# Copyright [2023-11-28] <sxc19@mails.tsinghua.edu.cn, Xingchen Song>
#            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 torch

from models.cosyvoice.transformer.activation import Swish
from models.cosyvoice.transformer.subsampling import (
    LinearNoSubsampling,
    EmbedinigNoSubsampling,
    Conv1dSubsampling2,
    Conv2dSubsampling4,
    Conv2dSubsampling6,
    Conv2dSubsampling8,
)
from models.cosyvoice.transformer.embedding import (PositionalEncoding,
                                             RelPositionalEncoding,
                                             WhisperPositionalEncoding,
                                             LearnablePositionalEncoding,
                                             NoPositionalEncoding)
from models.cosyvoice.transformer.attention import (MultiHeadedAttention,
                                             RelPositionMultiHeadedAttention)
from models.cosyvoice.transformer.embedding import EspnetRelPositionalEncoding
from models.cosyvoice.transformer.subsampling import LegacyLinearNoSubsampling
from models.cosyvoice.llm.llm import TransformerLM, Qwen2LM, CosyVoice3LM
from models.cosyvoice.flow.flow import MaskedDiffWithXvec, CausalMaskedDiffWithXvec, CausalMaskedDiffWithDiT
from models.cosyvoice.hifigan.generator import HiFTGenerator, CausalHiFTGenerator
from models.cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model


COSYVOICE_ACTIVATION_CLASSES = {
    "hardtanh": torch.nn.Hardtanh,
    "tanh": torch.nn.Tanh,
    "relu": torch.nn.ReLU,
    "selu": torch.nn.SELU,
    "swish": getattr(torch.nn, "SiLU", Swish),
    "gelu": torch.nn.GELU,
}

COSYVOICE_SUBSAMPLE_CLASSES = {
    "linear": LinearNoSubsampling,
    "linear_legacy": LegacyLinearNoSubsampling,
    "embed": EmbedinigNoSubsampling,
    "conv1d2": Conv1dSubsampling2,
    "conv2d": Conv2dSubsampling4,
    "conv2d6": Conv2dSubsampling6,
    "conv2d8": Conv2dSubsampling8,
    'paraformer_dummy': torch.nn.Identity
}

COSYVOICE_EMB_CLASSES = {
    "embed": PositionalEncoding,
    "abs_pos": PositionalEncoding,
    "rel_pos": RelPositionalEncoding,
    "rel_pos_espnet": EspnetRelPositionalEncoding,
    "no_pos": NoPositionalEncoding,
    "abs_pos_whisper": WhisperPositionalEncoding,
    "embed_learnable_pe": LearnablePositionalEncoding,
}

COSYVOICE_ATTENTION_CLASSES = {
    "selfattn": MultiHeadedAttention,
    "rel_selfattn": RelPositionMultiHeadedAttention,
}


def get_model_type(configs):
    # NOTE CosyVoice2Model inherits CosyVoiceModel
    if isinstance(configs['llm'], TransformerLM) and isinstance(configs['flow'], MaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator):
        return CosyVoiceModel
    if isinstance(configs['llm'], Qwen2LM) and isinstance(configs['flow'], CausalMaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator):
        return CosyVoice2Model
    if isinstance(configs['llm'], CosyVoice3LM) and isinstance(configs['flow'], CausalMaskedDiffWithDiT) and isinstance(configs['hift'], CausalHiFTGenerator):
        return CosyVoice3Model
    raise TypeError('No valid model type found!')