def _prepare_content(self, inputs: list[dict[str, str]], dataset: str | None = None) -> list[dict[str, str]]:
        """
        inputs list[dict[str, str]], each dict has keys: ['type', 'value']
        """
        content = []
        for s in inputs:
            if s['type'] == 'image':
                item = {'type': 'image', 'image': ensure_image_url(s['value'])}
                if dataset == 'OCRBench':
                    item['min_pixels'] = 10 * 10 * 28 * 28
                    warnings.warn(f"OCRBench dataset uses custom min_pixels={item['min_pixels']}")
                    if self.max_pixels is not None:
                        item['max_pixels'] = self.max_pixels
                else:
                    if self.min_pixels is not None:
                        item['min_pixels'] = self.min_pixels
                    if self.max_pixels is not None:
                        item['max_pixels'] = self.max_pixels
            elif s['type'] == 'text':
                item = {'type': 'text', 'text': s['value']}
            else:
                raise ValueError(f"Invalid message type: {s['type']}, {s}")
            content.append(item)
        return content

        inputs list[dict[str, str]], each dict has keys: ['type', 'value']
        
vlmeval/api/qwen_vl_api.py