# ******************************************************************************
# Copyright 2017-2019 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
import logging
from typing import List, Union

import torch
from torch.nn import CrossEntropyLoss, Dropout, Linear
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
from transformers import (
    ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    BertForTokenClassification,
    BertPreTrainedModel,
    RobertaConfig,
    RobertaModel,
    XLNetModel,
    XLNetPreTrainedModel,
)

from nlp_architect.data.sequential_tagging import TokenClsInputExample
from nlp_architect.models.transformers.base_model import InputFeatures, TransformerBase
from nlp_architect.models.transformers.quantized_bert import QuantizedBertForTokenClassification
from nlp_architect.utils.metrics import tagging

logger = logging.getLogger()
logger.setLevel(logging.INFO)


def _bert_token_tagging_head_fw(
    bert,
    input_ids,
    token_type_ids=None,
    attention_mask=None,
    labels=None,
    head_mask=None,
    valid_ids=None,
):
    outputs = bert.bert(
        input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, head_mask=head_mask
    )
    sequence_output = outputs[0]
    sequence_output = bert.dropout(sequence_output)
    logits = bert.classifier(sequence_output)

    if labels is not None:
        loss_fct = CrossEntropyLoss(ignore_index=0)
        active_positions = valid_ids.view(-1) != 0.0
        active_labels = labels.view(-1)[active_positions]
        active_logits = logits.view(-1, bert.num_labels)[active_positions]
        loss = loss_fct(active_logits, active_labels)
        return (
            loss,
            logits,
        )
    return (logits,)


class BertTokenClassificationHead(BertForTokenClassification):
    """BERT token classification head with linear classifier.
       This head's forward ignores word piece tokens in its linear layer.

       The forward requires an additional 'valid_ids' map that maps the tensors
       for valid tokens (e.g., ignores additional word piece tokens generated by
       the tokenizer, as in NER task the 'X' label).
    """

    def forward(
        self,
        input_ids,
        token_type_ids=None,
        attention_mask=None,
        labels=None,
        position_ids=None,
        head_mask=None,
        valid_ids=None,
    ):
        return _bert_token_tagging_head_fw(
            self,
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            labels=labels,
            head_mask=head_mask,
            valid_ids=valid_ids,
        )


class QuantizedBertForTokenClassificationHead(QuantizedBertForTokenClassification):
    """Quantized BERT token classification head with linear classifier.
       This head's forward ignores word piece tokens in its linear layer.

       The forward requires an additional 'valid_ids' map that maps the tensors
       for valid tokens (e.g., ignores additional word piece tokens generated by
       the tokenizer, as in NER task the 'X' label).
    """

    def forward(
        self,
        input_ids,
        token_type_ids=None,
        attention_mask=None,
        labels=None,
        position_ids=None,
        head_mask=None,
        valid_ids=None,
    ):
        return _bert_token_tagging_head_fw(
            self,
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            labels=labels,
            head_mask=head_mask,
            valid_ids=valid_ids,
        )


class XLNetTokenClassificationHead(XLNetPreTrainedModel):
    """XLNet token classification head with linear classifier.
       This head's forward ignores word piece tokens in its linear layer.

       The forward requires an additional 'valid_ids' map that maps the tensors
       for valid tokens (e.g., ignores additional word piece tokens generated by
       the tokenizer, as in NER task the 'X' label).
    """

    def __init__(self, config):
        super(XLNetTokenClassificationHead, self).__init__(config)
        self.num_labels = config.num_labels

        self.transformer = XLNetModel(config)
        self.logits_proj = torch.nn.Linear(config.d_model, config.num_labels)
        self.dropout = torch.nn.Dropout(config.dropout)

        self.apply(self.init_weights)

    def forward(
        self,
        input_ids,
        token_type_ids=None,
        input_mask=None,
        attention_mask=None,
        mems=None,
        perm_mask=None,
        target_mapping=None,
        labels=None,
        head_mask=None,
        valid_ids=None,
    ):
        transformer_outputs = self.transformer(
            input_ids,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            head_mask=head_mask,
        )
        sequence_output = transformer_outputs[0]
        output = self.dropout(sequence_output)
        logits = self.logits_proj(output)

        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=0)
            active_positions = valid_ids.view(-1) != 0.0
            active_labels = labels.view(-1)[active_positions]
            active_logits = logits.view(-1, self.num_labels)[active_positions]
            loss = loss_fct(active_logits, active_labels)
            return (
                loss,
                logits,
            )
        return (logits,)


class RobertaForTokenClassificationHead(BertPreTrainedModel):
    """RoBERTa token classification head with linear classifier.
       This head's forward ignores word piece tokens in its linear layer.

       The forward requires an additional 'valid_ids' map that maps the tensors
       for valid tokens (e.g., ignores additional word piece tokens generated by
       the tokenizer, as in NER task the 'X' label).
    """

    config_class = RobertaConfig
    pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "roberta"

    def __init__(self, config):
        super(RobertaForTokenClassificationHead, self).__init__(config)
        self.num_labels = config.num_labels

        self.roberta = RobertaModel(config)
        self.dropout = Dropout(config.hidden_dropout_prob)
        self.classifier = Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    def forward(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        labels=None,
        valid_ids=None,
    ):

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=0)
            active_positions = valid_ids.view(-1) != 0.0
            active_labels = labels.view(-1)[active_positions]
            active_logits = logits.view(-1, self.num_labels)[active_positions]
            loss = loss_fct(active_logits, active_labels)
            return (
                loss,
                logits,
            )
        return (logits,)


class TransformerTokenClassifier(TransformerBase):
    """
    Transformer word tagging classifier

    Args:
        model_type(str): model family (classifier head), choose between bert/quant_bert/xlnet
        labels (List[str], optional): list of tag labels
    """

    MODEL_CLASS = {
        "bert": BertTokenClassificationHead,
        "quant_bert": QuantizedBertForTokenClassificationHead,
        "xlnet": XLNetTokenClassificationHead,
        "roberta": RobertaForTokenClassificationHead,
    }

    def __init__(
        self,
        model_type: str,
        labels: List[str] = None,
        training_args: bool = None,
        *args,
        load_quantized=False,
        **kwargs,
    ):
        assert model_type in self.MODEL_CLASS.keys(), "unsupported model type"
        self.labels = labels
        self.num_labels = len(labels) + 1  # +1 for padding label
        self.labels_id_map = {k: v for k, v in enumerate(self.labels, 1)}

        super(TransformerTokenClassifier, self).__init__(
            model_type, labels=self.labels, num_labels=self.num_labels, *args, **kwargs
        )

        self.model_class = self.MODEL_CLASS[model_type]
        if model_type == "quant_bert" and load_quantized:
            self.model = self.model_class.from_pretrained(
                self.model_name_or_path,
                from_tf=bool(".ckpt" in self.model_name_or_path),
                config=self.config,
                from_8bit=load_quantized,
            )
        else:
            self.model = self.model_class.from_pretrained(
                self.model_name_or_path,
                from_tf=bool(".ckpt" in self.model_name_or_path),
                config=self.config,
            )
        self.training_args = training_args
        self.to(self.device, self.n_gpus)

    def train(
        self,
        train_data_set: DataLoader,
        dev_data_set: Union[DataLoader, List[DataLoader]] = None,
        test_data_set: Union[DataLoader, List[DataLoader]] = None,
        gradient_accumulation_steps: int = 1,
        per_gpu_train_batch_size: int = 8,
        max_steps: int = -1,
        num_train_epochs: int = 3,
        max_grad_norm: float = 1.0,
        logging_steps: int = 50,
        save_steps: int = 100,
        best_result_file: str = None,
    ):
        """
        Run model training

        Args:
            train_data_set (DataLoader): training dataset
            dev_data_set (Union[DataLoader, List[DataLoader]], optional): development data set
            (can be list). Defaults to None.
            test_data_set (Union[DataLoader, List[DataLoader]], optional): test data set
            (can be list). Defaults to None.
            gradient_accumulation_steps (int, optional): gradient accumulation steps.
            Defaults to 1.
            per_gpu_train_batch_size (int, optional): per GPU train batch size (or GPU).
            Defaults to 8.
            max_steps (int, optional): max steps for training. Defaults to -1.
            num_train_epochs (int, optional): number of training epochs. Defaults to 3.
            max_grad_norm (float, optional): max gradient norm. Defaults to 1.0.
            logging_steps (int, optional): number of steps between logging. Defaults to 50.
            save_steps (int, optional): number of steps between model save. Defaults to 100.
            best_result_file (str, optional): path to save best dev results when it's updated.
        """
        self._train(
            train_data_set,
            dev_data_set,
            test_data_set,
            gradient_accumulation_steps,
            per_gpu_train_batch_size,
            max_steps,
            num_train_epochs,
            max_grad_norm,
            logging_steps=logging_steps,
            save_steps=save_steps,
            best_result_file=best_result_file,
        )

    def _batch_mapper(self, batch):
        mapping = {
            "input_ids": batch[0],
            "attention_mask": batch[1],
            # XLM don't use segment_ids
            "token_type_ids": batch[2],
            "valid_ids": batch[3],
        }
        if len(batch) > 4:
            mapping.update({"labels": batch[4]})
        return mapping

    def evaluate_predictions(self, logits, label_ids):
        """
        Run evaluation of given logist and truth labels

        Args:
            logits: model logits
            label_ids: truth label ids
        """
        active_positions = label_ids.view(-1) != 0.0
        active_labels = label_ids.view(-1)[active_positions]
        active_logits = logits.view(-1, len(self.labels_id_map) + 1)[active_positions]
        logits = torch.argmax(F.log_softmax(active_logits, dim=1), dim=1)
        logits = logits.detach().cpu().numpy()
        out_label_ids = active_labels.detach().cpu().numpy()
        _, _, f1 = self.extract_labels(out_label_ids, self.labels_id_map, logits)
        logger.info("Results on evaluation set: F1 = {}".format(f1))
        return f1

    @staticmethod
    def extract_labels(label_ids, label_map, logits):
        y_true = []
        y_pred = []
        for p, y in zip(logits, label_ids):
            y_pred.append(label_map.get(p, "O"))
            y_true.append(label_map.get(y, "O"))
        assert len(y_true) == len(y_pred)
        return tagging(y_pred, y_true)

    def convert_to_tensors(
        self,
        examples: List[TokenClsInputExample],
        max_seq_length: int = 128,
        include_labels: bool = True,
    ) -> TensorDataset:
        """
        Convert examples to tensor dataset

        Args:
            examples (List[SequenceClsInputExample]): examples
            max_seq_length (int, optional): max sequence length. Defaults to 128.
            include_labels (bool, optional): include labels. Defaults to True.

        Returns:
            TensorDataset:
        """
        features = self._convert_examples_to_features(
            examples,
            max_seq_length,
            self.tokenizer,
            include_labels,
            # xlnet has a cls token at the end
            cls_token_at_end=bool(self.model_type in ["xlnet"]),
            cls_token=self.tokenizer.cls_token,
            cls_token_segment_id=2 if self.model_type in ["xlnet"] else 0,
            sep_token=self.tokenizer.sep_token,
            sep_token_extra=bool(self.model_type in ["roberta"]),
            # pad on the left for xlnet
            pad_on_left=bool(self.model_type in ["xlnet"]),
            pad_token=self.tokenizer.convert_tokens_to_ids([self.tokenizer.pad_token])[0],
            pad_token_segment_id=4 if self.model_type in ["xlnet"] else 0,
        )
        # Convert to Tensors and build dataset
        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
        all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
        all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
        all_valid_ids = torch.tensor([f.valid_ids for f in features], dtype=torch.long)

        if include_labels:
            all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
            dataset = TensorDataset(
                all_input_ids, all_input_mask, all_segment_ids, all_valid_ids, all_label_ids
            )
        else:
            dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_valid_ids)
        return dataset

    def _convert_examples_to_features(
        self,
        examples: List[TokenClsInputExample],
        max_seq_length,
        tokenizer,
        include_labels=True,
        cls_token_at_end=False,
        pad_on_left=False,
        cls_token="[CLS]",
        sep_token="[SEP]",
        pad_token=0,
        sequence_segment_id=0,
        sep_token_extra=0,
        cls_token_segment_id=1,
        pad_token_segment_id=0,
        mask_padding_with_zero=True,
    ):
        """ Loads a data file into a list of `InputBatch`s
            `cls_token_at_end` define the location of the CLS token:
                - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
                - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
            `cls_token_segment_id` define the segment id associated to the CLS token
            (0 for BERT, 2 for XLNet)
        """

        if include_labels:
            label_map = {v: k for k, v in self.labels_id_map.items()}
            label_pad = 0

        features = []
        for (ex_index, example) in enumerate(examples):
            if ex_index % 10000 == 0:
                logger.info("Processing example %d of %d", ex_index, len(examples))

            tokens = []
            labels = []
            valid_tokens = []
            for i, token in enumerate(example.tokens):
                new_tokens = tokenizer.tokenize(token)
                tokens.extend(new_tokens)
                v_tok = [0] * (len(new_tokens))
                v_tok[0] = 1
                valid_tokens.extend(v_tok)
                if include_labels:
                    v_lbl = [label_pad] * (len(new_tokens))
                    v_lbl[0] = label_map.get(example.label[i])
                    labels.extend(v_lbl)

            # truncate by max_seq_length
            special_tokens_count = 3 if sep_token_extra else 2
            tokens = tokens[: (max_seq_length - special_tokens_count)]
            valid_tokens = valid_tokens[: (max_seq_length - special_tokens_count)]
            if include_labels:
                labels = labels[: (max_seq_length - special_tokens_count)]

            tokens += [sep_token]
            if include_labels:
                labels += [label_pad]
            valid_tokens += [0]
            if sep_token_extra:  # roberta special case
                tokens += [sep_token]
                valid_tokens += [0]
                if include_labels:
                    labels += [label_pad]
            segment_ids = [sequence_segment_id] * len(tokens)

            if cls_token_at_end:
                tokens = tokens + [cls_token]
                segment_ids = segment_ids + [cls_token_segment_id]
                if include_labels:
                    labels = labels + [label_pad]
                valid_tokens = valid_tokens + [0]
            else:
                tokens = [cls_token] + tokens
                segment_ids = [cls_token_segment_id] + segment_ids
                if include_labels:
                    labels = [label_pad] + labels
                valid_tokens = [0] + valid_tokens

            input_ids = tokenizer.convert_tokens_to_ids(tokens)

            input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = max_seq_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
                if include_labels:
                    labels = ([label_pad] * padding_length) + labels
                valid_tokens = ([0] * padding_length) + valid_tokens
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
                segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
                if include_labels:
                    labels = labels + ([label_pad] * padding_length)
                valid_tokens = valid_tokens + ([0] * padding_length)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length
            assert len(valid_tokens) == max_seq_length
            if include_labels:
                assert len(labels) == max_seq_length

            features.append(
                InputFeatures(
                    input_ids=input_ids,
                    input_mask=input_mask,
                    segment_ids=segment_ids,
                    label_id=labels,
                    valid_ids=valid_tokens,
                )
            )
        return features

    def inference(
        self, examples: List[TokenClsInputExample], max_seq_length: int, batch_size: int = 64
    ):
        """
        Run inference on given examples

        Args:
            examples (List[SequenceClsInputExample]): examples
            batch_size (int, optional): batch size. Defaults to 64.

        Returns:
            logits
        """
        data_set = self.convert_to_tensors(
            examples, max_seq_length=max_seq_length, include_labels=False
        )
        inf_sampler = SequentialSampler(data_set)
        inf_dataloader = DataLoader(data_set, sampler=inf_sampler, batch_size=batch_size)
        logits = self._evaluate(inf_dataloader)
        active_positions = data_set.tensors[-1].view(len(data_set), -1) != 0.0
        logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
        res_ids = []
        for i in range(logits.size()[0]):
            res_ids.append(logits[i][active_positions[i]].detach().cpu().numpy())
        output = []
        for tag_ids, ex in zip(res_ids, examples):
            tokens = ex.tokens
            tags = [self.labels_id_map.get(t, "O") for t in tag_ids]
            output.append((tokens, tags))
        return output
