# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# 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.
import onnxruntime
import torch
import numpy as np
import whisper
from typing import Callable
import torchaudio.compliance.kaldi as kaldi


class CosyVoiceFrontEnd:

    def __init__(
        self,
        feat_extractor: Callable,
        campplus_model: str,
        speech_tokenizer_model: str,
    ):
        self.feat_extractor = feat_extractor
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        option = onnxruntime.SessionOptions()
        option.graph_optimization_level = (
            onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
        )
        option.intra_op_num_threads = 1
        self.campplus_session = onnxruntime.InferenceSession(
            campplus_model, sess_options=option, providers=["CPUExecutionProvider"]
        )
        self.speech_tokenizer_session = onnxruntime.InferenceSession(
            speech_tokenizer_model,
            sess_options=option,
            providers=[
                (
                    "CUDAExecutionProvider"
                    if torch.cuda.is_available()
                    else "CPUExecutionProvider"
                )
            ],
        )

    def _extract_speech_token(self, speech):
        assert (
            speech.shape[1] / 16000 <= 30
        ), "do not support extract speech token for audio longer than 30s"
        feat = whisper.log_mel_spectrogram(speech, n_mels=128)
        speech_token = (
            self.speech_tokenizer_session.run(
                None,
                {
                    self.speech_tokenizer_session.get_inputs()[0]
                    .name: feat.detach()
                    .cpu()
                    .numpy(),
                    self.speech_tokenizer_session.get_inputs()[1].name: np.array(
                        [feat.shape[2]], dtype=np.int32
                    ),
                },
            )[0]
            .flatten()
            .tolist()
        )
        speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device)
        speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(
            self.device
        )
        return speech_token, speech_token_len

    def _extract_spk_embedding(self, speech):
        feat = kaldi.fbank(speech, num_mel_bins=80, dither=0, sample_frequency=16000)
        feat = feat - feat.mean(dim=0, keepdim=True)
        embedding = (
            self.campplus_session.run(
                None,
                {
                    self.campplus_session.get_inputs()[0]
                    .name: feat.unsqueeze(dim=0)
                    .cpu()
                    .numpy()
                },
            )[0]
            .flatten()
            .tolist()
        )
        embedding = torch.tensor([embedding]).to(self.device)
        return embedding

    def _extract_speech_feat(self, speech):
        speech_feat = (
            self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device)
        )
        speech_feat = speech_feat.unsqueeze(dim=0)
        speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(
            self.device
        )
        return speech_feat, speech_feat_len