# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# Copyright (c) 2018 Ryan Leary
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# SOFTWARE.
# This file contains code artifacts adapted from https://github.com/ryanleary/patter
import math

import librosa
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from librosa.util import tiny
from torch.autograd import Variable
from torch_stft import STFT

from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
from nemo.collections.common.parts.patch_utils import stft_patch
from nemo.utils import logging

CONSTANT = 1e-5


def normalize_batch(x, seq_len, normalize_type):
    if normalize_type == "per_feature":
        x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device)
        x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device)
        for i in range(x.shape[0]):
            if x[i, :, : seq_len[i]].shape[1] == 1:
                raise ValueError(
                    "normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result "
                    "in torch.std() returning nan"
                )
            x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1)
            x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1)
        # make sure x_std is not zero
        x_std += CONSTANT
        return (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2)
    elif normalize_type == "all_features":
        x_mean = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
        x_std = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
        for i in range(x.shape[0]):
            x_mean[i] = x[i, :, : seq_len[i].item()].mean()
            x_std[i] = x[i, :, : seq_len[i].item()].std()
        # make sure x_std is not zero
        x_std += CONSTANT
        return (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1)
    elif "fixed_mean" in normalize_type and "fixed_std" in normalize_type:
        x_mean = torch.tensor(normalize_type["fixed_mean"], device=x.device)
        x_std = torch.tensor(normalize_type["fixed_std"], device=x.device)
        return (x - x_mean.view(x.shape[0], x.shape[1]).unsqueeze(2)) / x_std.view(x.shape[0], x.shape[1]).unsqueeze(2)
    else:
        return x


def splice_frames(x, frame_splicing):
    """ Stacks frames together across feature dim

    input is batch_size, feature_dim, num_frames
    output is batch_size, feature_dim*frame_splicing, num_frames

    """
    seq = [x]
    for n in range(1, frame_splicing):
        seq.append(torch.cat([x[:, :, :n], x[:, :, n:]], dim=2))
    return torch.cat(seq, dim=1)


class WaveformFeaturizer(object):
    def __init__(self, sample_rate=16000, int_values=False, augmentor=None):
        self.augmentor = augmentor if augmentor is not None else AudioAugmentor()
        self.sample_rate = sample_rate
        self.int_values = int_values

    def max_augmentation_length(self, length):
        return self.augmentor.max_augmentation_length(length)

    def process(self, file_path, offset=0, duration=0, trim=False, orig_sr=None):
        audio = AudioSegment.from_file(
            file_path,
            target_sr=self.sample_rate,
            int_values=self.int_values,
            offset=offset,
            duration=duration,
            trim=trim,
            orig_sr=orig_sr,
        )
        return self.process_segment(audio)

    def process_segment(self, audio_segment):
        self.augmentor.perturb(audio_segment)
        return torch.tensor(audio_segment.samples, dtype=torch.float)

    @classmethod
    def from_config(cls, input_config, perturbation_configs=None):
        if perturbation_configs is not None:
            aa = AudioAugmentor.from_config(perturbation_configs)
        else:
            aa = None

        sample_rate = input_config.get("sample_rate", 16000)
        int_values = input_config.get("int_values", False)

        return cls(sample_rate=sample_rate, int_values=int_values, augmentor=aa)


class FeaturizerFactory(object):
    def __init__(self):
        pass

    @classmethod
    def from_config(cls, input_cfg, perturbation_configs=None):
        return WaveformFeaturizer.from_config(input_cfg, perturbation_configs=perturbation_configs)


# Create helper class to patch forward func for use with AMP
class STFTPatch(STFT):
    def forward(self, input_data):
        return super().transform(input_data)[0]


# Create helper class for STFT that yields num_frames = num_samples // hop_length
class STFTExactPad(STFTPatch):
    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""

    def __init__(self, *params, **kw_params):
        super().__init__(*params, **kw_params)
        self.pad_amount = (self.filter_length - self.hop_length) // 2

    def inverse(self, magnitude, phase):
        recombine_magnitude_phase = torch.cat([magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1)

        inverse_transform = F.conv_transpose1d(
            recombine_magnitude_phase,
            Variable(self.inverse_basis, requires_grad=False),
            stride=self.hop_length,
            padding=0,
        )

        if self.window is not None:
            window_sum = librosa.filters.window_sumsquare(
                self.window,
                magnitude.size(-1),
                hop_length=self.hop_length,
                win_length=self.win_length,
                n_fft=self.filter_length,
                dtype=np.float32,
            )
            # remove modulation effects
            approx_nonzero_indices = torch.from_numpy(np.where(window_sum > tiny(window_sum))[0])
            window_sum = torch.autograd.Variable(torch.from_numpy(window_sum), requires_grad=False).to(
                magnitude.device
            )
            inverse_transform[..., approx_nonzero_indices] /= window_sum[approx_nonzero_indices]

            # scale by hop ratio
            inverse_transform *= self.filter_length / self.hop_length

        inverse_transform = inverse_transform[..., self.pad_amount :]
        inverse_transform = inverse_transform[..., : -self.pad_amount :]
        inverse_transform = inverse_transform.squeeze(1)

        return inverse_transform


class FilterbankFeatures(nn.Module):
    """Featurizer that converts wavs to Mel Spectrograms.
    See AudioToMelSpectrogramPreprocessor for args.
    """

    def __init__(
        self,
        sample_rate=16000,
        n_window_size=320,
        n_window_stride=160,
        window="hann",
        normalize="per_feature",
        n_fft=None,
        preemph=0.97,
        nfilt=64,
        lowfreq=0,
        highfreq=None,
        log=True,
        log_zero_guard_type="add",
        log_zero_guard_value=2 ** -24,
        dither=CONSTANT,
        pad_to=16,
        max_duration=16.7,
        frame_splicing=1,
        exact_pad=False,
        stft_exact_pad=False,  # TODO: Remove this in 1.1.0
        stft_conv=False,  # TODO: Remove this in 1.1.0
        pad_value=0,
        mag_power=2.0,
        use_grads=False,
    ):
        super().__init__()
        if stft_conv or stft_exact_pad:
            logging.warning(
                "Using torch_stft is deprecated and will be removed in 1.1.0. Please set stft_conv and stft_exact_pad "
                "to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True "
                "as needed."
            )
        if (exact_pad or stft_exact_pad) and n_window_stride % 2 == 1:
            raise NotImplementedError(
                f"{self} received exact_pad == True, but hop_size was odd. If audio_length % hop_size == 0. Then the "
                "returned spectrogram would not be of length audio_length // hop_size. Please use an even hop_size."
            )
        self.log_zero_guard_value = log_zero_guard_value
        if (
            n_window_size is None
            or n_window_stride is None
            or not isinstance(n_window_size, int)
            or not isinstance(n_window_stride, int)
            or n_window_size <= 0
            or n_window_stride <= 0
        ):
            raise ValueError(
                f"{self} got an invalid value for either n_window_size or "
                f"n_window_stride. Both must be positive ints."
            )
        logging.info(f"PADDING: {pad_to}")

        self.win_length = n_window_size
        self.hop_length = n_window_stride
        self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))
        self.stft_pad_amount = (self.n_fft - self.hop_length) // 2 if exact_pad else None
        self.stft_exact_pad = stft_exact_pad
        self.stft_conv = stft_conv

        if stft_conv:
            logging.info("STFT using conv")
            if stft_exact_pad:
                logging.info("STFT using exact pad")
                self.stft = STFTExactPad(self.n_fft, self.hop_length, self.win_length, window)
            else:
                self.stft = STFTPatch(self.n_fft, self.hop_length, self.win_length, window)
        else:
            logging.info("STFT using torch")
            if exact_pad:
                logging.info("STFT using exact pad")
            torch_windows = {
                'hann': torch.hann_window,
                'hamming': torch.hamming_window,
                'blackman': torch.blackman_window,
                'bartlett': torch.bartlett_window,
                'none': None,
            }
            window_fn = torch_windows.get(window, None)
            window_tensor = window_fn(self.win_length, periodic=False) if window_fn else None
            self.register_buffer("window", window_tensor)
            self.stft = lambda x: stft_patch(
                x,
                n_fft=self.n_fft,
                hop_length=self.hop_length,
                win_length=self.win_length,
                center=False if exact_pad else True,
                window=self.window.to(dtype=torch.float),
                return_complex=False,
            )

        self.normalize = normalize
        self.log = log
        self.dither = dither
        self.frame_splicing = frame_splicing
        self.nfilt = nfilt
        self.preemph = preemph
        self.pad_to = pad_to
        highfreq = highfreq or sample_rate / 2

        filterbanks = torch.tensor(
            librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq, fmax=highfreq), dtype=torch.float
        ).unsqueeze(0)
        self.register_buffer("fb", filterbanks)

        # Calculate maximum sequence length
        max_length = self.get_seq_len(torch.tensor(max_duration * sample_rate, dtype=torch.float))
        max_pad = pad_to - (max_length % pad_to) if pad_to > 0 else 0
        self.max_length = max_length + max_pad
        self.pad_value = pad_value
        self.mag_power = mag_power

        # We want to avoid taking the log of zero
        # There are two options: either adding or clamping to a small value
        if log_zero_guard_type not in ["add", "clamp"]:
            raise ValueError(
                f"{self} received {log_zero_guard_type} for the "
                f"log_zero_guard_type parameter. It must be either 'add' or "
                f"'clamp'."
            )

        self.use_grads = use_grads
        if not use_grads:
            self.forward = torch.no_grad()(self.forward)

        # log_zero_guard_value is the the small we want to use, we support
        # an actual number, or "tiny", or "eps"
        self.log_zero_guard_type = log_zero_guard_type
        logging.debug(f"sr: {sample_rate}")
        logging.debug(f"n_fft: {self.n_fft}")
        logging.debug(f"win_length: {self.win_length}")
        logging.debug(f"hop_length: {self.hop_length}")
        logging.debug(f"n_mels: {nfilt}")
        logging.debug(f"fmin: {lowfreq}")
        logging.debug(f"fmax: {highfreq}")
        logging.debug(f"using grads: {use_grads}")

    def log_zero_guard_value_fn(self, x):
        if isinstance(self.log_zero_guard_value, str):
            if self.log_zero_guard_value == "tiny":
                return torch.finfo(x.dtype).tiny
            elif self.log_zero_guard_value == "eps":
                return torch.finfo(x.dtype).eps
            else:
                raise ValueError(
                    f"{self} received {self.log_zero_guard_value} for the "
                    f"log_zero_guard_type parameter. It must be either a "
                    f"number, 'tiny', or 'eps'"
                )
        else:
            return self.log_zero_guard_value

    def get_seq_len(self, seq_len):
        if isinstance(self.stft, STFT):
            pad_amount = self.stft.pad_amount * 2
        else:
            # Assuming that center is True is stft_pad_amount = 0
            pad_amount = self.stft_pad_amount * 2 if self.stft_pad_amount is not None else self.n_fft // 2 * 2
        seq_len = torch.floor((seq_len + pad_amount - self.n_fft) / self.hop_length) + 1
        return seq_len.to(dtype=torch.long)

    @property
    def filter_banks(self):
        return self.fb

    def forward(self, x, seq_len):
        seq_len = self.get_seq_len(seq_len.float())

        if self.stft_pad_amount is not None:
            x = torch.nn.functional.pad(
                x.unsqueeze(1), (self.stft_pad_amount, self.stft_pad_amount), "reflect"
            ).squeeze(1)

        # dither (only in training mode for eval determinism)
        if self.training and self.dither > 0:
            x += self.dither * torch.randn_like(x)

        # do preemphasis
        if self.preemph is not None:
            x = torch.cat((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]), dim=1)

        # disable autocast to get full range of stft values
        with torch.cuda.amp.autocast(enabled=False):
            x = self.stft(x)

        # torch returns real, imag; so convert to magnitude
        if not self.stft_conv:
            # guard is needed for sqrt if grads are passed through
            guard = 0 if not self.use_grads else CONSTANT
            if x.dtype in [torch.cfloat, torch.cdouble]:
                x = torch.view_as_real(x)
            x = torch.sqrt(x.pow(2).sum(-1) + guard)

        # get power spectrum
        if self.mag_power != 1.0:
            x = x.pow(self.mag_power)

        # dot with filterbank energies
        x = torch.matmul(self.fb.to(x.dtype), x)

        # log features if required
        if self.log:
            if self.log_zero_guard_type == "add":
                x = torch.log(x + self.log_zero_guard_value_fn(x))
            elif self.log_zero_guard_type == "clamp":
                x = torch.log(torch.clamp(x, min=self.log_zero_guard_value_fn(x)))
            else:
                raise ValueError("log_zero_guard_type was not understood")

        # frame splicing if required
        if self.frame_splicing > 1:
            x = splice_frames(x, self.frame_splicing)

        # normalize if required
        if self.normalize:
            x = normalize_batch(x, seq_len, normalize_type=self.normalize)

        # mask to zero any values beyond seq_len in batch, pad to multiple of `pad_to` (for efficiency)
        max_len = x.size(-1)
        mask = torch.arange(max_len).to(x.device)
        mask = mask.expand(x.size(0), max_len) >= seq_len.unsqueeze(1)
        x = x.masked_fill(mask.unsqueeze(1).type(torch.bool).to(device=x.device), self.pad_value)
        del mask
        pad_to = self.pad_to
        if pad_to == "max":
            x = nn.functional.pad(x, (0, self.max_length - x.size(-1)), value=self.pad_value)
        elif pad_to > 0:
            pad_amt = x.size(-1) % pad_to
            if pad_amt != 0:
                x = nn.functional.pad(x, (0, pad_to - pad_amt), value=self.pad_value)

        return x, seq_len