BlipVisionConfig default image size is incorrect
Author: Hangsiin
Pull request: no

### System Info

I used the Huggingface version of the 'BLIP' model to do some image capturing fine-tuning.
The BLIP introduction link and BlipVisionConfig had a default image size of 224, 
so I scaled the image size of my training data to 224 and used it.
But when I got a lower score than I expected,
I was puzzled and realized that the default image size applied to the actual 'pretrained' model is 384, not 224

+ description of 'patch_size' is also incorrect. (in desc : patch_size = 32, actual : 16)
+ description of 'initializer_range' is also incorrect. (in desc : initializer_range=0.02, actual : 1e-10)

https://huggingface.co/docs/transformers/model_doc/blip#transformers.BlipVisionConfig

### Who can help?

_No response_

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

class BlipVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a
    BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
    configuration defaults will yield a similar configuration to that of the Blip-base
    [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        **image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.**
        patch_size (`int`, *optional*, defaults to 32):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        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 BlipVisionConfig, BlipVisionModel

    >>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
    >>> configuration = BlipVisionConfig()

    >>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
    >>> model = BlipVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "blip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        projection_dim=512,
        num_hidden_layers=12,
        num_attention_heads=12,
        **image_size=384,**
        patch_size=16,
        hidden_act="gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=1e-10,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.patch_size = patch_size
        self.image_size = image_size
        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act


### Expected behavior

actual image size has to be '224' or '384' .

Comments:
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Hi @Hangsiin, thanks for raising this issue! Indeed, it seems the configuration docstrings don't match the default values. Would you like to open a PR to fix these? This way you get the github contribution. 

@amyeroberts sounds good! I like to do it.