# 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
from typing import Generator
import torch
import numpy as np
import threading
import time
from torch.nn import functional as F
from contextlib import nullcontext
import uuid
from cosyvoice.utils.common import fade_in_out
from cosyvoice.utils.file_utils import convert_onnx_to_trt


class CosyVoiceModel:

    def __init__(self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        self.llm.fp16 = fp16
        self.flow.fp16 = fp16
        if self.fp16 is True:
            self.llm.half()
            self.flow.half()
        self.token_min_hop_len = 2 * self.flow.input_frame_rate
        self.token_max_hop_len = 4 * self.flow.input_frame_rate
        self.token_overlap_len = 20
        # here we fix set flow.decoder.estimator.static_chunk_size = 0 for compatibability
        self.flow.decoder.estimator.static_chunk_size = 0
        # mel fade in out
        self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
        self.mel_window = np.hamming(2 * self.mel_overlap_len)
        # hift cache
        self.mel_cache_len = 20
        self.source_cache_len = int(self.mel_cache_len * 256)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.stream_scale_factor = 1
        assert (
            self.stream_scale_factor >= 1
        ), "stream_scale_factor should be greater than 1, change it according to your actual rtf"
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.mel_overlap_dict = {}
        self.flow_cache_dict = {}
        self.hift_cache_dict = {}

    def load(self, llm_model, flow_model, hift_model):
        self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
        self.llm.to(self.device).eval()
        self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
        self.flow.to(self.device).eval()
        # in case hift_model is a hifigan model
        hift_state_dict = {
            k.replace("generator.", ""): v for k, v in torch.load(hift_model, map_location=self.device).items()
        }
        self.hift.load_state_dict(hift_state_dict, strict=True)
        self.hift.to(self.device).eval()

    def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
        llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device)
        self.llm.text_encoder = llm_text_encoder
        llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
        self.llm.llm = llm_llm
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16):
        assert torch.cuda.is_available(), "tensorrt only supports gpu!"
        if not os.path.exists(flow_decoder_estimator_model):
            convert_onnx_to_trt(flow_decoder_estimator_model, flow_decoder_onnx_model, fp16)
        if os.path.getsize(flow_decoder_estimator_model) == 0:
            raise ValueError("{} is empty file, delete it and export again!".format(flow_decoder_estimator_model))
        del self.flow.decoder.estimator
        import tensorrt as trt

        with open(flow_decoder_estimator_model, "rb") as f:
            self.flow.decoder.estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(
                f.read()
            )
        if self.flow.decoder.estimator_engine is None:
            raise ValueError("failed to load trt {}".format(flow_decoder_estimator_model))
        self.flow.decoder.estimator = self.flow.decoder.estimator_engine.create_execution_context()

    def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
        with self.llm_context:
            if isinstance(text, Generator):
                assert isinstance(self, CosyVoice2Model), "streaming input text is only implemented for CosyVoice2!"
                for i in self.llm.inference_bistream(
                    text=text,
                    prompt_text=prompt_text.to(self.device),
                    prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                    prompt_speech_token=llm_prompt_speech_token.to(self.device),
                    prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(
                        self.device
                    ),
                    embedding=llm_embedding.to(self.device),
                ):
                    self.tts_speech_token_dict[uuid].append(i)
            else:
                for i in self.llm.inference(
                    text=text.to(self.device),
                    text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
                    prompt_text=prompt_text.to(self.device),
                    prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                    prompt_speech_token=llm_prompt_speech_token.to(self.device),
                    prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(
                        self.device
                    ),
                    embedding=llm_embedding.to(self.device),
                ):
                    self.tts_speech_token_dict[uuid].append(i)
        self.llm_end_dict[uuid] = True

    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
        tts_mel, flow_cache = self.flow.inference(
            token=token.to(self.device),
            token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_token=prompt_token.to(self.device),
            prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_feat=prompt_feat.to(self.device),
            prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
            embedding=embedding.to(self.device),
            flow_cache=self.flow_cache_dict[uuid],
        )
        self.flow_cache_dict[uuid] = flow_cache

        # mel overlap fade in out
        if self.mel_overlap_dict[uuid].shape[2] != 0:
            tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]["mel"], self.hift_cache_dict[uuid]["source"]
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len :]
            tts_mel = tts_mel[:, :, : -self.mel_overlap_len]
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
            self.hift_cache_dict[uuid] = {
                "mel": tts_mel[:, :, -self.mel_cache_len :],
                "source": tts_source[:, :, -self.source_cache_len :],
                "speech": tts_speech[:, -self.source_cache_len :],
            }
            tts_speech = tts_speech[:, : -self.source_cache_len]
        else:
            if speed != 1.0:
                assert self.hift_cache_dict[uuid] is None, "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear")
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
        return tts_speech

    def tts(
        self,
        text,
        flow_embedding,
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
            self.hift_cache_dict[this_uuid] = None
            self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        p = threading.Thread(
            target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)
        )
        p.start()
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                time.sleep(0.1)
                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][: token_hop_len + self.token_overlap_len]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        finalize=False,
                    )
                    yield {"tts_speech": this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len
                ):
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            p.join()
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.mel_overlap_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
            self.flow_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()

    def vc(
        self,
        source_speech_token,
        flow_prompt_speech_token,
        prompt_speech_feat,
        flow_embedding,
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                source_speech_token.flatten().tolist(),
                True,
            )
            self.hift_cache_dict[this_uuid] = None
            self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][: token_hop_len + self.token_overlap_len]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        finalize=False,
                    )
                    yield {"tts_speech": this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len
                ):
                    break
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.mel_overlap_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()


class CosyVoice2Model(CosyVoiceModel):

    def __init__(self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        self.llm.fp16 = fp16
        self.flow.fp16 = fp16
        if self.fp16 is True:
            self.llm.half()
            self.flow.half()
        self.token_hop_len = 2 * self.flow.input_frame_rate
        # here we fix flow encoder/decoder decoding_chunk_size, in the future we will send it as arguments, or use cache
        self.flow.encoder.static_chunk_size = 2 * self.flow.input_frame_rate
        self.flow.decoder.estimator.static_chunk_size = 2 * self.flow.input_frame_rate * self.flow.token_mel_ratio
        # hift cache
        self.mel_cache_len = 8
        self.source_cache_len = int(self.mel_cache_len * 480)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.stream_scale_factor = 1
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.hift_cache_dict = {}

    def load_jit(self, flow_encoder_model):
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
        tts_mel, _ = self.flow.inference(
            token=token.to(self.device),
            token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_token=prompt_token.to(self.device),
            prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_feat=prompt_feat.to(self.device),
            prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
            embedding=embedding.to(self.device),
            finalize=finalize,
        )
        tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio :]
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]["mel"], self.hift_cache_dict[uuid]["source"]
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
            self.hift_cache_dict[uuid] = {
                "mel": tts_mel[:, :, -self.mel_cache_len :],
                "source": tts_source[:, :, -self.source_cache_len :],
                "speech": tts_speech[:, -self.source_cache_len :],
            }
            tts_speech = tts_speech[:, : -self.source_cache_len]
        else:
            if speed != 1.0:
                assert self.hift_cache_dict[uuid] is None, "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear")
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
        return tts_speech

    def tts(
        self,
        text,
        flow_embedding,
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
            self.hift_cache_dict[this_uuid] = None
        p = threading.Thread(
            target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)
        )
        p.start()
        if stream is True:
            token_offset = 0
            while True:
                time.sleep(0.1)
                if (
                    len(self.tts_speech_token_dict[this_uuid]) - token_offset
                    >= self.token_hop_len + self.flow.pre_lookahead_len
                ):
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][
                            : token_offset + self.token_hop_len + self.flow.pre_lookahead_len
                        ]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        token_offset=token_offset,
                        finalize=False,
                    )
                    token_offset += self.token_hop_len
                    yield {"tts_speech": this_tts_speech.cpu()}
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) - token_offset
                    < self.token_hop_len + self.flow.pre_lookahead_len
                ):
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                token_offset=token_offset,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            p.join()
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                token_offset=0,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
        torch.cuda.empty_cache()

    def t2w(
        self,
        source_speech_token,
        flow_prompt_speech_token,
        prompt_speech_feat,
        flow_embedding,
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                source_speech_token.flatten().tolist(),
                True,
            )
            self.hift_cache_dict[this_uuid] = None
            # self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            # self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][: token_hop_len + self.token_overlap_len]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        finalize=False,
                    )
                    yield {"tts_speech": this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len
                ):
                    break
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=0,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=0,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            # self.mel_overlap_dict.pop(this_uuid)
            # self.hift_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()


class CosyVoice2FlowModel(CosyVoiceModel):

    def __init__(self, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        # self.llm.fp16 = fp16
        self.flow.fp16 = fp16
        if self.fp16 is True:
            self.flow.half()
        self.token_hop_len = 2 * self.flow.input_frame_rate
        # here we fix flow encoder/decoder decoding_chunk_size, in the future we will send it as arguments, or use cache
        self.flow.encoder.static_chunk_size = 2 * self.flow.input_frame_rate
        self.flow.decoder.estimator.static_chunk_size = 2 * self.flow.input_frame_rate * self.flow.token_mel_ratio
        # hift cache
        self.mel_cache_len = 8
        self.source_cache_len = int(self.mel_cache_len * 480)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.stream_scale_factor = 1
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.hift_cache_dict = {}

    def load_jit(self, flow_encoder_model):
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
        tts_mel, _ = self.flow.inference(
            token=token.to(self.device),
            token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_token=prompt_token.to(self.device),
            prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
            prompt_feat=prompt_feat.to(self.device),
            prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
            embedding=embedding.to(self.device),
            finalize=finalize,
        )
        tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio :]
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]["mel"], self.hift_cache_dict[uuid]["source"]
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
            self.hift_cache_dict[uuid] = {
                "mel": tts_mel[:, :, -self.mel_cache_len :],
                "source": tts_source[:, :, -self.source_cache_len :],
                "speech": tts_speech[:, -self.source_cache_len :],
            }
            tts_speech = tts_speech[:, : -self.source_cache_len]
        else:
            if speed != 1.0:
                assert self.hift_cache_dict[uuid] is None, "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear")
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window)
        return tts_speech

    def load(self, flow_model, hift_model):
        self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
        self.flow.to(self.device).eval()
        # in case hift_model is a hifigan model
        hift_state_dict = {
            k.replace("generator.", ""): v for k, v in torch.load(hift_model, map_location=self.device).items()
        }
        self.hift.load_state_dict(hift_state_dict, strict=True)
        self.hift.to(self.device).eval()

    def t2w(
        self,
        source_speech_token,
        flow_prompt_speech_token,
        prompt_speech_feat,
        flow_embedding,
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                source_speech_token.flatten().tolist(),
                True,
            )
            self.hift_cache_dict[this_uuid] = None
            # self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            # self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][: token_hop_len + self.token_overlap_len]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        finalize=False,
                    )
                    yield {"tts_speech": this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len
                ):
                    break
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=0,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=0,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            # self.mel_overlap_dict.pop(this_uuid)
            # self.hift_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()
