# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
# Copyright 2022, 2023 Jim O'Regan for Språkbanken Tal
#
# 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.

# Lint as: python3
"""Datasets loader for Waxholm speech corpus"""

from io import BytesIO
import os
import soundfile as sf
from collections import namedtuple
from copy import deepcopy
from difflib import SequenceMatcher

import datasets
from datasets.tasks import AutomaticSpeechRecognition
from datasets.features import Audio

TRAIN_LIST = "alloktrainfiles"
TEST_LIST = "testfiles"


_DESCRIPTION = """\
The Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH.
"""


_CITATION = """
@article{bertenstam1995spoken,
  title={Spoken dialogue data collected in the {W}axholm project},
  author={Bertenstam, Johan and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
  journal={STH-QPSR, KTH},
  volume={1},
  pages={49--74},
  year={1995}
}
@inproceedings{bertenstam1995waxholm,
  title={The {W}axholm application database.},
  author={Bertenstam, J and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
  booktitle={EUROSPEECH},
  year={1995}
}"""


_URL = "http://www.speech.kth.se/waxholm/waxholm2.html"


class FRExpected(Exception):
    """Exception to raise when FR line was expected, but not read"""
    def __init__(self, line):
        msg = "Unknown line type (does not begin with 'FR'): "
        super().__init__(msg + line)


class WaxholmDataset(datasets.GeneratorBasedBuilder):
    """Dataset script for Waxholm."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="waxholm"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "text": datasets.Value("string"),
                "phonemes": datasets.Sequence(datasets.Value("string")),
                "audio": datasets.Audio(sampling_rate=16_000)
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_URL,
            citation=_CITATION,
            task_templates=[
                AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")
            ],
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split": "train",
                    "files": TRAIN_LIST
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "split": "test",
                    "files": TEST_LIST
                },
            ),
        ]

    def _generate_examples(self, split, files):
        with open(f"./waxholm/{files}") as input_file:
            for line in input_file.readlines():
                line = line.strip()
                parts = line.split(".")
                subdir = parts[0]
                audio_file = f"./waxholm/scenes_formatted/{subdir}/{line}"
                if not os.path.exists(audio_file):
                    print(f"{audio_file} does not exist: skipping")
                    continue
                text_file = f"{audio_file}.mix"
                if not os.path.exists(text_file):
                    print(f"{text_file} does not exist: skipping")
                    continue
                mix = Mix(text_file)
                samples, sr = smp_read_sf(audio_file)
                buffer = BytesIO()
                sf.write(buffer, samples, sr, format="wav")
                blank = Audio()
                yield line, {
                    "id": line,
                    "text": mix.text,
                    "phonemes": mix.get_phoneme_list(),
                    "audio": {
                        "bytes": buffer.getvalue(),
                        "sampling_rate": sr,
                    }
                }


def fix_text(text: str) -> str:
    replacements = text.maketrans("{}|\\[]", "äåöÖÄÅ")
    return text.translate(replacements)


Label = namedtuple('Label', ['start', 'end', 'label'])


class FR:
    def __init__(self, text="", **kwargs):  # C901
        if text and text != "":
            self.from_text(text)
        else:
            for arg in kwargs:
                prms = ["pm", "pm_type", "type", "frame",
                        "seconds", "phone", "phone_type",
                        "word", "pseudoword"]
                if arg in prms:
                    self.__dict__[arg] = kwargs[arg]
                else:
                    print(f"Unrecognised argument: {arg}")

    def from_text(self, text: str):
        if not text.startswith("FR"):
            raise FRExpected(text)
        parts = [a.strip() for a in text.split("\t")]
        self.frame = parts[0][2:].strip()
        if parts[-1].strip().endswith(" sec"):
            self.seconds = parts[-1].strip()[0:-4]
        def split_phone(phone):
            if phone.startswith("$#"):
                phtype = 'I'
                phone_type = fix_text(phone[0:2])
                phone_out = fix_text(phone[2:])
            elif phone.startswith("$") or phone.startswith("#"):
                phtype = 'I'
                phone_type = fix_text(phone[0:1])
                phone_out = fix_text(phone[1:])
            else:
                print(phone)
                return None
            return {
                "type": phtype,
                "phone_type": phone_type,
                "phone": phone_out
            }
        for subpart in parts[1:-1]:
            subpart = subpart.strip()
            if subpart.startswith("$#") or subpart.startswith("$") or subpart.startswith("#"):
                phparts = split_phone(subpart)
                if phparts is not None:
                    self.type = phparts['type']
                    self.phone_type = phparts['phone_type']
                    self.phone = phparts['phone']
            elif subpart.startswith(">pm "):
                phparts = split_phone(subpart[4:])
                if phparts is not None:
                    self.pm_type = phparts['phone_type']
                    self.pm = phparts['phone']
            elif subpart.startswith(">pm. "):
                phparts = split_phone(subpart[5:])
                if phparts is not None:
                    self.pm_type = phparts['phone_type']
                    self.pm = phparts['phone']
            elif subpart.startswith(">w "):
                self.type = 'B'
                self.word = fix_text(subpart[3:])
                self.pseudoword = False
            elif subpart.startswith(">w. "):
                self.type = 'B'
                self.word = fix_text(subpart[4:])
                self.pseudoword = False
            elif subpart == "> XklickX" or subpart == "> XutandX":
                self.type = 'B'
                self.word = subpart[2:]
                self.pseudoword = True
            elif subpart.startswith("X"):
                if hasattr(self, 'type'):
                    print(self.type, self.type == 'B')
                self.type = getattr(self, 'type', 'B')
                self.word = fix_text(subpart)
                self.pseudoword = True
            elif subpart == "OK":
                self.type = 'E'
            elif subpart == "PROBLEMS":
                self.type = 'E'

    def get_type(self):
        if "type" in self.__dict__:
            return self.type
        else:
            return ""

    def __repr__(self):
        parts = []
        parts.append(f"type: {self.get_type()}")
        parts.append(f"frame: {self.frame}")
        if self.get_type() != 'E':
            parts.append(f"phone: {self.get_phone()}")
        if 'word' in self.__dict__:
            parts.append(f"word: {self.word}")
        if 'pm_type' in self.__dict__:
            parts.append(f"pm_type: {self.pm_type}")
        if 'pm' in self.__dict__:
            parts.append(f"pm: {self.pm}")
        if 'seconds' in self.__dict__:
            parts.append(f"sec: {self.seconds}")
        return "FR(" + ", ".join(parts) + ")"

    def fix_type(self):
        if self.is_type("B") and self.get_word() == "":
            self.pm_type = "$"
            self.phone_type = "$"
            self.type = "I"

    def get_phone(self, fix_accents=True):
        def fix_accents(phone, fix_accents=True):
            if not fix_accents:
                return phone
            return phone.replace("'", "ˈ").replace('"', "ˌ")
        if 'pm' in self.__dict__:
            return fix_accents(self.pm, fix_accents)
        elif 'phone' in self.__dict__:
            return fix_accents(self.phone, fix_accents)
        else:
            return None

    def is_silence_word(self, noise=False):
        if 'word' in self.__dict__:
            if not noise:
                return self.word == "XX"
            else:
                return self.word.startswith("X") and self.word.endswith("X")
        else:
            return False
    
    def is_type(self, type):
        if "type" in self.__dict__:
            return type == self.type
        else:
            return False

    def has_seconds(self):
        return "seconds" in self.__dict__

    def get_seconds(self):
        if not self.has_seconds() and "frame" in self.__dict__:
            return int(self.frame) / 16000.0
        else:
            return self.seconds

    def get_word(self):
        if self.has_word():
            return self.word
        else:
            return ""

    def has_word(self):
        return "word" in self.__dict__

    def has_pseudoword(self):
        return "pseudoword" in self.__dict__


def merge_frs(fr1, fr2, check_time=False):
    """
    Merge FRS entries for plosives: by default, the
    period of glottal closure and the burst are separately
    annotated.
    """
    if fr2.has_word():
        return None
    if check_time:
        if fr1.get_seconds() != fr2.get_seconds():
            return None
    if _is_glottal_closure(fr1.get_phone(), fr2.get_phone()):
        if not fr1.has_word():
            return fr2
        else:
            word = None
            if fr1.has_word():
                word = fr1.word
            pword = None
            if fr1.has_pseudoword():
                pword = fr1.pseudoword
            return FR(pm=fr2.pm, pm_type=fr2.pm_type, type=fr2.type,
                      frame=fr2.frame, seconds=fr2.seconds, phone=fr2.phone,
                      phone_type=fr2.phone_type, word=word, pseudoword=pword)


SILS = {
    "K": "k",
    "G": "g",
    "T": "t",
    "D": "d",
    "2T": "2t",
    "2D": "2d",
    "P": "p",
    "B": "b"
}
def _is_glottal_closure(cur, next):
    return cur in SILS and next == SILS[cur]


def _replace_glottal_closures(input):
    input += ' '
    for sil in SILS:
        input = input.replace(f"{sil} {SILS[sil]} ", f"{SILS[sil]} ")
    return input[:-1]

def _fix_duration_markers(input):
    input += ' '
    input = input.replace(":+ ", ": ")
    return input[:-1]


class Mix():
    def __init__(self, filepath: str, stringfile=None, fix_type=True):
        self.fr = []
        self.path = filepath
        if stringfile is None:
            with open(filepath) as inpf:
                self.read_data(inpf.readlines())
        else:
            self.read_data(stringfile.split("\n"))
        if fix_type:
            for fr in self.fr:
                fr.fix_type()

    def read_data(self, inpf):  # C901
        """read data from text of a .mix file"""
        saw_text = False
        saw_phoneme = False
        saw_labels = False
        for line in inpf:
            if line.startswith("Waxholm dialog."):
                self.filepath = line[15:].strip()
            if line.startswith("TEXT:"):
                saw_text = True
                continue
            if saw_text:
                self.text = fix_text(line.strip())
                saw_text = False
            if line.startswith("PHONEME:"):
                saw_phoneme = True
                self.phoneme = fix_text(line[8:].strip())
                if line[8:].strip().endswith("."):
                    saw_phoneme = False
                continue
            if saw_phoneme:
                self.phoneme = fix_text(line.strip())
                if line[8:].strip().endswith("."):
                    saw_phoneme = False
            if line.startswith("FR "):
                if saw_labels:
                    saw_labels = False
                self.fr.append(FR(text=line))
            if line.startswith("Labels: "):
                self.labels = line[8:].strip()
                saw_labels = True
            if saw_labels and line.startswith(" "):
                self.labels += line.strip()

    def check_fr(self, verbose=False) -> bool:
        """
        Simple sanity check: that there were FR lines,
        and that the first was a start type, and
        last was an end type.
        """
        if 'fr' not in self.__dict__:
            return False
        if len(self.fr) == 0:
            return False
        start_end = self.fr[0].is_type("B") and self.fr[-1].is_type("E")
        if verbose and not start_end:
            if not self.fr[0].is_type("B"):
                print(f"{self.path}: missing start type")
            if not self.fr[-1].is_type("E"):
                print(f"{self.path}: missing end type")
        return start_end

    def get_times(self, as_frames=False):
        """
        get the times of each phoneme
        """
        if not self.check_fr(verbose=True):
            return []
        if as_frames:
            times = [int(x.frame) for x in self.fr]
        else:
            times = [float(x.seconds) for x in self.fr]
        return times

    def get_time_pairs(self, as_frames=False):
        """
        get a list of tuples containing start and end times
        By default, the times are in seconds; if `as_frames`
        is set, the number of frames are returned instead.
        """
        times = self.get_times(as_frames=as_frames)
        starts = times[0:-1]
        ends = times[1:]
        return [x for x in zip(starts, ends)]

    def prune_empty_presilences(self, verbose=False, include_noises=False):
        """
        Remove empty silence markers (i.e., those with no distinct duration)
        """
        self.orig_fr = deepcopy(self.fr)
        i = 0
        warned = False
        def check_cur(cur, next):
            if verbose and not cur.has_seconds():
                print(f"Missing seconds: {self.path}\nLine: {cur}")
            if verbose and not next.has_seconds():
                print(f"Missing seconds: {self.path}\nLine: {next}")
            return cur.get_seconds() == next.get_seconds() and cur.is_silence_word()
        todel = []
        while i < len(self.fr) - 1:
            if check_cur(self.fr[i], self.fr[i + 1]):
                if verbose:
                    if not warned:
                        warned = True
                        print(f"Empty silence in {self.path}:")
                    print(self.fr[i])
                todel.append(i)
            i += 1
        if todel is not None and todel != []:
            for chaff in todel.reverse():
                del(self.fr[chaff])

    def prune_empty_postsilences(self, verbose=False, include_noises=False):
        """
        Remove empty silence markers (i.e., those with no distinct duration)
        """
        if not "orig_fr" in self.__dict__:
            self.orig_fr = deepcopy(self.fr)
        i = 1
        warned = False
        def check_cur(cur, prev):
            if verbose and not cur.has_seconds():
                print(f"Missing seconds: {self.path}\nLine: {cur}")
            if verbose and not prev.has_seconds():
                print(f"Missing seconds: {self.path}\nLine: {prev}")
            return cur.get_seconds() == prev.get_seconds() and cur.is_silence_word()
        todel = []
        while i < len(self.fr):
            if check_cur(self.fr[i], self.fr[i - 1]):
                if verbose:
                    if not warned:
                        warned = True
                        print(f"Empty silence in {self.path}:")
                    print(self.fr[i])
                todel.append(i)
            i += 1
        if todel is not None and todel != []:
            for chaff in todel.reverse():
                del(self.fr[chaff])

    def prune_empty_segments(self, verbose=False):
        """
        Remove empty segments (i.e., those with no distinct duration)
        """
        if not "orig_fr" in self.__dict__:
            self.orig_fr = deepcopy(self.fr)
        times = self.get_time_pairs(as_frames=True)
        if len(times) != (len(self.fr) - 1):
            print("Uh oh: time pairs and items don't match")
        else:
            keep = []
            for fr in zip(self.fr[:-1], times):
                cur_time = fr[1]
                if cur_time[0] == cur_time[1]:
                    if verbose:
                        print(f"Empty segment {fr[0].get_phone()} ({cur_time[0]} --> {cur_time[1]})")
                else:
                    keep.append(fr[0])
            keep.append(self.fr[-1])
            self.fr = keep

    def prune_empty_silences(self, verbose = False):
        self.prune_empty_presilences(verbose)
        self.prune_empty_postsilences(verbose)

    def merge_plosives(self, verbose=False):
        """
        Merge plosives in FRs
        (in Waxholm, as in TIMIT, the silence before the burst and the burst
        are annotated separately).
        """
        if not "orig_fr" in self.__dict__:
            self.orig_fr = deepcopy(self.fr)
        tmp = []
        i = 0
        while i < len(self.fr)-1:
            merged = merge_frs(self.fr[i], self.fr[i+1])
            if merged is not None:
                if verbose:
                    print(f"Merging {self.fr[i]} and {self.fr[i+1]}")
                i += 1
                tmp.append(merged)
            else:
                tmp.append(self.fr[i])
            i += 1
        tmp.append(self.fr[-1])
        self.fr = tmp

    def get_phone_label_tuples(self, as_frames=False, fix_accents=True):
        times = self.get_time_pairs(as_frames=as_frames)
        if self.check_fr():
            labels = [fr.get_phone(fix_accents) for fr in self.fr[0:-1]]
        else:
            labels = []
        if len(times) == len(labels):
            out = []
            for z in zip(times, labels):
                out.append((z[0][0], z[0][1], z[1]))
            return out
        else:
            return []

    def get_merged_plosives(self, noop=False, prune_empty=True):
        """
        Returns a list of phones with plosives merged
        (in Waxholm, as in TIMIT, the silence before the burst and the burst
        are annotated separately).
        If `noop` is True, it simply returns the output of `prune_empty_labels()`
        """
        if noop:
            if not prune_empty:
                print("Warning: not valid to set noop to True and prune_empty to false")
                print("Ignoring prune_empty")
            return self.prune_empty_labels()
        i = 0
        out = []
        if prune_empty:
            labels = self.prune_empty_labels()
        else:
            labels = self.get_phone_label_tuples()
        while i < len(labels)-1:
            cur = labels[i]
            next = labels[i+1]
            if _is_glottal_closure(cur[2], next[2]):
                tmp = Label(start = cur[0], end = next[1], label = next[2])
                out.append(tmp)
                i += 2
            else:
                tmp = Label(start = cur[0], end = cur[1], label = cur[2])
                out.append(tmp)
                i += 1
        return out

    def get_word_label_tuples(self, verbose=True):
        times = self.get_time_pairs()
        if len(times) == len(self.fr[0:-1]):
            out = []
            labels_raw = [x for x in zip(times, self.fr[0:-1])]
            i = 0
            cur = None
            while i < len(labels_raw) - 1:
                if labels_raw[i][1].is_type("B"):
                    if cur is not None:
                        out.append(cur)
                    if labels_raw[i+1][1].is_type("B"):
                        if verbose and labels_raw[i][1].get_word() == "":
                            print("Expected word", labels_raw[i][1])
                        out.append((labels_raw[i][0][0], labels_raw[i][0][1], labels_raw[i][1].get_word()))
                        cur = None
                        i += 1
                        continue
                    else:
                        if verbose and labels_raw[i][1].get_word() == "":
                            print("Expected word", labels_raw[i][1])
                        cur = (labels_raw[i][0][0], labels_raw[i][0][1], labels_raw[i][1].get_word())
                if labels_raw[i+1][1].is_type("B"):
                    if cur is not None:
                        cur = (cur[0], labels_raw[i][0][1], cur[2])
                i += 1
            out.append(cur)
            return out
        else:
            return []

    def get_dictionary(self, fix_accents=True):
        """
        Get pronunciation dictionary entries from the .mix file.
        These entries are based on the corrected pronunciations; for
        the lexical pronunciations, use the `phoneme` property.
        """
        output = {}
        current_phones = []
        prev_word = ''

        for fr in self.fr:
            if 'word' in fr.__dict__:
                phone = fr.get_phone(fix_accents)
                if prev_word != "":
                    if prev_word not in output:
                        output[prev_word] = []
                    output[prev_word].append(current_phones.copy())
                    current_phones.clear()
                prev_word = fr.word
                current_phones.append(phone)
            elif fr.is_type("I"):
                phone = fr.get_phone(fix_accents)
                current_phones.append(phone)
            else:
                if prev_word not in output:
                    output[prev_word] = []
                output[prev_word].append(current_phones.copy())
                return output

    def get_dictionary_list(self, fix_accents=True):
        """
        Get pronunciation dictionary entries from the .mix file.
        These entries are based on the corrected pronunciations; for
        the lexical pronunciations, use the `phoneme` property.
        This version creates a list of tuples (word, phones) that
        preserves the order of the entries.
        """
        output = []
        current_phones = []
        prev_word = ''

        for fr in self.fr:
            if 'word' in fr.__dict__:
                phone = fr.get_phone(fix_accents)
                if prev_word != "":
                    output.append((prev_word, " ".join(current_phones)))
                    current_phones.clear()
                prev_word = fr.word
                current_phones.append(phone)
            elif fr.is_type("I"):
                phone = fr.get_phone(fix_accents)
                current_phones.append(phone)
            else:
                output.append((prev_word, " ".join(current_phones)))
                return output

    def get_phoneme_string(self, insert_pauses=True, fix_accents=True):
        """
        Get an opinionated phoneme string

        Args:
            insert_pauses (bool, optional): Insert pauses between words. Defaults to True.
            fix_accents (bool, optional): IPA-ify accents. Defaults to True.
        """
        dict_list = self.get_dictionary_list(fix_accents)
        skip = ['p:', '.']
        if insert_pauses:
            phone_strings = [x[1] for x in dict_list if x[1] not in skip]
            joined = ' p: '.join(phone_strings)
        else:
            phone_strings = [x[1] for x in dict_list if x[1] != "."]
            joined = ' '.join(phone_strings)
        joined = _replace_glottal_closures(joined)
        joined = _fix_duration_markers(joined)
        return joined

    def get_phoneme_list(self, insert_pauses=True, fix_accents=True):
        return self.get_phoneme_string(insert_pauses, fix_accents).split(' ')

    def get_compare_dictionary(self, fix_accents=True, merge_plosives=True, only_changed=True):
        """
        Get pronunciation dictionary for comparision: i.e., where there is a difference
        between the canonical pronunciation and what was spoken
        """
        if merge_plosives:
            self.merge_plosives()
        orig = self.get_dictionary_list(fix_accents)
        self.prune_empty_segments()
        new = self.get_dictionary_list(fix_accents)
        if len(orig) != len(new):
            words_orig = [w[0] for w in orig]
            words_new = [w[0] for w in new]
            skippables = []
            for tag, i, j, _, _ in SequenceMatcher(None, words_orig, words_new).get_opcodes():
                if tag in ('delete', 'replace'):
                    skippables += [a for a in range(i, j)]
            for c in skippables.reverse():
                del(orig[c])
        out = []
        i = 0
        while i < len(orig):
            if orig[i][0] == new[i][0]:
                if orig[i][1] == new[i][1]:
                    if not only_changed:
                        out.append(orig)
                else:
                    out.append((orig[i][0], orig[i][1], new[i][1]))
            i += 1
        return out


def smp_probe(filename: str) -> bool:
    with open(filename, "rb") as f:
        return f.read(9) == b"file=samp"


def smp_headers(filename: str):
    with open(filename, "rb") as f:
        f.seek(0)
        raw_headers = f.read(1024)
        raw_headers = raw_headers.rstrip(b'\x00')
        asc_headers = raw_headers.decode("ascii")
        asc_headers.rstrip('\x00')
        tmp = [a for a in asc_headers.split("\r\n")]
        back = -1
        while abs(back) > len(tmp) + 1:
            if tmp[back] == '=':
                break
            back -= 1
        tmp = tmp[0:back-1]
        return dict(a.split("=") for a in tmp)


def smp_read_sf(filename: str):
    headers = smp_headers(filename)
    if headers["msb"] == "last":
        ENDIAN = "LITTLE"
    else:
        ENDIAN = "BIG"

    data, sr = sf.read(filename, channels=int(headers["nchans"]),
                       samplerate=16000, endian=ENDIAN, start=512,
                       dtype="int16", format="RAW", subtype="PCM_16")
    return (data, sr)


def _write_wav(filename, arr):
    import wave

    with wave.open(filename, "w") as f:
        f.setnchannels(1)
        f.setsampwidth(2)
        f.setframerate(16000)
        f.writeframes(arr)


#arr, sr = smp_read_sf("/Users/joregan/Playing/waxholm/scenes_formatted//fp2060/fp2060.pr.09.smp")
#write_wav("out.wav", arr)