# Pix2Struct

## Overview

The Pix2Struct model was proposed in [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://huggingface.co/papers/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.

The abstract from the paper is the following:

> Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.

Tips:

Pix2Struct has been fine tuned on a variety of tasks and datasets, ranging from image captioning, visual question answering (VQA) over different inputs (books, charts, science diagrams), captioning UI components etc. The full list can be found in Table 1 of the paper.
We therefore advise you to use these models for the tasks they have been fine tuned on. For instance, if you want to use Pix2Struct for UI captioning, you should use the model fine tuned on the UI dataset. If you want to use Pix2Struct for image captioning, you should use the model fine tuned on the natural images captioning dataset and so on.

If you want to use the model to perform conditional text captioning, make sure to use the processor with `add_special_tokens=False`.

This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
The original code can be found [here](https://github.com/google-research/pix2struct).

## Resources

- [Fine-tuning Notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_pix2struct.ipynb)
- [All models](https://huggingface.co/models?search=pix2struct)

## Pix2StructConfig[[transformers.Pix2StructConfig]]

#### transformers.Pix2StructConfig[[transformers.Pix2StructConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/configuration_pix2struct.py#L142)

This is the configuration class to store the configuration of a Pix2StructModel. It is used to instantiate a Pix2Struct
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 [google/pix2struct-base](https://huggingface.co/google/pix2struct-base)

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

Example:

```python
>>> from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration

>>> # Initializing a Pix2StructConfig with google/pix2struct-base style configuration
>>> configuration = Pix2StructConfig()

>>> # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration
>>> model = Pix2StructForConditionalGeneration(configuration)

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

>>> # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig

>>> # Initializing a Pix2Struct text and Pix2Struct vision configuration
>>> config_text = Pix2StructTextConfig()
>>> config_vision = Pix2StructVisionConfig()

>>> config = Pix2StructConfig(text_config=config_text, vision_config=config_vision)
```

**Parameters:**

is_encoder_decoder (`bool`, *optional*, defaults to `True`) : Whether the model is used as an encoder/decoder or not.

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.

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.

is_vqa (`bool`, *optional*, defaults to `False`) : Whether the model has been fine-tuned for VQA or not.

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

## Pix2StructTextConfig[[transformers.Pix2StructTextConfig]]

#### transformers.Pix2StructTextConfig[[transformers.Pix2StructTextConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/configuration_pix2struct.py#L27)

This is the configuration class to store the configuration of a Pix2StructModel. It is used to instantiate a Pix2Struct
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 [google/pix2struct-base](https://huggingface.co/google/pix2struct-base)

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

Example:

```python
>>> from transformers import Pix2StructTextConfig, Pix2StructTextModel

>>> # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration
>>> configuration = Pix2StructTextConfig()

>>> # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration
>>> model = Pix2StructTextModel(configuration)

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

**Parameters:**

vocab_size (`int`, *optional*, defaults to `50244`) : 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.

d_kv (`int`, *optional*, defaults to `64`) : Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will be defined as `num_heads * d_kv`.

d_ff (`int`, *optional*, defaults to `2048`) : Dimension of the MLP representations.

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

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

relative_attention_num_buckets (`int`, *optional*, defaults to 32) : The number of buckets to use for each attention layer.

relative_attention_max_distance (`int`, *optional*, defaults to 128) : The maximum distance of the longer sequences for the bucket separation.

dropout_rate (`Union[float, int]`, *optional*, defaults to `0.1`) : The ratio for all dropout layers.

layer_norm_epsilon (`float`, *optional*, defaults to `1e-06`) : The epsilon used by the layer normalization layers.

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).

dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`) : The non-linear activation function (function or string).

decoder_start_token_id (`int`, *optional*, defaults to `0`) : If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.

use_cache (`bool`, *optional*, defaults to `False`) : 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.

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

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `1`) : Token id used for end-of-stream in the vocabulary.

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

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

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.

add_cross_attention (`bool`, *optional*, defaults to `False`) : Whether cross-attention layers should be added to the model.

## Pix2StructVisionConfig[[transformers.Pix2StructVisionConfig]]

#### transformers.Pix2StructVisionConfig[[transformers.Pix2StructVisionConfig]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/configuration_pix2struct.py#L87)

This is the configuration class to store the configuration of a Pix2StructModel. It is used to instantiate a Pix2Struct
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 [google/pix2struct-base](https://huggingface.co/google/pix2struct-base)

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

Example:

```python
>>> from transformers import Pix2StructVisionConfig, Pix2StructVisionModel

>>> # Initializing a Pix2StructVisionConfig with google/pix2struct-base style configuration
>>> configuration = Pix2StructVisionConfig()

>>> # Initializing a Pix2StructVisionModel (with random weights) from the google/pix2struct-base style configuration
>>> model = Pix2StructVisionModel(configuration)

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

**Parameters:**

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

patch_embed_hidden_size (`int`, *optional*, defaults to 768) : Dimensionality of the input patch_embedding layer in the Transformer encoder.

d_ff (`int`, *optional*, defaults to 2048) : Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.

d_kv (`int`, *optional*, defaults to 64) : Dimensionality of the key, query, value projections per attention head.

The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, : `"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported.

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.

dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`) : The non-linear activation function (function or string).

layer_norm_eps (`float`, *optional*, defaults to `1e-06`) : The epsilon used by the layer normalization layers.

dropout_rate (`Union[float, int]`, *optional*, defaults to `0.0`) : The ratio for all dropout 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.

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).

seq_len (`int`, *optional*, defaults to 4096) : Maximum sequence length (here number of patches) supported by the model.

relative_attention_num_buckets (`int`, *optional*, defaults to 32) : The number of buckets to use for each attention layer.

relative_attention_max_distance (`int`, *optional*, defaults to 128) : The maximum distance (in tokens) to use for each attention layer.

## Pix2StructProcessor[[transformers.Pix2StructProcessor]]

#### transformers.Pix2StructProcessor[[transformers.Pix2StructProcessor]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/processing_pix2struct.py#L47)

Constructs a Pix2StructProcessor which wraps a image processor and a tokenizer into a single processor.

[Pix2StructProcessor](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructProcessor) offers all the functionalities of [Pix2StructImageProcessor](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructImageProcessor) and [T5Tokenizer](/docs/transformers/main/en/model_doc/t5#transformers.T5Tokenizer). See the
[~Pix2StructImageProcessor](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructImageProcessor) and [~T5Tokenizer](/docs/transformers/main/en/model_doc/t5#transformers.T5Tokenizer) for more information.

__call__transformers.Pix2StructProcessor.__call__https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/processing_pix2struct.py#L52[{"name": "images", "val": " = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.pix2struct.processing_pix2struct.Pix2StructProcessorKwargs]"}]- **images** (``) --
  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`.
- **text** (`Union[str, list[str], list[list[str]]]`, *optional*) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors of a particular framework. Acceptable values are:

  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
- ****kwargs** ([ProcessingKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) --
  Additional processing options for each modality (text, images, videos, audio). Model-specific parameters
  are listed above; see the TypedDict class for the complete list of supported arguments.0`Union[~tokenization_utils_base.BatchEncoding, ~feature_extraction_utils.BatchFeature]`

**Parameters:**

image_processor (`Pix2StructImageProcessor`) : The image processor is a required input.

tokenizer (`T5Tokenizer`) : The tokenizer is a required input.

**Returns:**

`Union[~tokenization_utils_base.BatchEncoding, ~feature_extraction_utils.BatchFeature]`

## Pix2StructImageProcessor[[transformers.Pix2StructImageProcessor]]

#### transformers.Pix2StructImageProcessor[[transformers.Pix2StructImageProcessor]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/image_processing_pix2struct.py#L151)

Constructs a Pix2StructImageProcessor image processor.

preprocesstransformers.Pix2StructImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/image_processing_pix2struct.py#L331[{"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": "header_text", "val": ": str | list[str] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.pix2struct.image_processing_pix2struct.Pix2StructImageProcessorKwargs]"}]- **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`.
- **header_text** (`Union[str, list[str]]`, *optional*) --
  Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`.
- **max_patches** (`int`, *kwargs*, *optional*) --
  Maximum number of patches to extract.
- **patch_size** (`dict[str, *kwargs*, int]`, *optional*, defaults to `{"height" -- 16, "width": 16}`):
  The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16.
- **is_vqa** (`bool`, *kwargs*, *optional*, defaults to `False`) --
  Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is
  rendered onto the input images.
- **header_text** (`Union[list[str], *kwargs*, str]`, *optional*) --
  Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.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](/docs/transformers/main/en/main_classes/processors#transformers.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.

## Pix2StructImageProcessorPil[[transformers.Pix2StructImageProcessorPil]]

#### transformers.Pix2StructImageProcessorPil[[transformers.Pix2StructImageProcessorPil]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/image_processing_pil_pix2struct.py#L163)

Constructs a Pix2StructImageProcessor image processor.

preprocesstransformers.Pix2StructImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/image_processing_pil_pix2struct.py#L321[{"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": "header_text", "val": ": str | list[str] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.pix2struct.image_processing_pil_pix2struct.Pix2StructImageProcessorKwargs]"}]- **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`.
- **header_text** (`Union[str, list[str]]`, *optional*) --
  Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`.
- **max_patches** (`int`, *kwargs*, *optional*) --
  Maximum number of patches to extract.
- **patch_size** (`dict[str, *kwargs*, int]`, *optional*, defaults to `{"height" -- 16, "width": 16}`):
  The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16.
- **is_vqa** (`bool`, *kwargs*, *optional*, defaults to `False`) --
  Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is
  rendered onto the input images.
- **header_text** (`Union[list[str], *kwargs*, str]`, *optional*) --
  Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.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](/docs/transformers/main/en/main_classes/processors#transformers.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.

## Pix2StructTextModel[[transformers.Pix2StructTextModel]]

#### transformers.Pix2StructTextModel[[transformers.Pix2StructTextModel]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L918)

The standalone text decoder of Pix2Struct

This model inherits from [PreTrainedModel](/docs/transformers/main/en/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.Pix2StructTextModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L947[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_hidden_states", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position
  embeddings so you should be able to pad the inputs on both the right and the left.

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

  [What are input IDs?](../glossary#input-ids)

  To know more on how to prepare `input_ids` for pretraining take a look a [Pix2StructText
  Training](./t5#training).
- **attention_mask** (`torch.FloatTensor` 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)
- **encoder_hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *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` of shape `(batch_size, sequence_length)`, *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**.
- **inputs_embeds** (`torch.LongTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `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`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **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]`.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[CausalLMOutputWithCrossAttentions](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithCrossAttentions](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) 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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.
The [Pix2StructTextModel](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructTextModel) 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 (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).
- **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.
- **cross_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)`.

  Cross attentions weights after the attention softmax, used to compute the weighted average in the
  cross-attention heads.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.

Example:

```python
>>> from transformers import AutoProcessor, Pix2StructTextModel

>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
>>> model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base")

>>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> loss = outputs.loss
```

**Parameters:**

config ([Pix2StructTextModel](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructTextModel)) : 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/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

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

A [CausalLMOutputWithCrossAttentions](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) 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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.

## Pix2StructVisionModel[[transformers.Pix2StructVisionModel]]

#### transformers.Pix2StructVisionModel[[transformers.Pix2StructVisionModel]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L432)

The bare Pix2Struct Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/main/en/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.Pix2StructVisionModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L454[{"name": "flattened_patches", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **flattened_patches** (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_channels x patch_height x patch_width)`) --
  Flattened and padded pixel values. These values can be obtained using [AutoImageProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor). See
  `Pix2StructVisionImageProcessor.__call__` for details. Check the [original
  paper](https://huggingface.co/papers/2210.03347) (figure 5) for more details.
- **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)
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.
The [Pix2StructVisionModel](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructVisionModel) 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.

Example:

```python
>>> import httpx
>>> from io import BytesIO
>>> from PIL import Image
>>> from transformers import AutoProcessor, Pix2StructVisionModel

>>> image_processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
>>> model = Pix2StructVisionModel.from_pretrained("google/pix2struct-textcaps-base")

>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 2048, 768]
```

**Parameters:**

config ([Pix2StructVisionConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructVisionConfig)) : 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/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

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

A [BaseModelOutputWithPooling](/docs/transformers/main/en/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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.

## Pix2StructForConditionalGeneration[[transformers.Pix2StructForConditionalGeneration]]

#### transformers.Pix2StructForConditionalGeneration[[transformers.Pix2StructForConditionalGeneration]]

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L1139)

A conditional generation model with a language modeling head. Can be used for sequence generation tasks.

This model inherits from [PreTrainedModel](/docs/transformers/main/en/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.Pix2StructForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/modeling_pix2struct.py#L1166[{"name": "flattened_patches", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.BoolTensor | None = None"}, {"name": "encoder_outputs", "val": ": tuple[tuple[torch.FloatTensor]] | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_inputs_embeds", "val": ": torch.Tensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **flattened_patches** (`torch.FloatTensor` of shape `(batch_size, seq_length, hidden_size)`) --
  Flattened pixel patches. the `hidden_size` is obtained by the following formula: `hidden_size` =
  `num_channels` * `patch_size` * `patch_size`

  The process of flattening the pixel patches is done by `Pix2StructProcessor`.
- **attention_mask** (`torch.FloatTensor` 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)
- **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/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
  `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
  `past_key_values`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
  Training](./t5#training).
- **decoder_attention_mask** (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.
- **encoder_outputs** (`tuple[tuple[torch.FloatTensor]]`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss for the decoder.
- **decoder_inputs_embeds** (`torch.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
  representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
  input (see `past_key_values`). This is useful if you want more control over how to convert
  `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **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`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[Seq2SeqModelOutput](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`A [Seq2SeqModelOutput](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) 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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.
The [Pix2StructForConditionalGeneration](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration) 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 decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [EncoderDecoderCache](/docs/transformers/main/en/internal/generation_utils#transformers.EncoderDecoderCache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
- **decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
- **encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.

Example:

Inference:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration

>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
>>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")

>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

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

>>> # autoregressive generation
>>> generated_ids = model.generate(**inputs, max_new_tokens=50)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> print(generated_text)
A stop sign is on a street corner.

>>> # conditional generation
>>> text = "A picture of"
>>> inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False)

>>> generated_ids = model.generate(**inputs, max_new_tokens=50)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> print(generated_text)
A picture of a stop sign with a red stop sign
```

Training:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration

>>> processor = AutoProcessor.from_pretrained("google/pix2struct-base")
>>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-base")

>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))
>>> text = "A stop sign is on the street corner."

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

>>> # forward pass
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> print(f"{loss.item():.5f}")
5.94282
```

**Parameters:**

config ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) : 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/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[Seq2SeqModelOutput](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)``

A [Seq2SeqModelOutput](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) 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 ([Pix2StructConfig](/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig)) and inputs.