# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# 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.
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging

logger = logging.get_logger(__name__)


class Qwen3TTSSpeakerEncoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3TTSSpeakerEncoder`].
    It is used to instantiate a Qwen3TTS speaker encoder model according to the specified arguments, defining the model
    architecture. The architecture is based on the ECAPA-TDNN model.

    Args:
        mel_dim (`int`, *optional*, defaults to 128):
            The dimension of the input mel-spectrogram.
        enc_dim (`int`, *optional*, defaults to 192):
            The dimension of the final speaker embedding.
        enc_channels (`list[int]`, *optional*, defaults to `[512, 512, 512, 512, 1536]`):
            A list of output channels for each TDNN/SERes2Net layer in the encoder.
            The first channel size is for the initial TDNN layer,
            the intermediate ones for the `SqueezeExcitationRes2NetBlock` layers,
            and the last one for the multi-layer feature aggregation.
        enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
            A list of kernel sizes for each layer in the encoder, corresponding to `enc_channels`.
        enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
            A list of dilations for each layer in the encoder, corresponding to `enc_channels`.
        enc_attention_channels (`int`, *optional*, defaults to 128):
            The number of attention channels in the `AttentiveStatisticsPooling` layer.
        enc_res2net_scale (`int`, *optional*,defaults to 8):
            The scale of the `Res2NetBlock` in the encoder.
        enc_se_channels (`int`, *optional*, defaults to 128):
            The number of channels in the squeeze part of the `SqueezeExcitationBlock`.
    """

    def __init__(
        self,
        mel_dim=128,
        enc_dim=1024,
        enc_channels=[512, 512, 512, 512, 1536],
        enc_kernel_sizes=[5, 3, 3, 3, 1],
        enc_dilations=[1, 2, 3, 4, 1],
        enc_attention_channels=128,
        enc_res2net_scale=8,
        enc_se_channels=128,
        sample_rate=24000,
    ):
        self.mel_dim = mel_dim
        self.enc_dim = enc_dim
        self.enc_channels = enc_channels
        self.enc_kernel_sizes = enc_kernel_sizes
        self.enc_dilations = enc_dilations
        self.enc_attention_channels = enc_attention_channels
        self.enc_res2net_scale = enc_res2net_scale
        self.enc_se_channels = enc_se_channels
        self.sample_rate = sample_rate


class Qwen3TTSTalkerCodePredictorConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3TTSTalkerCodePredictorModel`].
    It is used to instantiate a Qwen3TTSTalkerCodePredictor model according to the specified arguments,
    defining the model architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 151936):
            Vocabulary size of the Qwen3TTSTalkerCodePredictor model.
            Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen3TTSTalkerCodePredictorModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 22016):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
        head_dim (`int`, *optional*, defaults to 128):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 32768):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        use_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        max_window_layers (`int`, *optional*, defaults to 28):
            The number of layers using full attention.
            The first `max_window_layers` layers will use full attention, while any
            additional layer afterwards will use SWA (Sliding Window Attention).
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    """

    model_type = "qwen3_tts_talker_code_predictor"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `Qwen3TTSTalkerCodePredictor`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        vocab_size=2048,
        hidden_size=1024,
        intermediate_size=3072,
        num_hidden_layers=5,
        num_attention_heads=16,
        num_key_value_heads=8,
        head_dim=128,
        hidden_act="silu",
        max_position_embeddings=32768,
        initializer_range=0.02,
        rms_norm_eps=0.000001,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000,
        rope_scaling=None,
        attention_bias=False,
        use_sliding_window=False,
        sliding_window=4096,
        max_window_layers=28,
        layer_types=None,
        attention_dropout=0,
        num_code_groups=32,
        **kwargs,
    ):
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if self.use_sliding_window else None
        self.max_window_layers = max_window_layers

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.head_dim = head_dim
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)

        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention"
                if self.sliding_window is not None and i >= self.max_window_layers
                else "full_attention"
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)
        self.num_code_groups = num_code_groups


class Qwen3TTSTalkerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3TTSTalkerModel`]. It is used to instantiate a
    Qwen3TTSTalker model according to the specified arguments, defining the model architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 151936):
            Vocabulary size of the Qwen3TTSTalker model.
            Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen3TTSTalkerModel`]
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 6144):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 4):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.

        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 32768):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        use_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
    """

    model_type = "qwen3_tts_talker"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `Qwen3TTSTalker`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    sub_configs = {"code_predictor_config": Qwen3TTSTalkerCodePredictorConfig}

    def __init__(
        self,
        code_predictor_config=None,
        vocab_size=3072,
        hidden_size=1024,
        intermediate_size=2048,
        num_hidden_layers=20,
        num_attention_heads=16,
        num_key_value_heads=2,
        hidden_act="silu",
        max_position_embeddings=32768,
        initializer_range=0.02,
        rms_norm_eps=0.000001,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000,
        rope_scaling=None,
        attention_bias=False,
        use_sliding_window=False,
        sliding_window=4096,
        attention_dropout=0,
        num_code_groups=32,
        text_hidden_size=2048,
        codec_eos_token_id=4198,
        codec_think_id=4202,
        codec_nothink_id=4203,
        codec_think_bos_id=4204,
        codec_think_eos_id=4205,
        codec_pad_id=4196,
        codec_bos_id=4197,
        spk_id=None,
        spk_is_dialect=None,
        codec_language_id=None,
        **kwargs,
    ):
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if use_sliding_window else None

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]

        if code_predictor_config is None:
            code_predictor_config = {}
            self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig()
            logger.info("code_predictor_config is None. Initializing code_predictor model with default values")
        elif isinstance(code_predictor_config, Qwen3TTSTalkerCodePredictorConfig):
            self.code_predictor_config = code_predictor_config
        else:
            self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig(**code_predictor_config)
        self.num_code_groups = num_code_groups
        self.text_hidden_size = text_hidden_size
        self.codec_eos_token_id = codec_eos_token_id
        self.codec_think_id = codec_think_id
        self.codec_language_id = codec_language_id
        self.codec_nothink_id = codec_nothink_id
        self.codec_think_bos_id = codec_think_bos_id
        self.codec_think_eos_id = codec_think_eos_id
        self.codec_pad_id = codec_pad_id
        self.codec_bos_id = codec_bos_id
        self.spk_id = spk_id
        self.spk_is_dialect = spk_is_dialect


class Qwen3TTSConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`Qwen3TTSForConditionalGeneration`].
    """

    model_type = "qwen3_tts"
    sub_configs = {
        "talker_config": Qwen3TTSTalkerConfig,
        "speaker_encoder_config": Qwen3TTSSpeakerEncoderConfig,
    }

    def __init__(
        self,
        talker_config=None,
        speaker_encoder_config=None,
        tokenizer_type=None,
        tts_model_size=None,
        tts_model_type=None,
        im_start_token_id=151644,
        im_end_token_id=151645,
        tts_pad_token_id=151671,
        tts_bos_token_id=151672,
        tts_eos_token_id=151673,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if talker_config is None:
            talker_config = {}
            logger.info("talker_config is None. Initializing talker model with default values")
        if speaker_encoder_config is None:
            speaker_encoder_config = {}
            logger.info("speaker_encoder_config is None. Initializing talker model with default values")

        self.talker_config = Qwen3TTSTalkerConfig(**talker_config)
        self.speaker_encoder_config = Qwen3TTSSpeakerEncoderConfig(**speaker_encoder_config)

        self.tokenizer_type = tokenizer_type
        self.tts_model_size = tts_model_size
        self.tts_model_type = tts_model_type

        self.im_start_token_id = im_start_token_id
        self.im_end_token_id = im_end_token_id
        self.tts_pad_token_id = tts_pad_token_id
        self.tts_bos_token_id = tts_bos_token_id
        self.tts_eos_token_id = tts_eos_token_id

        # TODO: remove these dummy values after
        self.image_token_id = 0  # dummy image token id
        self.video_token_id = 0  # dummy video token id
        self.vision_start_token_id = 0  # dummy vision start token id
        self.vision_config = PretrainedConfig()  # dummy vision config
        self.vision_config.spatial_merge_size = 1

    def get_text_config(self, **kwargs):
        # vLLM expects text config to expose hidden_size/num_attention_heads.
        # For Qwen3 TTS, the talker config is the text model config.
        config = self.talker_config
        # if hasattr(config, "rope_parameters"):
        #     delattr(config, "rope_parameters")
        return config


__all__ = ["Qwen3TTSConfig", "Qwen3TTSTalkerConfig", "Qwen3TTSSpeakerEncoderConfig"]
