# 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 os
import time
from typing import Generator
from tqdm import tqdm
from hyperpyyaml import load_hyperpyyaml

# from modelscope import snapshot_download
import torch
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice2FlowModel
from cosyvoice.utils.file_utils import logging
from cosyvoice.utils.class_utils import get_model_type


class CosyVoice:

    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
        self.instruct = True if "-Instruct" in model_dir else False
        self.model_dir = model_dir
        self.fp16 = fp16
        # if not os.path.exists(model_dir):
        #     model_dir = snapshot_download(model_dir)
        with open("{}/cosyvoice.yaml".format(model_dir), "r") as f:
            configs = load_hyperpyyaml(f)
        assert get_model_type(configs) != CosyVoice2Model, "do not use {} for CosyVoice initialization!".format(
            model_dir
        )
        self.frontend = CosyVoiceFrontEnd(
            configs["get_tokenizer"],
            configs["feat_extractor"],
            "{}/campplus.onnx".format(model_dir),
            "{}/speech_tokenizer_v1.onnx".format(model_dir),
            "{}/spk2info.pt".format(model_dir),
            configs["allowed_special"],
        )
        self.sample_rate = configs["sample_rate"]
        if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
            load_jit, load_trt, fp16 = False, False, False
            logging.warning("no cuda device, set load_jit/load_trt/fp16 to False")
        self.model = CosyVoiceModel(configs["llm"], configs["flow"], configs["hift"], fp16)
        self.model.load("{}/llm.pt".format(model_dir), "{}/flow.pt".format(model_dir), "{}/hift.pt".format(model_dir))
        if load_jit:
            self.model.load_jit(
                "{}/llm.text_encoder.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
                "{}/llm.llm.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
                "{}/flow.encoder.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
            )
        if load_trt:
            self.model.load_trt(
                "{}/flow.decoder.estimator.{}.mygpu.plan".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
                "{}/flow.decoder.estimator.fp32.onnx".format(model_dir),
                self.fp16,
            )
        del configs

    def list_available_spks(self):
        spks = list(self.frontend.spk2info.keys())
        return spks

    def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
            model_input = self.frontend.frontend_sft(i, spk_id)
            start_time = time.time()
            logging.info("synthesis text {}".format(i))
            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
                speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
                logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
                yield model_output
                start_time = time.time()

    def inference_zero_shot(
        self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True
    ):
        prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
            if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
                logging.warning(
                    "synthesis text {} too short than prompt text {}, this may lead to bad performance".format(
                        i, prompt_text
                    )
                )
            model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
            start_time = time.time()
            logging.info("synthesis text {}".format(i))
            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
                speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
                logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
                yield model_output
                start_time = time.time()

    def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
            model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
            start_time = time.time()
            logging.info("synthesis text {}".format(i))
            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
                speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
                logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
                yield model_output
                start_time = time.time()

    def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
        assert isinstance(self.model, CosyVoiceModel), "inference_instruct is only implemented for CosyVoice!"
        if self.instruct is False:
            raise ValueError("{} do not support instruct inference".format(self.model_dir))
        instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
            model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
            start_time = time.time()
            logging.info("synthesis text {}".format(i))
            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
                speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
                logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
                yield model_output
                start_time = time.time()

    def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
        model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
        start_time = time.time()
        for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
            speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
            logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
            yield model_output
            start_time = time.time()


class CosyVoice2(CosyVoice):
    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
        # import pdb ; pdb.set_trace()
        self.instruct = True if "-Instruct" in model_dir else False
        self.model_dir = model_dir
        self.fp16 = fp16
        # if not os.path.exists(model_dir):
        #     model_dir = snapshot_download(model_dir)
        with open("{}/cosyvoice.yaml".format(model_dir), "r") as f:
            configs = load_hyperpyyaml(
                f, overrides={"qwen_pretrain_path": os.path.join(model_dir, "CosyVoice-BlankEN")}
            )
        assert get_model_type(configs) == CosyVoice2Model, "do not use {} for CosyVoice2 initialization!".format(
            model_dir
        )
        self.frontend = CosyVoiceFrontEnd(
            configs["get_tokenizer"],
            configs["feat_extractor"],
            "{}/campplus.onnx".format(model_dir),
            "{}/speech_tokenizer_v2.onnx".format(model_dir),
            "{}/spk2info.pt".format(model_dir),
            configs["allowed_special"],
        )
        self.sample_rate = configs["sample_rate"]
        if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
            load_jit, load_trt, fp16 = False, False, False
            logging.warning("no cuda device, set load_jit/load_trt/fp16 to False")
        self.model = CosyVoice2Model(configs["llm"], configs["flow"], configs["hift"], fp16)
        self.model.load("{}/llm.pt".format(model_dir), "{}/flow.pt".format(model_dir), "{}/hift.pt".format(model_dir))
        if load_jit:
            self.model.load_jit("{}/flow.encoder.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32"))
        if load_trt:
            self.model.load_trt(
                "{}/flow.decoder.estimator.{}.mygpu.plan".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
                "{}/flow.decoder.estimator.fp32.onnx".format(model_dir),
                self.fp16,
            )
        del configs

    def inference_instruct(self, *args, **kwargs):
        raise NotImplementedError("inference_instruct is not implemented for CosyVoice2!")

    def inference_instruct2(
        self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True
    ):
        assert isinstance(self.model, CosyVoice2Model), "inference_instruct2 is only implemented for CosyVoice2!"
        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
            model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate)
            start_time = time.time()
            logging.info("synthesis text {}".format(i))
            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
                speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
                logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
                yield model_output
                start_time = time.time()

    def inference_flow(self, prompt_speech_16k, source_speech_token, stream=False, speed=1.0):
        model_input = self.frontend.frontend_token(prompt_speech_16k, self.sample_rate)
        # model_input = self.inference_token(prompt_speech_16k)
        start_time = time.time()
        model_input["source_speech_token"] = source_speech_token
        for model_output in self.model.t2w(**model_input, stream=stream, speed=speed):
            speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
            logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
            yield model_output
            start_time = time.time()

    def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
        model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
        start_time = time.time()
        for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
            speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
            logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
            yield model_output
            start_time = time.time()


class CosyVoice2Flow(CosyVoice):
    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
        # import pdb ; pdb.set_trace()
        self.instruct = True if "-Instruct" in model_dir else False
        self.model_dir = model_dir
        self.fp16 = fp16
        # if not os.path.exists(model_dir):
        #     model_dir = snapshot_download(model_dir)
        with open("{}/cosyvoice2flow.yaml".format(model_dir), "r") as f:
            configs = load_hyperpyyaml(
                f, overrides={"qwen_pretrain_path": os.path.join(model_dir, "CosyVoice-BlankEN")}
            )
        self.frontend = CosyVoiceFrontEnd(
            configs["get_tokenizer"],
            configs["feat_extractor"],
            "{}/campplus.onnx".format(model_dir),
            "{}/speech_tokenizer_v2.onnx".format(model_dir),
            "{}/spk2info.pt".format(model_dir),
            configs["allowed_special"],
        )
        self.sample_rate = configs["sample_rate"]
        if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
            load_jit, load_trt, fp16 = False, False, False
            logging.warning("no cuda device, set load_jit/load_trt/fp16 to False")
        self.model = CosyVoice2FlowModel(configs["flow"], configs["hift"], fp16)
        self.model.load("{}/flow.pt".format(model_dir), "{}/hift.pt".format(model_dir))
        if load_jit:
            self.model.load_jit("{}/flow.encoder.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32"))
        if load_trt:
            self.model.load_trt(
                "{}/flow.decoder.estimator.{}.mygpu.plan".format(model_dir, "fp16" if self.fp16 is True else "fp32"),
                "{}/flow.decoder.estimator.fp32.onnx".format(model_dir),
                self.fp16,
            )
        del configs

    def inference_flow(self, prompt_speech_16k, source_speech_token, stream=False, speed=1.0):
        model_input = self.frontend.frontend_token(prompt_speech_16k, self.sample_rate)
        # model_input = self.inference_token(prompt_speech_16k)
        start_time = time.time()
        model_input["source_speech_token"] = source_speech_token
        for model_output in self.model.t2w(**model_input, stream=stream, speed=speed):
            speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
            logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
            yield model_output
            start_time = time.time()

    def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
        model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
        start_time = time.time()
        for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
            speech_len = model_output["tts_speech"].shape[1] / self.sample_rate
            logging.info("yield speech len {}, rtf {}".format(speech_len, (time.time() - start_time) / speech_len))
            yield model_output
            start_time = time.time()
