import logging
import numpy as np
from gradio_client import Client
import os
import json
import time
import httpx

logger = logging.getLogger(__name__)

from chord_extractor.extractors import Chordino
from chord_extractor import clear_conversion_cache, LabelledChordSequence

_CONSTANT_GAP_SECS = 2
_SEQ_EMBED_SPACE = 'ohollo/chord-seq-embed'
_POST_PROCESS_CHORD_LEN_RATIO = 0.7
_MAX_RETRIES = 3
_RETRY_DELAY_SECS = 2


def _create_client():
    for attempt in range(_MAX_RETRIES):
        try:
            return Client(_SEQ_EMBED_SPACE)
        except httpx.ReadTimeout:
            if attempt < _MAX_RETRIES - 1:
                time.sleep(_RETRY_DELAY_SECS)
            else:
                raise

_client = _create_client()

def _call_embedding_service(chords_w_timestamps):
    logger.info(chords_w_timestamps)
    result = _client.predict(json.dumps(chords_w_timestamps), api_name="/predict")
    return json.loads(result)


def get_embeddings_from_chord_sequences(chord_sequences: list[list[str]], constant_gap_secs: float = _CONSTANT_GAP_SECS) -> np.ndarray:
    """
    Converts chord sequences into its corresponding embeddings.

    :param chord_sequence: List of chords representing the chord sequence.
    :return: 2-d numpy array of embeddings per chord sequence.
    """
    chords_w_timestamps = [
        {'label': chord_sequence, 'timestamp': [i* constant_gap_secs for i, _ in enumerate(chord_sequence)]} 
        for chord_sequence in chord_sequences
    ]
    return np.array(_call_embedding_service(chords_w_timestamps)['embeddings'])


def get_embedding_from_filepaths(file_paths: list[str]) -> tuple[np.ndarray, list[int]]:
    """
    Reads chord sequences from a given filepath and converts them into embeddings.

    :param file_paths: List of paths to the audio files. Can be anything supported by chord-extractor - .mid, .wav, .mp3, .ogg
    :return: 2-d numpy array of embeddings per chord sequence.
    """
    chords_w_timestamps = []
    chord_lengths = []
    chordino = Chordino() 
    for file_path in file_paths:
        if not os.path.isfile(file_path):
            raise FileNotFoundError(f"File not found: {file_path}")
        
        conversion_file_path = chordino.preprocess(file_path)

        chords = chordino.extract(conversion_file_path if conversion_file_path else file_path)
        chords_w_timestamps.append({
            'label': [chord.chord for chord in chords],
            'timestamp': [chord.timestamp for chord in chords]
        })
        chord_lengths.append(int(len(chords) * _POST_PROCESS_CHORD_LEN_RATIO))
    return np.array(_call_embedding_service(chords_w_timestamps)['embeddings']), chord_lengths