# coding=utf-8
# Copyright 2023 Mikel Penagarikano
#
# 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
"""Albayzin automatic speech recognition  dataset.
"""

import os
from pathlib import Path

import datasets
from datasets.tasks import AutomaticSpeechRecognition

_CITATION = """\
@inproceedings{conf/interspeech/MorenoPBLLMN93,
  title = {Albayzin speech database: design of the phonetic corpus.},
  author = {Moreno, Asunción and Poch, Dolors and Bonafonte, Antonio and Lleida, Eduardo and Llisterri, Joaquim and Mariño, José B. and Nadeu, Climent},
  booktitle = {EUROSPEECH},
  crossref = {conf/interspeech/1993},
  ee = {http://www.isca-speech.org/archive/eurospeech_1993/e93_0175.html},
  publisher = {ISCA},
  url = {https://dblp.uni-trier.de/rec/conf/interspeech/MorenoPBLLMN93.html},
  year = 1993
}
"""

_DESCRIPTION = """\
Albayzín, an Spanish Speech Recognition Database.

Albayzin is a spoken database for Spanish designed for speech recognition purposes.
It is composed by utterances from a set of phonetically balanced sentences, containing
a balanced set of males and females of ages from 18 to 55.
"""

_HOMEPAGE = "http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/"


class AlbayzinConfig(datasets.BuilderConfig):
    """BuilderConfig for Albayzin."""

    def __init__(self, **kwargs):
        """
        Args:
          data_dir: `string`, the path to the folder containing the files in the
            downloaded .tar
          citation: `string`, citation for the data set
          url: `string`, url for information about the data set
          **kwargs: keyword arguments forwarded to super.
        """
        super(AlbayzinConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class Albayzin(datasets.GeneratorBasedBuilder):
    """Albayzin dataset."""

    BUILDER_CONFIGS = [AlbayzinConfig(name="default", description="'Default' configuration.")]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "text": datasets.Value("string"),
                    "id": datasets.Value("string"),
                }
            ),
            supervised_keys=("file", "text"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
        )

    def _split_generators(self, dl_manager):

        if dl_manager.manual_dir == None :
            raise ValueError(
                f"Make sure you insert a manual dir via `datasets.load_dataset('Albayzin', data_dir=...)` that points to the Albayzin dataset directory."
            )
        
        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        if not os.path.exists(data_dir):
            raise FileNotFoundError(
                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('Albayzin', data_dir=...)` that points to the Albayzin dataseti directory."
            )

        #assert False
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}),
        ]


    def _load_transcripts(self, split, data_dir):
        # replacement for things such as 'a --> á
        replacements = {
            "'a":"á" , "'e":"é" , "'i":"í" , "'o":"ó" , "'u":"ú" ,
            "'A":"Á" , "'E":"É" , "'I":"Í" , "'O":"Ó" , "'U":"Ú" ,
            "~n":"ñ" , "'~N":"Ñ" , "~u":"ü"
        } 
        split2dir = {'train':'FA.TXT' , 'test':'FT.TXT'}
        path = Path(data_dir,'texto',split2dir[split])
        with path.open(encoding="ascii") as f:
            text = {}
            for line in f:
                for k,v in replacements.items():
                    line = line.replace(k,v)
                words = line.split()
                # ASCII text, with CRLF, CR line terminators
                if len(words) < 3 : continue
                # text_id_number - text
                text[int(words[0])] = ' '.join(words[2:])
            return text


    def _generate_examples(self, split, data_dir):
        """Generate examples from Albayzin archive_path."""
        # Iterating the contents of the data to extract the relevant information
        split2dir = {'train':'SUB_APRE' , 'test':'SUB_PRUE'}
        wav_paths = sorted(Path(data_dir,'wav/CF',split2dir[split]).glob(f"**/*.wav"))
        transcripts = self._load_transcripts(split, data_dir)
        for key,wav_path in enumerate(wav_paths):
            id_ = wav_path.stem
            transcript = transcripts[int(id_[-4:])]
            
            example = {
                "file": str(wav_path),
                "audio": str(wav_path),
                "text": transcript,
                "id": id_,
            }

            yield key, example