""".. _attacked_text:

Attacked Text Class
=====================

A helper class that represents a string that can be attacked.
"""

from collections import OrderedDict
import math

import flair
from flair.data import Sentence
import numpy as np
import torch

import textattack

from .utils import device, words_from_text

flair.device = device


class AttackedText:

    """A helper class that represents a string that can be attacked.

    Models that take multiple sentences as input separate them by ``SPLIT_TOKEN``.
    Attacks "see" the entire input, joined into one string, without the split token.

    ``AttackedText`` instances that were perturbed from other ``AttackedText``
    objects contain a pointer to the previous text
    (``attack_attrs["previous_attacked_text"]``), so that the full chain of
    perturbations might be reconstructed by using this key to form a linked
    list.

    Args:
       text (string): The string that this AttackedText represents
       attack_attrs (dict): Dictionary of various attributes stored
           during the course of an attack.
    """

    SPLIT_TOKEN = "<SPLIT>"

    def __init__(self, text_input, attack_attrs=None):
        # Read in ``text_input`` as a string or OrderedDict.
        if isinstance(text_input, str):
            self._text_input = OrderedDict([("text", text_input)])
        elif isinstance(text_input, OrderedDict):
            self._text_input = text_input
        else:
            raise TypeError(
                f"Invalid text_input type {type(text_input)} (required str or OrderedDict)"
            )
        # Process input lazily.
        self._words = None
        self._words_per_input = None
        self._pos_tags = None
        self._ner_tags = None
        # Format text inputs.
        self._text_input = OrderedDict([(k, v) for k, v in self._text_input.items()])
        if attack_attrs is None:
            self.attack_attrs = dict()
        elif isinstance(attack_attrs, dict):
            self.attack_attrs = attack_attrs
        else:
            raise TypeError(f"Invalid type for attack_attrs: {type(attack_attrs)}")
        # Indices of words from the *original* text. Allows us to map
        # indices between original text and this text, and vice-versa.
        self.attack_attrs.setdefault("original_index_map", np.arange(self.num_words))
        # A list of all indices in *this* text that have been modified.
        self.attack_attrs.setdefault("modified_indices", set())

    def __eq__(self, other):
        """Compares two text instances to make sure they have the same attack
        attributes.

        Since some elements stored in ``self.attack_attrs`` may be numpy
        arrays, we have to take special care when comparing them.
        """
        if not (self.text == other.text):
            return False
        if len(self.attack_attrs) != len(other.attack_attrs):
            return False
        for key in self.attack_attrs:
            if key not in other.attack_attrs:
                return False
            elif isinstance(self.attack_attrs[key], np.ndarray):
                if not (self.attack_attrs[key].shape == other.attack_attrs[key].shape):
                    return False
                elif not (self.attack_attrs[key] == other.attack_attrs[key]).all():
                    return False
            else:
                if not self.attack_attrs[key] == other.attack_attrs[key]:
                    return False
        return True

    def __hash__(self):
        return hash(self.text)

    def free_memory(self):
        """Delete items that take up memory.

        Can be called once the AttackedText is only needed to display.
        """
        if "previous_attacked_text" in self.attack_attrs:
            self.attack_attrs["previous_attacked_text"].free_memory()
            self.attack_attrs.pop("previous_attacked_text", None)

        self.attack_attrs.pop("last_transformation", None)

        for key in self.attack_attrs:
            if isinstance(self.attack_attrs[key], torch.Tensor):
                self.attack_attrs.pop(key, None)

    def text_window_around_index(self, index, window_size):
        """The text window of ``window_size`` words centered around
        ``index``."""
        length = self.num_words
        half_size = (window_size - 1) / 2.0
        if index - half_size < 0:
            start = 0
            end = min(window_size - 1, length - 1)
        elif index + half_size >= length:
            start = max(0, length - window_size)
            end = length - 1
        else:
            start = index - math.ceil(half_size)
            end = index + math.floor(half_size)
        text_idx_start = self._text_index_of_word_index(start)
        text_idx_end = self._text_index_of_word_index(end) + len(self.words[end])
        return self.text[text_idx_start:text_idx_end]

    def pos_of_word_index(self, desired_word_idx):
        """Returns the part-of-speech of the word at index `word_idx`.

        Uses FLAIR part-of-speech tagger.
        """
        if not self._pos_tags:
            sentence = Sentence(
                self.text,
                use_tokenizer=textattack.shared.utils.TextAttackFlairTokenizer(),
            )
            textattack.shared.utils.flair_tag(sentence)
            self._pos_tags = sentence
        flair_word_list, flair_pos_list = textattack.shared.utils.zip_flair_result(
            self._pos_tags
        )

        for word_idx, word in enumerate(self.words):
            assert (
                word in flair_word_list
            ), "word absent in flair returned part-of-speech tags"
            word_idx_in_flair_tags = flair_word_list.index(word)
            if word_idx == desired_word_idx:
                return flair_pos_list[word_idx_in_flair_tags]
            else:
                flair_word_list = flair_word_list[word_idx_in_flair_tags + 1 :]
                flair_pos_list = flair_pos_list[word_idx_in_flair_tags + 1 :]

        raise ValueError(
            f"Did not find word from index {desired_word_idx} in flair POS tag"
        )

    def ner_of_word_index(self, desired_word_idx, model_name="ner"):
        """Returns the ner tag of the word at index `word_idx`.

        Uses FLAIR ner tagger.
        """
        if not self._ner_tags:
            sentence = Sentence(
                self.text,
                use_tokenizer=textattack.shared.utils.TextAttackFlairTokenizer(),
            )
            textattack.shared.utils.flair_tag(sentence, model_name)
            self._ner_tags = sentence
        flair_word_list, flair_ner_list = textattack.shared.utils.zip_flair_result(
            self._ner_tags, "ner"
        )

        for word_idx, word in enumerate(flair_word_list):
            word_idx_in_flair_tags = flair_word_list.index(word)
            if word_idx == desired_word_idx:
                return flair_ner_list[word_idx_in_flair_tags]
            else:
                flair_word_list = flair_word_list[word_idx_in_flair_tags + 1 :]
                flair_ner_list = flair_ner_list[word_idx_in_flair_tags + 1 :]

        raise ValueError(
            f"Did not find word from index {desired_word_idx} in flair POS tag"
        )

    def _text_index_of_word_index(self, i):
        """Returns the index of word ``i`` in self.text."""
        pre_words = self.words[: i + 1]
        lower_text = self.text.lower()
        # Find all words until `i` in string.
        look_after_index = 0
        for word in pre_words:
            look_after_index = lower_text.find(word.lower(), look_after_index) + len(
                word
            )
        look_after_index -= len(self.words[i])
        return look_after_index

    def text_until_word_index(self, i):
        """Returns the text before the beginning of word at index ``i``."""
        look_after_index = self._text_index_of_word_index(i)
        return self.text[:look_after_index]

    def text_after_word_index(self, i):
        """Returns the text after the end of word at index ``i``."""
        # Get index of beginning of word then jump to end of word.
        look_after_index = self._text_index_of_word_index(i) + len(self.words[i])
        return self.text[look_after_index:]

    def first_word_diff(self, other_attacked_text):
        """Returns the first word in self.words that differs from
        other_attacked_text.

        Useful for word swap strategies.
        """
        w1 = self.words
        w2 = other_attacked_text.words
        for i in range(min(len(w1), len(w2))):
            if w1[i] != w2[i]:
                return w1[i]
        return None

    def first_word_diff_index(self, other_attacked_text):
        """Returns the index of the first word in self.words that differs from
        other_attacked_text.

        Useful for word swap strategies.
        """
        w1 = self.words
        w2 = other_attacked_text.words
        for i in range(min(len(w1), len(w2))):
            if w1[i] != w2[i]:
                return i
        return None

    def all_words_diff(self, other_attacked_text):
        """Returns the set of indices for which this and other_attacked_text
        have different words."""
        indices = set()
        w1 = self.words
        w2 = other_attacked_text.words
        for i in range(min(len(w1), len(w2))):
            if w1[i] != w2[i]:
                indices.add(i)
        return indices

    def ith_word_diff(self, other_attacked_text, i):
        """Returns whether the word at index i differs from
        other_attacked_text."""
        w1 = self.words
        w2 = other_attacked_text.words
        if len(w1) - 1 < i or len(w2) - 1 < i:
            return True
        return w1[i] != w2[i]

    def words_diff_num(self, other_attacked_text):
        # using edit distance to calculate words diff num
        def generate_tokens(words):
            result = {}
            idx = 1
            for w in words:
                if w not in result:
                    result[w] = idx
                    idx += 1
            return result

        def words_to_tokens(words, tokens):
            result = []
            for w in words:
                result.append(tokens[w])
            return result

        def edit_distance(w1_t, w2_t):
            matrix = [
                [i + j for j in range(len(w2_t) + 1)] for i in range(len(w1_t) + 1)
            ]

            for i in range(1, len(w1_t) + 1):
                for j in range(1, len(w2_t) + 1):
                    if w1_t[i - 1] == w2_t[j - 1]:
                        d = 0
                    else:
                        d = 1
                    matrix[i][j] = min(
                        matrix[i - 1][j] + 1,
                        matrix[i][j - 1] + 1,
                        matrix[i - 1][j - 1] + d,
                    )

            return matrix[len(w1_t)][len(w2_t)]

        def cal_dif(w1, w2):
            tokens = generate_tokens(w1 + w2)
            w1_t = words_to_tokens(w1, tokens)
            w2_t = words_to_tokens(w2, tokens)
            return edit_distance(w1_t, w2_t)

        w1 = self.words
        w2 = other_attacked_text.words
        return cal_dif(w1, w2)

    def convert_from_original_idxs(self, idxs):
        """Takes indices of words from original string and converts them to
        indices of the same words in the current string.

        Uses information from
        ``self.attack_attrs['original_index_map']``, which maps word
        indices from the original to perturbed text.
        """
        if len(self.attack_attrs["original_index_map"]) == 0:
            return idxs
        elif isinstance(idxs, set):
            idxs = list(idxs)

        elif not isinstance(idxs, [list, np.ndarray]):
            raise TypeError(
                f"convert_from_original_idxs got invalid idxs type {type(idxs)}"
            )

        return [self.attack_attrs["original_index_map"][i] for i in idxs]

    def replace_words_at_indices(self, indices, new_words):
        """This code returns a new AttackedText object where the word at
        ``index`` is replaced with a new word."""
        if len(indices) != len(new_words):
            raise ValueError(
                f"Cannot replace {len(new_words)} words at {len(indices)} indices."
            )
        words = self.words[:]
        for i, new_word in zip(indices, new_words):
            if not isinstance(new_word, str):
                raise TypeError(
                    f"replace_words_at_indices requires ``str`` words, got {type(new_word)}"
                )
            if (i < 0) or (i > len(words)):
                raise ValueError(f"Cannot assign word at index {i}")
            words[i] = new_word
        return self.generate_new_attacked_text(words)

    def replace_word_at_index(self, index, new_word):
        """This code returns a new AttackedText object where the word at
        ``index`` is replaced with a new word."""
        if not isinstance(new_word, str):
            raise TypeError(
                f"replace_word_at_index requires ``str`` new_word, got {type(new_word)}"
            )
        return self.replace_words_at_indices([index], [new_word])

    def delete_word_at_index(self, index):
        """This code returns a new AttackedText object where the word at
        ``index`` is removed."""
        return self.replace_word_at_index(index, "")

    def insert_text_after_word_index(self, index, text):
        """Inserts a string before word at index ``index`` and attempts to add
        appropriate spacing."""
        if not isinstance(text, str):
            raise TypeError(f"text must be an str, got type {type(text)}")
        word_at_index = self.words[index]
        new_text = " ".join((word_at_index, text))
        return self.replace_word_at_index(index, new_text)

    def insert_text_before_word_index(self, index, text):
        """Inserts a string before word at index ``index`` and attempts to add
        appropriate spacing."""
        if not isinstance(text, str):
            raise TypeError(f"text must be an str, got type {type(text)}")
        word_at_index = self.words[index]
        # TODO if ``word_at_index`` is at the beginning of a sentence, we should
        # optionally capitalize ``text``.
        new_text = " ".join((text, word_at_index))
        return self.replace_word_at_index(index, new_text)

    def get_deletion_indices(self):
        return self.attack_attrs["original_index_map"][
            self.attack_attrs["original_index_map"] == -1
        ]

    def generate_new_attacked_text(self, new_words):
        """Returns a new AttackedText object and replaces old list of words
        with a new list of words, but preserves the punctuation and spacing of
        the original message.

        ``self.words`` is a list of the words in the current text with
        punctuation removed. However, each "word" in ``new_words`` could
        be an empty string, representing a word deletion, or a string
        with multiple space-separated words, representation an insertion
        of one or more words.
        """
        perturbed_text = ""
        original_text = AttackedText.SPLIT_TOKEN.join(self._text_input.values())
        new_attack_attrs = dict()
        if "label_names" in self.attack_attrs:
            new_attack_attrs["label_names"] = self.attack_attrs["label_names"]
        new_attack_attrs["newly_modified_indices"] = set()
        # Point to previously monitored text.
        new_attack_attrs["previous_attacked_text"] = self
        # Use `new_attack_attrs` to track indices with respect to the original
        # text.
        new_attack_attrs["modified_indices"] = self.attack_attrs[
            "modified_indices"
        ].copy()
        new_attack_attrs["original_index_map"] = self.attack_attrs[
            "original_index_map"
        ].copy()
        new_i = 0
        # Create the new attacked text by swapping out words from the original
        # text with a sequence of 0+ words in the new text.
        for i, (input_word, adv_word_seq) in enumerate(zip(self.words, new_words)):
            word_start = original_text.index(input_word)
            word_end = word_start + len(input_word)
            perturbed_text += original_text[:word_start]
            original_text = original_text[word_end:]
            adv_words = words_from_text(adv_word_seq)
            adv_num_words = len(adv_words)
            num_words_diff = adv_num_words - len(words_from_text(input_word))
            # Track indices on insertions and deletions.
            if num_words_diff != 0:
                # Re-calculated modified indices. If words are inserted or deleted,
                # they could change.
                shifted_modified_indices = set()
                for modified_idx in new_attack_attrs["modified_indices"]:
                    if modified_idx < i:
                        shifted_modified_indices.add(modified_idx)
                    elif modified_idx > i:
                        shifted_modified_indices.add(modified_idx + num_words_diff)
                    else:
                        pass
                new_attack_attrs["modified_indices"] = shifted_modified_indices
                # Track insertions and deletions wrt original text.
                # original_modification_idx = i
                new_idx_map = new_attack_attrs["original_index_map"].copy()
                if num_words_diff == -1:
                    # Word deletion
                    new_idx_map[new_idx_map == i] = -1
                new_idx_map[new_idx_map > i] += num_words_diff

                if num_words_diff > 0 and input_word != adv_words[0]:
                    # If insertion happens before the `input_word`
                    new_idx_map[new_idx_map == i] += num_words_diff

                new_attack_attrs["original_index_map"] = new_idx_map
            # Move pointer and save indices of new modified words.
            for j in range(i, i + adv_num_words):
                if input_word != adv_word_seq:
                    new_attack_attrs["modified_indices"].add(new_i)
                    new_attack_attrs["newly_modified_indices"].add(new_i)
                new_i += 1
            # Check spaces for deleted text.
            if adv_num_words == 0 and len(original_text):
                # Remove extra space (or else there would be two spaces for each
                # deleted word).
                # @TODO What to do with punctuation in this case? This behavior is undefined.
                if i == 0:
                    # If the first word was deleted, take a subsequent space.
                    if original_text[0] == " ":
                        original_text = original_text[1:]
                else:
                    # If a word other than the first was deleted, take a preceding space.
                    if perturbed_text[-1] == " ":
                        perturbed_text = perturbed_text[:-1]
            # Add substitute word(s) to new sentence.
            perturbed_text += adv_word_seq
        perturbed_text += original_text  # Add all of the ending punctuation.

        # Add pointer to self so chain of replacements can be reconstructed.
        new_attack_attrs["prev_attacked_text"] = self

        # Reform perturbed_text into an OrderedDict.
        perturbed_input_texts = perturbed_text.split(AttackedText.SPLIT_TOKEN)
        perturbed_input = OrderedDict(
            zip(self._text_input.keys(), perturbed_input_texts)
        )
        return AttackedText(perturbed_input, attack_attrs=new_attack_attrs)

    def words_diff_ratio(self, x):
        """Get the ratio of words difference between current text and `x`.

        Note that current text and `x` must have same number of words.
        """
        assert self.num_words == x.num_words
        return float(np.sum(self.words != x.words)) / self.num_words

    def align_with_model_tokens(self, model_wrapper):
        """Align AttackedText's `words` with target model's tokenization scheme
        (e.g. word, character, subword). Specifically, we map each word to list
        of indices of tokens that compose the word (e.g. embedding --> ["em",
        "##bed", "##ding"])

        Args:
            model_wrapper (textattack.models.wrappers.ModelWrapper): ModelWrapper of the target model

        Returns:
            word2token_mapping (dict[int, list[int]]): Dictionary that maps i-th word to list of indices.
        """
        tokens = model_wrapper.tokenize([self.tokenizer_input], strip_prefix=True)[0]
        word2token_mapping = {}
        j = 0
        last_matched = 0

        for i, word in enumerate(self.words):
            matched_tokens = []
            while j < len(tokens) and len(word) > 0:
                token = tokens[j].lower()
                idx = word.lower().find(token)
                if idx == 0:
                    word = word[idx + len(token) :]
                    matched_tokens.append(j)
                    last_matched = j
                j += 1

            if not matched_tokens:
                word2token_mapping[i] = None
                j = last_matched
            else:
                word2token_mapping[i] = matched_tokens

        return word2token_mapping

    @property
    def tokenizer_input(self):
        """The tuple of inputs to be passed to the tokenizer."""
        input_tuple = tuple(self._text_input.values())
        # Prefer to return a string instead of a tuple with a single value.
        if len(input_tuple) == 1:
            return input_tuple[0]
        else:
            return input_tuple

    @property
    def column_labels(self):
        """Returns the labels for this text's columns.

        For single-sequence inputs, this simply returns ['text'].
        """
        return list(self._text_input.keys())

    @property
    def words_per_input(self):
        """Returns a list of lists of words corresponding to each input."""
        if not self._words_per_input:
            self._words_per_input = [
                words_from_text(_input) for _input in self._text_input.values()
            ]
        return self._words_per_input

    @property
    def words(self):
        if not self._words:
            self._words = words_from_text(self.text)
        return self._words

    @property
    def text(self):
        """Represents full text input.

        Multiply inputs are joined with a line break.
        """
        return "\n".join(self._text_input.values())

    @property
    def num_words(self):
        """Returns the number of words in the sequence."""
        return len(self.words)

    @property
    def newly_swapped_words(self):
        return [self.words[i] for i in self.attack_attrs["newly_modified_indices"]]

    def printable_text(self, key_color="bold", key_color_method=None):
        """Represents full text input. Adds field descriptions.

        For example, entailment inputs look like:
            ```
            premise: ...
            hypothesis: ...
            ```
        """
        # For single-sequence inputs, don't show a prefix.
        if len(self._text_input) == 1:
            return next(iter(self._text_input.values()))
        # For multiple-sequence inputs, show a prefix and a colon. Optionally,
        # color the key.
        else:
            if key_color_method:

                def ck(k):
                    return textattack.shared.utils.color_text(
                        k, key_color, key_color_method
                    )

            else:

                def ck(k):
                    return k

            return "\n".join(
                f"{ck(key.capitalize())}: {value}"
                for key, value in self._text_input.items()
            )

    def __repr__(self):
        return f'<AttackedText "{self.text}">'
