[Refactor] Refactor the Generate Interface

[kennymckormick]

After refactoring, all VLMs (no matter API ones or OpenSource ones), will follow the following input format:

1. A list of dictionary: A multi-modal message is represented by a list of dictionary, each dictionary has two keys: type and value:

   1. type: We currently support two types, choices are ["image", "text"].
   2. value: When type=='text' , the value is the text message (a single string); when type=='image', the value can be the local path of an image file, or the image URL.

   Example:

   IMAGE_PTH = 'assets/apple.jpg'
   IMAGE_URL = 'https://raw.githubusercontent.com/open-compass/VLMEvalKit/main/assets/apple.jpg'
   msg1 = [
       dict(type='image', value=IMAGE_PTH),
       dict(type='text', value='What is in this image?')
   ]
   msg2 = [
       dict(type='image', value=IMAGE_URL),
       dict(type='image', value=IMAGE_URL),
       dict(type='text', value='How many apples are there in these images?')
   ]
   response = model.generate(msg1)

2. For convenience sake, we also support to take a list of string as inputs. In that case, we will check if a string is an image path or image URL and automatically convert it to the list[dict] format.

   Example:

   IMAGE_PTH = 'assets/apple.jpg'
   IMAGE_URL = 'https://raw.githubusercontent.com/open-compass/VLMEvalKit/main/assets/apple.jpg'
   msg1 = [IMAGE_PTH, 'What is in this image?']
   msg2 = [IMAGE_URL, IMAGE_URL,  'How many apples are there in these images?']
   response = model.generate(msg1)

3. Though the input format accepts multiple images in a multi-modal message, sometimes the VLM does not support that feature (for example, some VLMs only accept a single image). In such case, only the first image will be processed by the VLM, and there will be a warning message.

4. Another advantage of the new format is, it can be compatible with potential future updates. For examples, in the future, new types can be added to be compatible with audio or signals from custom sensors.