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# 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 RopeParameters


class Glm4vVisionConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vVisionModel`]. It is used to instantiate an Glm4vVisionModel
    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:
            depth (`int`, *optional*, defaults to 24):
                Number of layers (depth) in the model.
            hidden_size (`int`, *optional*, defaults to 1536):
                Dimensionality of the encoder layers and the pooler layer.
            hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
                The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
                `"relu"`, `"selu"` and `"gelu_new"` are supported.
            attention_bias (`bool`, *optional*, defaults to `False`):
                Whether to add a bias to the queries, keys and values.
            attention_dropout (`float`, *optional*, defaults to 0.0):
                Dropout probability for attention weights.
            num_heads (`<fill_type>`, *optional*, defaults to 12): <fill_docstring>
            in_channels (`<fill_type>`, *optional*, defaults to 3): <fill_docstring>
            image_size (`int` or `list[int]`, *optional*, defaults to 336):
                The size (resolution) of each image.
            patch_size (`int`, *optional*, defaults to 14):
                The size (resolution) of each patch.
            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.
            out_hidden_size (`int`, *optional*, defaults to 4096):
                The output hidden size of the vision model.
            intermediate_size (`int`, *optional*, defaults to 13696):
                Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
            initializer_range (`float`, *optional*, defaults to 0.02):
                The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
    Example:

    ```python
    >>> from transformers import Glm4vVisionConfig, Glm4vVisionModel

    >>> # Initializing a Glm4vVisionConfig GLM-4.1V-9B style configuration
    >>> configuration = Glm4vVisionConfig()

    >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration
    >>> model = Glm4vVisionModel(configuration)

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

    model_type = "glm4v_vision"
    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 Glm4vTextConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a
    GLM-4.1V 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).

    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 151552):
            Vocabulary size of the Glm4v model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Glm4vModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 13696):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 40):
            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 2):
            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://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-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`.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary 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`.
        pad_token_id (`int`, *optional*):
            The id of the padding token.


    ```python
    >>> from transformers import Glm4vTextModel, Glm4vConfig

    >>> # Initializing a GLM-4.1V style configuration
    >>> configuration = Glm4vConfig()

    >>> # Initializing a model from the GLM-4.1V style configuration
    >>> model = Glm4vTextModel(configuration)

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

    model_type = "glm4v_text"
    base_config_key = "text_config"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `Glm4v`
    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_gather_output",  # we need to replicate here due to the `chunk` operation
        "layers.*.mlp.down_proj": "rowwise_split_input",  # input is replicated 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"]),
    }

    def __init__(
        self,
        vocab_size: int | None = 151552,
        hidden_size: int | None = 4096,
        intermediate_size: int | None = 13696,
        num_hidden_layers: int | None = 40,
        num_attention_heads: int | None = 32,
        num_key_value_heads: int | None = 2,
        hidden_act: str | None = "silu",
        max_position_embeddings: int | None = 32768,
        initializer_range: float | None = 0.02,
        rms_norm_eps: int | None = 1e-05,
        use_cache: bool | None = True,
        attention_dropout: float | None = 0.0,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        pad_token_id: int | None = 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

        # 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_dropout = attention_dropout
        self.rope_parameters = rope_parameters
        self.pad_token_id = pad_token_id

        super().__init__(ignore_keys_at_rope_validation={"mrope_section"}, **kwargs)


class Glm4vConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a
    GLM-4.1V 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).

    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 `Glm4vTextConfig`):
            The config object or dictionary of the text backbone.
        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Glm4vVisionConfig`):
            The config object or dictionary of the vision backbone.
        image_token_id (`int`, *optional*, defaults to 151343):
            The image token index to encode the image prompt.
        video_token_id (`int`, *optional*, defaults to 151344):
            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.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.

    ```python
    >>> from transformers import Glm4vForConditionalGeneration, Glm4vConfig

    >>> # Initializing a GLM-4.1V style configuration
    >>> configuration = Glm4vConfig()

    >>> # Initializing a model from the GLM-4.1V style configuration
    >>> model = Glm4vForConditionalGeneration(configuration)

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

    model_type = "glm4v"
    sub_configs = {"vision_config": Glm4vVisionConfig, "text_config": Glm4vTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        image_token_id=151343,
        video_token_id=151344,
        image_start_token_id=151339,
        image_end_token_id=151340,
        video_start_token_id=151341,
        video_end_token_id=151342,
        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"](**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
        self.tie_word_embeddings = tie_word_embeddings

        super().__init__(**kwargs)


__all__ = ["Glm4vConfig", "Glm4vTextConfig", "Glm4vVisionConfig"]