import os
from typing import Any, Dict, List, Optional, Tuple, Union

from transformers import PretrainedConfig

from transformers.utils import logging

logger = logging.get_logger(__name__)

class Qwen2VLVisionConfig(PretrainedConfig):
    model_type = "qwen2_vit"

    def __init__(
        self,
        depth=32,
        embed_dim=1280,
        hidden_size=3584,
        hidden_act="quick_gelu",
        mlp_ratio=4,
        num_heads=16,
        in_channels=3,
        patch_size=14,
        spatial_merge_size=2,
        temporal_patch_size=2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.depth = depth
        self.embed_dim = embed_dim
        self.hidden_size = hidden_size
        self.hidden_act = hidden_act
        self.mlp_ratio = mlp_ratio
        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

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        if config_dict.get("model_type") == "qwen2_vl":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)