# Copyright 2022 The HuggingFace Datasets 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.
"""The TEDLIUM dataset for automatic speech recognition."""
import csv

import datasets
from datasets.tasks import AutomaticSpeechRecognition

from huggingface_hub import list_repo_files


import pyarrow.parquet as pq
import pyarrow as pa


_DESCRIPTION = """\
The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.
"""

_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC97S62"

_LICENSE = "licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)"

_DATA_REPO_ID = "sanchit-gandhi/tedlium-data"

_WHISPER_TRANSCRIPT_URL = "https://huggingface.co/datasets/distil-whisper/whisper_transcriptions_greedy/resolve/main/tedlium"

_WHISPER_TRANSCRIPT_URLs = _WHISPER_TRANSCRIPT_URL + "/{split}-transcription.csv"

class TedLium(datasets.ArrowBasedBuilder):
    """The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech."""

    VERSION = datasets.Version("1.1.0")

    # This version of the dataset is hard-coded to work with release3 and release3 only.
    DEFAULT_CONFIG_NAME = "release3"
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="release3", version=VERSION, description=_DESCRIPTION),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "audio": datasets.features.Audio(sampling_rate=16_000),
                "text": datasets.Value("string"),
                "speaker_id": datasets.Value("string"),
                "gender": datasets.features.ClassLabel(names=["unknown", "female", "male"]),
                "file": datasets.Value("string"),
                "id": datasets.Value("string"),
                "whisper_transcript": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("audio", "text"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
        )

    def _split_generators(self, dl_manager):
        data_repo_download = f"https://huggingface.co/datasets/{_DATA_REPO_ID}/resolve/main/"
        all_files = list_repo_files(_DATA_REPO_ID, repo_type="dataset")

        train_files = [file for file in all_files if file.startswith("data/train")]
        validation_files = [file for file in all_files if file.startswith("data/validation")]
        test_files = [file for file in all_files if file.startswith("data/test")]

        split_to_ids = {
            "train": train_files,
            "validation": validation_files,
            "test": test_files,
        }

        dl_urls = {}
        for split, split_ids in split_to_ids.items():
            dl_urls[split] = [data_repo_download + source_id for source_id in split_ids]
        archive_paths = dl_manager.download(dl_urls)

        local_extracted_archive_paths = (
            dl_manager.extract(archive_paths)
            if not dl_manager.is_streaming
            else {split: [None] * len(archive_paths[split]) for split in split_to_ids}
        )

        transcription_urls = {split: _WHISPER_TRANSCRIPT_URLs.format(split=split.replace(".", "-")) for split in split_to_ids}
        transcript_archive_path = dl_manager.download(transcription_urls)

        train_split = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_extracted_archive_paths": local_extracted_archive_paths["train"],
                    "archives": [dl_manager.iter_files(path) for path in archive_paths["train"]],
                    "whisper_transcript": transcript_archive_path["train"],
                },
            ),
        ]
        dev_split = [
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "local_extracted_archive_paths": local_extracted_archive_paths["validation"],
                    "archives": [dl_manager.iter_files(path) for path in archive_paths["validation"]],
                    "whisper_transcript": transcript_archive_path["validation"],
                },
            ),
        ]
        test_split = [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "local_extracted_archive_paths": local_extracted_archive_paths["test"],
                    "archives": [dl_manager.iter_files(path) for path in archive_paths["test"]],
                    "whisper_transcript": transcript_archive_path["test"],
                },
            ),
        ]
        return train_split + dev_split + test_split

    def _generate_tables(self, local_extracted_archive_paths, archives, whisper_transcript):
        whisper_transcriptions = dict()
        with open(whisper_transcript, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter=",")
            for line in reader:
                whisper_transcriptions[line["file_id"]] = line["whisper_transcript"]

        idx = 0
        for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
            # Here we iterate over all the files within the TAR archive:
            for audio_file in archive:
                with open(audio_file, "rb") as f:
                    pf = pq.ParquetFile(f)
                    for record_batch in pf.iter_batches():
                        pa_table = pa.Table.from_batches([record_batch])

                    batch_whisper_transcript = []
                    for text, file_id in zip(pa_table["text"], pa_table["id"]):
                        transcription = whisper_transcriptions.get(str(file_id), None)
                        batch_whisper_transcript.append(transcription if str(text) != "ignore_time_segment_in_scoring" else "ignore_time_segment_in_scoring")

                    batch_whisper_transcript = pa.array(batch_whisper_transcript, pa.string())
                    pa_table = pa_table.append_column("whisper_transcript", batch_whisper_transcript)
                    yield idx, pa_table
                    idx += 1