import argparse
import csv
import os
import re
import shutil
import tarfile
import tempfile
from tqdm import tqdm

from pydub import AudioSegment
import requests
from pocketbase import PocketBase

parser = argparse.ArgumentParser(description="Command description.")

pb = PocketBase('https://pocketbase.nenadb.dev/')

def contains_interruption(transcription: str, translation: str) -> bool:
    boundaries = r"[\s\-꞊ˈ…,\.?!]|$"
    languages = r"(A|Az|E|H|K|P|R)"

    # Check if transcription is just a string enclosed with parenthesis
    if re.fullmatch(r'\(.*\)', transcription):
        return True

    # Check if transcription contains any language abbreviation followed by a boundary
    pattern = f'{languages}(?={boundaries})'
    if re.search(pattern, transcription):
        return True

    # Check if translation contains square brackets
    if '[' in translation and ']' in translation:
        return True

    # If none of the above conditions are met, return False
    return False

def build_dataset(test_split=0.10, dev_split=0.10):
    dialects = pb.collection("dialects").get_full_list(query_params={
        "sort": "name",
    })

    dialects = {
        dialect.name.lower(): dialect.name
        for dialect in dialects
    }

    examples = pb.collection("examples").get_full_list(query_params={
        "expand": "dialect",
        "filter": "validated=true",
    })

    stats = {
        "dialects": {
            dialect : {
                "buckets": {
                    "dev": 0,
                    "test": 0,
                    "train": 0,
                },
                "splits": {
                    "proficiency": {},
                    "age": {},
                    "locale": {},
                    "crowdsourced": 0,
                },
                "speakers": set(),
                "size": 0,
                "totalExamples": 0,
                "examplesTranslated": 0,
                "durationLabelled": 0,
                "durationUnlabelled": 0,
            }
            for dialect in dialects.keys()
        },
        "totalExamples": 0,
        "examplesTranslated": 0,
        "durationLabelled": 0,
        "durationUnlabelled": 0,
        "version": "1.0.0",
        "date": "2023-10-7",
        "name": "NENA Speech Dataset",
        "multilingual": True,
    }

    def split_examples(examples):
        test_end = int(test_split * len(examples))
        dev_end = int((dev_split + test_split) * len(examples))
        
        return {
            'test': examples[:test_end],
            'dev': examples[test_end:dev_end],
            'train': examples[dev_end:],
        }

    subsets = {
        dialect: split_examples([
            example for example in examples
            if example.expand['dialect'].name.lower() == dialect
        ])
        for dialect in dialects.keys()
    }

    with tqdm(total=len(examples)) as pbar:
        for dialect, subset in subsets.items():
            for split, examples in subset.items():
                audio_dir_path = os.path.join("audio", dialect, split)
                os.makedirs(audio_dir_path, exist_ok=True)

                transcripts = []
                transcript_dir_path = os.path.join("transcript", dialect)
                os.makedirs(transcript_dir_path, exist_ok=True)
                
                for example in examples:
                    pbar.set_description(f"Downloading audios ({dialect}/{split})")
                    pbar.update(1)
                    audio_url = pb.get_file_url(example, example.speech, {})
                    response = requests.get(audio_url)
                    with tempfile.NamedTemporaryFile() as f:
                        f.write(response.content)
                        f.flush()
                        audio = AudioSegment.from_file(f.name)
                    audio = audio.set_frame_rate(48000)
                    audio_file_name = f"nena_speech_{example.id}.mp3"
                    audio_file_path = os.path.join(audio_dir_path, audio_file_name)
                    audio.export(audio_file_path, format="mp3")
                    
                    transcripts.append({
                        'client_id': example.speaker,
                        'transcription': example.transcription,
                        'translation': example.translation,
                        'path': audio_file_name,
                        'locale': example.locale,
                        'proficiency': example.proficiency,
                        'age': example.age,
                        'crowdsourced': example.crowdsourced,
                        'unlabelled': not example.transcription,
                        'interrupted': contains_interruption(example.transcription, example.translation),
                    })

                    dialect_stats = stats["dialects"][dialect]

                    stats["totalExamples"] += 1
                    dialect_stats["totalExamples"] += 1
                    if example.translation:
                        stats["examplesTranslated"] += 1
                        dialect_stats["examplesTranslated"] += 1
                    if example.transcription:
                        stats["durationLabelled"] += len(audio) / 1000
                        dialect_stats["durationLabelled"] += len(audio) / 1000
                    else:
                        stats["durationUnlabelled"] += len(audio) / 1000
                        dialect_stats["durationUnlabelled"] += len(audio) / 1000

                    dialect_stats["buckets"][split] += 1
                    dialect_stats["speakers"].add(example.speaker)
                    dialect_stats["splits"]["proficiency"][example.proficiency] = dialect_stats["splits"]["proficiency"].get(example.proficiency, 0) + 1 / len(examples)
                    dialect_stats["splits"]["age"][example.age] = dialect_stats["splits"]["age"].get(example.age, 0) + 1 / len(examples)
                    dialect_stats["splits"]["locale"][example.locale] = dialect_stats["splits"]["locale"].get(example.locale, 0) + 1 / len(examples)
                    if example.crowdsourced:
                        dialect_stats["splits"]["crowdsourced"] += 1 / len(examples)

                pbar.set_description(f"Saving audios ({dialect}/{split})")
                audio_tar_path = f"{audio_dir_path}.tar"
                with tarfile.open(audio_tar_path, 'w') as tar:
                    tar.add(audio_dir_path, arcname=os.path.basename(audio_dir_path))

                pbar.set_description(f"Saving transcripts ({dialect}/{split})")

                with open(os.path.join(transcript_dir_path, f"{split}.tsv"), 'w', newline='') as f:
                    fieldnames = [] if len(transcripts) == 0 else transcripts[0].keys()
                    writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter='\t')
                    writer.writeheader()
                    writer.writerows(transcripts)

                shutil.rmtree(audio_dir_path) 
            stats["dialects"][dialect]["speakers"] = len(stats["dialects"][dialect]["speakers"])

    with open('dialect.py', 'w') as f:
        python_code = f'DIALECT = {repr(dialects)}\n'
        f.write(python_code)

    with open('release_stats.py', 'w') as f:
        python_code = f'STATS = {repr(stats)}\n'
        f.write(python_code)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Generate text from prompt")

    parser.add_argument(
        "-b",
        "--build",
        action="store_true",
        help="Download text prompts from GCS bucket",
    )

    args = parser.parse_args()

    build_dataset()