"""SVQ data reading."""

import io
import os
try:
  from array_record.python import array_record_module as array_record
except:
  import array_record
import datasets
import librosa
import numpy as np
import pandas as pd
from scipy.io import wavfile


def read_wav_bytes_to_normalized_float(
    wav_bytes, resample_hz: float | None = None
):
  """Reads WAV bytes object and returns normalized float numpy array.

  Args:
    wav_bytes: WAV bytes object.
    resample_hz: Optional resample rate.
  Returns:
    (waveform, original sample rate before any resample)
  """
  rate, data = wavfile.read(io.BytesIO(wav_bytes))

  if data.ndim > 1 and data.shape[1] > 1:
    raise ValueError("Only mono WAV files are supported.")

  # Convert data to float and normalize
  if data.dtype == np.int16:
    x = data.astype(np.float32) / np.iinfo(np.int16).max
  elif data.dtype == np.int32:
    x = data.astype(np.float32) / np.iinfo(np.int32).max
  elif data.dtype == np.float32:
    x = data
  else:
    raise TypeError(f"Unsupported data type: {data.dtype}")
  if resample_hz is not None and resample_hz != rate:
    x = librosa.resample(x, orig_sr=rate, target_sr=resample_hz)
  return x, rate


def read_utt_index(basepath):
  """Read utt_index.jsonl file to a dict of {uttid: path:index}."""
  df = pd.read_json(os.path.join(basepath, "utt_index.jsonl"), lines=True)
  return dict(zip(df["utt_id"], df["index"]))


class UttLookup:
  """Lookup utterances by utt_id with optional resampling.

  Usage:
    utt_lookup = UttLookup(basepath)
    waveform = utt_lookup(utt_id)
  """

  def __init__(self, basepath, resample_hz: float | None = None):
    self.basepath = basepath
    self.resample_hz = resample_hz
    self.utt_id_to_path_idx = read_utt_index(basepath)
    self.readers = {}
    self.orig_sample_rate_ = None

  @property
  def orig_sample_rate(self):
    if self.orig_sample_rate_ is None:
      utt_id = next(iter(self.utt_id_to_path_idx))
      self(utt_id)
    return self.orig_sample_rate_

  def __call__(self, utt_id: str):
    path, idx = self.utt_id_to_path_idx[utt_id].split(":")
    if path not in self.readers:
      array_record_path = os.path.join(self.basepath, f"{path}.array_record")
      self.readers[path] = array_record.ArrayRecordReader(
          array_record_path
      )
    b = self.readers[path].read([int(idx)])
    waveform, sample_rate = read_wav_bytes_to_normalized_float(
        b[0], resample_hz=self.resample_hz
    )
    if self.orig_sample_rate_ is None:
      self.orig_sample_rate_ = sample_rate
    if sample_rate != self.orig_sample_rate_:
      raise ValueError(
          f"Sample rate mismatch: {sample_rate} != {self.orig_sample_rate_}"
      )
    return waveform


def generate_examples(filepath, resample_hz: float | None = None):
  """Generate examples from a jsonl task file."""
  basepath = os.path.dirname(filepath)
  utt_lookup = UttLookup(basepath, resample_hz=resample_hz)
  task = pd.read_json(filepath, lines=True)
  for ex in task.to_dict(orient="records"):
    utt = utt_lookup(ex["utt_id"])
    ex["waveform"] = utt
    yield ex


_CITATION = """\
@InProceedings{mseb,
title = {Massive Sound Embedding Benchmark (MSEB)},
author={Georg Heigold, Ehsan Variani, Tom Bagby, Ji Ma, Cyril Allauzen, Shankar Kumar, Michael Riley}
year={2025}
}
"""

_NUM_SHARDS = 128  # Internal sharding for parallel data loading.


class SvqDataset(datasets.GeneratorBasedBuilder):
  """SVQ dataset."""

  VERSION = datasets.Version("1.1.0")

  BUILDER_CONFIGS = [
      datasets.BuilderConfig(name=name, description=desc)
      for name, desc in [
          ("span_reasoning_in_lang", "Span reasoning in language."),
          ("span_retrieval_in_lang", "Span retrieval in language."),
          ("span_reasoning_cross_lang", "Span reasoning cross language."),
          ("span_retrieval_cross_lang", "Span retrieval cross language."),
          ("passage_retrieval_in_lang", "Passage retrieval in language."),
          ("passage_retrieval_cross_lang", "Passage retrieval cross language."),
          ("document_retrieval_in_lang", "Document retrieval in language."),
          (
              "document_retrieval_cross_lang",
              "Document retrieval cross language.",
          ),
      ]
  ]

  DEFAULT_WRITER_BATCH_SIZE = 64

  def _info(self):
    task = self.config.name
    features = {
        "utt_id": datasets.Value("string"),
        "waveform": datasets.Sequence(datasets.Value("float32")),
        "text": datasets.Value("string"),
        "locale": datasets.Value("string"),
        "environment": datasets.Value("string"),
        "speaker_id": datasets.Value("string"),
        "speaker_age": datasets.Value("int32"),
        "speaker_gender": datasets.Value("string"),
        "page_id": datasets.Value("string"),
        "page_title": datasets.Value("string"),
        "passage_id": datasets.Value("string"),
        "passage_text": datasets.Value("string"),
    }
    if "span" in task:
      features["span"] = datasets.Value("string")
    return datasets.DatasetInfo(
        description=(
            "Simple Voice Queries (SVQ) dataset, Task: span reasoning in"
            " language."
        ),
        features=datasets.Features(**features),
        homepage="https://huggingface.co/datasets/google/svq",
        license="Apache 2.0",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    basepath = os.getcwd()
    task = self.config.name
    return [
        datasets.SplitGenerator(
            name="eval",
            gen_kwargs={
                "filepath": os.path.join(
                    basepath, f"{task}.jsonl"
                ),
                "shards": list(range(_NUM_SHARDS)),
                "resample_hz": 16000,
                "task_name": task,
            },
        ),
    ]

  def _generate_examples(
      self, filepath=None, shards=None, resample_hz=None, task_name=None
  ):
    basepath = os.path.dirname(filepath)
    utt_lookup = UttLookup(basepath, resample_hz=resample_hz)
    task = pd.read_json(filepath, lines=True)
    task = np.array_split(task, _NUM_SHARDS)
    task_shards = [task[idx].to_dict(orient="records") for idx in shards]
    del task
    for shard in task_shards:
      for ex in shard:
        utt = utt_lookup(ex["utt_id"])
        ex["waveform"] = utt
        del ex["task"]
        if "span" not in task_name:
          del ex["span"]
        yield "_".join([ex["utt_id"], ex["passage_id"]]), ex