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# coding=utf-8
# Copyright 2025 The ZhipuAI Inc. team and 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 ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation


class Glm4vMoeVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vMoeVisionModel`]. It is used to instantiate an Glm4vMoeVisionModel
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield
    a similar configuration to that of
    GLM-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking).

    Args:
        hidden_size (`int`, *optional*, defaults to 1536):
            Dimensionality of the encoder layers and the pooler layer.
        depth (`int`, *optional*, defaults to 24):
            Number of layers (depth) in the model.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to add a bias to the queries, keys and values.
        intermediate_size (`int`, *optional*, defaults to 13696):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"selu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            Dropout probability for attention weights.
        projection_dropout (`float`, *optional*, defaults to 0.0):
            Dropout probability for the projection layer.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        image_size (`int` or `list[int]`, *optional*, defaults to `[336, 336]`):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to `14`):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        out_hidden_size (`int`, *optional*, defaults to 4096):
            The output hidden size of the vision model.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        spatial_merge_size (`int`, *optional*, defaults to 2):
            The size used for merging spatial dimensions.
        temporal_patch_size (`int`, *optional*, defaults to 2):
            The size used for patches along the temporal dimension.
    Example:

    ```python
    >>> from transformers import Glm4vMoeVisionConfig, Glm4vMoeVisionModel

    >>> # Initializing a Glm4vMoeVisionConfig GLM-4.1V-9B style configuration
    >>> configuration = Glm4vMoeVisionConfig()

    >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration
    >>> model = Glm4vMoeVisionModel(configuration)

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

    model_type = "glm4v_moe"
    base_config_key = "vision_config"

    def __init__(
        self,
        depth=24,
        hidden_size=1536,
        hidden_act="silu",
        attention_bias=False,
        attention_dropout=0.0,
        num_heads=12,
        in_channels=3,
        image_size=336,
        patch_size=14,
        rms_norm_eps=1e-05,
        spatial_merge_size=2,
        temporal_patch_size=2,
        out_hidden_size=4096,
        intermediate_size=13696,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.depth = depth
        self.hidden_size = hidden_size
        self.hidden_act = hidden_act
        self.num_heads = num_heads
        self.in_channels = in_channels
        self.image_size = image_size
        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.intermediate_size = intermediate_size
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout


class Glm4vMoeTextConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vMoeModel`]. It is used to instantiate a
    GLM-4.5V model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of
    GLM-4.5V [THUDM/GLM-4.5V](https://huggingface.co/THUDM/GLM-4.5V).

    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 151424):
            Vocabulary size of the Glm4vMoe model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Glm4vMoeModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 10944):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 46):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 96):
            Number of attention heads for each attention layer in the Transformer encoder.
        partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            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 65536):
            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-05):
            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.
        attention_bias (`bool`, defaults to `True`, *optional*, defaults to `True`):
            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.
        moe_intermediate_size (`int`, *optional*, defaults to 1408):
            Intermediate size of the routed expert.
        num_experts_per_tok (`int`, *optional*, defaults to 8):
            number of experts per token.
        n_shared_experts (`int`, *optional*, defaults to 1):
            Number of shared experts.
        n_routed_experts (`int`, *optional*, defaults to 128):
            Number of routed experts.
        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor or routed experts.
        n_group (`int`, *optional*, defaults to 1):
            Number of groups for routed experts.
        topk_group (`int`, *optional*, defaults to 1):
            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
        first_k_dense_replace (`int`, *optional*, defaults to 1):
            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                                    \--k dense layers--/
        norm_topk_prob (`bool`, *optional*, defaults to `True`):
            Whether to normalize the topk probabilities.

    ```python
    >>> from transformers import Glm4vMoeTextModel, Glm4vMoeConfig

    >>> # Initializing a GLM-4.5V style configuration
    >>> configuration = Glm4vMoeConfig()

    >>> # Initializing a model from the GLM-4.5V style configuration
    >>> model = Glm4vMoeTextModel(configuration)

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

    model_type = "Glm4vMoe_text"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `Glm4vMoe`
    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_up_proj": "colwise_rep",  # we need to replicate here due to the `chunk` operation
        "layers.*.mlp.down_proj": "rowwise_rep",  # we need to replicate here due to the `chunk` operation
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    base_config_key = "text_config"

    def __init__(
        self,
        vocab_size=151424,
        hidden_size=4096,
        intermediate_size=10944,
        num_hidden_layers=46,
        num_attention_heads=96,
        partial_rotary_factor=0.5,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=65536,
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=True,
        attention_dropout=0.0,
        moe_intermediate_size=1408,
        num_experts_per_tok=8,
        n_shared_experts=1,
        n_routed_experts=128,
        routed_scaling_factor=1.0,
        n_group=1,
        topk_group=1,
        first_k_dense_replace=1,
        norm_topk_prob=True,
        **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.partial_rotary_factor = partial_rotary_factor

        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"]
        rope_config_validation(self, ignore_keys={"mrope_section"})

        # MoE arguments
        self.moe_intermediate_size = moe_intermediate_size
        self.num_experts_per_tok = num_experts_per_tok
        self.n_group = n_group
        self.topk_group = topk_group
        self.n_shared_experts = n_shared_experts
        self.n_routed_experts = n_routed_experts
        self.routed_scaling_factor = routed_scaling_factor
        self.first_k_dense_replace = first_k_dense_replace
        self.norm_topk_prob = norm_topk_prob


class Glm4vMoeConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vMoeModel`]. It is used to instantiate a
    GLM-4.5V model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of
    GLM-4.5V [zai_org/GLM-4.5V](https://huggingface.co/zai_org/GLM-4.5V).

    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 `Glm4vMoeTextConfig`):
            The config object or dictionary of the text backbone.
        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Glm4vMoeVisionConfig`):
            The config object or dictionary of the vision backbone.
        image_token_id (`int`, *optional*, defaults to 151363):
            The image token index to encode the image prompt.
        video_token_id (`int`, *optional*, defaults to 151364):
            The video token index to encode the image prompt.
        image_start_token_id (`int`, *optional*, defaults to 151339):
            The image start token index to encode the start of image.
        image_end_token_id (`int`, *optional*, defaults to 151340):
            The image end token index to encode the end of image.
        video_start_token_id (`int`, *optional*, defaults to 151341):
            The video start token index to encode the start of video.
        video_end_token_id (`int`, *optional*, defaults to 151342):
            The video end token index to encode the end of video.

    ```python
    >>> from transformers import Glm4vMoeForConditionalGeneration, Glm4vMoeConfig

    >>> # Initializing a GLM-4.5V style configuration
    >>> configuration = Glm4vMoeConfig()

    >>> # Initializing a model from the GLM-4.5V style configuration
    >>> model = Glm4vMoeForConditionalGeneration(configuration)

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

    model_type = "glm4v_moe"
    sub_configs = {"vision_config": Glm4vMoeVisionConfig, "text_config": Glm4vMoeTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        image_token_id=151363,
        video_token_id=151364,
        image_start_token_id=151339,
        image_end_token_id=151340,
        video_start_token_id=151341,
        video_end_token_id=151342,
        **kwargs,
    ):
        super().__init__(**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:
            # For BC use all kwargs to init `TextConfig`
            self.text_config = self.sub_configs["text_config"](**kwargs)

        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.video_start_token_id = video_start_token_id
        self.video_end_token_id = video_end_token_id
        self.image_start_token_id = image_start_token_id
        self.image_end_token_id = image_end_token_id


__all__ = ["Glm4vMoeConfig", "Glm4vMoeTextConfig"]
