configuration_qwen3vlmoetext.py

# coding=utf-8
# Copyright 2024 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.
"""RWKV079Qwen3 model configuration"""

#Never gonna give you up
from typing import Optional
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 RWKV07BMoEConfig(PretrainedConfig):
#     r"""
#     This is the configuration class to store the configuration of a [`RWKV07BMoEModel`]. It is used to instantiate a
#     RWKV079Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
#     with the defaults will yield a similar configuration to that of
#     Qwen3-7B-beta [Qwen/Qwen3-7B-beta](https://huggingface.co/Qwen/Qwen3-7B-beta).

#     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 RWKV079Qwen3 model. Defines the number of different tokens that can be represented by the
#             `inputs_ids` passed when calling [`RWKV07BMoEModel`]
#         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 checkout [this
#             paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
#         lora_rank_decay (`int`, *optional*):
#             The rank of the lora used to generate decay.
#         lora_rank_iclr (`int`, *optional*):
#             The rank of the lora used to generate the in-context learning rate.
#         lora_rank_value_residual_mix (`int`, *optional*):
#             The rank of the lora used to generate the value residual mix amount.
#         lora_rank_value_gate (`int`, *optional*):
#             The rank of the lora used to generate the gate.
#         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
#         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 that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
#         attention_dropout (`float`, *optional*, defaults to 0.0):
#             The dropout ratio for the attention probabilities.

#     ```python
#     >>> from transformers import RWKV07BMoEModel, RWKV079Qwen3Config

#     >>> # Initializing a RWKV079Qwen3 style configuration
#     >>> configuration = RWKV079Qwen3Config()

#     >>> # Initializing a model from the RWKV079Qwen3-7B style configuration
#     >>> model = RWKV07BMoEModel(configuration)

#     >>> # Accessing the model configuration
#     >>> configuration = model.config
#     ```"""

#     model_type = "rwkv07b_moe"
#     keys_to_ignore_at_inference = ["past_key_values"]

#     # Default tensor parallel plan for base model `Qwen3Moe`
#     base_model_tp_plan = {
#         #NoPE-GQA
#         "layers.*.self_attn.q_proj": "colwise",
#         "layers.*.self_attn.k_proj": "colwise",
#         "layers.*.self_attn.v_proj": "colwise",
#         "layers.*.self_attn.o_proj": "rowwise",
#         #RoPE-RWKV
#         "layers.*.self_attn.receptance": "colwise",
#         "layers.*.self_attn.key": "colwise",
#         "layers.*.self_attn.value": "colwise",
#         "layers.*.self_attn.output": "rowwise",
#         "layers.*.mlp.experts.*.gate_proj": "colwise",
#         "layers.*.mlp.experts.*.up_proj": "colwise",
#         "layers.*.mlp.experts.*.down_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=151936,
#         hidden_size=4096,
#         intermediate_size=22016,
#         num_hidden_layers=32,
#         num_attention_heads=32,
#         num_key_value_heads=32,
#         lora_rank_tokenshift=None,
#         lora_rank_decay=None,
#         lora_rank_iclr=None,
#         lora_rank_value_residual_mix=None,
#         lora_rank_value_key_mix=None,
#         lora_rank_gate=None,
#         hidden_act="silu",
#         max_position_embeddings=32768,
#         initializer_range=0.02,
#         rms_norm_eps=1e-6,
#         use_cache=True,
#         tie_word_embeddings=False,
#         use_rope=True,
#         rope_theta=10000.0,
#         rope_scaling=None,
#         use_sliding_window=False,
#         sliding_window=4096,
#         max_window_layers=28,
#         first_attention_layer=9999,
#         first_post_attention_layer=9999,
#         attention_striping=1,
#         last_striping_layer=99999,
#         layer_types=None,
#         attention_dropout=0.0,
#         attention_bias=True,
#         attention_output_bias=False,
#         gate_rank_type=2,
#         balance_state=True,
#         groupnorm_att=False,
#         use_tokenshift=False,

#         decoder_sparse_step=1,
#         moe_intermediate_size=768,
#         num_experts_per_tok=8,
#         num_experts=128,
#         norm_topk_prob=False,
#         output_router_logits=False,
#         router_aux_loss_coef=0.001,
#         mlp_only_layers=None,
#         **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.max_window_layers = max_window_layers
#         self.first_attention_layer = first_attention_layer
#         self.first_post_attention_layer = first_post_attention_layer
#         self.attention_striping = attention_striping
#         self.last_striping_layer = last_striping_layer

#         # 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.lora_rank_tokenshift = lora_rank_tokenshift
#         self.lora_rank_decay = lora_rank_decay
#         self.lora_rank_iclr = lora_rank_iclr
#         self.lora_rank_value_residual_mix = lora_rank_value_residual_mix
#         self.lora_rank_gate = lora_rank_gate
#         self.hidden_act = hidden_act
#         self.initializer_range = initializer_range
#         self.rms_norm_eps = rms_norm_eps
#         self.use_cache = use_cache
#         self.use_rope = use_rope
#         self.rope_theta = rope_theta
#         self.rope_scaling = rope_scaling
#         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"]
#         self.rope_parameters = rope_scaling
#         #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.attention_bias = attention_bias
#         self.attention_output_bias = attention_output_bias
#         self.gate_rank_type = gate_rank_type
#         self.balance_state = balance_state
#         self.groupnorm_att = groupnorm_att
#         self.use_tokenshift = use_tokenshift

#         # MoE arguments
#         self.decoder_sparse_step = decoder_sparse_step
#         self.moe_intermediate_size = moe_intermediate_size
#         self.num_experts_per_tok = num_experts_per_tok
#         self.num_experts = num_experts
#         self.norm_topk_prob = norm_topk_prob
#         self.output_router_logits = output_router_logits
#         self.router_aux_loss_coef = router_aux_loss_coef
#         self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers

#         super().__init__(
#             tie_word_embeddings=tie_word_embeddings,
#             **kwargs,
#         )
from typing import Optional, TypedDict
class RopeParameters(TypedDict):
    """
    Args:
        rope_theta (`float`):
            The base period of the RoPE embeddings.
        rope_type (`str`, *optional*, defaults to "default"):
            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
    """

    rope_theta: float
    rope_type: Optional[str]
    factor: Optional[float]
    original_max_position_embeddings: Optional[int]
    attention_factor: Optional[float]
    beta_fast: Optional[float]
    beta_slow: Optional[float]
    short_factor: Optional[list[float]]
    long_factor: Optional[list[float]]
    low_freq_factor: Optional[float]
    high_freq_factor: Optional[float]

class Qwen3VLMoeTextConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3VLMoeTextModel`]. It is used to instantiate a
    Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct).

    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 Qwen2MoE model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen2MoeModel`]
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 5632):
            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 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 16):
            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 checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). 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 128000):
            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.
        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.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        decoder_sparse_step (`int`, *optional*, defaults to 1):
            The frequency of the MoE layer.
        moe_intermediate_size (`int`, *optional*, defaults to 1408):
            Intermediate size of the routed expert.
        num_experts_per_tok (`int`, *optional*, defaults to 4):
            Number of selected experts.
        num_experts (`int`, *optional*, defaults to 60):
            Number of routed experts.
        mlp_only_layers (`List[int]`, *optional*, defaults to `[]`):
            Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock
            The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
            If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        head_dim (`int`, *optional*):
            The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.

    ```python
    >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig

    >>> # Initializing a Qwen3VLMoe style configuration
    >>> configuration = Qwen3VLMoeConfig()

    >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
    >>> model = Qwen3VLMoeForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "qwen3_vl_moe_text"
    base_config_key = "text_config"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `Qwen3VLMoe`
    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: Optional[int] = 151936,
        hidden_size: Optional[int] = 2048,
        intermediate_size: Optional[int] = 5632,
        num_hidden_layers: Optional[int] = 24,
        num_attention_heads: Optional[int] = 16,
        num_key_value_heads: Optional[int] = 16,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 128000,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[float] = 1e-6,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        attention_bias: Optional[bool] = False,
        attention_dropout: Optional[float] = 0.0,
        decoder_sparse_step: Optional[int] = 1,
        moe_intermediate_size: Optional[int] = 1408,
        num_experts_per_tok: Optional[int] = 4,
        num_experts: Optional[int] = 60,
        mlp_only_layers: Optional[list[int]] = None,
        rope_parameters: Optional[RopeParameters] = None,
        head_dim: Optional[int] = None,
        **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.layer_types = None

        # 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.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.head_dim = head_dim or hidden_size // num_attention_heads
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters

        self.sliding_window = None
        self.max_window_layers = 0

        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)
            ]

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = kwargs.get("rope_theta", 5000000.0)
       # standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"})

        # MoE arguments
        self.decoder_sparse_step = decoder_sparse_step
        self.moe_intermediate_size = moe_intermediate_size
        self.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers

        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)


class Qwen3VLMoeVisionConfig(PretrainedConfig):
    model_type = "qwen3_vl_moe"
    base_config_key = "vision_config"

    def __init__(
        self,
        depth=27,
        hidden_size=1152,
        hidden_act="gelu_pytorch_tanh",
        intermediate_size=4304,
        num_heads=16,
        in_channels=3,
        patch_size=16,
        spatial_merge_size=2,
        temporal_patch_size=2,
        out_hidden_size=3584,
        num_position_embeddings=2304,
        deepstack_visual_indexes=[8, 16, 24],
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.depth = depth
        self.hidden_size = hidden_size
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.num_heads = num_heads
        self.in_channels = in_channels
        self.patch_size = patch_size
        self.spatial_merge_size = spatial_merge_size
        self.temporal_patch_size = temporal_patch_size
        self.out_hidden_size = out_hidden_size
        self.num_position_embeddings = num_position_embeddings
        self.initializer_range = initializer_range
        self.deepstack_visual_indexes = deepstack_visual_indexes


class RWKV07BMoEConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3VLMoeModel`]. It is used to instantiate a
    Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct).

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


    Args:
        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeTextConfig`):
            The config object or dictionary of the text backbone.
        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3VLMoeVisionConfig`):
            The config object or dictionary of the vision backbone.
        image_token_id (`int`, *optional*, defaults to 151655):
            The image token index to encode the image prompt.
        video_token_id (`int`, *optional*, defaults to 151656):
            The video token index to encode the image prompt.
        vision_start_token_id (`int`, *optional*, defaults to 151652):
            The start token index to encode the image prompt.
        vision_end_token_id (`int`, *optional*, defaults to 151653):
            The end token index to encode the image prompt.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie the word embeddings.

    ```python
    >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig

    >>> # Initializing a Qwen3-VL-MOE style configuration
    >>> configuration = Qwen3VLMoeConfig()

    >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
    >>> model = Qwen3VLMoeForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "rwkv07b_moe"
    sub_configs = {"vision_config": Qwen3VLMoeVisionConfig, "text_config": Qwen3VLMoeTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        image_token_id=151655,
        video_token_id=151656,
        vision_start_token_id=151652,
        vision_end_token_id=151653,
        tie_word_embeddings=False,
        **kwargs,
    ):
        if isinstance(vision_config, dict):
            self.vision_config = self.sub_configs["vision_config"](**vision_config)
        elif vision_config is None:
            self.vision_config = self.sub_configs["vision_config"]()

        if isinstance(text_config, dict):
            self.text_config = self.sub_configs["text_config"](**text_config)
        elif text_config is None:
            self.text_config = self.sub_configs["text_config"]()

        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.vision_start_token_id = vision_start_token_id
        self.vision_end_token_id = vision_end_token_id
        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)


__all__ = ["Qwen3VLMoeConfig", "Qwen3VLMoeTextConfig"]