate(batch.cs_objs):
            num_chunks = len(cs_obj.chunks)
            if num_chunks == 0:
                logits = None
            else:
                # span_hidden: (num_chunks, hid_dim)
                span_hidden = span_query_hidden[i, : len(cs_obj.chunks)]

                if hasattr(self, "size_embedding"):
                    # size_embedded: (num_chunks, emb_dim)
                    size_embedded = self.size_embedding(cs_obj.span_size_ids)
                    span_hidden = torch.cat([span_hidden, size_embedded], dim=-1)

                if hasattr(self, "label_embedding"):
                    # label_embedded: (num_chunks, emb_dim)
                    label_embedded = self.label_embedding(cs_obj.ck_label_ids)
                    span_hidden = torch.cat([span_hidden, label_embedded], dim=-1)

                span_hidden = self.in_dropout(span_hidden)

                if hasattr(self, "reduction"):
                    reduced = self.reduction(span_hidden)
                    logits = self.hid2logit(self.hid_dropout(reduced))
                else:
                    # logits: (num_chunks, logit_dim)
                    logits = self.hid2logit(span_hidden)

                if self.ck_loss_weight > 0:
                    ck_logits = self.ck_hid2logit(span_hidden)
                    logits = (logits, ck_logits)

            batch_logits.append(logits)

        return batch_logits

    def forward(
        self, batch: Batch, full_hidden: torch.Tensor, span_query_hidden: torch.Tensor
    ):
        batch_logits = self.get_logits(batch, full_hidden, span_query_hidden)

        losses = []
        for logits, cs_obj in zip(batch_logits, batch.cs_objs):
            if len(cs_obj.chunks) == 0:
                loss = torch.tensor(0.0, device=full_hidden.device)
            else:
                label_ids = cs_obj.cs2label_id
                ck_label_ids_gold = cs_obj.ck_label_ids_gold
                if self.ck_loss_weight > 0:
                    logits, ck_logits = logits
                if hasattr(cs_obj, "non_mask"):
                    non_mask = cs_obj.non_mask
                    logits, label_ids = logits[non_mask], label_ids[non_mask]
                loss = self.criterion(logits, label_ids)
                if self.ck_loss_weight > 0:
                    loss = loss + self.ck_loss_weight * self.criterion(
                        ck_logits, ck_label_ids_gold
                    )
            losses.append(loss)
        return torch.stack(losses)

    def decode(
        self, batch: Batch, full_hidden: torch.Tensor, span_query_hidden: torch.Tensor
    ):
        batch_logits = self.get_logits(batch, full_hidden, span_query_hidden)

        batch_attributes = []
        for logits, cs_obj in zip(batch_logits, batch.cs_objs):
            if len(cs_obj.chunks) == 0:
                attributes = []
            else:
                if self.ck_loss_weight > 0:
                    logits, _ = logits
                if not self.multilabel:
                    confidences, label_ids = logits.softmax(dim=-1).max(dim=-1)
                    labels = [self.idx2label[i] for i in label_ids.cpu().tolist()]
                    attributes = [
                        (label, chunk)
                        for label, chunk in zip(labels, cs_obj.chunks)
                        if label != self.none_label
                    ]
                    confidences = [
                        conf
                        for label, conf in zip(labels, confidences.cpu().tolist())
                        if label != self.none_label
                    ]
                else:
                    all_confidences = logits.sigmoid()
                    # Zero-out all entities according to <none> labels
                    all_confidences[
                        all_confidences[:, self.none_idx] > (1 - self.conf_thresh)
                    ] = 0
                    # Zero-out <none> labels for all entities
                    all_confidences[:, self.none_idx] = 0
                    assert all_confidences.size(0) == len(cs_obj.chunks)

                    all_confidences_list = all_confidences.cpu().tolist()
                    pos_entries = (
                        torch.nonzero(all_confidences > self.conf_thresh).cpu().tolist()
                    )

                    attributes = [
                        (self.idx2label[i], cs_obj.chunks[cidx])
                        for cidx, i in pos_entries
                    ]
                    confidences = [
                        all_confidences_list[cidx][i] for cidx, i in pos_entries
                    ]
                assert len(confidences) == len(attributes)
                attributes = self._filter(attributes)
            batch_attributes.append(attributes)

        return batch_attributes
