transformers/main/ko/model_doc/blip.md

# BLIP[[blip]]

## 개요[[overview]]

BLIP 모델은 Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi의 [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://huggingface.co/papers/2201.12086) 논문에서 제안되었습니다.

BLIP은 여러 멀티모달 작업을 수행할 수 있는 모델입니다:

- 시각 질문 응답 (Visual Question Answering, VQA)
- 이미지-텍스트 검색 (이미지-텍스트 매칭)
- 이미지 캡셔닝

논문의 초록은 다음과 같습니다:

*비전-언어 사전 학습(Vision-Language Pre-training, VLP)은 다양한 비전-언어 작업의 성능을 크게 향상시켰습니다. 하지만, 대부분의 기존 사전 학습 모델들은 이해 기반 작업이나 생성 기반 작업 중 하나에서만 뛰어난 성능을 발휘합니다. 또한 성능 향상은 주로 웹에서 수집한 노이즈가 많은 이미지-텍스트 쌍으로 데이터셋의 규모를 키우는 방식으로 이루어졌는데, 이는 최적의 지도 학습 방식이라고 보기 어렵습니다. 본 논문에서는 BLIP이라는 새로운 VLP 프레임워크를 제안합니다. 이 프레임워크는 비전-언어 이해 및 생성 작업 모두에 유연하게 적용될 수 있습니다. BLIP는 캡셔너가 합성 캡션을 생성하고 필터가 노이즈 캡션을 제거하는 부트스트래핑 방법을 통해 웹 데이터의 노이즈를 효과적으로 활용합니다. 우리는 이미지-텍스트 검색(Recall@1에서 +2.7%), 이미지 캡셔닝(CIDEr에서 +2.8%), 그리고 VQA(VQA 점수에서 +1.6%)와 같은 다양한 비전-언어 작업에서 최신 성과를 달성했습니다. 또한 BLIP은 제로샷 방식으로 비디오-언어 작업에 직접 전이될 때도 강력한 일반화 능력을 보여줍니다. 이 논문의 코드, 모델, 데이터셋은 공개되었습니다.*

![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif)

이 모델은 [ybelkada](https://huggingface.co/ybelkada)가 기여했습니다.
원본 코드는 [여기](https://github.com/salesforce/BLIP)에서 찾을 수 있습니다.

## 자료[[resources]]

- [Jupyter notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb): 사용자 정의 데이터셋에서 BLIP를 이미지 캡셔닝으로 미세 조정하는 방법

## BlipConfig[[transformers.BlipConfig]][[transformers.BlipConfig]]

#### transformers.BlipConfig[[transformers.BlipConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/configuration_blip.py#L112)

This is the configuration class to store the configuration of a BlipModel. It is used to instantiate a Blip
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 [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import BlipConfig, BlipModel

>>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipConfig()

>>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

>>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig

>>> # Initializing a BLIPText and BLIPVision configuration
>>> config_text = BlipTextConfig()
>>> config_vision = BlipVisionConfig()

>>> config = BlipConfig(text_config=config_text, vision_config=config_vision)
```

**Parameters:**

text_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the text backbone.

vision_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the vision backbone.

projection_dim (`int`, *optional*, defaults to `512`) : Dimensionality of text and vision projection layers.

logit_scale_init_value (`float`, *optional*, defaults to `2.6592`) : The initial value of the *logit_scale* parameter.

image_text_hidden_size (`int`, *optional*, defaults to 256) : Dimensionality of the hidden state of the image-text fusion layer.

label_smoothing (`float`, *optional*) : A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in `Rethinking the Inception Architecture for Computer Vision <https://huggingface.co/papers/1512.00567>`__. Default: :math:`0.0`.

tie_word_embeddings (`bool`, *optional*, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

initializer_factor (`float`, *optional*, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

## BlipTextConfig[[transformers.BlipTextConfig]][[transformers.BlipTextConfig]]

#### transformers.BlipTextConfig[[transformers.BlipTextConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/configuration_blip.py#L27)

This is the configuration class to store the configuration of a BlipModel. It is used to instantiate a Blip
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 [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import BlipTextConfig, BlipTextModel

>>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipTextConfig()

>>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipTextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

vocab_size (`int`, *optional*, defaults to `30524`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations.

encoder_hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations.

projection_dim (`int`, *optional*, defaults to `768`) : Dimensionality of text and vision projection layers.

num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `8`) : Number of attention heads for each attention layer in the Transformer decoder.

max_position_embeddings (`int`, *optional*, defaults to `512`) : The maximum sequence length that this model might ever be used with.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

layer_norm_eps (`float`, *optional*, defaults to `1e-12`) : The epsilon used by the layer normalization layers.

hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

attention_probs_dropout_prob (`Union[float, int]`, *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.

bos_token_id (`int`, *optional*, defaults to `30522`) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `2`) : Token id used for end-of-stream in the vocabulary.

pad_token_id (`int`, *optional*, defaults to `0`) : Token id used for padding in the vocabulary.

sep_token_id (`int`, *optional*, defaults to `102`) : Token id used for separator in the vocabulary.

is_decoder (`bool`, *optional*, defaults to `True`) : Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.

use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model.

tie_word_embeddings (`bool`, *optional*, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

label_smoothing (`float`, *optional*) : A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in `Rethinking the Inception Architecture for Computer Vision <https://huggingface.co/papers/1512.00567>`__. Default: :math:`0.0`.

## BlipVisionConfig[[transformers.BlipVisionConfig]][[transformers.BlipVisionConfig]]

#### transformers.BlipVisionConfig[[transformers.BlipVisionConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/configuration_blip.py#L77)

This is the configuration class to store the configuration of a BlipModel. It is used to instantiate a Blip
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 [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

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
```

**Parameters:**

hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations.

projection_dim (`int`, *optional*, defaults to `512`) : Dimensionality of text and vision projection layers.

num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder.

image_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `384`) : The size (resolution) of each image.

patch_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `16`) : The size (resolution) of each patch.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

layer_norm_eps (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities.

initializer_range (`float`, *optional*, defaults to `1e-10`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

## BlipProcessor[[transformers.BlipProcessor]][[transformers.BlipProcessor]]

#### transformers.BlipProcessor[[transformers.BlipProcessor]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/processing_blip.py#L39)

Constructs a BlipProcessor which wraps a image processor and a tokenizer into a single processor.

[BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) offers all the functionalities of [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) and [BertTokenizer](/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer). See the
[~BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) and [~BertTokenizer](/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer) for more information.

**Parameters:**

image_processor (`BlipImageProcessor`) : The image processor is a required input.

tokenizer (`BertTokenizer`) : The tokenizer is a required input.

## BlipImageProcessor[[transformers.BlipImageProcessor]][[transformers.BlipImageProcessor]]

#### transformers.BlipImageProcessor[[transformers.BlipImageProcessor]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/image_processing_blip.py#L22)

Constructs a BlipImageProcessor image processor.

preprocesstransformers.BlipImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/ko/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** (`ImagesKwargs`, *optional*) --
  Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
  for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

**Parameters:**

- ****kwargs** (`ImagesKwargs`, *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

**Returns:**

``~image_processing_base.BatchFeature``

- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

## BlipModel[[transformers.BlipModel]][[transformers.BlipModel]]

`BlipModel`은 향후 버전에서 더 이상 지원되지 않을 예정입니다. 목적에 따라 `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` 또는 `BlipForQuestionAnswering`을 사용하십시오.

#### transformers.BlipModel[[transformers.BlipModel]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L522)

This model is going to be deprecated in future versions. Please use `BlipForConditionalGeneration`, `BlipForQuestionAnswering` or `BlipForImageTextRetrieval` depending on your usecase.

This model inherits from [PreTrainedModel](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.BlipModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L691[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "return_loss", "val": ": bool | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **return_loss** (`bool`, *optional*) --
  Whether or not to return the contrastive loss.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0`BlipOutput` or `tuple(torch.FloatTensor)`A `BlipOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
The [BlipModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`) -- Contrastive loss for image-text similarity.
- **logits_per_image** (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
  similarity scores.
- **logits_per_text** (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
  similarity scores.
- **text_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of [BlipTextModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipTextModel).
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of [BlipVisionModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipVisionModel).
- **text_model_output** (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [BlipTextModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipTextModel).
- **vision_model_output** (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [BlipVisionModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipVisionModel).

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, BlipModel

>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> inputs = processor(
...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
... )

>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
```

**Parameters:**

config ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``BlipOutput` or `tuple(torch.FloatTensor)``

A `BlipOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
#### get_text_features[[transformers.BlipModel.get_text_features]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L567)

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> from transformers import AutoProcessor, BlipModel

>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
```

**Parameters:**

input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are input IDs?](../glossary#input-ids)

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:  - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**.  [What are attention masks?](../glossary#attention-mask)

position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.  [What are position IDs?](../glossary#position-ids)

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
#### get_image_features[[transformers.BlipModel.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L600)

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, BlipModel

>>> model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> inputs = processor(images=image, return_tensors="pt")

>>> image_features = model.get_image_features(**inputs)
```

**Parameters:**

pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) : The tensors corresponding to the input images. Pixel values can be obtained using [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).

interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the pre-trained position encodings.

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.

## BlipTextModel[[transformers.BlipTextModel]][[transformers.BlipTextModel]]

#### transformers.BlipTextModel[[transformers.BlipTextModel]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip_text.py#L462)

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an
`encoder_hidden_states` is then expected as an input to the forward pass.

forwardtransformers.BlipTextModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip_text.py#L487[{"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.Tensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor | None = None"}, {"name": "encoder_embeds", "val": ": torch.Tensor | None = None"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "is_decoder", "val": ": bool | None = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]

encoder_hidden_states  (`torch.FloatTensor`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Cache`, *optional*):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).

## BlipVisionModel[[transformers.BlipVisionModel]][[transformers.BlipVisionModel]]

#### transformers.BlipVisionModel[[transformers.BlipVisionModel]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L463)

forwardtransformers.BlipVisionModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L483[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0[BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
The [BlipVisionModel](/docs/transformers/main/ko/model_doc/blip#transformers.BlipVisionModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

**Parameters:**

pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) : The tensors corresponding to the input images. Pixel values can be obtained using [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).

interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the pre-trained position encodings.

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.

## BlipForConditionalGeneration[[transformers.BlipForConditionalGeneration]][[transformers.BlipForConditionalGeneration]]

#### transformers.BlipForConditionalGeneration[[transformers.BlipForConditionalGeneration]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L782)

BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass
`input_ids` to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise,
the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption
from the text input. If no text input is provided, the decoder will start with the [BOS] token only.

This model inherits from [PreTrainedModel](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.BlipForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L809[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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]`.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.
- **logits_to_keep** (`Union[int, torch.Tensor]`, *optional*, defaults to `0`) --
  If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
  `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
  token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
  If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
  This is useful when using packed tensor format (single dimension for batch and sequence length).0`BlipForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`A `BlipForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
The [BlipForConditionalGeneration](/docs/transformers/main/ko/model_doc/blip#transformers.BlipForConditionalGeneration) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) -- Language modeling loss from the text decoder.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*) -- Prediction scores of the language modeling head of the text decoder model.
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*) -- The image embeddings obtained after applying the Vision Transformer model to the input image.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, BlipForConditionalGeneration

>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
>>> model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))
>>> text = "A picture of"

>>> inputs = processor(images=image, text=text, return_tensors="pt")

>>> outputs = model(**inputs)
```

**Parameters:**

config ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``BlipForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)``

A `BlipForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.

## BlipForImageTextRetrieval[[transformers.BlipForImageTextRetrieval]][[transformers.BlipForImageTextRetrieval]]

#### transformers.BlipForImageTextRetrieval[[transformers.BlipForImageTextRetrieval]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L1178)

BLIP Model with a vision and text projector, and a classification head on top. The model is used in the context of
image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to
the image.

This model inherits from [PreTrainedModel](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.BlipForImageTextRetrieval.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L1217[{"name": "input_ids", "val": ": LongTensor"}, {"name": "pixel_values", "val": ": FloatTensor"}, {"name": "use_itm_head", "val": ": bool | None = True"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **use_itm_head** (`bool`, *optional*, defaults to `True`) --
  Whether or not to use the image-text matching head.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0`BlipTextVisionModelOutput` or `tuple(torch.FloatTensor)`A `BlipTextVisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
The [BlipForImageTextRetrieval](/docs/transformers/main/ko/model_doc/blip#transformers.BlipForImageTextRetrieval) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss from the text decoder.
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The image embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, BlipForImageTextRetrieval

>>> model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))
>>> text = "an image of a cat"

>>> inputs = processor(images=image, text=text, return_tensors="pt")
>>> outputs = model(**inputs)
```

**Parameters:**

config ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``BlipTextVisionModelOutput` or `tuple(torch.FloatTensor)``

A `BlipTextVisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.

## BlipForQuestionAnswering[[transformers.BlipForQuestionAnswering]][[transformers.BlipForQuestionAnswering]]

#### transformers.BlipForQuestionAnswering[[transformers.BlipForQuestionAnswering]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L955)

BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text
decoder. The vision encoder will encode the input image, the text encoder will encode the input question together
with the encoding of the image, and the text decoder will output the answer to the question.

This model inherits from [PreTrainedModel](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.BlipForQuestionAnswering.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py#L983[{"name": "input_ids", "val": ": LongTensor"}, {"name": "pixel_values", "val": ": FloatTensor"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([BlipProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor) uses
  [BlipImageProcessor](/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)
- **decoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to
  make sure the model can only look at previous inputs in order to predict the future.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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]`.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0`BlipTextVisionModelOutput` or `tuple(torch.FloatTensor)`A `BlipTextVisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.
The [BlipForQuestionAnswering](/docs/transformers/main/ko/model_doc/blip#transformers.BlipForQuestionAnswering) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss from the text decoder.
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The image embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, BlipForQuestionAnswering

>>> model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> # training
>>> text = "How many cats are in the picture?"
>>> label = "2"
>>> inputs = processor(images=image, text=text, return_tensors="pt")
>>> labels = processor(text=label, return_tensors="pt").input_ids

>>> inputs["labels"] = labels
>>> outputs = model(**inputs)
>>> loss = outputs.loss
>>> loss.backward()

>>> # inference
>>> text = "How many cats are in the picture?"
>>> inputs = processor(images=image, text=text, return_tensors="pt")
>>> outputs = model.generate(**inputs)
>>> print(processor.decode(outputs[0], skip_special_tokens=True))
2
```

**Parameters:**

config ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``BlipTextVisionModelOutput` or `tuple(torch.FloatTensor)``

A `BlipTextVisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BlipConfig](/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig)) and inputs.