# coding=utf-8
# Copyright 2022 The PolyAI and HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""
EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French
that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification
for spoken dialogue systems.
"""


import csv
import json
import os
from pathlib import Path

import datasets

logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{Spithourakis2022evi,
    author      = {Georgios P. Spithourakis and Ivan Vuli\'{c} and Micha\l{} Lis and I\~{n}igo Casanueva and Pawe\l{} Budzianowski},
    title       = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification},
    year        = {2022},
    note        = {Data available at https://github.com/PolyAI-LDN/evi-paper},
    url         = {https://arxiv.org/abs/2204.13496},
    booktitle   = {Findings of NAACL (publication pending)}
}
"""  # noqa

_ALL_CONFIGS = sorted([
    "en-GB", "fr-FR", "pl-PL"
])

_LANGS = sorted(["en", "fr", "pl"])

_DESCRIPTION = """
EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French 
that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification 
for spoken dialogue systems.
"""  # noqa

_LICENSE = "CC-BY-4.0"

_HOMEPAGE = "https://github.com/PolyAI-LDN/evi-paper"

_BASE_URL = "https://huggingface.co/datasets/PolyAI/evi/resolve/main/data"

_TEXT_URL = {
    lang: os.path.join(_BASE_URL, f"dialogues.{lang.split('-')[0]}.tsv") for lang in _LANGS
}

_AUDIO_DATA_URL = "https://poly-public-data.s3.eu-west-2.amazonaws.com/evi-paper/audios.zip"  # noqa

_VERSION = datasets.Version("0.0.1", "")


class EviConfig(datasets.BuilderConfig):
    """BuilderConfig for EVI"""

    def __init__(
        self, name, *args, **kwargs
    ):
        super().__init__(name=name, *args, **kwargs)
        self.languages = _LANGS if name == "all" else [name.split("-")[0]]  # all langs if config == "all"


class Evi(datasets.GeneratorBasedBuilder):

    DEFAULT_WRITER_BATCH_SIZE = 512
    BUILDER_CONFIGS = [EviConfig(name) for name in _ALL_CONFIGS + ["all"]]

    def _info(self):
        features = datasets.Features(
            {
                "language": datasets.ClassLabel(names=_LANGS),
                "dialogue_id": datasets.Value("string"),
                "speaker_id": datasets.Value("string"),
                "turn_id": datasets.Value("int32"),
                "target_profile_id": datasets.Value("string"),
                "asr_transcription": datasets.Value("string"),
                "asr_nbest": datasets.Sequence(datasets.Value("string")),
                "path": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=8_000),
            }
        )

        return datasets.DatasetInfo(
            version=_VERSION,
            description=_DESCRIPTION,
            license=_LICENSE,
            citation=_CITATION,
            features=features,
            homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        langs = self.config.languages
        lang2text_urls = {
            lang: _TEXT_URL[lang] for lang in langs
        }
        lang2text_paths = dl_manager.download_and_extract(lang2text_urls)
        audio_data_path = dl_manager.download_and_extract(_AUDIO_DATA_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "audio_data_path": audio_data_path,
                    "text_paths": lang2text_paths,
                },
            )
        ]

    def _generate_examples(self, audio_data_path, text_paths):

        for lang in text_paths.keys():
            text_path = text_paths[lang]
            with open(text_path, encoding="utf-8") as fin:
                reader = csv.DictReader(
                    fin, delimiter="\t", skipinitialspace=True
                )
                for dictrow in reader:
                    dialogue_id = dictrow["dialogue_id"]
                    turn_id = dictrow["turn_num"]
                    file_path = os.path.join(
                        "audios",
                        lang,
                        dialogue_id,
                        f'{turn_id}.wav'
                    )
                    full_path = os.path.join(audio_data_path, file_path)

                    yield file_path, {
                        "language": lang,
                        "dialogue_id": dialogue_id,
                        "speaker_id": dictrow["speaker_id"],
                        "turn_id": turn_id,
                        "target_profile_id": dictrow["scenario_id"],
                        "asr_transcription": dictrow["transcription"],
                        "asr_nbest": json.loads(dictrow["nbest"]),
                        "path": file_path,
                        "audio": str(full_path)
                    }