#!/usr/bin/env python3
# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
#                                                  Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.


"""

This script takes as input `lang_dir`, which should contain::

    - lang_dir/text_with_bpe,
    - lang_dir/words.txt

and generates the following files in the directory `lang_dir`:

    - lexicon.txt
    - lexicon_disambig.txt
    - L.pt
    - L_disambig.pt
    - tokens.txt
"""

import re
from pathlib import Path
from typing import Dict, List

import k2
import sentencepiece as spm
import torch
from prepare_lang import (
    Lexicon,
    add_disambig_symbols,
    add_self_loops,
    write_lexicon,
    write_mapping,
)


def lexicon_to_fst_no_sil(
    lexicon: Lexicon,
    token2id: Dict[str, int],
    word2id: Dict[str, int],
    need_self_loops: bool = False,
) -> k2.Fsa:
    """Convert a lexicon to an FST (in k2 format).

    Args:
      lexicon:
        The input lexicon. See also :func:`read_lexicon`
      token2id:
        A dict mapping tokens to IDs.
      word2id:
        A dict mapping words to IDs.
      need_self_loops:
        If True, add self-loop to states with non-epsilon output symbols
        on at least one arc out of the state. The input label for this
        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
    Returns:
      Return an instance of `k2.Fsa` representing the given lexicon.
    """
    loop_state = 0  # words enter and leave from here
    next_state = 1  # the next un-allocated state, will be incremented as we go

    arcs = []

    # The blank symbol <blk> is defined in local/train_bpe_model.py
    assert token2id["<blk>"] == 0
    assert word2id["<eps>"] == 0

    eps = 0

    for word, pieces in lexicon:
        assert len(pieces) > 0, f"{word} has no pronunciations"
        cur_state = loop_state

        word = word2id[word]
        pieces = [token2id[i] if i in token2id else token2id["<unk>"] for i in pieces]

        for i in range(len(pieces) - 1):
            w = word if i == 0 else eps
            arcs.append([cur_state, next_state, pieces[i], w, 0])

            cur_state = next_state
            next_state += 1

        # now for the last piece of this word
        i = len(pieces) - 1
        w = word if i == 0 else eps
        arcs.append([cur_state, loop_state, pieces[i], w, 0])

    if need_self_loops:
        disambig_token = token2id["#0"]
        disambig_word = word2id["#0"]
        arcs = add_self_loops(
            arcs,
            disambig_token=disambig_token,
            disambig_word=disambig_word,
        )

    final_state = next_state
    arcs.append([loop_state, final_state, -1, -1, 0])
    arcs.append([final_state])

    arcs = sorted(arcs, key=lambda arc: arc[0])
    arcs = [[str(i) for i in arc] for arc in arcs]
    arcs = [" ".join(arc) for arc in arcs]
    arcs = "\n".join(arcs)

    fsa = k2.Fsa.from_str(arcs, acceptor=False)
    return fsa


def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
    """Check if all the given tokens are in token symbol table.

    Args:
      token_sym_table:
        Token symbol table that contains all the valid tokens.
      tokens:
        A list of tokens.
    Returns:
      Return True if there is any token not in the token_sym_table,
      otherwise False.
    """
    for tok in tokens:
        if tok not in token_sym_table:
            return True
    return False


def generate_lexicon(
    token_sym_table: Dict[str, int],
    words: List[str],
    bpe_model=None,
) -> Lexicon:
    """Generate a lexicon from a word list and token_sym_table.

    Args:
      token_sym_table:
        Token symbol table that mapping token to token ids.
      words:
        A list of strings representing words.
    Returns:
      Return a dict whose keys are words and values are the corresponding
          tokens.
    """
    sp = ""
    if bpe_model is not None:
        sp = spm.SentencePieceProcessor()
        sp.load(str(bpe_model))

    lexicon = []
    zhPattern = re.compile(r"([\u4e00-\u9fa5])")
    for word in words:
        match = zhPattern.search(word)
        tokens = []
        if match:
            tokens = list(word.strip(" \t"))
        else:
            tokens = sp.encode_as_pieces(word.strip(" \t"))

        if contain_oov(token_sym_table, tokens):
            continue
        lexicon.append((word, tokens))

    # The OOV word is <UNK>
    lexicon.append(("<UNK>", ["<unk>"]))
    return lexicon


def generate_tokens(text_file: str) -> Dict[str, int]:
    """Generate tokens from the given text file.

    Args:
      text_file:
        A file that contains text lines to generate tokens.
    Returns:
      Return a dict whose keys are tokens and values are token ids ranged
      from 0 to len(keys) - 1.
    """
    tokens: Dict[str, int] = dict()
    tokens["<blk>"] = 0
    tokens["<sos/eos>"] = 1
    tokens["<unk>"] = 2
    whitespace = re.compile(r"([\t\r\n]+)")
    with open(text_file, "r", encoding="utf-8") as f:
        for line in f:
            line = re.sub(whitespace, "", line)
            chars = line.split(" ")
            for char in chars:
                if char not in tokens:
                    tokens[char] = len(tokens)

    return tokens


def main():
    lang_dir = Path("data/lang_char")
    text_file = lang_dir / "text_with_bpe"
    bpe_model = lang_dir / "bpe.model"
    words_file = lang_dir / "words.txt"

    word_sym_table = k2.SymbolTable.from_file(words_file)

    words = word_sym_table.symbols

    excluded = ["<eps>", "!SIL", "<SPOKEN_NOISE>", "<UNK>", "#0", "<s>", "</s>"]
    for w in excluded:
        if w in words:
            words.remove(w)

    token_sym_table = generate_tokens(text_file)

    lexicon = generate_lexicon(token_sym_table, words, bpe_model=bpe_model)

    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)

    next_token_id = max(token_sym_table.values()) + 1
    for i in range(max_disambig + 1):
        disambig = f"#{i}"
        assert disambig not in token_sym_table
        token_sym_table[disambig] = next_token_id
        next_token_id += 1

    word_sym_table.add("#0")
    word_sym_table.add("<s>")
    word_sym_table.add("</s>")

    write_mapping(lang_dir / "tokens.txt", token_sym_table)

    write_lexicon(lang_dir / "lexicon.txt", lexicon)
    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)

    L = lexicon_to_fst_no_sil(
        lexicon,
        token2id=token_sym_table,
        word2id=word_sym_table,
    )

    L_disambig = lexicon_to_fst_no_sil(
        lexicon_disambig,
        token2id=token_sym_table,
        word2id=word_sym_table,
        need_self_loops=True,
    )
    torch.save(L.as_dict(), lang_dir / "L.pt")
    torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")


if __name__ == "__main__":
    main()