# coding=utf-8
# Lint as: python3
"""test set"""


import csv
import os
import json

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
import os

import datasets

_CITATION = """\
@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}
"""

_DESCRIPTION = """\
Lorem ipsum
"""
_URL = "https://huggingface.co/datasets/j-krzywdziak/test2"
_AUDIO_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/resolve/main"
_DATA_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/raw/main"
_DL_URLS = {
    "clean": {
        "of": _AUDIO_URL + "/clean/of/examples.zip",
        "on": _AUDIO_URL + "/clean/on/examples.zip",
        "example": _DATA_URL + "/clean/example.tsv",
        "keyword": _DATA_URL + "/clean/keyword.tsv"
    },
    "other": {
        "of": _AUDIO_URL + "/other/of/examples.zip",
        "on": _AUDIO_URL + "/other/on/examples.zip",
        "example": _DATA_URL + "/other/example.tsv",
        "keyword": _DATA_URL + "/other/keyword.tsv"
    },
    "all": {
        "clean.of": _AUDIO_URL + "/clean/of/examples.zip",
        "clean.on": _AUDIO_URL + "/clean/on/examples.zip",
        "other.of": _AUDIO_URL + "/other/of/examples.zip",
        "other.on": _AUDIO_URL + "/other/on/examples.zip",
        "clean.example": _DATA_URL + "/clean/example.tsv",
        "clean.keyword": _DATA_URL + "/clean/keyword.tsv",
        "other.example": _DATA_URL + "/other/example.tsv",
        "other.keyword": _DATA_URL + "/other/keyword.tsv"

    },
}



class TestASR(datasets.GeneratorBasedBuilder):
    """Lorem ipsum."""
    VERSION = "0.0.0"
    DEFAULT_CONFIG_NAME = "all"

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="clean", description="'Clean' speech."),
        datasets.BuilderConfig(name="other", description="'Other', more challenging, speech."),
        datasets.BuilderConfig(name="all", description="Combined clean and other dataset."),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "path": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "ngram": datasets.Value("string"),
                    "type": datasets.Value("string")
                }
            ),
            supervised_keys=("file", "text"),
            homepage=_URL,
            citation=_CITATION
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_DL_URLS[self.config.name])
        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
        if self.config.name == "clean":
            of_split = [
                datasets.SplitGenerator(
                    name="of",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("of"),
                        "files": dl_manager.iter_archive(archive_path["of"]),
                        "examples": archive_path["example"],
                        "keywords": archive_path["keyword"]
                    },
                )
            ]
            on_split = [
                datasets.SplitGenerator(
                    name="on",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("on"),
                        "files": dl_manager.iter_archive(archive_path["on"]),
                        "examples": archive_path["example"],
                        "keywords": archive_path["keyword"]
                    },
                )
            ]
        elif self.config.name == "other":
            of_split = [
                datasets.SplitGenerator(
                    name="of",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("of"),
                        "files": dl_manager.iter_archive(archive_path["of"]),
                        "examples": archive_path["example"],
                        "keywords": archive_path["keyword"]
                    },
                )
            ]
            on_split = [
                datasets.SplitGenerator(
                    name="on",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("on"),
                        "files": dl_manager.iter_archive(archive_path["on"]),
                        "examples": archive_path["example"],
                        "keywords": archive_path["keyword"]
                    },
                )
            ]
        elif self.config.name == "all":
            of_split = [
                datasets.SplitGenerator(
                    name="clean.of",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("clean.of"),
                        "files": dl_manager.iter_archive(archive_path["clean.of"]),
                        "examples": archive_path["clean.example"],
                        "keywords": archive_path["clean.keyword"]
                    },
                ),
                datasets.SplitGenerator(
                    name="other.of",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("other.of"),
                        "files": dl_manager.iter_archive(archive_path["other.of"]),
                        "examples": archive_path["other.example"],
                        "keywords": archive_path["other.keyword"]
                    }
                )
            ]
            on_split = [
                datasets.SplitGenerator(
                    name="clean.on",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("clean.on"),
                        "files": dl_manager.iter_archive(archive_path["clean.on"]),
                        "examples": archive_path["clean.example"],
                        "keywords": archive_path["clean.keyword"]
                    },
                ),
                datasets.SplitGenerator(
                    name="other.on",
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("other.on"),
                        "files": dl_manager.iter_archive(archive_path["other.on"]),
                        "examples": archive_path["other.example"],
                        "keywords": archive_path["other.keyword"]
                    }
                )
            ]
        return on_split + of_split

    def _generate_examples(self, files, local_extracted_archive, examples, keywords):
        """Lorem ipsum."""
        audio_data = {}
        transcripts = []
        key = 0
        for path, f in files:
            audio_data[path] = f.read()
        with open(keywords, encoding="utf-8") as f:
            next(f)
            for row in f:
                r = row.split("\t")
                path = 'examples/'+r[0]
                ngram = r[1]
                transcripts.append({
                    "path": path,
                    "ngram": ngram,
                    "type": "keyword"
                })
        with open(examples, encoding="utf-8") as f2:
            for row in f2:
                r = row.split("\t")
                path = 'examples/'+r[0]
                ngram = r[1]
                transcripts.append({
                    "path": path,
                    "ngram": ngram,
                    "type": "example"
                })
        if audio_data and len(audio_data) == len(transcripts):
            for transcript in transcripts:
                audio = {"path": transcript["path"], "bytes": audio_data[transcript["path"]]}
                yield key, {"audio": audio, **transcript}
                key += 1
            audio_data = {}
            transcripts = []