from typing import Dict, List, Union, Set, Iterator
import logging
import textwrap

from overrides import overrides
import torch

from allennlp.common.checks import ConfigurationError
from allennlp.common.util import pad_sequence_to_length
from allennlp.data.fields.field import Field
from allennlp.data.fields.sequence_field import SequenceField
from allennlp.data.vocabulary import Vocabulary

logger = logging.getLogger(__name__)


class SequenceLabelField(Field[torch.Tensor]):
    """
    A `SequenceLabelField` assigns a categorical label to each element in a
    :class:`~allennlp.data.fields.sequence_field.SequenceField`.
    Because it's a labeling of some other field, we take that field as input here, and we use it to
    determine our padding and other things.

    This field will get converted into a list of integer class ids, representing the correct class
    for each element in the sequence.

    # Parameters

    labels : `Union[List[str], List[int]]`
        A sequence of categorical labels, encoded as strings or integers.  These could be POS tags like [NN,
        JJ, ...], BIO tags like [B-PERS, I-PERS, O, O, ...], or any other categorical tag sequence. If the
        labels are encoded as integers, they will not be indexed using a vocab.
    sequence_field : `SequenceField`
        A field containing the sequence that this `SequenceLabelField` is labeling.  Most often, this is a
        `TextField`, for tagging individual tokens in a sentence.
    label_namespace : `str`, optional (default='labels')
        The namespace to use for converting tag strings into integers.  We convert tag strings to
        integers for you, and this parameter tells the `Vocabulary` object which mapping from
        strings to integers to use (so that "O" as a tag doesn't get the same id as "O" as a word).
    """

    # It is possible that users want to use this field with a namespace which uses OOV/PAD tokens.
    # This warning will be repeated for every instantiation of this class (i.e for every data
    # instance), spewing a lot of warnings so this class variable is used to only log a single
    # warning per namespace.
    _already_warned_namespaces: Set[str] = set()

    def __init__(
        self,
        labels: Union[List[str], List[int]],
        sequence_field: SequenceField,
        label_namespace: str = "labels",
    ) -> None:
        self.labels = labels
        self.sequence_field = sequence_field
        self._label_namespace = label_namespace
        self._indexed_labels = None
        self._maybe_warn_for_namespace(label_namespace)
        if len(labels) != sequence_field.sequence_length():
            raise ConfigurationError(
                "Label length and sequence length "
                "don't match: %d and %d" % (len(labels), sequence_field.sequence_length())
            )

        self._skip_indexing = False
        if all(isinstance(x, int) for x in labels):
            self._indexed_labels = labels
            self._skip_indexing = True

        elif not all(isinstance(x, str) for x in labels):
            raise ConfigurationError(
                "SequenceLabelFields must be passed either all "
                "strings or all ints. Found labels {} with "
                "types: {}.".format(labels, [type(x) for x in labels])
            )

    def _maybe_warn_for_namespace(self, label_namespace: str) -> None:
        if not (self._label_namespace.endswith("labels") or self._label_namespace.endswith("tags")):
            if label_namespace not in self._already_warned_namespaces:
                logger.warning(
                    "Your label namespace was '%s'. We recommend you use a namespace "
                    "ending with 'labels' or 'tags', so we don't add UNK and PAD tokens by "
                    "default to your vocabulary.  See documentation for "
                    "`non_padded_namespaces` parameter in Vocabulary.",
                    self._label_namespace,
                )
                self._already_warned_namespaces.add(label_namespace)

    # Sequence methods
    def __iter__(self) -> Iterator[Union[str, int]]:
        return iter(self.labels)

    def __getitem__(self, idx: int) -> Union[str, int]:
        return self.labels[idx]

    def __len__(self) -> int:
        return len(self.labels)

    @overrides
    def count_vocab_items(self, counter: Dict[str, Dict[str, int]]):
        if self._indexed_labels is None:
            for label in self.labels:
                counter[self._label_namespace][label] += 1  # type: ignore

    @overrides
    def index(self, vocab: Vocabulary):
        if not self._skip_indexing:
            self._indexed_labels = [
                vocab.get_token_index(label, self._label_namespace)  # type: ignore
                for label in self.labels
            ]

    @overrides
    def get_padding_lengths(self) -> Dict[str, int]:
        return {"num_tokens": self.sequence_field.sequence_length()}

    @overrides
    def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor:
        desired_num_tokens = padding_lengths["num_tokens"]
        padded_tags = pad_sequence_to_length(self._indexed_labels, desired_num_tokens)
        tensor = torch.LongTensor(padded_tags)
        return tensor

    @overrides
    def empty_field(self) -> "SequenceLabelField":
        # The empty_list here is needed for mypy
        empty_list: List[str] = []
        sequence_label_field = SequenceLabelField(empty_list, self.sequence_field.empty_field())
        sequence_label_field._indexed_labels = empty_list
        return sequence_label_field

    def __str__(self) -> str:
        length = self.sequence_field.sequence_length()
        formatted_labels = "".join(
            "\t\t" + labels + "\n" for labels in textwrap.wrap(repr(self.labels), 100)
        )
        return (
            f"SequenceLabelField of length {length} with "
            f"labels:\n {formatted_labels} \t\tin namespace: '{self._label_namespace}'."
        )
