# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and 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.

# Lint as: python3
"""RAVDESS multimodal dataset for emotion recognition."""


import os
from pathlib import Path, PurePath, PurePosixPath
from collections import OrderedDict
import pandas as pd
import datasets


_CITATION = """\

"""

_DESCRIPTION = """\

"""

_URL = "https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip"
_HOMEPAGE = "https://smartlaboratory.org/ravdess/"

_CLASS_NAMES = [
    'neutral',
    'calm',
    'happy', 
    'sad', 
    'angry', 
    'fearful', 
    'disgust', 
    'surprised'
]



_FEAT_DICT = OrderedDict([
    ('Modality', ['full-AV', 'video-only', 'audio-only']),
    ('Vocal channel', ['speech', 'song']),
    ('Emotion', ['neutral', 'calm', 'happy', 'sad', 'angry', 'fearful', 'disgust', 'surprised']),
    ('Emotion intensity', ['normal', 'strong']),
    ('Statement', ["Kids are talking by the door", "Dogs are sitting by the door"]),
    ('Repetition', ["1st repetition", "2nd repetition"]),
])


def filename2feats(filename):
    codes = filename.stem.split('-')
    d = {}
    for i, k in enumerate(_FEAT_DICT.keys()):
        d[k] = _FEAT_DICT[k][int(codes[i])-1]
    d['Actor'] = codes[-1]
    d['Gender'] = 'female' if int(codes[-1]) % 2 == 0 else 'male'
    d['Path_to_Wav'] = str(filename)
    return d


def preprocess(data_root_path):
    output_dir = data_root_path / "RAVDESS_ser"
    output_dir.mkdir(parents=True, exist_ok=True)

    data = []
    for actor_dir in data_root_path.iterdir():
        if actor_dir.is_dir() and "Actor" in actor_dir.name:
            for f in actor_dir.iterdir():
                data.append(filename2feats(f))

    df = pd.DataFrame(data, columns=list(_FEAT_DICT.keys()) + ['Actor', 'Gender', 'Path_to_Wav'])
    df.to_csv(output_dir / 'data.csv')



class RAVDESSConfig(datasets.BuilderConfig):
    """BuilderConfig for RAVDESS."""

    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(RAVDESSConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs)


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

    BUILDER_CONFIGS = [] #RAVDESSConfig(name="clean", description="'Clean' speech.")]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=48000),
                    "text": datasets.Value("string"),
                    "labels": datasets.ClassLabel(names=_CLASS_NAMES),
                    "speaker_id": datasets.Value("string"),
                    "speaker_gender": datasets.Value("string")
#                    "id": datasets.Value("string"),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION
        )


    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download_and_extract(_URL)
        archive_path = Path(archive_path)
        preprocess(archive_path)
        csv_path = os.path.join(archive_path, "RAVDESS_ser/data.csv")


        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
                                    gen_kwargs={"data_info_csv": csv_path}),
        ]


    def _generate_examples(self, data_info_csv):
        print("\nGenerating an example")

        # Read the data info to extract rows mentioning about non-converted audio only
        data_info = pd.read_csv(open(data_info_csv, encoding="utf8"))

        # Iterating the contents of the data to extract the relevant information
        for audio_idx in range(data_info.shape[0]):
            audio_data = data_info.iloc[audio_idx]
            
#            subpath = str(audio_data["Path_to_Wav"])
            # import pathlib
            # subpath = subpath.replace('\\', '/')
            # p2 = pathlib.PurePosixPath(subpath)
            # wav_path = str(pathlib.PurePath(data_path) / p2)
#            labels = audio_data["Emotion"] #.lower().split(',')

          #  labels = [l for l in labels if len(l) > 1]

            example = {
                "audio": audio_data['Path_to_Wav'],  #wav_path,
                "text": audio_data['Statement'],
                "labels": audio_data['Emotion'],
                "speaker_id": audio_data["Actor"],
                "speaker_gender": audio_data["Gender"]
            }

            yield audio_idx, example

            
            
            
            
            
            
            
            
            
            
            
            
            
            
    # def class_names(self):
    #     return _CLASS_NAMES

    
    
    
    
    #            transcript = 
            # # extract transcript
            # with open(wav_path.replace(".WAV", ".TXT"), encoding="utf-8") as op:
            #     transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number