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# Copyright 2025 The HuggingFace 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 ..auto import CONFIG_MAPPING, AutoConfig


class FastVlmConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FastVlmForConditionalGeneration`]. It is used to instantiate a
    FastVLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield the same configuration as the one of FastVLM-7B.

    e.g. [KamilaMila/FastVLM-7B](https://huggingface.co/KamilaMila/FastVLM-7B)

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

    Args:
        vision_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `TimmWrapperConfig` for `fastvit_mci3`):
            The config object or dictionary of the vision backbone.
        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
            The config object or dictionary of the text backbone.
        image_token_id (`int`, *optional*, defaults to 151646):
            The image token index to encode the image prompt.
        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The activation function used by the multimodal projector.
        vision_feature_select_strategy (`str`, *optional*, defaults to `"full"`):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Only "full" supported.
        vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -1):
            The index of the layer to select the vision feature. If multiple indices are provided,
            the vision feature of the corresponding indices will be concatenated to form the
            vision features. Only -1 supported.
        multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
            Whether to use bias in the multimodal projector.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings

    Example:

    ```python
    >>> from transformers import FastVlmForConditionalGeneration, FastVlmConfig

    >>> # Initializing a FastVLM-7B style configuration
    >>> configuration = FastVlmConfig()

    >>> # Initializing a model from the FastVLM-7B style configuration
    >>> model = FastVlmForConditionalGeneration(configuration)

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

    model_type = "fast_vlm"
    attribute_map = {
        "image_token_id": "image_token_index",
    }
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}

    def __init__(
        self,
        vision_config=None,
        text_config=None,
        image_token_id=151646,
        projector_hidden_act="gelu",
        vision_feature_select_strategy="full",
        vision_feature_layer=-1,
        multimodal_projector_bias=True,
        tie_word_embeddings=False,
        **kwargs,
    ):
        self.image_token_id = image_token_id
        self.projector_hidden_act = projector_hidden_act

        if vision_feature_select_strategy != "full":
            raise ValueError(
                f"Unexpected select feature strategy: {vision_feature_select_strategy}. Only 'full' is supported in FastVLM."
            )

        if vision_feature_layer != -1:
            raise ValueError(
                f"Unexpected vision feature layer: {vision_feature_layer}. Only -1 is supported in FastVLM."
            )

        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.vision_feature_layer = vision_feature_layer

        if isinstance(vision_config, dict):
            vision_config["model_type"] = vision_config.get("model_type", "timm_wrapper")
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        elif vision_config is None:
            vision_config = CONFIG_MAPPING["timm_wrapper"](
                architecture="fastvit_mci3",
                do_pooling=True,
                global_pool="avg",
                hidden_size=3072,
                initializer_range=0.02,
                model_args={"inference_mode": True},
            )

        self.vision_config = vision_config

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config.get("model_type", "qwen2")
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["qwen2"](
                hidden_size=3584,
                vocab_size=152128,
                intermediate_size=18944,
                num_attention_heads=28,
                num_key_value_heads=4,
                num_hidden_layers=28,
            )

        self.text_config = text_config
        self.multimodal_projector_bias = multimodal_projector_bias
        self.tie_word_embeddings = tie_word_embeddings

        # The default value is `False` but this config is used with many model types
        # Attr `tie_word_embeddings` was saved in text config for those models, so we
        # need an ugly workaround and forward-pass the attr from text config
        if not tie_word_embeddings and self.text_config.tie_word_embeddings:
            self.tie_word_embeddings = self.text_config.tie_word_embeddings

        super().__init__(**kwargs)


__all__ = ["FastVlmConfig"]
