T^(hCdddlmZmZmZmZddlZddlZddlmZddlm Z ddl m Z ddl m Z mZdd lmZdd lmZmZmZmZmZe j.eZGd d eZGd deZGddeZGdde ZGddeZGddeZGddeZ GddeZ!gdZ"y))ListOptionalTupleUnionN)nn) DynamicCache)logging)Idefics3ConfigIdefics3VisionConfig)Idefics3ImageProcessor)Idefics3BaseModelOutputWithPast Idefics3ForConditionalGeneration Idefics3ModelIdefics3PreTrainedModelIdefics3VisionTransformerceZdZdZdZy)SmolVLMVisionConfiga This is the configuration class to store the configuration of a [`SmolVLMVisionModel`]. It is used to instantiate a SmolVLM vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) used in SmolVLM [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct). 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 1152): 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 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. 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_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): 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.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer >>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig >>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration >>> configuration = SmolVLMVisionConfig() >>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration >>> model = SmolVLMVisionTransformer(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```smolvlm_visionN__name__ __module__ __qualname____doc__ model_typei/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/smolvlm/modular_smolvlm.pyrr(s1 f"Jrrc eZdZy)SmolVLMPreTrainedModelNrrrrrrr!r!`rr!c eZdZy)SmolVLMVisionTransformerNr"rrrr%r%dr#rr%ceZdZdZdZy) SmolVLMConfiga This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a SmolVLM 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 model of the SmolVLM [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should cache the key/value pairs of the attention mechanism. Only relevant if `config.is_decoder=True`. image_token_id (`int`, *optional*, defaults to 128257): The id of the "image" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to tie the word embeddings with the token embeddings. vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`): Custom vision config or dict for the vision tower text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`): Custom text config or dict for the text model scale_factor (`int`, *optional*, defaults to 2): The scale factor for the image encoder. pad_token_id (`int`, *optional*, defaults to 128002): The id of the padding token. Example: ```python >>> from transformers import SmolVLMModel, SmolVLMConfig >>> # Initializing configuration >>> configuration = SmolVLMConfig() >>> # Initializing a model from the configuration >>> model = SmolVLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```smolvlmNrrrrr'r'hs# JJrr'c eZdZy)SmolVLMImageProcessorNr"rrrr*r*r#rr*c eZdZy)SmolVLMBaseModelOutputWithPastNr"rrrr,r,r#rr,ceZdZdZdej dej dej fdZ ddeej deej deej d ee ejdeejd eejd eejdeejd ee d ee dee dee deej de eeffdZy) SmolVLMModelz A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger in forward. Instead, we override inputs_merger here with custom logic. input_ids inputs_embedsimage_hidden_statescH|j\}}}||jk(}|jd}tj||zdk(s t d||z}tj jj|jddd} | dd} |jd} | dz |z} | dz |z} | jd| z}tj|}|||| |ddf||<tj|jd||}|S)NdimrzCAt least one sample has tokens not divisible by patch_size.)r3r)value) shapeimage_token_idsumtorchall ValueErrorr functionalpadcumsum unsqueeze zeros_likewhere)selfr/r0r1_ patch_size image_masknum_image_tokensblocks_per_sampleoffsets block_offsetrow_cum chunk_idx local_idx block_idx image_embeds merged_embedss r inputs_mergerzSmolVLMModel.inputs_mergers:/44:q$"5"55 %>>a>0yy)J6!;<bc c, :((%%))*;*B*Bq*B*I6YZ)[s| ###+q[Z/ q[J.  **1- 9 '' 6 #6y7LiXbNcef7f#g Z  J$8$8$|j }|||jA|||}|j ||||| | | | |  }| stCdg||DStE|j:|jF|jH|jJ|Scc}w)NzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fz5You have to specify either input_ids or inputs_embedsrzWWhen first calling the model, if input_embeds are passed, input_ids should not be None.zMYou cannot specify both pixel_values and image_hidden_states at the same timer r3g)r7r4T)rr r)sizedtypedevice) dimensionrastep)r7r_)rVpatch_attention_mask)rbrc)r/r0r1) r0rSrTrUrXrYrZr[r\c3&K|] }|| yw)Nr).0vs r z'SmolVLMModel.forward..1sUqq}Us)last_hidden_staterU hidden_states attentionsr1)&configrYrZrXuse_return_dicttraining text_modelgradient_checkpointinglogger warning_oncer8r=r get_seq_lengthget_input_embeddingstorcviewnumelr:any contiguousr;onesbool vision_configrFunfold vision_modelrk connectorrbrRtupler,rUrlrm)rDr/rSrTrUr0rVrWr1rXrYrZr[r\ batch_size seq_lengthrEpast_seen_tokens num_images num_channelsheightwidthnb_values_per_imagereal_images_indsirFpatches_subgridrfoutputss rforwardzSmolVLMModel.forwards 2C1N-TXT_T_TqTq$8$D $++JjJj "+!6IDKK">"@  , 388\8JNaa '(&* #'(89DDFL$+',zz9BCA,,,Q/C**'..($(A';'@'@+(.B.H.H.L($(<@ $D>@ 'tn@ d^@ !!1!12@  u44 5@ rr.c,eZdZdZfdZfdZxZS)SmolVLMForConditionalGenerationzy A subclass of Idefics3ForConditionalGeneration that uses SmolVLMModel instead of the default Idefics3Model. ct||t||_t j |j j|j jd|_ |jy)NF)bias) super__init__r.modelrLinear text_config hidden_size vocab_sizelm_head post_init)rDrn __class__s rrz(SmolVLMForConditionalGeneration.__init__BsS  !&) yy!3!3!?!?ASASA^A^ejk  rc $t|di|y)a Args: 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 `model.image_token_id` (where `model` is your instance of `SmolVLMForConditionalGeneration`). Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> import requests >>> import torch >>> from PIL import Image >>> from io import BytesIO >>> from transformers import AutoProcessor, AutoModelForImageTextToText >>> from transformers.image_utils import load_image >>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible >>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg") >>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg") >>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg") >>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") >>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") >>> # Create inputs >>> messages = [ ... { ... "role": "user", ... "content": [ ... {"type": "video", "path": path/to/video}, ... {"type": "text", "text": "What is happening in this video?"}, ... ] ... } ... ] >>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True) >>> # Generate >>> generated_ids = model.generate(**inputs, max_new_tokens=256) >>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_texts) ```Nr)rr)rD super_kwargsrs rrz'SmolVLMForConditionalGeneration.forwardHs` ','r)rrrrrr __classcell__)rs@rrr<s  0(0(rr)rr'r*rr!r.r%)#typingrrrrr;torch.utils.checkpointr cache_utilsr utilsr idefics3.configuration_idefics3r r "idefics3.image_processing_idefics3ridefics3.modeling_idefics3rrrrr get_loggerrrsrr!r%r'r*r,r.r__all__rrrrs 0/ 'SG   H %5 .5 p 4  8 ' N' T 2  %D _ =_ D<(&F<(~ r