from typing import Dict, Optional

import numpy
from overrides import overrides
import torch
from torch.nn.modules.linear import Linear
import torch.nn.functional as F

from allennlp.common import Params
from allennlp.common.checks import ConfigurationError
from allennlp.data import Vocabulary
from allennlp.modules import Seq2SeqEncoder, TimeDistributed, TextFieldEmbedder
from allennlp.models.model import Model
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits
from allennlp.training.metrics import CategoricalAccuracy


@Model.register("simple_tagger")
class SimpleTagger(Model):
    """
    This ``SimpleTagger`` simply encodes a sequence of text with a stacked ``Seq2SeqEncoder``, then
    predicts a tag for each token in the sequence.

    Parameters
    ----------
    vocab : ``Vocabulary``, required
        A Vocabulary, required in order to compute sizes for input/output projections.
    text_field_embedder : ``TextFieldEmbedder``, required
        Used to embed the ``tokens`` ``TextField`` we get as input to the model.
    stacked_encoder : ``Seq2SeqEncoder``
        The encoder (with its own internal stacking) that we will use in between embedding tokens
        and predicting output tags.
    initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
        Used to initialize the model parameters.
    regularizer : ``RegularizerApplicator``, optional (default=``None``)
        If provided, will be used to calculate the regularization penalty during training.
    """

    def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 stacked_encoder: Seq2SeqEncoder,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 regularizer: Optional[RegularizerApplicator] = None) -> None:
        super(SimpleTagger, self).__init__(vocab, regularizer)

        self.text_field_embedder = text_field_embedder
        self.num_classes = self.vocab.get_vocab_size("labels")
        self.stacked_encoder = stacked_encoder
        self.tag_projection_layer = TimeDistributed(Linear(self.stacked_encoder.get_output_dim(),
                                                           self.num_classes))

        if text_field_embedder.get_output_dim() != stacked_encoder.get_input_dim():
            raise ConfigurationError("The output dimension of the text_field_embedder must match the "
                                     "input dimension of the phrase_encoder. Found {} and {}, "
                                     "respectively.".format(text_field_embedder.get_output_dim(),
                                                            stacked_encoder.get_input_dim()))
        self.metrics = {
                "accuracy": CategoricalAccuracy(),
                "accuracy3": CategoricalAccuracy(top_k=3)
        }

        initializer(self)

    @overrides
    def forward(self,  # type: ignore
                tokens: Dict[str, torch.LongTensor],
                tags: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
        # pylint: disable=arguments-differ
        """
        Parameters
        ----------
        tokens : Dict[str, torch.LongTensor], required
            The output of ``TextField.as_array()``, which should typically be passed directly to a
            ``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
            tensors.  At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
            Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
            for the ``TokenIndexers`` when you created the ``TextField`` representing your
            sequence.  The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
            which knows how to combine different word representations into a single vector per
            token in your input.
        tags : torch.LongTensor, optional (default = None)
            A torch tensor representing the sequence of integer gold class labels of shape
            ``(batch_size, num_tokens)``.

        Returns
        -------
        An output dictionary consisting of:
        logits : torch.FloatTensor
            A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
            unnormalised log probabilities of the tag classes.
        class_probabilities : torch.FloatTensor
            A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
            a distribution of the tag classes per word.
        loss : torch.FloatTensor, optional
            A scalar loss to be optimised.

        """
        embedded_text_input = self.text_field_embedder(tokens)
        batch_size, sequence_length, _ = embedded_text_input.size()
        mask = get_text_field_mask(tokens)
        encoded_text = self.stacked_encoder(embedded_text_input, mask)

        logits = self.tag_projection_layer(encoded_text)
        reshaped_log_probs = logits.view(-1, self.num_classes)
        class_probabilities = F.softmax(reshaped_log_probs, dim=-1).view([batch_size,
                                                                          sequence_length,
                                                                          self.num_classes])

        output_dict = {"logits": logits, "class_probabilities": class_probabilities}

        if tags is not None:
            loss = sequence_cross_entropy_with_logits(logits, tags, mask)
            for metric in self.metrics.values():
                metric(logits, tags, mask.float())
            output_dict["loss"] = loss

        return output_dict

    @overrides
    def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Does a simple position-wise argmax over each token, converts indices to string labels, and
        adds a ``"tags"`` key to the dictionary with the result.
        """
        all_predictions = output_dict['class_probabilities']
        all_predictions = all_predictions.cpu().data.numpy()
        if all_predictions.ndim == 3:
            predictions_list = [all_predictions[i] for i in range(all_predictions.shape[0])]
        else:
            predictions_list = [all_predictions]
        all_tags = []
        for predictions in predictions_list:
            argmax_indices = numpy.argmax(predictions, axis=-1)
            tags = [self.vocab.get_token_from_index(x, namespace="labels")
                    for x in argmax_indices]
            all_tags.append(tags)
        output_dict['tags'] = all_tags
        return output_dict

    @overrides
    def get_metrics(self, reset: bool = False) -> Dict[str, float]:
        return {metric_name: metric.get_metric(reset) for metric_name, metric in self.metrics.items()}

    @classmethod
    def from_params(cls, vocab: Vocabulary, params: Params) -> 'SimpleTagger':
        embedder_params = params.pop("text_field_embedder")
        text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
        stacked_encoder = Seq2SeqEncoder.from_params(params.pop("stacked_encoder"))

        initializer = InitializerApplicator.from_params(params.pop('initializer', []))
        regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))

        return cls(vocab=vocab,
                   text_field_embedder=text_field_embedder,
                   stacked_encoder=stacked_encoder,
                   initializer=initializer,
                   regularizer=regularizer)
