import logging

import os

import re

from collections import Counter, namedtuple

from math import ceil

EntTuple = namedtuple('EntTuple', ['tag', 'start', 'end', 'text'])

class Annotations:

    """

    An Annotations object stores all relevant information needed to manage Annotations over a document.

    The Annotation object is utilized by medaCy to structure input to models and output from models.

    This object wraps a list of tuples representing the entities in a document.

    :ivar ann_path: the path to the .ann file

    :ivar source_text_path: path to the related .txt file

    :ivar annotations: a list of annotation tuples

    """

    brat_pattern = re.compile(r'T(\d+)\t(\S+) ((\d+ \d+;)*\d+ \d+)\t(.+)')

    def __init__(self, annotation_data, source_text_path=None):

        """

        :param annotation_data: a file path to an annotation file, or a list of annotation tuples.

        Construction from a list of tuples is intended for internal use.

        :param source_text_path: optional; path to the text file from which the annotations were derived.

        """

        if isinstance(annotation_data, list) and all(isinstance(e, tuple) for e in annotation_data):

            self.annotations = annotation_data

            self.source_text_path = source_text_path

            return

        elif not os.path.isfile(annotation_data):

            raise FileNotFoundError("annotation_data must be a list of tuples or a valid file path, but is %s" % repr(annotation_data))

        self.ann_path = annotation_data

        self.source_text_path = source_text_path

        self.annotations = self._init_from_file(annotation_data)

    @staticmethod

    def _init_from_file(file_path):

        """

        Creates a list of annotation tuples from a file path

        :param file_path: the path to an ann file

        :return: a list of annotation tuples

        """

        annotations = []

        with open(file_path, 'r', encoding='utf-8') as f:

            data = f.read()

        for match in re.finditer(Annotations.brat_pattern, data):

            tag = match.group(2)

            spans = match.group(3)

            mention = match.group(5)

            spans = re.findall(r'\d+', spans)

            start, end = int(spans[0]), int(spans[-1])

            new_ent = EntTuple(tag, start, end, mention)

            annotations.append(new_ent)

        return annotations

    @property

    def annotations(self):

        return self._annotations

    @annotations.setter

    def annotations(self, value):

        """Ensures that annotations are always sorted"""

        self._annotations = sorted([EntTuple(*e) for e in value], key=lambda x: (x.start, x.end))

    def get_labels(self, as_list=False):

        """

        Get the set of labels from this collection of annotations.

        :param as_list: bool for if to return the results as a list; defaults to False

        :return: The set of labels.

        """

        labels = {e[0] for e in self.annotations}

        if as_list:

            return list(labels)

        return labels

    def add_entity(self, label, start, end, text=""):

        """

        Adds an entity to the Annotations

        :param label: the label of the annotation you are appending

        :param start: the start index in the document of the annotation you are appending.

        :param end: the end index in the document of the annotation you are appending

        :param text: the raw text of the annotation you are appending

        """

        self.annotations.append(EntTuple(label, start, end, text))

    def to_ann(self, write_location=None):

        """

        Formats the Annotations object into a string representing a valid ANN file. Optionally writes the formatted

        string to a destination.

        :param write_location: path of location to write ann file to

        :return: returns string formatted as an ann file, if write_location is valid path also writes to that path.

        """

        ann_string = ""

        for num, tup in enumerate(self.annotations, 1):

            mention = tup.text.replace('\n', ' ')

            ann_string += f"T{num}\t{tup.tag} {tup.start} {tup.end}\t{mention}\n"

        if write_location is not None:

            if os.path.isfile(write_location):

                logging.warning("Overwriting file at: %s", write_location)

            with open(write_location, 'w') as f:

                f.write(ann_string)

        return ann_string

    def difference(self, other, leniency=0):

        """

        Identifies the difference between two Annotations objects. Useful for checking if an unverified annotation

        matches an annotation known to be accurate. This is done returning a list of all annotations in the operated on

        Annotation object that do not exist in the passed in annotation object. This is a set difference.

        :param other: Another Annotations object.

        :param leniency: a floating point value between [0,1] defining the leniency of the character spans to count

        as different. A value of zero considers only exact character matches while a positive value considers entities

        that differ by up to :code:`ceil(leniency * len(span)/2)` on either side.

        :return: A set of tuples of non-matching annotations.

        """

        if not isinstance(other, Annotations):

            raise ValueError("Annotations.diff() can only accept another Annotations object as an argument.")

        if leniency == 0:

            return set(self.annotations) - set(other.annotations)

        if not 0 <= leniency <= 1:

            raise ValueError("Leniency must be a floating point between [0,1]")

        matches = set()

        for ann in self.annotations:

            label, start, end, text = ann

            window = ceil(leniency * (end - start))

            for c_label, c_start, c_end, c_text in other.annotations:

                if label == c_label:

                    if start - window <= c_start and end + window >= c_end:

                        matches.add(ann)

                        break

        return set(self.annotations) - matches

    def intersection(self, other, leniency=0):

        """

        Computes the intersection of the operated annotation object with the operand annotation object.

        :param other: Another Annotations object.

        :param leniency: a floating point value between [0,1] defining the leniency of the character spans to count as

        different. A value of zero considers only exact character matches while a positive value considers entities that

         differ by up to :code:`ceil(leniency * len(span)/2)` on either side.

        :return A set of annotations that appear in both Annotation objects

        """

        if not isinstance(other, Annotations):

            raise ValueError("An Annotations object is requried as an argument.")

        if leniency == 0:

            return set(self.annotations) & set(other.annotations)

        if not 0 <= leniency <= 1:

            raise ValueError("Leniency must be a floating point between [0,1]")

        matches = set()

        for ann in self.annotations:

            label, start, end, text = ann

            window = ceil(leniency * (end - start))

            for c_label, c_start, c_end, c_text in other:

                if label == c_label and start - window <= c_start and end + window >= c_end:

                    matches.add(ann)

                    break

        return matches

    def compute_ambiguity(self, other):

        """

        Finds occurrences of spans from 'annotations' that intersect with a span from this annotation but do not have this spans label.

        label. If 'annotation' comprises a models predictions, this method provides a strong indicators

        of a model's in-ability to dis-ambiguate between entities. For a full analysis, compute a confusion matrix.

        :param other: Another Annotations object.

        :return: a dictionary containing incorrect label predictions for given spans

        """

        if not isinstance(other, Annotations):

            raise ValueError("An Annotations object is required as an argument.")

        ambiguity_dict = {}

        for label, start, end, text in self.annotations:

            for c_label, c_start, c_end, c_text in other.annotations:

                if label == c_label:

                    continue

                overlap = max(0, min(end, c_end) - max(c_start, start))

                if overlap != 0:

                    ambiguity_dict[(label, start, end, text)] = [(c_label, c_start, c_end, c_text)]

        return ambiguity_dict

    def compute_confusion_matrix(self, other, entities, leniency=0):

        """

        Computes a confusion matrix representing span level ambiguity between this annotation and the argument annotation.

        An annotation in 'annotations' is ambiguous is it overlaps with a span in this Annotation but does not have the

        same entity label. The main diagonal of this matrix corresponds to entities in this Annotation that match spans

        in 'annotations' and have equivalent class label.

        :param other: Another Annotations object.

        :param entities: a list of entities to use in computing matrix ambiguity.

        :param leniency: leniency to utilize when computing overlapping entities. This is the same definition of leniency as in intersection.

        :return: a square matrix with dimension len(entities) where matrix[i][j] indicates that entities[i] in this annotation was predicted as entities[j] in 'annotation' matrix[i][j] times.

        """

        if not isinstance(other, Annotations):

            raise ValueError("An Annotations object is required as an argument.")

        if not isinstance(entities, list):

            raise ValueError("entities must be a list of entities, but is %s" % repr(entities))

        entity_encoding = {entity: i for i, entity in enumerate(entities)}

        # Create 2-d array of len(entities) ** 2

        confusion_matrix = [[0 for _ in range(len(entities))] for _ in range(len(entities))]

        ambiguity_dict = self.compute_ambiguity(other)

        intersection = self.intersection(other, leniency=leniency)

        # Compute all off diagonal scores

        for gold_annotation in ambiguity_dict:

            gold_label, start, end, text = gold_annotation

            for ambiguous_annotation in ambiguity_dict[gold_annotation]:

                ambiguous_label = ambiguous_annotation[0]

                confusion_matrix[entity_encoding[gold_label]][entity_encoding[ambiguous_label]] += 1

        # Compute diagonal scores (correctly predicted entities with correct spans)

        for matching_annotation in intersection:

            matching_label, start, end, text = matching_annotation

            confusion_matrix[entity_encoding[matching_label]][entity_encoding[matching_label]] += 1

        return confusion_matrix

    def compute_counts(self):

        """

        Computes counts of each entity type in this annotation.

        :return: a Counter of the entity counts

        """

        return Counter(e[0] for e in self.annotations)

    def __str__(self):

        return str(self.annotations)

    def __len__(self):

        return len(self.annotations)

    def __iter__(self):

        return iter(self.annotations)

    def __or__(self, other):

        """

        Creates an Annotations object containing annotations from two instances

        :param other: the other Annotations instance

        :return: a new Annotations object containing entities from both

        """

        new_entities = list(set(self.annotations) | set(other.annotations))

        new_annotations = Annotations(new_entities, source_text_path=self.source_text_path or other.source_text_path)

        new_annotations.ann_path = 'None'

        return new_annotations

    def __ior__(self, other):

        self.annotations = list(set(self.annotations) | set(other.annotations))

        self.ann_path = 'None'

        return self