import logging
import math
import time
from pathlib import Path
from typing import Tuple

import cv2
import numpy as np
import pandas as pd
import torch
import torchaudio
import torchaudio.transforms as T
import yaml
from PIL import Image
from tqdm import tqdm
from ultralytics import YOLO


def yaml_read(path: Path) -> dict:
    """Returns yaml content as a python dict."""
    with open(path, "r") as f:
        return yaml.safe_load(f)


def clip(
    waveform: torch.Tensor,
    offset: float,
    duration: float,
    sample_rate: int,
) -> torch.Tensor:
    """
    Returns a clipped waveform of `duration` seconds at `offset` in seconds.
    """
    offset_frames_start = int(offset * sample_rate)
    offset_frames_end = offset_frames_start + int(duration * sample_rate)
    return waveform[:, offset_frames_start:offset_frames_end]


def chunk(
    waveform: torch.Tensor,
    sample_rate: int,
    duration: float,
    overlap: float,
) -> list[torch.Tensor]:
    """
    Returns a list of waveforms as torch.Tensor. Each of these waveforms have the specified
    duration and the specified overlap in seconds.
    """
    total_seconds = waveform.shape[1] / sample_rate
    number_spectrograms = total_seconds / (duration - overlap)
    offsets = [
        idx * (duration - overlap) for idx in range(0, math.ceil(number_spectrograms))
    ]
    return [
        clip(
            waveform=waveform,
            offset=offset,
            duration=duration,
            sample_rate=sample_rate,
        )
        for offset in offsets
    ]


def load_audio(audio_filepath: Path) -> Tuple[torch.Tensor, int]:
    """
    Loads an audio_filepath and returns the waveform and sample_rate of the file.
    """
    start_time = time.time()
    waveform, sample_rate = torchaudio.load(audio_filepath)
    end_time = time.time()
    elapsed_time = end_time - start_time
    logging.info(
        f"Elapsed time to load audio file {audio_filepath.name}: {elapsed_time:.2f}s"
    )
    return waveform, sample_rate


def waveform_to_spectrogram(
    waveform: torch.Tensor,
    sample_rate: int,
    n_fft: int,
    hop_length: int,
    freq_max: float,
) -> torch.Tensor:
    """
    Returns a spectrogram as a torch.Tensor given the provided arguments.
    See torchaudio.transforms.Spectrogram for more details about the parameters.

    Args:
      waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
      sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
      n_fft (int): Size of FFT
      hop_length (int): Length of hop between STFT windows.
      freq_max (float): cutoff frequency (Hz)
    """
    filtered_waveform = torchaudio.functional.lowpass_biquad(
        waveform=waveform, sample_rate=sample_rate, cutoff_freq=freq_max
    )
    transform = T.Spectrogram(n_fft=n_fft, hop_length=hop_length, power=2)
    spectrogram = transform(filtered_waveform)
    spectrogram_db = torchaudio.transforms.AmplitudeToDB()(spectrogram)
    frequencies = torch.linspace(0, sample_rate // 2, spectrogram_db.size(1))
    max_freq_bin = torch.searchsorted(frequencies, freq_max).item()
    filtered_spectrogram_db = spectrogram_db[:, :max_freq_bin, :]
    return filtered_spectrogram_db


def normalize(x: np.ndarray, max_value: int = 255) -> np.ndarray:
    """
    Returns the normalized array, value in [0 - max_value]
    Useful for image conversion.
    """
    _min, _max = x.min(), x.max()
    x_normalized = max_value * (x - _min) / (_max - _min)
    return x_normalized.astype(np.uint8)


def spectrogram_tensor_to_np_image(
    spectrogram: torch.Tensor, width: int, height: int
) -> np.ndarray:
    """
    Returns a numpy array of shape (height, width) that represents the spectrogram tensor as an image.
    """
    spectrogram_db_np = spectrogram[0].numpy()
    # Normalize to [0, 255] for image conversion
    spectrogram_db_normalized = normalize(spectrogram_db_np, max_value=255)
    resized_spectrogram_array = cv2.resize(
        spectrogram_db_normalized, (width, height), interpolation=cv2.INTER_LINEAR
    )
    # Horizontal flip to make it show the low frequency range at the bottom left of the image instead of the top left
    flipped_resized_spectrogram_array = np.flipud(resized_spectrogram_array)
    return flipped_resized_spectrogram_array


def waveform_to_np_image(
    waveform: torch.Tensor,
    sample_rate: int,
    n_fft: int,
    hop_length: int,
    freq_max: float,
    width: int,
    height: int,
) -> np.ndarray:
    """
    Returns a numpy image of shape (height, width) that represents the waveform tensor as an image of its spectrogram.

    Args:
      waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
      sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
      duration (float): time in seconds of the waveform
      n_fft (int): Size of FFT
      hop_length (int): Length of hop between STFT windows.
      freq_max (float): cutoff frequency (Hz)
      width (int): width of the generated image
      height (int): height of the generated image
    """
    spectrogram = waveform_to_spectrogram(
        waveform=waveform,
        sample_rate=sample_rate,
        n_fft=n_fft,
        hop_length=hop_length,
        freq_max=freq_max,
    )
    return spectrogram_tensor_to_np_image(
        spectrogram=spectrogram,
        width=width,
        height=height,
    )


def batch_sequence(xs: list, batch_size: int):
    """
    Yields successive n-sized batches from xs.
    """
    for i in range(0, len(xs), batch_size):
        yield xs[i : i + batch_size]


def inference(
    model: YOLO,
    audio_filepath: Path,
    duration: float,
    overlap: float,
    width: int,
    height: int,
    freq_max: float,
    n_fft: int,
    hop_length: int,
    batch_size: int,
    output_dir: Path,
    save_spectrograms: bool,
    save_predictions: bool,
    verbose: bool,
) -> list:
    """
    Inference entry point for running on an entire audio_filepath sound file.
    """
    logging.info(f"Loading audio filepath {audio_filepath}")
    # waveform, sample_rate = torchaudio.load(audio_filepath)
    waveform, sample_rate = load_audio(audio_filepath)
    waveforms = chunk(
        waveform=waveform,
        sample_rate=sample_rate,
        duration=duration,
        overlap=overlap,
    )
    logging.info(f"Chunking the waveform into {len(waveforms)} overlapping clips")
    logging.info(f"Generating {len(waveforms)} spectrograms")
    images = [
        Image.fromarray(
            waveform_to_np_image(
                waveform=y,
                sample_rate=sample_rate,
                n_fft=n_fft,
                hop_length=hop_length,
                freq_max=freq_max,
                width=width,
                height=height,
            )
        )
        for y in tqdm(waveforms)
    ]
    if save_spectrograms:
        save_dir = output_dir / "spectrograms"
        logging.info(f"Saving spectrograms in {save_dir}")
        save_dir.mkdir(exist_ok=True, parents=True)
        for i, image in tqdm(enumerate(images), total=len(images)):
            image.save(save_dir / f"spectrogram_{i}.png")

    results = []

    batches = list(batch_sequence(images, batch_size=batch_size))
    logging.info(f"Running inference on the spectrograms, {len(batches)} batches")
    for batch in tqdm(batches):
        results.extend(model.predict(batch, verbose=verbose))

    if save_predictions:
        save_dir = output_dir / "predictions"
        save_dir.mkdir(parents=True, exist_ok=True)
        logging.info(f"Saving predictions in {save_dir}")
        for i, yolov8_prediction in tqdm(enumerate(results), total=len(results)):
            yolov8_prediction.save(str(save_dir / f"prediction_{i}.png"))

    return results


def index_to_relative_offset(idx: int, duration: float, overlap: float) -> float:
    """
    Returns the relative offset in seconds based on the provided spectrogram index, the duration and the overlap.
    """
    return idx * (duration - overlap)


def from_yolov8_prediction(
    yolov8_prediction,
    idx: int,
    duration: float,
    overlap: float,
    freq_min: float,
    freq_max: float,
) -> list[dict]:
    results = []
    for k, box_xyxyn in enumerate(yolov8_prediction.boxes.xyxyn):
        conf = yolov8_prediction.boxes.conf[k].item()
        x1, y1, x2, y2 = box_xyxyn.numpy()
        xmin = min(x1, x2)
        xmax = max(x1, x2)
        ymin = min(y1, y2)
        ymax = max(y1, y2)
        freq_start = ymin * (freq_max - freq_min)
        freq_end = ymax * (freq_max - freq_min)
        t_start = xmin * duration + index_to_relative_offset(
            idx=idx, duration=duration, overlap=overlap
        )
        t_end = xmax * duration + index_to_relative_offset(
            idx=idx, duration=duration, overlap=overlap
        )
        data = {
            "probability": conf,
            "freq_start": freq_start,
            "freq_end": freq_end,
            "t_start": t_start,
            "t_end": t_end,
        }
        results.append(data)
    return results


def to_dataframe(
    yolov8_predictions,
    duration: float,
    overlap: float,
    freq_min: float,
    freq_max: float,
) -> pd.DataFrame:
    """
    Turns the yolov8 predictions into a pandas dataframe, taking into account the relative offset of each prediction.
    The dataframes contains the following columns
      probability (float): float in 0-1 that represents the probability that this is an actual rumble
      freq_start (float): Hz - where the box starts on the frequency axis
      freq_end (float): Hz - where the box ends on the frequency axis
      t_start (float): Hz - where the box starts on the time axis
      t_end (float): Hz - where the box ends on the time axis
    """
    results = []
    for idx, yolov8_prediction in enumerate(yolov8_predictions):
        results.extend(
            from_yolov8_prediction(
                yolov8_prediction,
                idx=idx,
                duration=duration,
                overlap=overlap,
                freq_min=freq_min,
                freq_max=freq_max,
            )
        )
    return pd.DataFrame(results)


def bgr_to_rgb(a: np.ndarray) -> np.ndarray:
    """
    Turn a BGR numpy array into a RGB numpy array when the array `a` represents
    an image.
    """
    return a[:, :, ::-1]

def get_concat_v(im1: Image.Image, im2: Image.Image) -> Image.Image:
    """
    Concatenate vertically two PIL images.
    """
    dst = Image.new('RGB', (im1.width, im1.height + im2.height))
    dst.paste(im1, (0, 0))
    dst.paste(im2, (0, im1.height))
    return dst