Update for new transformers release (#6)

- remove mdoeling files, change mdoel id (6e24ab01218e19c6b3b15146011d44246c16e53d)


Co-authored-by: Anna Banaszak <[email protected]>

README.md

@@ -89,7 +89,7 @@ You can apply it using the dedicated [`.apply_chat_template()`](https://huggingf
 
 ## 🏃 How to run LFM2-VL
 
-You can run LFM2-VL with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.55 or more recent as follows:
+You can run LFM2-VL with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.57 or more recent as follows:
 
 ```bash
 pip install -U transformers pillow
@@ -106,10 +106,9 @@ model_id = "LiquidAI/LFM2-VL-450M"
 model = AutoModelForImageTextToText.from_pretrained(
     model_id,
     device_map="auto",
-    torch_dtype="bfloat16",
-    trust_remote_code=True
+    dtype="bfloat16"
 )
-processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_id)
 
 # Load image and create conversation
 url = "https://www.ilankelman.org/stopsigns/australia.jpg"

config.json

@@ -2,10 +2,6 @@
   "architectures": [
     "Lfm2VlForConditionalGeneration"
   ],
-  "auto_map": {
-    "AutoConfig": "modeling_lfm2_vl.Lfm2VlConfig",
-    "AutoModelForImageTextToText": "modeling_lfm2_vl.Lfm2VlForConditionalGeneration"
-  },
   "do_image_splitting": true,
   "downsample_factor": 2,
   "encoder_patch_size": 16,
@@ -16,7 +12,7 @@
   "max_tiles": 10,
   "min_image_tokens": 64,
   "min_tiles": 2,
-  "model_type": "lfm2-vl",
+  "model_type": "lfm2_vl",
   "projector_bias": true,
   "projector_hidden_act": "gelu",
   "projector_hidden_size": 2560,
@@ -94,6 +90,5 @@
     "patch_size": 16,
     "torch_dtype": "bfloat16",
     "vision_use_head": false
-  },
-  "vision_feature_layer": -1
+  }
 }

modeling_lfm2_vl.py

@@ -1,688 +0,0 @@
-"""PyTorch LFM2-VL model."""
-
-from dataclasses import dataclass
-
-import torch
-from torch import nn
-from transformers import AutoConfig, AutoModel
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache
-from transformers.configuration_utils import PretrainedConfig
-from transformers.generation import GenerationMixin
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
-from transformers.modeling_utils import PreTrainedModel
-from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
-from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
-from transformers.models.siglip2.modeling_siglip2 import Siglip2VisionModel
-from transformers.processing_utils import Unpack
-from transformers.utils import can_return_tuple, logging
-
-logger = logging.get_logger(__name__)
-
-
-class Lfm2VlConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Lfm2VlForConditionalGeneration`]. It is used to instantiate an
-    Lfm2Vl model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Lfm2-VL-1.6B.
-
-    e.g. [LiquidAI/LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B)
-
-    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 (`AutoConfig | dict`,  *optional*, defaults to `Siglip2ImageConfig`):
-            The config object or dictionary of the vision backbone.
-        text_config (`AutoConfig | dict`, *optional*, defaults to `Lfm2Config`):
-            The config object or dictionary of the text backbone.
-        image_token_id (`int`, *optional*, defaults to 396):
-            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.
-        projector_hidden_size (`int`, *optional*, defaults to 2056):
-            The hidden size of the multimodal projector.
-        projector_bias (`bool`, *optional*, defaults to `True`):
-            Whether to use bias in the multimodal projector.
-        downsample_factor (`int`, *optional*, defaults to 2):
-            The downsample_factor factor of the vision backbone.
-        vision_feature_layer (`int`, *optional*, defaults to -1):
-            The layer of the vision tower to use as features.
-        min_image_tokens (`int`, *optional*, defaults to 64):
-            The minimum number of image tokens for smart resize.
-        max_image_tokens (`int`, *optional*, defaults to 256):
-            The maximum number of image tokens for smart resize.
-        encoder_patch_size (`int`, *optional*, defaults to 16):
-            The patch size of the encoder.
-        max_num_patches (`int`, *optional*, defaults to 1024):
-            The maximum number of image tokens passed to the encoder per image or tile.
-        use_image_special_tokens (`bool`, *optional*, defaults to `True`):
-            Whether to use image special tokens.
-        do_image_splitting (`bool`, *optional*, defaults to `True`):
-            Whether to split large images into tiles.
-        min_tiles (`int`, *optional*, defaults to 2):
-            The minimum number of tiles to split the image into.
-        max_tiles (`int`, *optional*, defaults to 10):
-            The maximum number of tiles to split the image into.
-        tile_size (`int`, *optional*, defaults to 512):
-            The size of the tile to split the image into.
-        max_pixels_tolerance (`float`, *optional*, defaults to 2.0):
-            The maximum tolerance for the number of pixels in the image before splitting.
-        use_thumbnail (`bool`, *optional*, defaults to `True`):
-            Whether to append the thumbnail of the image when splitting.
-    """
-
-    model_type = "lfm2-vl"
-    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_index=396,
-        projector_hidden_act="gelu",
-        projector_hidden_size=2560,
-        projector_bias=True,
-        downsample_factor=2,
-        vision_feature_layer=-1,
-        min_image_tokens=64,
-        max_image_tokens=256,
-        encoder_patch_size=16,
-        max_num_patches=1024,
-        use_image_special_tokens=True,
-        do_image_splitting=True,
-        min_tiles=2,
-        max_tiles=10,
-        tile_size=512,
-        max_pixels_tolerance=2.0,
-        use_thumbnail=True,
-        torch_dtype=torch.bfloat16,
-        **kwargs,
-    ):
-        self.vision_config = vision_config
-        self.text_config = text_config
-        self.image_token_index = image_token_index
-        self.projector_hidden_act = projector_hidden_act
-        self.projector_hidden_size = projector_hidden_size
-        self.projector_bias = projector_bias
-        self.downsample_factor = downsample_factor
-        self.vision_feature_layer = vision_feature_layer
-        self.min_image_tokens = min_image_tokens
-        self.max_image_tokens = max_image_tokens
-        self.encoder_patch_size = encoder_patch_size
-        self.max_num_patches = max_num_patches
-        self.use_image_special_tokens = use_image_special_tokens
-        self.do_image_splitting = do_image_splitting
-        self.min_tiles = min_tiles
-        self.max_tiles = max_tiles
-        self.tile_size = tile_size
-        self.max_pixels_tolerance = max_pixels_tolerance
-        self.use_thumbnail = use_thumbnail
-        self.torch_dtype = torch_dtype
-        
-        if isinstance(vision_config, dict):
-            vision_config = Siglip2VisionConfig(**vision_config)
-        elif vision_config is None:
-            vision_config = Siglip2VisionConfig()
-        self.vision_config = vision_config
-
-        self.vision_config = vision_config
-
-        if isinstance(text_config, dict):
-            text_config = Lfm2Config(**text_config)
-        elif text_config is None:
-            text_config = Lfm2Config()
-
-        self.text_config = text_config
-
-        super().__init__(**kwargs)
-
-
-@dataclass
-class Lfm2VlModelOutputWithPast(BaseModelOutputWithPast):
-    r"""
-    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
-
-        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-        `past_key_values` input) to speed up sequential decoding.
-    image_hidden_states (`torch.FloatTensor`, *optional*):
-        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
-        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
-    """
-
-    image_hidden_states: torch.FloatTensor | None = None
-
-
-@dataclass
-class Lfm2VlCausalLMOutputWithPast(ModelOutput):
-    r"""
-    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-        Language modeling loss (for next-token prediction).
-    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
-
-        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-        `past_key_values` input) to speed up sequential decoding.
-    image_hidden_states (`torch.FloatTensor`, *optional*):
-        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
-        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
-    """
-
-    loss: torch.FloatTensor | None = None
-    logits: torch.FloatTensor | None = None
-    past_key_values: list[torch.FloatTensor] | None = None
-    hidden_states: tuple[torch.FloatTensor] | None = None
-    attentions: tuple[torch.FloatTensor] | None = None
-    image_hidden_states: torch.FloatTensor | None = None
-
-
-class Lfm2VlMultiModalProjector(nn.Module):
-    def __init__(self, config: Lfm2VlConfig):
-        super().__init__()
-        in_channels = config.vision_config.hidden_size * (config.downsample_factor**2)
-        self.layer_norm = nn.LayerNorm(in_channels)
-        self.linear_1 = nn.Linear(
-            in_channels,
-            config.projector_hidden_size,
-            bias=config.projector_bias,
-        )
-        self.act = ACT2FN[config.projector_hidden_act]
-        self.linear_2 = nn.Linear(
-            config.projector_hidden_size,
-            config.text_config.hidden_size,
-            bias=config.projector_bias,
-        )
-
-    def forward(self, image_features):
-        image_features = self.layer_norm(image_features)
-        hidden_states = self.linear_1(image_features)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.linear_2(hidden_states)
-        return hidden_states
-
-
-class PixelUnshuffleBlock(nn.Module):
-    def __init__(self, factor: int):
-        super().__init__()
-        self.factor = factor
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        n, w, h, c = x.size()
-        if w % self.factor != 0:
-            x = torch.concat(
-                [
-                    x,
-                    torch.zeros(
-                        (n, self.factor - (w % self.factor), h, c), dtype=x.dtype
-                    ).to(x.device),
-                ],
-                dim=1,
-            ).contiguous()
-            n, w, h, c = x.size()
-        x = x.contiguous()
-        if h % self.factor != 0:
-            x = torch.concat(
-                [
-                    x,
-                    torch.zeros(
-                        (n, w, self.factor - (h % self.factor), c), dtype=x.dtype
-                    ).to(x.device),
-                ],
-                dim=2,
-            ).contiguous()
-            n, w, h, c = x.size()
-        x = x.view(n, w, int(h / self.factor), int(c * self.factor))
-        x = x.permute(0, 2, 1, 3).contiguous()
-        x = x.view(
-            n, int(h / self.factor), int(w / self.factor), int(c * self.factor**2)
-        )
-        x = x.permute(0, 2, 1, 3).contiguous()
-        return x
-
-
-class Lfm2VlPreTrainedModel(PreTrainedModel):
-    config: Lfm2VlConfig
-    base_model_prefix = ""
-    supports_gradient_checkpointing = True
-    _skip_keys_device_placement = ["past_key_values"]
-
-    _supports_flash_attn = True
-    _supports_sdpa = True
-
-    _can_compile_fullgraph = False
-    _supports_flex_attn = True
-    _supports_attention_backend = True
-
-
-class Lfm2VlModel(Lfm2VlPreTrainedModel):
-    _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
-
-    def __init__(self, config: Lfm2VlConfig):
-        super().__init__(config)
-        self.vision_tower = Siglip2VisionModel(config.vision_config)
-
-        if config.vision_feature_layer != -1:
-            self.vision_tower.vision_model.encoder.layers = (
-                self.vision_tower.vision_model.encoder.layers[
-                    : config.vision_feature_layer + 1
-                ]
-            )
-        if config.downsample_factor > 1:
-            self.pixel_unshuffle = PixelUnshuffleBlock(config.downsample_factor)
-        else:
-            self.pixel_unshuffle = nn.Identity()
-
-        self.multi_modal_projector = Lfm2VlMultiModalProjector(config)
-        self.language_model = AutoModel.from_config(config.text_config)
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.language_model.get_input_embeddings()
-
-    def set_input_embeddings(self, value):
-        self.language_model.set_input_embeddings(value)
-
-    def set_decoder(self, decoder):
-        self.language_model = decoder
-
-    def get_decoder(self):
-        return self.language_model
-
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        spatial_shapes: torch.Tensor,
-        pixel_attention_mask: torch.Tensor,
-        **kwargs,
-    ) -> list[torch.Tensor]:
-        """
-        Obtains image last hidden states from the vision tower and apply multimodal projection.
-
-        Args:
-            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
-               The tensors corresponding to the input images.
-            spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`):
-                The spatial shapes of the input images.
-            pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`):
-                The pixel attention mask of the input images.
-        Returns:
-            image_features (`list[torch.Tensor]`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
-        """
-        image_outputs = self.vision_tower(
-            pixel_values=pixel_values,
-            spatial_shapes=spatial_shapes,
-            pixel_attention_mask=pixel_attention_mask,
-        ).last_hidden_state
-
-        img_feature_lengths = pixel_attention_mask.sum(dim=1)
-        image_features = []
-
-        for img_idx in range(image_outputs.size(0)):
-            feature = image_outputs[img_idx]
-            # unpad the image representation
-            feature = feature[: img_feature_lengths[img_idx], :].unsqueeze(0)
-
-            feature_org_h, feature_org_w = spatial_shapes[img_idx]
-            feature = feature.reshape(1, feature_org_h, feature_org_w, -1)
-            feature = self.pixel_unshuffle(feature)
-
-            # project the image representation
-            img_embedding = self.multi_modal_projector(feature)
-
-            # flatten here to handle variable length in naflex
-            img_embedding = img_embedding.reshape(-1, img_embedding.size(-1))
-            image_features.append(img_embedding)
-
-        return image_features
-
-    def get_placeholder_mask(
-        self,
-        input_ids: torch.LongTensor | None,
-        inputs_embeds: torch.FloatTensor,
-        image_features: torch.FloatTensor,
-    ):
-        """
-        Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
-        equal to the length of multimodal features. If the lengths are different, an error is raised.
-        """
-        if input_ids is None:
-            special_image_mask = inputs_embeds == self.get_input_embeddings()(
-                torch.tensor(
-                    self.config.image_token_id,
-                    dtype=torch.long,
-                    device=inputs_embeds.device,
-                )
-            )
-            special_image_mask = special_image_mask.all(-1)
-        else:
-            special_image_mask = input_ids == self.config.image_token_id
-        n_image_tokens = special_image_mask.sum()
-        special_image_mask = (
-            special_image_mask.unsqueeze(-1)
-            .expand_as(inputs_embeds)
-            .to(inputs_embeds.device)
-        )
-        n_image_features = image_features.shape[0]
-        if inputs_embeds[special_image_mask].numel() != image_features.numel():
-            raise ValueError(
-                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-            )
-        return special_image_mask
-
-    @can_return_tuple
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        pixel_values: torch.FloatTensor = None,
-        spatial_shapes: torch.Tensor = None,
-        pixel_attention_mask: torch.Tensor = None,
-        past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        cache_position: torch.LongTensor | None = None,
-        image_sizes: torch.Tensor = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple | Lfm2VlModelOutputWithPast:
-        """
-        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
-            The spatial shapes of the input images.
-        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
-            The pixel attention mask of the input images.
-        """
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You must specify exactly one of input_ids or inputs_embeds"
-            )
-
-        if inputs_embeds is None:
-            inputs_embeds = self.get_input_embeddings()(input_ids)
-
-        if pixel_values is not None:
-            image_features = self.get_image_features(
-                pixel_values=pixel_values,
-                spatial_shapes=spatial_shapes,
-                pixel_attention_mask=pixel_attention_mask,
-            )
-            image_features = torch.cat(image_features, dim=0).to(
-                inputs_embeds.device, inputs_embeds.dtype
-            )
-            special_image_mask = self.get_placeholder_mask(
-                input_ids=input_ids,
-                inputs_embeds=inputs_embeds,
-                image_features=image_features,
-            )
-            inputs_embeds = inputs_embeds.masked_scatter(
-                special_image_mask, image_features
-            )
-
-        outputs = self.language_model(
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        return Lfm2VlModelOutputWithPast(
-            last_hidden_state=outputs.last_hidden_state,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            image_hidden_states=image_features if pixel_values is not None else None,
-        )
-
-
-class Lfm2VlForConditionalGeneration(Lfm2VlPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: Lfm2VlConfig):
-        super().__init__(config)
-        self.model = Lfm2VlModel(config)
-        self.lm_head = nn.Linear(
-            config.text_config.hidden_size, config.text_config.vocab_size, bias=False
-        )
-        self.post_init()
-
-    def _supports_default_dynamic_cache(self):
-        return False
-
-    def get_input_embeddings(self):
-        return self.model.get_input_embeddings()
-
-    def set_input_embeddings(self, value):
-        self.model.set_input_embeddings(value)
-
-    def get_output_embeddings(self) -> nn.Module:
-        return self.lm_head
-
-    def set_decoder(self, decoder):
-        self.model.set_decoder(decoder)
-
-    def get_decoder(self):
-        return self.model.get_decoder()
-
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        spatial_shapes: torch.Tensor,
-        pixel_attention_mask: torch.Tensor,
-        **kwargs,
-    ):
-        return self.model.get_image_features(
-            pixel_values=pixel_values,
-            spatial_shapes=spatial_shapes,
-            pixel_attention_mask=pixel_attention_mask,
-            **kwargs,
-        )
-
-    @property
-    def language_model(self):
-        return self.model.language_model
-
-    @property
-    def vision_tower(self):
-        return self.model.vision_tower
-
-    @property
-    def multi_modal_projector(self):
-        return self.model.multi_modal_projector
-
-    @can_return_tuple
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        spatial_shapes: torch.Tensor = None,
-        pixel_attention_mask: torch.Tensor = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
-        labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        cache_position: torch.LongTensor | None = None,
-        logits_to_keep: int | torch.Tensor = 0,
-        image_sizes: torch.Tensor | None = None,
-        **kwargs,
-    ) -> tuple | Lfm2VlCausalLMOutputWithPast:
-        r"""
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`, *optional*):
-            The input image tensors.
-        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
-            The spatial shapes of the input images.
-        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
-            The pixel attention mask of the input images.
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Example:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, AutoModelForImageTextToText
-        >>> from transformers.image_utils import load_image
-
-        >>> model = AutoModelForImageTextToText.from_pretrained(
-        ...     "LiquidAI/LFM2-VL-1.6B",
-        ...     trust_remote_code=True
-        ... )
-        >>> processor = AutoProcessor.from_pretrained(
-        ...     "LiquidAI/LFM2-VL-1.6B",
-        ...     trust_remote_code=True
-        ... )
-
-        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-        >>> image = load_image(url)
-
-        >>> conversation = [
-        ...     {
-        ...         "role": "user",
-        ...         "content": [
-        ...             {"type": "image", "image": image},
-        ...             {"type": "text", "text": "What is in this image?"},
-        ...         ],
-        ...     },
-        ... ]
-
-        >>> inputs = processor.apply_chat_template(
-        ...     conversation,
-        ...     add_generation_prompt=True,
-        ...     tokenize=True,
-        ...     return_dict=True,
-        ...     return_tensors="pt"
-        ... )
-
-        >>> # Generate
-        >>> outputs = model.generate(**inputs, max_new_tokens=45)
-        >>> processor.batch_decode(outputs, skip_special_tokens=True)[0]
-        'This image depicts a vibrant street scene in what appears to be a Chinatown or similar cultural area. The focal point is a large red stop sign with white lettering, mounted on a pole.'
-        ```"""
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        outputs = self.model(
-            input_ids=input_ids,
-            pixel_values=pixel_values,
-            spatial_shapes=spatial_shapes,
-            pixel_attention_mask=pixel_attention_mask,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=True,
-            cache_position=cache_position,
-            image_sizes=image_sizes,
-            **kwargs,
-        )
-
-        hidden_states = outputs[0]
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = (
-            slice(-logits_to_keep, None)
-            if isinstance(logits_to_keep, int)
-            else logits_to_keep
-        )
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.text_config.vocab_size,
-                **kwargs,
-            )
-
-        return Lfm2VlCausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            image_hidden_states=outputs.image_hidden_states,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        inputs_embeds=None,
-        pixel_values=None,
-        attention_mask=None,
-        cache_position=None,
-        logits_to_keep=None,
-        **kwargs,
-    ):
-        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
-        model_inputs = super().prepare_inputs_for_generation(
-            input_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            logits_to_keep=logits_to_keep,
-            **kwargs,
-        )
-
-        if cache_position[0] == 0:
-            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
-            # Otherwise we need pixel values to be passed to model
-            model_inputs["pixel_values"] = pixel_values
-
-        return model_inputs
-
-
-__all__ = ["Lfm2VlForConditionalGeneration", "Lfm2VlModel", "Lfm2VlPreTrainedModel"]

processing_lfm2_vl.py

@@ -1,645 +0,0 @@
-import math
-from typing import Union
-
-from PIL import Image
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_utils import ImageInput, make_nested_list_of_images
-from transformers.image_transforms import to_pil_image
-from transformers.processing_utils import (
-    ImagesKwargs,
-    ProcessingKwargs,
-    ProcessorMixin,
-    Unpack,
-)
-from transformers.tokenization_utils_base import BatchEncoding, TextInput
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-# resize adapted from qwen2.5
-# https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
-def round_by_factor(number: float, factor: int) -> int:
-    """Returns the closest integer to 'number' that is divisible by 'factor'."""
-    return round(number / factor) * factor
-
-
-def ceil_by_factor(number: float, factor: int) -> int:
-    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
-    return math.ceil(number / factor) * factor
-
-
-def floor_by_factor(number: float, factor: int) -> int:
-    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
-    return math.floor(number / factor) * factor
-
-
-def find_closest_aspect_ratio(
-    aspect_ratio: float,
-    target_ratios: list[tuple[int, int]],
-    width: int,
-    height: int,
-    image_size: int,
-) -> tuple[int, int]:
-    """Find the closest aspect ratio from target_ratios to match the input aspect ratio.
-
-    Args:
-        aspect_ratio: The aspect ratio to match (width/height).
-        target_ratios: List of possible aspect ratios as tuples of (width, height) integers.
-        width: Original image width in pixels.
-        height: Original image height in pixels.
-        image_size: Base size for calculating target area.
-
-    Returns:
-        tuple[int, int]: The best matching ratio as (width, height) integers.
-    """
-    best_ratio_diff = float("inf")
-    best_ratio = (1, 1)
-    area = width * height
-
-    for ratio in target_ratios:
-        target_aspect_ratio = ratio[0] / ratio[1]
-        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-
-        # update best ratio if we found a closer match
-        if ratio_diff < best_ratio_diff:
-            best_ratio_diff = ratio_diff
-            best_ratio = ratio
-        # if equally close, prefer the ratio that better matches the original image area
-        elif ratio_diff == best_ratio_diff:
-            target_area = image_size * image_size * ratio[0] * ratio[1]
-            if area > 0.5 * target_area:
-                best_ratio = ratio
-
-    return best_ratio
-
-
-class Lfm2VlImagesKwargs(ImagesKwargs, total=False):
-    return_row_col_info: bool | None
-    max_image_size: dict[str, int] | None
-
-
-class Lfm2VlProcessorKwargs(ProcessingKwargs, total=False):
-    images_kwargs: Lfm2VlImagesKwargs
-
-    _defaults = {
-        "text_kwargs": {
-            "add_special_tokens": False,
-            "padding": False,
-            "is_split_into_words": False,
-        },
-        "images_kwargs": {
-            "do_resize": False,
-        },
-    }
-
-
-class Lfm2VlProcessor(ProcessorMixin):
-    r"""
-    Constructs a Lfm2Vl processor which wraps a Lfm2Tokenizer tokenizer and Lfm2Vl image processor into a single processor.
-
-    [`Lfm2VlProcessor`] offers all the functionalities of [`Siglip2ImageProcessor`] and [`Lfm2Tokenizer`].
-
-    Args:
-        image_processor (`Siglip2ImageProcessor`):
-            An instance of [`Siglip2ImageProcessor`]. The image processor is a required input.
-        tokenizer (`PreTrainedTokenizerBase`):
-            An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "Siglip2ImageProcessor"
-    tokenizer_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        image_processor,
-        tokenizer,
-        chat_template: str,
-        use_image_special_tokens: bool,
-        downsample_factor: int,
-        do_image_splitting: bool,
-        min_tiles: int,
-        max_tiles: int,
-        use_thumbnail: bool,
-        min_image_tokens: int,
-        max_image_tokens: int,
-        encoder_patch_size: int,
-        tile_size: int,
-        max_pixels_tolerance: float,
-        max_num_patches: int,
-        auto_map: dict[str, str] = None,
-        **kwargs,
-    ):
-        self.image_token = getattr(tokenizer, "image_token", "<image>")
-        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
-        self.use_image_special_tokens = use_image_special_tokens
-        self.image_start_token = getattr(
-            tokenizer, "image_start_token", "<|image_start|>"
-        )
-        self.image_end_token = getattr(tokenizer, "image_end_token", "<|image_end|>")
-        self.image_thumbnail_token = getattr(
-            tokenizer, "image_thumbnail", "<|img_thumbnail|>"
-        )
-        self.downsample_factor = downsample_factor
-        self.do_image_splitting = do_image_splitting
-        self.min_tiles = min_tiles
-        self.max_tiles = max_tiles
-        self.use_thumbnail = use_thumbnail
-        self.min_image_tokens = min_image_tokens
-        self.max_image_tokens = max_image_tokens
-        self.encoder_patch_size = encoder_patch_size
-        self.tile_size = tile_size
-        self.max_pixels_tolerance = max_pixels_tolerance
-        self.chat_template = chat_template
-        self.auto_map = auto_map
-        super().__init__(
-            image_processor, tokenizer, chat_template=chat_template, **kwargs
-        )
-        self.max_num_patches = max_num_patches
-        self.image_processor.max_num_patches = max_num_patches
-
-    def _high_res_preprocessor(
-        self,
-        image: Image.Image,
-        min_tiles,
-        max_tiles,
-        tile_size,
-    ) -> tuple[list[Image.Image], int, int, int]:
-        """Process a high resolution image into patches.
-        This method splits a high resolution image into a grid of smaller patches while trying to maintain
-        the original aspect ratio. It finds the optimal grid configuration within the specified tile constraints.
-        """
-        orig_width, orig_height = image.size
-        aspect_ratio = orig_width / orig_height
-
-        # generate valid patch grid configurations (width, height)
-        target_ratios = [
-            (w, h)
-            for n in range(min_tiles, max_tiles + 1)
-            for w in range(1, n + 1)
-            for h in range(1, n + 1)
-            if min_tiles <= w * h <= max_tiles
-        ]
-        target_ratios = sorted(set(target_ratios), key=lambda x: x[0] * x[1])
-
-        # default to 1x1 if no valid configurations found
-        if not target_ratios:
-            return [], 0, 0
-
-        # find best matching grid configuration
-        grid_width, grid_height = find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-
-        target_width = tile_size * grid_width
-        target_height = tile_size * grid_height
-        total_patches = grid_width * grid_height
-
-        # resize and split image into patches
-        resized_img = image.resize((target_width, target_height))
-        patches = []
-
-        for i in range(total_patches):
-            # calculate patch coordinates
-            col = i % grid_width
-            row = i // grid_width
-            box = (
-                col * tile_size,
-                row * tile_size,
-                (col + 1) * tile_size,
-                (row + 1) * tile_size,
-            )
-            patch = resized_img.crop(box)
-            patches.append(patch)
-
-        num_rows = grid_height
-        num_columns = grid_width
-
-        return patches, num_rows, num_columns
-
-    def _smart_resize(
-        self,
-        image: Image.Image,
-        downsample_factor: int,
-        min_image_tokens: int,
-        max_image_tokens: int,
-        encoder_patch_size: int,
-    ) -> Image.Image:
-        """
-        Rescales the image so that the following conditions are met:
-        1. Both dimensions (height and width) are divisible by 'encoder_patch_size' * 'downsample_factor'.
-           This ensures no padding is needed in the downsampling step.
-        2. The total number of pixels is within the range ['smart_resize_min_pixels', 'smart_resize_max_pixels'].
-        3. The aspect ratio of the image is maintained as closely as possible.
-        """
-        width, height = image.size
-
-        total_factor = encoder_patch_size * downsample_factor
-        smart_resize_min_pixels = (
-            min_image_tokens
-            * encoder_patch_size ** 2
-            * downsample_factor ** 2
-        )
-        smart_resize_max_pixels = (
-            max_image_tokens
-            * encoder_patch_size ** 2
-            * downsample_factor ** 2
-        )
-
-        h_bar = max(total_factor, round_by_factor(height, total_factor))
-        w_bar = max(total_factor, round_by_factor(width, total_factor))
-
-        if h_bar * w_bar > smart_resize_max_pixels:
-            beta = math.sqrt((height * width) / smart_resize_max_pixels)
-            h_bar = max(total_factor, floor_by_factor(height / beta, total_factor))
-            w_bar = max(total_factor, floor_by_factor(width / beta, total_factor))
-        elif h_bar * w_bar < smart_resize_min_pixels:
-            beta = math.sqrt(smart_resize_min_pixels / (height * width))
-            h_bar = ceil_by_factor(height * beta, total_factor)
-            w_bar = ceil_by_factor(width * beta, total_factor)
-
-        resized_img = image.resize((w_bar, h_bar))
-        return resized_img
-
-    def _get_tokens_num(self, image_height: int, image_width: int) -> int:
-        num_patches_height = image_height // self.encoder_patch_size
-        num_patches_width = image_width // self.encoder_patch_size
-
-        dwn_num_patches_height = math.ceil(num_patches_height / self.downsample_factor)
-        dwn_num_patches_width = math.ceil(num_patches_width / self.downsample_factor)
-
-        return dwn_num_patches_height * dwn_num_patches_width
-
-    def _is_img_too_large(
-        self,
-        image: Image.Image,
-        max_image_tokens: int,
-        encoder_patch_size: int,
-        max_pixels_tolerance: float,
-    ) -> bool:
-        """Check if the image is too large to be processed as one tile."""
-        width, height = image.size
-
-        h_bar = max(encoder_patch_size, round_by_factor(height, encoder_patch_size))
-        w_bar = max(encoder_patch_size, round_by_factor(width, encoder_patch_size))
-        return (
-            h_bar * w_bar
-            > max_image_tokens
-            * encoder_patch_size ** 2
-            * self.downsample_factor ** 2
-            * max_pixels_tolerance
-        )
-
-    def _resize_and_maybe_split(
-        self,
-        image: ImageInput,
-        downsample_factor: int,
-        min_tiles: int,
-        max_tiles: int,
-        use_thumbnail: bool,
-        min_image_tokens: int,
-        max_image_tokens: int,
-        encoder_patch_size: int,
-        tile_size: int,
-        max_pixels_tolerance: float,
-    ) -> tuple[list[Image.Image], int, int, int, int]:
-        """Apply smart resize and maybe split the image into tiles if image too large.
-        Return:
-            image_tiles: ImageInput
-            num_tokens_per_tile: int
-            num_rows: int
-            num_cols: int
-            num_thumbnail_tokens: int
-        """
-        image = to_pil_image(image)
-        do_image_splitting = not min_tiles == max_tiles == 1
-        if (
-            self._is_img_too_large(
-                image,
-                max_image_tokens,
-                encoder_patch_size,
-                max_pixels_tolerance,
-            )
-            and do_image_splitting
-        ):
-            image_tiles, num_rows, num_cols = self._high_res_preprocessor(
-                image, min_tiles, max_tiles, tile_size
-            )
-            if len(image_tiles) > 1:
-                num_thumbnail_tokens = 0
-                if use_thumbnail:
-                    thumbnail_image = self._smart_resize(
-                        image,
-                        downsample_factor,
-                        min_image_tokens,
-                        max_image_tokens,
-                        encoder_patch_size,
-                    )
-                    num_thumbnail_tokens = self._get_tokens_num(
-                        thumbnail_image.height, thumbnail_image.width
-                    )
-                    image_tiles.append(thumbnail_image)
-
-                return (
-                    image_tiles,
-                    self._get_tokens_num(tile_size, tile_size),
-                    num_rows,
-                    num_cols,
-                    num_thumbnail_tokens,
-                )
-        else:
-            image = self._smart_resize(
-                image,
-                downsample_factor,
-                min_image_tokens,
-                max_image_tokens,
-                encoder_patch_size,
-            )
-            return [image], self._get_tokens_num(image.height, image.width), 1, 1, 0
-
-    def process_vision(
-        self,
-        text: list[str],
-        images: list[list[ImageInput]],
-        use_image_special_tokens: bool,
-        downsample_factor: int,
-        min_tiles: int,
-        max_tiles: int,
-        use_thumbnail: bool,
-        min_image_tokens: int,
-        max_image_tokens: int,
-        encoder_patch_size: int,
-        tile_size: int,
-        max_pixels_tolerance: float,
-        output_kwargs: dict,
-    ):
-        if text is not None:
-            n_images_in_text = [sample.count(self.image_token) for sample in text]
-
-        n_images_in_images = [len(sublist) for sublist in images]
-
-        if n_images_in_images != n_images_in_text:
-            raise ValueError(
-                f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
-            )
-
-        prompt_strings = []
-        image_inputs = []
-
-        for sample_text, sample_images in zip(text, images, strict=False):
-            split_sample = sample_text.split(self.image_token)
-            sample_tiles = []
-            sample_text_with_image_tokens = ""
-            for i, image in enumerate(sample_images):
-                sample_text_with_image_tokens += split_sample[i]
-                if use_image_special_tokens:
-                    sample_text_with_image_tokens += self.image_start_token
-                (
-                    image_tiles,
-                    num_tokens_per_tile,
-                    num_rows,
-                    num_cols,
-                    num_thumbnail_tokens,
-                ) = self._resize_and_maybe_split(
-                    image,
-                    downsample_factor,
-                    min_tiles,
-                    max_tiles,
-                    use_thumbnail,
-                    min_image_tokens,
-                    max_image_tokens,
-                    encoder_patch_size,
-                    tile_size,
-                    max_pixels_tolerance,
-                )
-
-                if len(image_tiles) > 1:
-                    for row in range(num_rows):
-                        for col in range(num_cols):
-                            if use_image_special_tokens:
-                                sample_text_with_image_tokens += (
-                                    f"<|img_row_{row + 1}_col_{col + 1}|>"
-                                )
-                            sample_text_with_image_tokens += (
-                                self.image_token * num_tokens_per_tile
-                            )
-
-                    if num_thumbnail_tokens > 0:
-                        if use_image_special_tokens:
-                            sample_text_with_image_tokens += self.image_thumbnail_token
-                        sample_text_with_image_tokens += (
-                            self.image_token * num_thumbnail_tokens
-                        )
-                else:
-                    sample_text_with_image_tokens += (
-                        self.image_token * num_tokens_per_tile
-                    )
-
-                if use_image_special_tokens:
-                    sample_text_with_image_tokens += self.image_end_token
-
-                sample_text_with_image_tokens += split_sample[i + 1]
-                sample_tiles.extend(image_tiles)
-
-            prompt_strings.append(sample_text_with_image_tokens)
-            image_inputs.append(sample_tiles)
-
-        image_inputs = self.image_processor(
-            image_inputs, **output_kwargs["images_kwargs"]
-        )
-
-        if text is None:
-            return None, image_inputs
-
-        return prompt_strings, image_inputs
-
-    def __call__(
-        self,
-        images: ImageInput | list[ImageInput] | list[list[ImageInput]] = None,
-        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
-        use_image_special_tokens: bool | None = None,
-        downsample_factor: int | None = None,
-        min_image_tokens: int | None = None,
-        max_image_tokens: int | None = None,
-        do_image_splitting: bool | None = None,
-        min_tiles: int | None = None,
-        max_tiles: int | None = None,
-        use_thumbnail: bool | None = None,
-        encoder_patch_size: int | None = None,
-        tile_size: int | None = None,
-        max_pixels_tolerance: float | None = None,
-        **kwargs: Unpack[Lfm2VlProcessorKwargs],
-    ) -> BatchEncoding:
-        """
-        Processes the input prompts and returns a BatchFeature.
-
-        Example:
-
-        ```python
-        >>> import requests
-        >>> from transformers import AutoProcessor
-        >>> from transformers.image_utils import load_image
-        >>> processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-1.6B", trust_remote_code=True)
-
-        >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
-        >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
-
-        >>> image1, image2 = load_image(url1), load_image(url2)
-        >>> images = [image1, image2]
-
-        >>> conversation = [
-        ...     {
-        ...         "role": "user",
-        ...         "content": [
-        ...             {"type": "image", "url": image1},
-        ...             {"type": "image", "url": image2},
-        ...             {"type": "text", "text": "Compare the two images."},
-        ...         ],
-        ...     },
-        ... ]
-        >>> chat_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
-        >>> outputs = processor(images=images, text=chat_inputs, return_tensors="pt")
-        >>> input_ids = outputs.input_ids
-        >>> input_tokens = processor.tokenizer.batch_decode(input_ids)
-        >>> print(input_tokens)
-        '['user\nCompare the two images.\nassistant\n']'
-        ```
-
-        Args:
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`, *optional*):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. If is of type `list[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
-            text (`TextInput`, *optional*):
-                The sequence or batch of sequences to be encoded.
-                Wherever an image token, `<image>` is encountered it is expanded to a proper sequence of image tokens.
-            return_tensors (`str | TensorType`, *optional*):
-                If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
-                information.
-        """
-        use_image_special_tokens = (
-            use_image_special_tokens
-            if use_image_special_tokens is not None
-            else self.use_image_special_tokens
-        )
-        downsample_factor = (
-            downsample_factor
-            if downsample_factor is not None
-            else self.downsample_factor
-        )
-        do_image_splitting = (
-            do_image_splitting
-            if do_image_splitting is not None
-            else self.do_image_splitting
-        )
-
-        min_tiles = min_tiles if min_tiles is not None else self.min_tiles
-        max_tiles = max_tiles if max_tiles is not None else self.max_tiles
-
-        if not do_image_splitting:
-            min_tiles = 1
-            max_tiles = 1
-            logger.debug(
-                "Image splitting is disabled, setting min_tiles and max_tiles to 1. Set do_image_splitting=True to enable splitting."
-            )
-
-        if do_image_splitting and min_tiles > max_tiles:
-            raise ValueError("min_tiles must be less than or equal to max_tiles")
-
-        use_thumbnail = (
-            use_thumbnail if use_thumbnail is not None else self.use_thumbnail
-        )
-        min_image_tokens = (
-            min_image_tokens if min_image_tokens is not None else self.min_image_tokens
-        )
-        max_image_tokens = (
-            max_image_tokens if max_image_tokens is not None else self.max_image_tokens
-        )
-        encoder_patch_size = (
-            encoder_patch_size
-            if encoder_patch_size is not None
-            else self.encoder_patch_size
-        )
-        tile_size = tile_size if tile_size is not None else self.tile_size
-        max_pixels_tolerance = (
-            max_pixels_tolerance
-            if max_pixels_tolerance is not None
-            else self.max_pixels_tolerance
-        )
-
-        if text is None and images is None:
-            raise ValueError("You must provide one of `text` or `images`.")
-
-        output_kwargs = self._merge_kwargs(
-            Lfm2VlProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if text is not None:
-            if isinstance(text, str):
-                text = [text]
-            elif not isinstance(text, list) and not isinstance(text[0], str):
-                raise ValueError(
-                    "Invalid input text. Please provide a string, or a list of strings"
-                )
-            n_images_in_text = sum([sample.count(self.image_token) for sample in text])
-            if n_images_in_text > 0 and (images is None):
-                raise ValueError(
-                    f"We detected {n_images_in_text} tokens in the text but no images were passed"
-                )
-
-        inputs = {}
-
-        if images is not None:
-            images = make_nested_list_of_images(images)
-            text, vision_inputs = self.process_vision(
-                text,
-                images,
-                use_image_special_tokens,
-                downsample_factor,
-                min_tiles,
-                max_tiles,
-                use_thumbnail,
-                min_image_tokens,
-                max_image_tokens,
-                encoder_patch_size,
-                tile_size,
-                max_pixels_tolerance,
-                output_kwargs,
-            )
-            inputs.update(vision_inputs)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-
-        if text is not None:
-            text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-            self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
-            inputs.update(text_inputs)
-
-        return BatchFeature(inputs, tensor_type=return_tensors)
-
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.batch_decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        batched_decode_output = self.tokenizer.batch_decode(*args, **kwargs)
-        return batched_decode_output
-
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.decode`]. Please refer to
-        the docstring of this method for more information.
-        """
-        decode_output = self.tokenizer.decode(*args, **kwargs)
-        return decode_output
-
-    @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(image_processor_input_names + tokenizer_input_names))
-
-
-__all__ = ["Lfm2VlProcessor"]