# Copyright (c) Alibaba, Inc. and its affiliates.

import importlib
import os
from typing import Any, Dict

import numpy as np
import scipy.io.wavfile as wav
import torch
import yaml

from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LinearAECAndFbank
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger

logger = get_logger()

FEATURE_MVN = 'feature.DEY.mvn.txt'

CONFIG_YAML = 'dey_mini.yaml'


def initialize_config(module_cfg):
    r"""According to config items, load specific module dynamically with params.
        1. Load the module corresponding to the "module" param.
        2. Call function (or instantiate class) corresponding to the "main" param.
        3. Send the param (in "args") into the function (or class) when calling ( or instantiating).

    Args:
        module_cfg (dict): config items, eg:
            {
                "module": "models.model",
                "main": "Model",
                "args": {...}
            }

    Returns:
        the module loaded.
    """
    module = importlib.import_module(module_cfg['module'])
    return getattr(module, module_cfg['main'])(**module_cfg['args'])


@PIPELINES.register_module(
    Tasks.acoustic_echo_cancellation,
    module_name=Pipelines.speech_dfsmn_aec_psm_16k)
class LinearAECPipeline(Pipeline):
    r"""AEC Inference Pipeline only support 16000 sample rate.

    When invoke the class with pipeline.__call__(), you should provide two params:
        Dict[str, Any]
            the path of wav files, eg:{
            "nearend_mic": "/your/data/near_end_mic_audio.wav",
            "farend_speech": "/your/data/far_end_speech_audio.wav"}
        output_path (str, optional): "/your/output/audio_after_aec.wav"
            the file path to write generate audio.
    """

    def __init__(self, model, **kwargs):
        """
        use `model` and `preprocessor` to create a kws pipeline for prediction
        Args:
            model: model id on modelscope hub.
        """
        super().__init__(model=model, **kwargs)
        self.check_trust_remote_code(
            'This pipeline requires `trust_remote_code=True` to load the module defined'
            ' in the `dey_mini.yaml`, setting this to True means you trust the code and files'
            ' listed in this model repo.',
            model_dir=model)

        self.use_cuda = torch.cuda.is_available()
        with open(
                os.path.join(self.model, CONFIG_YAML), encoding='utf-8') as f:
            self.config = yaml.full_load(f.read())
            self.config['io']['mvn'] = os.path.join(self.model, FEATURE_MVN)
        self._init_model()
        self.preprocessor = LinearAECAndFbank(self.config['io'])

        n_fft = self.config['loss']['args']['n_fft']
        hop_length = self.config['loss']['args']['hop_length']
        winlen = n_fft
        window = torch.hamming_window(winlen, periodic=False)

        def stft(x):
            return torch.view_as_real(
                torch.stft(
                    x,
                    n_fft,
                    hop_length,
                    winlen,
                    center=False,
                    window=window.to(x.device),
                    return_complex=True))

        def istft(x, slen):
            return torch.istft(
                torch.view_as_complex(x),
                n_fft,
                hop_length,
                winlen,
                window=window.to(x.device),
                center=False,
                length=slen)

        self.stft = stft
        self.istft = istft

    def _init_model(self):
        checkpoint = torch.load(
            os.path.join(self.model, ModelFile.TORCH_MODEL_BIN_FILE),
            map_location='cpu',
            weights_only=True)
        self.model = initialize_config(self.config['nnet'])
        if self.use_cuda:
            self.model = self.model.cuda()
        self.model.load_state_dict(checkpoint)

    def forward(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        r"""The AEC process.

        Args:
            inputs: dict={'feature': Tensor, 'base': Tensor}
                'feature' feature of input audio.
                'base' the base audio to mask.

        Returns:
            output_pcm: generated audio array
        """
        output_data = self._process(inputs['feature'], inputs['base'])
        output_data = output_data.astype(np.int16).tobytes()
        return {OutputKeys.OUTPUT_PCM: output_data}

    def postprocess(self, inputs: Dict[str, Any], **kwargs) -> Dict[str, Any]:
        r"""The post process. Will save audio to file, if the output_path is given.

        Args:
            inputs: a dict contains following keys:
                - output_pcm: generated audio array
            kwargs: accept 'output_path' which is the path to write generated audio

        Returns:
            output_pcm: generated audio array
        """
        if 'output_path' in kwargs.keys():
            wav.write(
                kwargs['output_path'], self.preprocessor.SAMPLE_RATE,
                np.frombuffer(inputs[OutputKeys.OUTPUT_PCM], dtype=np.int16))
        return inputs

    def _process(self, fbanks, mixture):
        if self.use_cuda:
            fbanks = fbanks.cuda()
            mixture = mixture.cuda()
        if self.model.vad:
            with torch.no_grad():
                masks, vad = self.model(fbanks.unsqueeze(0))
                masks = masks.permute([2, 1, 0])
        else:
            with torch.no_grad():
                masks = self.model(fbanks.unsqueeze(0))
                masks = masks.permute([2, 1, 0])
        spectrum = self.stft(mixture)
        masked_spec = spectrum * masks
        masked_sig = self.istft(masked_spec, len(mixture)).cpu().numpy()
        return masked_sig
