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+# coding=utf-8
+# 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
+
+
+__all__ = ["Qwen3TTSConfig", "Qwen3TTSTalkerConfig", "Qwen3TTSSpeakerEncoderConfig"]