import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = ''

try:
    import tflite_runtime.interpreter as tflite
except:
    from tensorflow import lite as tflite

import argparse
import operator
import librosa
import numpy as np
import math
import time

def loadModel():

    global INPUT_LAYER_INDEX
    global OUTPUT_LAYER_INDEX
    global MDATA_INPUT_INDEX
    global CLASSES

    print('LOADING TF LITE MODEL...', end=' ')

    # Load TFLite model and allocate tensors.
    interpreter = tflite.Interpreter(model_path='model/BirdNET_6K_GLOBAL_MODEL.tflite',num_threads=2)
    interpreter.allocate_tensors()

    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Get input tensor index
    INPUT_LAYER_INDEX = input_details[0]['index']
    MDATA_INPUT_INDEX = input_details[1]['index']
    OUTPUT_LAYER_INDEX = output_details[0]['index']

    # Load labels
    CLASSES = []
    with open('model/labels.txt', 'r') as lfile:
        for line in lfile.readlines():
            CLASSES.append(line.replace('\n', ''))

    print('DONE!')

    return interpreter

def loadCustomSpeciesList(path):

    slist = []
    if os.path.isfile(path):
        with open(path, 'r') as csfile:
            for line in csfile.readlines():
                slist.append(line.replace('\r', '').replace('\n', ''))

    return slist

def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):

    # Split signal with overlap
    sig_splits = []
    for i in range(0, len(sig), int((seconds - overlap) * rate)):
        split = sig[i:i + int(seconds * rate)]

        # End of signal?
        if len(split) < int(minlen * rate):
            break
        
        # Signal chunk too short? Fill with zeros.
        if len(split) < int(rate * seconds):
            temp = np.zeros((int(rate * seconds)))
            temp[:len(split)] = split
            split = temp
        
        sig_splits.append(split)

    return sig_splits

def readAudioData(path, overlap, sample_rate=48000):

    print('READING AUDIO DATA...', end=' ', flush=True)

    # Open file with librosa (uses ffmpeg or libav)
    sig, rate = librosa.load(path, sr=sample_rate, mono=True, res_type='kaiser_fast')

    # Split audio into 3-second chunks
    chunks = splitSignal(sig, rate, overlap)

    print('DONE! READ', str(len(chunks)), 'CHUNKS.')

    return chunks

def convertMetadata(m):

    # Convert week to cosine
    if m[2] >= 1 and m[2] <= 48:
        m[2] = math.cos(math.radians(m[2] * 7.5)) + 1 
    else:
        m[2] = -1

    # Add binary mask
    mask = np.ones((3,))
    if m[0] == -1 or m[1] == -1:
        mask = np.zeros((3,))
    if m[2] == -1:
        mask[2] = 0.0

    return np.concatenate([m, mask])

def custom_sigmoid(x, sensitivity=1.0):
    return 1 / (1.0 + np.exp(-sensitivity * x))

def predict(sample, interpreter, sensitivity):

    # Make a prediction
    interpreter.set_tensor(INPUT_LAYER_INDEX, np.array(sample[0], dtype='float32'))
    interpreter.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
    interpreter.invoke()
    prediction = interpreter.get_tensor(OUTPUT_LAYER_INDEX)[0]

    # Apply custom sigmoid
    p_sigmoid = custom_sigmoid(prediction, sensitivity)

    # Get label and scores for pooled predictions
    p_labels = dict(zip(CLASSES, p_sigmoid))

    # Sort by score
    p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)

    # Remove species that are on blacklist
    for i in range(min(10, len(p_sorted))):
        if p_sorted[i][0] in ['Human_Human', 'Non-bird_Non-bird', 'Noise_Noise']:
            p_sorted[i] = (p_sorted[i][0], 0.0)

    # Only return first the top ten results
    return p_sorted[:10]

def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap, interpreter):

    detections = {}
    start = time.time()
    print('ANALYZING AUDIO...', end=' ', flush=True)

    # Convert and prepare metadata
    mdata = convertMetadata(np.array([lat, lon, week]))
    mdata = np.expand_dims(mdata, 0)

    # Parse every chunk
    pred_start = 0.0
    for c in chunks:

        # Prepare as input signal
        sig = np.expand_dims(c, 0)

        # Make prediction
        p = predict([sig, mdata], interpreter, sensitivity)

        # Save result and timestamp
        pred_end = pred_start + 3.0
        detections[str(pred_start) + ';' + str(pred_end)] = p
        pred_start = pred_end - overlap

    print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')

    return detections

def writeResultsToFile(detections, min_conf, path):

    print('WRITING RESULTS TO', path, '...', end=' ')
    rcnt = 0
    with open(path, 'w') as rfile:
        rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
        for d in detections:
            for entry in detections[d]:
                if entry[1] >= min_conf and (entry[0] in WHITE_LIST or len(WHITE_LIST) == 0):
                    rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
                    rcnt += 1
    print('DONE! WROTE', rcnt, 'RESULTS.')

def main():

    global WHITE_LIST

    # Parse passed arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('--i', help='Path to input file.')
    parser.add_argument('--o', default='result.csv', help='Path to output file. Defaults to result.csv.')
    parser.add_argument('--lat', type=float, default=-1, help='Recording location latitude. Set -1 to ignore.')
    parser.add_argument('--lon', type=float, default=-1, help='Recording location longitude. Set -1 to ignore.')
    parser.add_argument('--week', type=int, default=-1, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.')
    parser.add_argument('--overlap', type=float, default=0.0, help='Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.')
    parser.add_argument('--sensitivity', type=float, default=1.0, help='Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.')
    parser.add_argument('--min_conf', type=float, default=0.1, help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')   
    parser.add_argument('--custom_list', default='', help='Path to text file containing a list of species. Not used if not provided.')

    args = parser.parse_args()

    # Load model
    interpreter = loadModel()

    # Load custom species list
    if not args.custom_list == '':
        WHITE_LIST = loadCustomSpeciesList(args.custom_list)
    else:
        WHITE_LIST = []


    # Read audio data
    audioData = readAudioData(args.i, args.overlap)

    # Process audio data and get detections
    week = max(1, min(args.week, 48))
    sensitivity = max(0.5, min(1.0 - (args.sensitivity - 1.0), 1.5))
    detections = analyzeAudioData(audioData, args.lat, args.lon, week, sensitivity, args.overlap, interpreter)

    # Write detections to output file
    min_conf = max(0.01, min(args.min_conf, 0.99))
    writeResultsToFile(detections, min_conf, args.o)
    for i in detections:
        print("\n", detections[i][0],"\n")


if __name__ == '__main__':

    main()

    # Example calls
    # python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' --lat 35.4244 --lon -120.7463 --week 18
    # python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766 --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity 1.25 --custom_list 'example/custom_species_list.txt'