import functools
import random
import warnings
from pathlib import Path
from typing import Optional, List, Callable, Union

import numpy as np

from audiomentations.core.audio_loading_utils import load_sound_file
from audiomentations.core.transforms_interface import BaseWaveformTransform
from audiomentations.core.utils import (
    calculate_desired_noise_rms,
    calculate_rms,
    convert_decibels_to_amplitude_ratio,
    find_audio_files_in_paths,
)


class AddBackgroundNoise(BaseWaveformTransform):
    """Mix in another sound, e.g. a background noise. Useful if your original sound is clean and
    you want to simulate an environment where background noise is present.
    Can also be used for mixup, as in https://arxiv.org/pdf/1710.09412.pdf
    A folder of (background noise) sounds to be mixed in must be specified. These sounds should
    ideally be at least as long as the input sounds to be transformed. Otherwise, the background
    sound will be repeated, which may sound unnatural.
    Note that the gain of the added noise is relative to the amount of signal in the input if the parameter noise_rms
    is set to "relative" (default option). This implies that if the input is completely silent, no noise will be added.
    Here are some examples of datasets that can be downloaded and used as background noise:
    * https://github.com/karolpiczak/ESC-50#download
    * https://github.com/microsoft/DNS-Challenge/
    """

    def __init__(
        self,
        sounds_path: Union[List[Path], List[str], Path, str],
        min_snr_in_db: float = None,
        max_snr_in_db: float = None,
        min_snr_db: float = None,
        max_snr_db: float = None,
        noise_rms: str = "relative",
        min_absolute_rms_in_db: float = None,
        max_absolute_rms_in_db: float = None,
        min_absolute_rms_db: float = None,
        max_absolute_rms_db: float = None,
        noise_transform: Optional[Callable[[np.ndarray, int], np.ndarray]] = None,
        p: float = 0.5,
        lru_cache_size: int = 2,
    ):
        """
        :param sounds_path: A path or list of paths to audio file(s) and/or folder(s) with
            audio files. Can be str or Path instance(s). The audio files given here are
            supposed to be background noises.
        :param min_snr_in_db: Deprecated. Use min_snr_db instead.
        :param max_snr_in_db: Deprecated. Use max_snr_db instead.
        :param min_snr_db: Minimum signal-to-noise ratio in dB. Is only used if noise_rms is set to "relative"
        :param max_snr_db: Maximum signal-to-noise ratio in dB. Is only used if noise_rms is set to "relative"
        :param noise_rms: Defines how the background noise will be added to the audio input. If the chosen
            option is "relative", the RMS of the added noise will be proportional to the RMS of
            the input sound. If the chosen option is "absolute", the background noise will have
            a RMS independent of the RMS of the input audio file. The default option is "relative".
        :param min_absolute_rms_db: Is only used if noise_rms is set to "absolute". It is
            the minimum RMS value in dB that the added noise can take. The lower the RMS is,
            the lower the added sound will be. Default: -45.0
        :param max_absolute_rms_db: Is only used if noise_rms is set to "absolute". It is
            the maximum RMS value in dB that the added noise can take. Note that this value
            can not exceed 0. Default: -15.0
        :param min_absolute_rms_in_db: Deprecated. Use min_absolute_rms_db instead.
        :param max_absolute_rms_in_db: Deprecated. Use max_absolute_rms_db instead.
        :param noise_transform: A callable waveform transform (or composition of transforms) that
            gets applied to the noise before it gets mixed in. The callable is expected
            to input audio waveform (numpy array) and sample rate (int).
        :param p: The probability of applying this transform
        :param lru_cache_size: Maximum size of the LRU cache for storing noise files in memory
        """
        super().__init__(p)
        self.sound_file_paths = find_audio_files_in_paths(sounds_path)
        self.sound_file_paths = [str(p) for p in self.sound_file_paths]

        assert len(self.sound_file_paths) > 0

        if min_snr_db is not None and min_snr_in_db is not None:
            raise ValueError(
                "Passing both min_snr_db and min_snr_in_db is not supported. Use only"
                " min_snr_db."
            )
        elif min_snr_db is not None:
            self.min_snr_db = min_snr_db
        elif min_snr_in_db is not None:
            warnings.warn(
                "The min_snr_in_db parameter is deprecated. Use min_snr_db instead.",
                DeprecationWarning,
            )
            self.min_snr_db = min_snr_in_db
        else:
            self.min_snr_db = 3.0  # the default

        if max_snr_db is not None and max_snr_in_db is not None:
            raise ValueError(
                "Passing both max_snr_db and max_snr_in_db is not supported. Use only"
                " max_snr_db."
            )
        elif max_snr_db is not None:
            self.max_snr_db = max_snr_db
        elif max_snr_in_db is not None:
            warnings.warn(
                "The max_snr_in_db parameter is deprecated. Use max_snr_db instead.",
                DeprecationWarning,
            )
            self.max_snr_db = max_snr_in_db
        else:
            self.max_snr_db = 30.0  # the default

        assert self.min_snr_db <= self.max_snr_db

        if min_absolute_rms_db is not None and min_absolute_rms_in_db is not None:
            raise ValueError(
                "Passing both min_absolute_rms_db and min_absolute_rms_in_db is not"
                " supported. Use only min_absolute_rms_db."
            )
        elif min_absolute_rms_db is not None:
            self.min_absolute_rms_db = min_absolute_rms_db
        elif min_absolute_rms_in_db is not None:
            warnings.warn(
                (
                    "The min_absolute_rms_in_db parameter is deprecated. Use"
                    " min_absolute_rms_db instead."
                ),
                DeprecationWarning,
            )
            self.min_absolute_rms_db = min_absolute_rms_in_db
        else:
            self.min_absolute_rms_db = -45.0  # the default

        if max_absolute_rms_db is not None and max_absolute_rms_in_db is not None:
            raise ValueError(
                "Passing both max_absolute_rms_db and max_absolute_rms_in_db is not"
                " supported. Use only max_absolute_rms_db."
            )
        elif max_absolute_rms_db is not None:
            self.max_absolute_rms_db = max_absolute_rms_db
        elif max_absolute_rms_in_db is not None:
            warnings.warn(
                (
                    "The max_absolute_rms_in_db parameter is deprecated. Use"
                    " max_absolute_rms_db instead."
                ),
                DeprecationWarning,
            )
            self.max_absolute_rms_db = max_absolute_rms_in_db
        else:
            self.max_absolute_rms_db = -45.0  # the default

        assert self.min_absolute_rms_db <= self.max_absolute_rms_db <= 0

        self.noise_rms = noise_rms
        self._load_sound = functools.lru_cache(maxsize=lru_cache_size)(
            AddBackgroundNoise._load_sound
        )
        self.noise_transform = noise_transform

    @staticmethod
    def _load_sound(file_path, sample_rate):
        return load_sound_file(file_path, sample_rate)

    def randomize_parameters(self, samples: np.ndarray, sample_rate: int):
        super().randomize_parameters(samples, sample_rate)
        if self.parameters["should_apply"]:
            self.parameters["snr_db"] = random.uniform(self.min_snr_db, self.max_snr_db)
            self.parameters["rms_db"] = random.uniform(
                self.min_absolute_rms_db, self.max_absolute_rms_db
            )
            self.parameters["noise_file_path"] = random.choice(self.sound_file_paths)

            num_samples = len(samples)
            noise_sound, _ = self._load_sound(
                self.parameters["noise_file_path"], sample_rate
            )

            num_noise_samples = len(noise_sound)
            min_noise_offset = 0
            max_noise_offset = max(0, num_noise_samples - num_samples - 1)
            self.parameters["noise_start_index"] = random.randint(
                min_noise_offset, max_noise_offset
            )
            self.parameters["noise_end_index"] = (
                self.parameters["noise_start_index"] + num_samples
            )

    def apply(self, samples: np.ndarray, sample_rate: int):
        noise_sound, _ = self._load_sound(
            self.parameters["noise_file_path"], sample_rate
        )
        noise_sound = noise_sound[
            self.parameters["noise_start_index"] : self.parameters["noise_end_index"]
        ]

        if self.noise_transform:
            noise_sound = self.noise_transform(noise_sound, sample_rate)

        noise_rms = calculate_rms(noise_sound)
        if noise_rms < 1e-9:
            warnings.warn(
                "The file {} is too silent to be added as noise. Returning the input"
                " unchanged.".format(self.parameters["noise_file_path"])
            )
            return samples

        clean_rms = calculate_rms(samples)

        if self.noise_rms == "relative":
            desired_noise_rms = calculate_desired_noise_rms(
                clean_rms, self.parameters["snr_db"]
            )

            # Adjust the noise to match the desired noise RMS
            noise_sound = noise_sound * (desired_noise_rms / noise_rms)

        if self.noise_rms == "absolute":
            desired_noise_rms_db = self.parameters["rms_db"]
            desired_noise_rms_amp = convert_decibels_to_amplitude_ratio(
                desired_noise_rms_db
            )
            gain = desired_noise_rms_amp / noise_rms
            noise_sound = noise_sound * gain

        # Repeat the sound if it shorter than the input sound
        num_samples = len(samples)
        while len(noise_sound) < num_samples:
            noise_sound = np.concatenate((noise_sound, noise_sound))

        if len(noise_sound) > num_samples:
            noise_sound = noise_sound[0:num_samples]

        # Return a mix of the input sound and the background noise sound
        return samples + noise_sound

    def __getstate__(self):
        state = self.__dict__.copy()
        warnings.warn(
            "Warning: the LRU cache of AddBackgroundNoise gets discarded when pickling"
            " it. E.g. this means the cache will not be used when using"
            " AddBackgroundNoise together with multiprocessing on Windows"
        )
        del state["_load_sound"]
        return state
