# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from functools import lru_cache
import os
import random
import traceback
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import torch
from einops import rearrange
from scipy import ndimage
from torch.special import gammaln
from tqdm import tqdm


def get_abs_rel_paths(input_path: Path, base_path: Path) -> Tuple[Path, Path]:
    """
    Get the absolute and relative paths of input file path.

    Args:
        input_path: An absolute or relative path.
        base_path: base directory the input is relative to.

    Returns:
        The absolute and relative paths of the file.
    """
    if os.path.isabs(input_path):
        abs_path = input_path
        rel_path = input_path.relative_to(base_path)
    else:
        rel_path = input_path
        abs_path = base_path / rel_path

    return abs_path, rel_path


def get_audio_filepaths(manifest_entry: Dict[str, Any], audio_dir: Path) -> Tuple[Path, Path]:
    """
    Get the absolute and relative paths of audio from a manifest entry.

    Args:
        manifest_entry: Manifest entry dictionary.
        audio_dir: base directory where audio is stored.

    Returns:
        The absolute and relative paths of the audio.
    """
    audio_filepath = Path(manifest_entry["audio_filepath"])
    audio_filepath_abs, audio_filepath_rel = get_abs_rel_paths(input_path=audio_filepath, base_path=audio_dir)
    return audio_filepath_abs, audio_filepath_rel


def normalize_volume(audio: np.array, volume_level: float = 0.95) -> np.array:
    """Apply peak normalization to the input audio.
    """
    if not (0.0 <= volume_level <= 1.0):
        raise ValueError(f"Volume must be in range [0.0, 1.0], received {volume_level}")

    if audio.size == 0:
        return audio

    max_sample = np.max(np.abs(audio))
    if max_sample == 0:
        return audio

    return volume_level * (audio / np.max(np.abs(audio)))


class BetaBinomialInterpolator:
    """
    This module calculates alignment prior matrices (based on beta-binomial distribution) using cached popular sizes and image interpolation.
    The implementation is taken from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/data_function.py
    """

    def __init__(self, round_mel_len_to=50, round_text_len_to=10, cache_size=500):
        self.round_mel_len_to = round_mel_len_to
        self.round_text_len_to = round_text_len_to

    @staticmethod
    def round(val, to):
        return max(1, int(np.round((val + 1) / to))) * to

    def __call__(self, w, h):
        bw = BetaBinomialInterpolator.round(w, to=self.round_mel_len_to)
        bh = BetaBinomialInterpolator.round(h, to=self.round_text_len_to)
        ret = ndimage.zoom(beta_binomial_prior_distribution(bw, bh).T, zoom=(w / bw, h / bh), order=1)
        assert ret.shape[0] == w, ret.shape
        assert ret.shape[1] == h, ret.shape
        return ret


def general_padding(item, item_len, max_len, pad_value=0):
    if item_len < max_len:
        item = torch.nn.functional.pad(item, (0, max_len - item_len), value=pad_value)
    return item


def stack_tensors(tensors: List[torch.Tensor], max_lens: List[int], pad_value: float = 0.0) -> torch.Tensor:
    """
    Create batch by stacking input tensor list along the time axes.

    Args:
        tensors: List of tensors to pad and stack
        max_lens: List of lengths to pad each axis to, starting with the last axis
        pad_value: Value for padding

    Returns:
        Padded and stacked tensor.
    """
    padded_tensors = []
    for tensor in tensors:
        padding = []
        for i, max_len in enumerate(max_lens, 1):
            padding += [0, max_len - tensor.shape[-i]]

        padded_tensor = torch.nn.functional.pad(tensor, pad=padding, value=pad_value)
        padded_tensors.append(padded_tensor)

    stacked_tensor = torch.stack(padded_tensors)
    return stacked_tensor


def logbeta(x, y):
    return gammaln(x) + gammaln(y) - gammaln(x + y)


def logcombinations(n, k):
    return gammaln(n + 1) - gammaln(k + 1) - gammaln(n - k + 1)


def logbetabinom(n, a, b, x):
    return logcombinations(n, x) + logbeta(x + a, n - x + b) - logbeta(a, b)

def beta_binomial_prior_distribution(phoneme_count: int, mel_count: int, scaling_factor: float = 1.0) -> np.array:
    x = rearrange(torch.arange(0, phoneme_count), "b -> 1 b")
    y = rearrange(torch.arange(1, mel_count + 1), "b -> b 1")
    a = scaling_factor * y
    b = scaling_factor * (mel_count + 1 - y)
    n = torch.FloatTensor([phoneme_count - 1])

    return logbetabinom(n, a, b, x).exp().numpy()


def get_base_dir(paths):
    def is_relative_to(path1, path2):
        try:
            path1.relative_to(path2)
            return True
        except ValueError:
            return False

    def common_path(path1, path2):
        while path1 is not None:
            if is_relative_to(path2, path1):
                return path1
            path1 = path1.parent if path1 != path1.parent else None
        return None

    base_dir = None
    for p in paths:
        audio_dir = Path(p).parent
        if base_dir is None:
            base_dir = audio_dir
            continue
        base_dir = common_path(base_dir, audio_dir)

    return base_dir


def filter_dataset_by_duration(entries: List[Dict[str, Any]], min_duration: float, max_duration: float):
    """
    Filter out manifest entries based on duration.

    Args:
        entries: List of manifest entry dictionaries.
        min_duration: Minimum duration below which entries are removed.
        max_duration: Maximum duration above which entries are removed.

    Returns:
        filtered_entries: List of manifest entries after filtering.
        total_hours: Total duration of original dataset, in hours
        filtered_hours: Total duration of dataset after filtering, in hours
    """
    filtered_entries = []
    total_duration = 0.0
    filtered_duration = 0.0
    for entry in entries:
        duration = entry["duration"]
        total_duration += duration
        if (min_duration and duration < min_duration) or (max_duration and duration > max_duration):
            continue

        filtered_duration += duration
        filtered_entries.append(entry)

    total_hours = total_duration / 3600.0
    filtered_hours = filtered_duration / 3600.0

    return filtered_entries, total_hours, filtered_hours


def get_weighted_sampler(
    sample_weights: List[float], batch_size: int, world_size: int, num_steps: int
) -> torch.utils.data.WeightedRandomSampler:
    """
    Create pytorch sampler for doing weighted random sampling.

    Args:
        sample_weights: List of sampling weights for all elements in the dataset.
        batch_size: Batch size to sample.
        world_size: Number of devices being used.
        num_steps: Number of steps to be considered an epoch.

    Returns:
        Pytorch sampler
    """
    weights = torch.tensor(sample_weights, dtype=torch.float64)
    num_samples = batch_size * world_size * num_steps
    sampler = torch.utils.data.WeightedRandomSampler(weights=weights, num_samples=num_samples)
    return sampler


import torch
from torch import nn


class ConvNorm(nn.Module):
    """ 1D Convolution """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size=1,
        stride=1,
        padding=None,
        dilation=1,
        bias=True,
        w_init_gain="linear",
        transpose=False,
    ):
        super(ConvNorm, self).__init__()

        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)

        self.conv = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=bias,
        )
        self.transpose = transpose

    def forward(self, x):
        if self.transpose:
            x = x.contiguous().transpose(1, 2)
        x = self.conv(x)
        if self.transpose:
            x = x.contiguous().transpose(1, 2)

        return x


class LinearNorm(nn.Module):
    """ LinearNorm Projection """

    def __init__(self, in_features, out_features, bias=False):
        super(LinearNorm, self).__init__()
        self.linear = nn.Linear(in_features, out_features, bias)

        nn.init.xavier_uniform_(self.linear.weight)
        if bias:
            nn.init.constant_(self.linear.bias, 0.0)

    def forward(self, x):
        x = self.linear(x)
        return x


class AlignmentEncoder(torch.nn.Module):
    """ Alignment Encoder for Unsupervised Duration Modeling """

    def __init__(self,
                n_mel_channels,
                n_att_channels,
                n_text_channels,
                temperature,
                multi_speaker,
                spk_channel):
        super().__init__()
        self.temperature = temperature
        self.softmax = torch.nn.Softmax(dim=3)
        self.log_softmax = torch.nn.LogSoftmax(dim=3)

        self.key_proj = nn.Sequential(
            ConvNorm(
                n_text_channels,
                n_text_channels * 2,
                kernel_size=3,
                bias=True,
                w_init_gain='relu'
            ),
            torch.nn.ReLU(),
            ConvNorm(
                n_text_channels * 2,
                n_att_channels,
                kernel_size=1,
                bias=True,
            ),
        )

        self.query_proj = nn.Sequential(
            ConvNorm(
                n_mel_channels,
                n_mel_channels * 2,
                kernel_size=3,
                bias=True,
                w_init_gain='relu',
            ),
            torch.nn.ReLU(),
            ConvNorm(
                n_mel_channels * 2,
                n_mel_channels,
                kernel_size=1,
                bias=True,
            ),
            torch.nn.ReLU(),
            ConvNorm(
                n_mel_channels,
                n_att_channels,
                kernel_size=1,
                bias=True,
            ),
        )

        if multi_speaker:
            self.key_spk_proj = LinearNorm(spk_channel, n_text_channels)
            self.query_spk_proj = LinearNorm(spk_channel, n_mel_channels)

    def forward(self, queries, keys, mask=None, attn_prior=None, speaker_embed=None):
        """Forward pass of the aligner encoder.
        Args:
            queries (torch.tensor): B x C x T1 tensor (probably going to be mel data).
            keys (torch.tensor): B x C2 x T2 tensor (text data).
            mask (torch.tensor): uint8 binary mask for variable length entries (should be in the T2 domain).
            attn_prior (torch.tensor): prior for attention matrix.
            speaker_embed (torch.tensor): B x C tnesor of speaker embedding for multi-speaker scheme.
        Output:
            attn (torch.tensor): B x 1 x T1 x T2 attention mask. Final dim T2 should sum to 1.
            attn_logprob (torch.tensor): B x 1 x T1 x T2 log-prob attention mask.
        """
        # print('keys', keys.shape, 'queries', queries.shape, 'mask', mask.shape, 'attn_prior', attn_prior.shape, 'speaker_embed', speaker_embed.shape)
        # exit()
        if speaker_embed is not None:
            keys = keys + self.key_spk_proj(speaker_embed.unsqueeze(1).expand(
                -1, keys.shape[-1], -1
            )).transpose(1, 2)
            queries = queries + self.query_spk_proj(speaker_embed.unsqueeze(1).expand(
                -1, queries.shape[-1], -1
            )).transpose(1, 2)
        keys_enc = self.key_proj(keys)  # B x n_attn_dims x T2
        queries_enc = self.query_proj(queries)

        # Simplistic Gaussian Isotopic Attention
        attn = (queries_enc[:, :, :, None] - keys_enc[:, :, None]) ** 2  # B x n_attn_dims x T1 x T2
        attn = -self.temperature * attn.sum(1, keepdim=True)

        if attn_prior is not None:
            #print(f"AlignmentEncoder \t| mel: {queries.shape} phone: {keys.shape} mask: {mask.shape} attn: {attn.shape} attn_prior: {attn_prior.shape}")
            attn = self.log_softmax(attn) + torch.log(attn_prior[:, None] + 1e-8)
            #print(f"AlignmentEncoder \t| After prior sum attn: {attn.shape}")

        attn_logprob = attn.clone()

        if mask is not None:
            attn.data.masked_fill_(mask.permute(0, 2, 1).unsqueeze(2), -float("inf"))

        attn = self.softmax(attn)  # softmax along T2
        return attn, attn_logprob

if __name__ == '__main__':
    import matplotlib.pyplot as plt
    beta_binomial_interpolator = BetaBinomialInterpolator()
    attn_prior = beta_binomial_prior_distribution(500, 1000)
    print(attn_prior.shape)

        # attn_prior = beta_binomial_interpolator(200, 600)
    # plt.imshow(attn_prior)
    # plt.show()