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layoutlmv2

mindnlp.transformers.models.layoutlmv2.configuration_layoutlmv2

LayoutLMv2 model configuration

mindnlp.transformers.models.layoutlmv2.configuration_layoutlmv2.LayoutLMv2Config

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [LayoutLMv2Model]. It is used to instantiate an LayoutLMv2 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 LayoutLMv2 microsoft/layoutlmv2-base-uncased architecture.

Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information.

PARAMETER DESCRIPTION
vocab_size

Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [LayoutLMv2Model] or [TFLayoutLMv2Model].

TYPE: `int`, *optional*, defaults to 30522 DEFAULT: 30522

hidden_size

Dimension of the encoder layers and the pooler layer.

TYPE: `int`, *optional*, defaults to 768 DEFAULT: 768

num_hidden_layers

Number of hidden layers in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 12 DEFAULT: 12

num_attention_heads

Number of attention heads for each attention layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 12 DEFAULT: 12

intermediate_size

Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 3072 DEFAULT: 3072

hidden_act

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

TYPE: `str` or `function`, *optional*, defaults to `"gelu"` DEFAULT: 'gelu'

hidden_dropout_prob

The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

TYPE: `float`, *optional*, defaults to 0.1 DEFAULT: 0.1

attention_probs_dropout_prob

The dropout ratio for the attention probabilities.

TYPE: `float`, *optional*, defaults to 0.1 DEFAULT: 0.1

max_position_embeddings

The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

TYPE: `int`, *optional*, defaults to 512 DEFAULT: 512

type_vocab_size

The vocabulary size of the token_type_ids passed when calling [LayoutLMv2Model] or [TFLayoutLMv2Model].

TYPE: `int`, *optional*, defaults to 2 DEFAULT: 2

initializer_range

The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

TYPE: `float`, *optional*, defaults to 0.02 DEFAULT: 0.02

layer_norm_eps

The epsilon used by the layer normalization layers.

TYPE: `float`, *optional*, defaults to 1e-12 DEFAULT: 1e-12

max_2d_position_embeddings

The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024).

TYPE: `int`, *optional*, defaults to 1024 DEFAULT: 1024

max_rel_pos

The maximum number of relative positions to be used in the self-attention mechanism.

TYPE: `int`, *optional*, defaults to 128 DEFAULT: 128

rel_pos_bins

The number of relative position bins to be used in the self-attention mechanism.

TYPE: `int`, *optional*, defaults to 32 DEFAULT: 32

fast_qkv

Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

max_rel_2d_pos

The maximum number of relative 2D positions in the self-attention mechanism.

TYPE: `int`, *optional*, defaults to 256 DEFAULT: 256

rel_2d_pos_bins

The number of 2D relative position bins in the self-attention mechanism.

TYPE: `int`, *optional*, defaults to 64 DEFAULT: 64

image_feature_pool_shape

The shape of the average-pooled feature map.

TYPE: `List[int]`, *optional*, defaults to [7, 7, 256] DEFAULT: [7, 7, 256]

coordinate_size

Dimension of the coordinate embeddings.

TYPE: `int`, *optional*, defaults to 128 DEFAULT: 128

shape_size

Dimension of the width and height embeddings.

TYPE: `int`, *optional*, defaults to 128 DEFAULT: 128

has_relative_attention_bias

Whether or not to use a relative attention bias in the self-attention mechanism.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

has_spatial_attention_bias

Whether or not to use a spatial attention bias in the self-attention mechanism.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

has_visual_segment_embedding

Whether or not to add visual segment embeddings.

TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

detectron2_config_args

Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to this file for details regarding default values.

TYPE: `dict`, *optional* DEFAULT: None

Example 
>>> from transformers import LayoutLMv2Config, LayoutLMv2Model
...
>>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
>>> configuration = LayoutLMv2Config()
...
>>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
>>> model = LayoutLMv2Model(configuration)
...
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in mindnlp\transformers\models\layoutlmv2\configuration_layoutlmv2.py 
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class LayoutLMv2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
    LayoutLMv2 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 LayoutLMv2
    [microsoft/layoutlmv2-base-uncased](https://hf-mirror.com/microsoft/layoutlmv2-base-uncased) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimension of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
            [`TFLayoutLMv2Model`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum value that the 2D position embedding might ever be used with. Typically set this to something
            large just in case (e.g., 1024).
        max_rel_pos (`int`, *optional*, defaults to 128):
            The maximum number of relative positions to be used in the self-attention mechanism.
        rel_pos_bins (`int`, *optional*, defaults to 32):
            The number of relative position bins to be used in the self-attention mechanism.
        fast_qkv (`bool`, *optional*, defaults to `True`):
            Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
        max_rel_2d_pos (`int`, *optional*, defaults to 256):
            The maximum number of relative 2D positions in the self-attention mechanism.
        rel_2d_pos_bins (`int`, *optional*, defaults to 64):
            The number of 2D relative position bins in the self-attention mechanism.
        image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]):
            The shape of the average-pooled feature map.
        coordinate_size (`int`, *optional*, defaults to 128):
            Dimension of the coordinate embeddings.
        shape_size (`int`, *optional*, defaults to 128):
            Dimension of the width and height embeddings.
        has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use a relative attention bias in the self-attention mechanism.
        has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use a spatial attention bias in the self-attention mechanism.
        has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
            Whether or not to add visual segment embeddings.
        detectron2_config_args (`dict`, *optional*):
            Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
            file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
            for details regarding default values.

    Example:
        ```python
        >>> from transformers import LayoutLMv2Config, LayoutLMv2Model
        ...
        >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
        >>> configuration = LayoutLMv2Config()
        ...
        >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
        >>> model = LayoutLMv2Model(configuration)
        ...
        >>> # Accessing the model configuration
        >>> configuration = model.config
        ```
    """
    model_type = "layoutlmv2"

    def __init__(
            self,
            vocab_size=30522,
            hidden_size=768,
            num_hidden_layers=12,
            num_attention_heads=12,
            intermediate_size=3072,
            hidden_act="gelu",
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=512,
            type_vocab_size=2,
            initializer_range=0.02,
            layer_norm_eps=1e-12,
            pad_token_id=0,
            max_2d_position_embeddings=1024,
            max_rel_pos=128,
            rel_pos_bins=32,
            fast_qkv=True,
            max_rel_2d_pos=256,
            rel_2d_pos_bins=64,
            image_feature_pool_shape=[7, 7, 256],
            coordinate_size=128,
            shape_size=128,
            has_relative_attention_bias=True,
            has_spatial_attention_bias=True,
            has_visual_segment_embedding=False,
            use_visual_backbone=True,
            detectron2_config_args=None,
            **kwargs,
    ):
        """
        Initializes a LayoutLMv2Config object with the specified parameters.

        Args:
            vocab_size (int): The size of the vocabulary.
            hidden_size (int): The hidden size for the model.
            num_hidden_layers (int): The number of hidden layers in the model.
            num_attention_heads (int): The number of attention heads in the model.
            intermediate_size (int): The size of the intermediate layer in the model.
            hidden_act (str): The activation function for the hidden layers.
            hidden_dropout_prob (float): The dropout probability for the hidden layers.
            attention_probs_dropout_prob (float): The dropout probability for the attention probabilities.
            max_position_embeddings (int): The maximum position embeddings allowed.
            type_vocab_size (int): The size of the type vocabulary.
            initializer_range (float): The range for parameter initialization.
            layer_norm_eps (float): The epsilon value for layer normalization.
            pad_token_id (int): The token ID for padding.
            max_2d_position_embeddings (int): The maximum 2D position embeddings allowed.
            max_rel_pos (int): The maximum relative position.
            rel_pos_bins (int): The number of relative position bins.
            fast_qkv (bool): Flag to enable fast query, key, value computation.
            max_rel_2d_pos (int): The maximum relative 2D position.
            rel_2d_pos_bins (int): The number of relative 2D position bins.
            image_feature_pool_shape (list): The shape of the image feature pool.
            coordinate_size (int): The size of coordinates.
            shape_size (int): The size of shapes.
            has_relative_attention_bias (bool): Flag indicating if relative attention bias is used.
            has_spatial_attention_bias (bool): Flag indicating if spatial attention bias is used.
            has_visual_segment_embedding (bool): Flag indicating if visual segment embedding is used.
            use_visual_backbone (bool): Flag indicating if visual backbone is used.
            detectron2_config_args (dict): Additional arguments for the Detectron2 configuration.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            intermediate_size=intermediate_size,
            hidden_act=hidden_act,
            hidden_dropout_prob=hidden_dropout_prob,
            attention_probs_dropout_prob=attention_probs_dropout_prob,
            max_position_embeddings=max_position_embeddings,
            type_vocab_size=type_vocab_size,
            initializer_range=initializer_range,
            layer_norm_eps=layer_norm_eps,
            pad_token_id=pad_token_id,
            **kwargs,
        )
        self.max_2d_position_embeddings = max_2d_position_embeddings
        self.max_rel_pos = max_rel_pos
        self.rel_pos_bins = rel_pos_bins
        self.fast_qkv = fast_qkv
        self.max_rel_2d_pos = max_rel_2d_pos
        self.rel_2d_pos_bins = rel_2d_pos_bins
        self.image_feature_pool_shape = image_feature_pool_shape
        self.coordinate_size = coordinate_size
        self.shape_size = shape_size
        self.has_relative_attention_bias = has_relative_attention_bias
        self.has_spatial_attention_bias = has_spatial_attention_bias
        self.has_visual_segment_embedding = has_visual_segment_embedding
        self.use_visual_backbone = use_visual_backbone
        self.detectron2_config_args = (
            detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config()
        )

    @classmethod
    def get_default_detectron2_config(cls):
        '''
        This method returns a dictionary containing the default configuration for the Detectron2 model.
        The configuration includes various settings related to the model's architecture, backbone, region of
        interest (ROI) heads, and other parameters.

        Args:
            cls (class): The class object.

        Returns:
            dict: A dictionary containing the default configuration for the Detectron2 model.

        Raises:
            None.
        '''
        return {
            "MODEL.MASK_ON": True,
            "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
            "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
            "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
            "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
            "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
            "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
            "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
            "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
            "MODEL.POST_NMS_TOPK_TEST": 1000,
            "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
            "MODEL.ROI_HEADS.NUM_CLASSES": 5,
            "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
            "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
            "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
            "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
            "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
            "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
            "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
            "MODEL.RESNETS.DEPTH": 101,
            "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
            "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
            "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
            "MODEL.RESNETS.NUM_GROUPS": 32,
            "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
            "MODEL.RESNETS.STRIDE_IN_1X1": False,
        }

    def get_detectron2_config(self):
        """
        This method generates a Detectron2 configuration for the LayoutLMv2 model.

        Args:
            self: The instance of the LayoutLMv2Config class.

        Returns:
            None.

        Raises:
            None
        """
        detectron2_config = Dict(
            {
                "MODEL": {
                    "MASK_ON": True,
                    "PIXEL_MEAN": [103.53, 116.28, 123.675],
                    "PIXEL_STD": [57.375, 57.120, 58.395],
                    "BACKBONE": {"NAME": "build_resnet_fpn_backbone"},
                    "FPN": {
                        "FUSE_TYPE": "sum",
                        "IN_FEATURES": ["res2", "res3", "res4", "res5"],
                        "NORM": "BN",
                        "OUT_CHANNELS": 256
                    },
                    "ANCHOR_GENERATOR": {"SIZES": [[32], [64], [128], [256], [512]]},
                    "RPN": {
                        "IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                        "PRE_NMS_TOPK_TRAIN": 2000,
                        "PRE_NMS_TOPK_TEST": 1000,
                        "POST_NMS_TOPK_TRAIN": 1000,
                    },
                    "POST_NMS_TOPK_TEST": 1000,
                    "ROI_HEADS": {
                        "NAME": "StandardROIHeads",
                        "NUM_CLASSES": 5,
                        "IN_FEATURES": ["p2", "p3", "p4", "p5"],
                    },
                    "ROI_BOX_HEAD": {
                        "NAME": "FastRCNNConvFCHead",
                        "NUM_FC": 2,
                        "POOLER_RESOLUTION": 14,
                    },
                    "ROI_MASK_HEAD": {
                        "NAME": "MaskRCNNConvUpsampleHead",
                        "NUM_CONV": 4,
                        "POOLER_RESOLUTION": 7,
                    },
                    "RESNETS": {
                        "DEPTH": 101,
                        "SIZES": [[32], [64], [128], [256], [512]],
                        "ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                        "FREEZE_AT": 2,
                        "NORM": "BN",
                        "NUM_GROUPS": 32,
                        "WIDTH_PER_GROUP": 8,
                        "STEM_IN_CHANNELS": 3,
                        "STEM_OUT_CHANNELS": 64,
                        "RES2_OUT_CHANNELS": 256,
                        "STRIDE_IN_1X1": False,
                        "RES5_DILATION": 1,
                        "NAME": "resnet101",
                        "PRETRAINED": True,
                        "NUM_CLASSES": 1000,
                        "OUT_FEATURES": ["res2", "res3", "res4", "res5"]
                    }
                }
            }
        )
        for k, v in self.detectron2_config_args.items():
            attributes = k.split(".")
            to_set = detectron2_config
            for attribute in attributes[:-1]:
                to_set = getattr(to_set, attribute)
            setattr(to_set, attributes[-1], v)

        return detectron2_config

mindnlp.transformers.models.layoutlmv2.configuration_layoutlmv2.LayoutLMv2Config.__init__(vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, max_2d_position_embeddings=1024, max_rel_pos=128, rel_pos_bins=32, fast_qkv=True, max_rel_2d_pos=256, rel_2d_pos_bins=64, image_feature_pool_shape=[7, 7, 256], coordinate_size=128, shape_size=128, has_relative_attention_bias=True, has_spatial_attention_bias=True, has_visual_segment_embedding=False, use_visual_backbone=True, detectron2_config_args=None, **kwargs)

Initializes a LayoutLMv2Config object with the specified parameters.

PARAMETER DESCRIPTION
vocab_size

The size of the vocabulary.

TYPE: int DEFAULT: 30522

hidden_size

The hidden size for the model.

TYPE: int DEFAULT: 768

num_hidden_layers

The number of hidden layers in the model.

TYPE: int DEFAULT: 12

num_attention_heads

The number of attention heads in the model.

TYPE: int DEFAULT: 12

intermediate_size

The size of the intermediate layer in the model.

TYPE: int DEFAULT: 3072

hidden_act

The activation function for the hidden layers.

TYPE: str DEFAULT: 'gelu'

hidden_dropout_prob

The dropout probability for the hidden layers.

TYPE: float DEFAULT: 0.1

attention_probs_dropout_prob

The dropout probability for the attention probabilities.

TYPE: float DEFAULT: 0.1

max_position_embeddings

The maximum position embeddings allowed.

TYPE: int DEFAULT: 512

type_vocab_size

The size of the type vocabulary.

TYPE: int DEFAULT: 2

initializer_range

The range for parameter initialization.

TYPE: float DEFAULT: 0.02

layer_norm_eps

The epsilon value for layer normalization.

TYPE: float DEFAULT: 1e-12

pad_token_id

The token ID for padding.

TYPE: int DEFAULT: 0

max_2d_position_embeddings

The maximum 2D position embeddings allowed.

TYPE: int DEFAULT: 1024

max_rel_pos

The maximum relative position.

TYPE: int DEFAULT: 128

rel_pos_bins

The number of relative position bins.

TYPE: int DEFAULT: 32

fast_qkv

Flag to enable fast query, key, value computation.

TYPE: bool DEFAULT: True

max_rel_2d_pos

The maximum relative 2D position.

TYPE: int DEFAULT: 256

rel_2d_pos_bins

The number of relative 2D position bins.

TYPE: int DEFAULT: 64

image_feature_pool_shape

The shape of the image feature pool.

TYPE: list DEFAULT: [7, 7, 256]

coordinate_size

The size of coordinates.

TYPE: int DEFAULT: 128

shape_size

The size of shapes.

TYPE: int DEFAULT: 128

has_relative_attention_bias

Flag indicating if relative attention bias is used.

TYPE: bool DEFAULT: True

has_spatial_attention_bias

Flag indicating if spatial attention bias is used.

TYPE: bool DEFAULT: True

has_visual_segment_embedding

Flag indicating if visual segment embedding is used.

TYPE: bool DEFAULT: False

use_visual_backbone

Flag indicating if visual backbone is used.

TYPE: bool DEFAULT: True

detectron2_config_args

Additional arguments for the Detectron2 configuration.

TYPE: dict DEFAULT: None

RETURNS DESCRIPTION

None.
Source code in mindnlp\transformers\models\layoutlmv2\configuration_layoutlmv2.py 
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def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        max_2d_position_embeddings=1024,
        max_rel_pos=128,
        rel_pos_bins=32,
        fast_qkv=True,
        max_rel_2d_pos=256,
        rel_2d_pos_bins=64,
        image_feature_pool_shape=[7, 7, 256],
        coordinate_size=128,
        shape_size=128,
        has_relative_attention_bias=True,
        has_spatial_attention_bias=True,
        has_visual_segment_embedding=False,
        use_visual_backbone=True,
        detectron2_config_args=None,
        **kwargs,
):
    """
    Initializes a LayoutLMv2Config object with the specified parameters.

    Args:
        vocab_size (int): The size of the vocabulary.
        hidden_size (int): The hidden size for the model.
        num_hidden_layers (int): The number of hidden layers in the model.
        num_attention_heads (int): The number of attention heads in the model.
        intermediate_size (int): The size of the intermediate layer in the model.
        hidden_act (str): The activation function for the hidden layers.
        hidden_dropout_prob (float): The dropout probability for the hidden layers.
        attention_probs_dropout_prob (float): The dropout probability for the attention probabilities.
        max_position_embeddings (int): The maximum position embeddings allowed.
        type_vocab_size (int): The size of the type vocabulary.
        initializer_range (float): The range for parameter initialization.
        layer_norm_eps (float): The epsilon value for layer normalization.
        pad_token_id (int): The token ID for padding.
        max_2d_position_embeddings (int): The maximum 2D position embeddings allowed.
        max_rel_pos (int): The maximum relative position.
        rel_pos_bins (int): The number of relative position bins.
        fast_qkv (bool): Flag to enable fast query, key, value computation.
        max_rel_2d_pos (int): The maximum relative 2D position.
        rel_2d_pos_bins (int): The number of relative 2D position bins.
        image_feature_pool_shape (list): The shape of the image feature pool.
        coordinate_size (int): The size of coordinates.
        shape_size (int): The size of shapes.
        has_relative_attention_bias (bool): Flag indicating if relative attention bias is used.
        has_spatial_attention_bias (bool): Flag indicating if spatial attention bias is used.
        has_visual_segment_embedding (bool): Flag indicating if visual segment embedding is used.
        use_visual_backbone (bool): Flag indicating if visual backbone is used.
        detectron2_config_args (dict): Additional arguments for the Detectron2 configuration.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(
        vocab_size=vocab_size,
        hidden_size=hidden_size,
        num_hidden_layers=num_hidden_layers,
        num_attention_heads=num_attention_heads,
        intermediate_size=intermediate_size,
        hidden_act=hidden_act,
        hidden_dropout_prob=hidden_dropout_prob,
        attention_probs_dropout_prob=attention_probs_dropout_prob,
        max_position_embeddings=max_position_embeddings,
        type_vocab_size=type_vocab_size,
        initializer_range=initializer_range,
        layer_norm_eps=layer_norm_eps,
        pad_token_id=pad_token_id,
        **kwargs,
    )
    self.max_2d_position_embeddings = max_2d_position_embeddings
    self.max_rel_pos = max_rel_pos
    self.rel_pos_bins = rel_pos_bins
    self.fast_qkv = fast_qkv
    self.max_rel_2d_pos = max_rel_2d_pos
    self.rel_2d_pos_bins = rel_2d_pos_bins
    self.image_feature_pool_shape = image_feature_pool_shape
    self.coordinate_size = coordinate_size
    self.shape_size = shape_size
    self.has_relative_attention_bias = has_relative_attention_bias
    self.has_spatial_attention_bias = has_spatial_attention_bias
    self.has_visual_segment_embedding = has_visual_segment_embedding
    self.use_visual_backbone = use_visual_backbone
    self.detectron2_config_args = (
        detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config()
    )

mindnlp.transformers.models.layoutlmv2.configuration_layoutlmv2.LayoutLMv2Config.get_default_detectron2_config() classmethod

This method returns a dictionary containing the default configuration for the Detectron2 model. The configuration includes various settings related to the model's architecture, backbone, region of interest (ROI) heads, and other parameters.

PARAMETER DESCRIPTION
cls

The class object.

TYPE: class

RETURNS DESCRIPTION
dict

A dictionary containing the default configuration for the Detectron2 model.
Source code in mindnlp\transformers\models\layoutlmv2\configuration_layoutlmv2.py 
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@classmethod
def get_default_detectron2_config(cls):
    '''
    This method returns a dictionary containing the default configuration for the Detectron2 model.
    The configuration includes various settings related to the model's architecture, backbone, region of
    interest (ROI) heads, and other parameters.

    Args:
        cls (class): The class object.

    Returns:
        dict: A dictionary containing the default configuration for the Detectron2 model.

    Raises:
        None.
    '''
    return {
        "MODEL.MASK_ON": True,
        "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
        "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
        "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
        "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
        "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
        "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
        "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
        "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
        "MODEL.POST_NMS_TOPK_TEST": 1000,
        "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
        "MODEL.ROI_HEADS.NUM_CLASSES": 5,
        "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
        "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
        "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
        "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
        "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
        "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
        "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
        "MODEL.RESNETS.DEPTH": 101,
        "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
        "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
        "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
        "MODEL.RESNETS.NUM_GROUPS": 32,
        "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
        "MODEL.RESNETS.STRIDE_IN_1X1": False,
    }

mindnlp.transformers.models.layoutlmv2.configuration_layoutlmv2.LayoutLMv2Config.get_detectron2_config()

This method generates a Detectron2 configuration for the LayoutLMv2 model.

PARAMETER DESCRIPTION
self

The instance of the LayoutLMv2Config class.
RETURNS DESCRIPTION

None.
Source code in mindnlp\transformers\models\layoutlmv2\configuration_layoutlmv2.py 
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def get_detectron2_config(self):
    """
    This method generates a Detectron2 configuration for the LayoutLMv2 model.

    Args:
        self: The instance of the LayoutLMv2Config class.

    Returns:
        None.

    Raises:
        None
    """
    detectron2_config = Dict(
        {
            "MODEL": {
                "MASK_ON": True,
                "PIXEL_MEAN": [103.53, 116.28, 123.675],
                "PIXEL_STD": [57.375, 57.120, 58.395],
                "BACKBONE": {"NAME": "build_resnet_fpn_backbone"},
                "FPN": {
                    "FUSE_TYPE": "sum",
                    "IN_FEATURES": ["res2", "res3", "res4", "res5"],
                    "NORM": "BN",
                    "OUT_CHANNELS": 256
                },
                "ANCHOR_GENERATOR": {"SIZES": [[32], [64], [128], [256], [512]]},
                "RPN": {
                    "IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                    "PRE_NMS_TOPK_TRAIN": 2000,
                    "PRE_NMS_TOPK_TEST": 1000,
                    "POST_NMS_TOPK_TRAIN": 1000,
                },
                "POST_NMS_TOPK_TEST": 1000,
                "ROI_HEADS": {
                    "NAME": "StandardROIHeads",
                    "NUM_CLASSES": 5,
                    "IN_FEATURES": ["p2", "p3", "p4", "p5"],
                },
                "ROI_BOX_HEAD": {
                    "NAME": "FastRCNNConvFCHead",
                    "NUM_FC": 2,
                    "POOLER_RESOLUTION": 14,
                },
                "ROI_MASK_HEAD": {
                    "NAME": "MaskRCNNConvUpsampleHead",
                    "NUM_CONV": 4,
                    "POOLER_RESOLUTION": 7,
                },
                "RESNETS": {
                    "DEPTH": 101,
                    "SIZES": [[32], [64], [128], [256], [512]],
                    "ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                    "FREEZE_AT": 2,
                    "NORM": "BN",
                    "NUM_GROUPS": 32,
                    "WIDTH_PER_GROUP": 8,
                    "STEM_IN_CHANNELS": 3,
                    "STEM_OUT_CHANNELS": 64,
                    "RES2_OUT_CHANNELS": 256,
                    "STRIDE_IN_1X1": False,
                    "RES5_DILATION": 1,
                    "NAME": "resnet101",
                    "PRETRAINED": True,
                    "NUM_CLASSES": 1000,
                    "OUT_FEATURES": ["res2", "res3", "res4", "res5"]
                }
            }
        }
    )
    for k, v in self.detectron2_config_args.items():
        attributes = k.split(".")
        to_set = detectron2_config
        for attribute in attributes[:-1]:
            to_set = getattr(to_set, attribute)
        setattr(to_set, attributes[-1], v)

    return detectron2_config

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2

Image processor class for LayoutLMv2.

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.LayoutLMv2ImageProcessor

Bases: BaseImageProcessor

Constructs a LayoutLMv2 image processor.

PARAMETER DESCRIPTION
do_resize

Whether to resize the image's (height, width) dimensions to (size["height"], size["width"]). Can be overridden by do_resize in preprocess.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

size

224, "width": 224}): Size of the image after resizing. Can be overridden bysizeinpreprocess`.

TYPE: `Dict[str, int]` *optional*, defaults to `{"height" DEFAULT: None

resample

Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the preprocess method.

TYPE: `PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR` DEFAULT: BILINEAR

apply_ocr

Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by apply_ocr in preprocess.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

ocr_lang

The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used. Can be overridden by ocr_lang in preprocess.

TYPE: `str`, *optional* DEFAULT: None

tesseract_config

Any additional custom configuration flags that are forwarded to the config parameter when calling Tesseract. For example: '--psm 6'. Can be overridden by tesseract_config in preprocess.

TYPE: `str`, *optional*, defaults to `""` DEFAULT: ''

Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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class LayoutLMv2ImageProcessor(BaseImageProcessor):
    r"""
    Constructs a LayoutLMv2 image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be
            overridden by `do_resize` in `preprocess`.
        size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
            Size of the image after resizing. Can be overridden by `size` in `preprocess`.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
            `preprocess` method.
        apply_ocr (`bool`, *optional*, defaults to `True`):
            Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
            `apply_ocr` in `preprocess`.
        ocr_lang (`str`, *optional*):
            The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
            used. Can be overridden by `ocr_lang` in `preprocess`.
        tesseract_config (`str`, *optional*, defaults to `""`):
            Any additional custom configuration flags that are forwarded to the `config` parameter when calling
            Tesseract. For example: '--psm 6'. Can be overridden by `tesseract_config` in `preprocess`.
    """
    model_input_names = ["pixel_values"]

    def __init__(
            self,
            do_resize: bool = True,
            size: Dict[str, int] = None,
            resample: PILImageResampling = PILImageResampling.BILINEAR,
            apply_ocr: bool = True,
            ocr_lang: Optional[str] = None,
            tesseract_config: Optional[str] = "",
            **kwargs,
    ) -> None:
        """
        Initializes a LayoutLMv2ImageProcessor object.

        Args:
            self: The LayoutLMv2ImageProcessor instance.
            do_resize (bool): Indicates whether to perform image resizing. Defaults to True.
            size (Dict[str, int]): A dictionary specifying the height and width for resizing the image.
                Defaults to {'height': 224, 'width': 224}.
            resample (PILImageResampling): The resampling filter to use when resizing the image.
                Defaults to PILImageResampling.BILINEAR.
            apply_ocr (bool): Indicates whether optical character recognition (OCR) should be applied. Defaults to True.
            ocr_lang (Optional[str]): The language for OCR. If None, the default language is used. Defaults to None.
            tesseract_config (Optional[str]): Configuration options for the Tesseract OCR engine.
                Defaults to an empty string.
            **kwargs: Additional keyword arguments.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 224, "width": 224}
        size = get_size_dict(size)

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.apply_ocr = apply_ocr
        self.ocr_lang = ocr_lang
        self.tesseract_config = tesseract_config
        self._valid_processor_keys = [
            "images",
            "do_resize",
            "size",
            "resample",
            "apply_ocr",
            "ocr_lang",
            "tesseract_config",
            "return_tensors",
            "data_format",
            "input_data_format",
        ]

    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
    def resize(
            self,
            image: np.ndarray,
            size: Dict[str, int],
            resample: PILImageResampling = PILImageResampling.BILINEAR,
            data_format: Optional[Union[str, ChannelDimension]] = None,
            input_data_format: Optional[Union[str, ChannelDimension]] = None,
            **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
        output_size = (size["height"], size["width"])
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def preprocess(
            self,
            images: ImageInput,
            do_resize: bool = None,
            size: Dict[str, int] = None,
            resample: PILImageResampling = None,
            apply_ocr: bool = None,
            ocr_lang: Optional[str] = None,
            tesseract_config: Optional[str] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            data_format: ChannelDimension = ChannelDimension.FIRST,
            input_data_format: Optional[Union[str, ChannelDimension]] = None,
            **kwargs,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Desired size of the output image after resizing.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. This can be one of the enum `PIL.Image` resampling
                filter. Only has an effect if `do_resize` is set to `True`.
            apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`):
                Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
            ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`):
                The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
                used.
            tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`):
                Any additional custom configuration flags that are forwarded to the `config` parameter when calling
                Tesseract.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:

                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:

                - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        size = get_size_dict(size)
        resample = resample if resample is not None else self.resample
        apply_ocr = apply_ocr if apply_ocr is not None else self.apply_ocr
        ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
        tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config

        images = make_list_of_images(images)

        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )
        validate_preprocess_arguments(
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        # All transformations expect numpy arrays.
        images = [to_numpy_array(image) for image in images]

        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

        if apply_ocr:
            requires_backends(self, "pytesseract")
            words_batch = []
            boxes_batch = []
            for image in images:
                words, boxes = apply_tesseract(image, ocr_lang, tesseract_config, input_data_format=input_data_format)
                words_batch.append(words)
                boxes_batch.append(boxes)

        if do_resize:
            images = [
                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        images = [flip_channel_order(image, input_data_format=input_data_format) for image in images]
        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)

        if apply_ocr:
            data["words"] = words_batch
            data["boxes"] = boxes_batch
        return data

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.LayoutLMv2ImageProcessor.__init__(do_resize=True, size=None, resample=PILImageResampling.BILINEAR, apply_ocr=True, ocr_lang=None, tesseract_config='', **kwargs)

Initializes a LayoutLMv2ImageProcessor object.

PARAMETER DESCRIPTION
self

The LayoutLMv2ImageProcessor instance.
do_resize

Indicates whether to perform image resizing. Defaults to True.

TYPE: bool DEFAULT: True

size

A dictionary specifying the height and width for resizing the image. Defaults to {'height': 224, 'width': 224}.

TYPE: Dict[str, int] DEFAULT: None

resample

The resampling filter to use when resizing the image. Defaults to PILImageResampling.BILINEAR.

TYPE: PILImageResampling DEFAULT: BILINEAR

apply_ocr

Indicates whether optical character recognition (OCR) should be applied. Defaults to True.

TYPE: bool DEFAULT: True

ocr_lang

The language for OCR. If None, the default language is used. Defaults to None.

TYPE: Optional[str] DEFAULT: None

tesseract_config

Configuration options for the Tesseract OCR engine. Defaults to an empty string.

TYPE: Optional[str] DEFAULT: ''

**kwargs

Additional keyword arguments.

DEFAULT: {}

RETURNS DESCRIPTION
None

None.
Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        apply_ocr: bool = True,
        ocr_lang: Optional[str] = None,
        tesseract_config: Optional[str] = "",
        **kwargs,
) -> None:
    """
    Initializes a LayoutLMv2ImageProcessor object.

    Args:
        self: The LayoutLMv2ImageProcessor instance.
        do_resize (bool): Indicates whether to perform image resizing. Defaults to True.
        size (Dict[str, int]): A dictionary specifying the height and width for resizing the image.
            Defaults to {'height': 224, 'width': 224}.
        resample (PILImageResampling): The resampling filter to use when resizing the image.
            Defaults to PILImageResampling.BILINEAR.
        apply_ocr (bool): Indicates whether optical character recognition (OCR) should be applied. Defaults to True.
        ocr_lang (Optional[str]): The language for OCR. If None, the default language is used. Defaults to None.
        tesseract_config (Optional[str]): Configuration options for the Tesseract OCR engine.
            Defaults to an empty string.
        **kwargs: Additional keyword arguments.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(**kwargs)
    size = size if size is not None else {"height": 224, "width": 224}
    size = get_size_dict(size)

    self.do_resize = do_resize
    self.size = size
    self.resample = resample
    self.apply_ocr = apply_ocr
    self.ocr_lang = ocr_lang
    self.tesseract_config = tesseract_config
    self._valid_processor_keys = [
        "images",
        "do_resize",
        "size",
        "resample",
        "apply_ocr",
        "ocr_lang",
        "tesseract_config",
        "return_tensors",
        "data_format",
        "input_data_format",
    ]

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.LayoutLMv2ImageProcessor.preprocess(images, do_resize=None, size=None, resample=None, apply_ocr=None, ocr_lang=None, tesseract_config=None, return_tensors=None, data_format=ChannelDimension.FIRST, input_data_format=None, **kwargs)

Preprocess an image or batch of images.

PARAMETER DESCRIPTION
images

Image to preprocess.

TYPE: `ImageInput`

do_resize

Whether to resize the image.

TYPE: `bool`, *optional*, defaults to `self.do_resize` DEFAULT: None

size

Desired size of the output image after resizing.

TYPE: `Dict[str, int]`, *optional*, defaults to `self.size` DEFAULT: None

resample

Resampling filter to use if resizing the image. This can be one of the enum PIL.Image resampling filter. Only has an effect if do_resize is set to True.

TYPE: `PILImageResampling`, *optional*, defaults to `self.resample` DEFAULT: None

apply_ocr

Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.

TYPE: `bool`, *optional*, defaults to `self.apply_ocr` DEFAULT: None

ocr_lang

The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used.

TYPE: `str`, *optional*, defaults to `self.ocr_lang` DEFAULT: None

tesseract_config

Any additional custom configuration flags that are forwarded to the config parameter when calling Tesseract.

TYPE: `str`, *optional*, defaults to `self.tesseract_config` DEFAULT: None

return_tensors

The type of tensors to return. Can be one of:

  • Unset: Return a list of np.ndarray.
  • TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
  • TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
  • TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
  • TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.

TYPE: `str` or `TensorType`, *optional* DEFAULT: None

data_format

The channel dimension format for the output image. Can be one of:

  • ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • ChannelDimension.LAST: image in (height, width, num_channels) format.

TYPE: `ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST` DEFAULT: FIRST

Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        apply_ocr: bool = None,
        ocr_lang: Optional[str] = None,
        tesseract_config: Optional[str] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
) -> PIL.Image.Image:
    """
    Preprocess an image or batch of images.

    Args:
        images (`ImageInput`):
            Image to preprocess.
        do_resize (`bool`, *optional*, defaults to `self.do_resize`):
            Whether to resize the image.
        size (`Dict[str, int]`, *optional*, defaults to `self.size`):
            Desired size of the output image after resizing.
        resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
            Resampling filter to use if resizing the image. This can be one of the enum `PIL.Image` resampling
            filter. Only has an effect if `do_resize` is set to `True`.
        apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`):
            Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
        ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`):
            The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
            used.
        tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`):
            Any additional custom configuration flags that are forwarded to the `config` parameter when calling
            Tesseract.
        return_tensors (`str` or `TensorType`, *optional*):
            The type of tensors to return. Can be one of:

            - Unset: Return a list of `np.ndarray`.
            - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
            - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
            - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
            - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
        data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
            The channel dimension format for the output image. Can be one of:

            - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
    """
    do_resize = do_resize if do_resize is not None else self.do_resize
    size = size if size is not None else self.size
    size = get_size_dict(size)
    resample = resample if resample is not None else self.resample
    apply_ocr = apply_ocr if apply_ocr is not None else self.apply_ocr
    ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
    tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config

    images = make_list_of_images(images)

    validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)

    if not valid_images(images):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray."
        )
    validate_preprocess_arguments(
        do_resize=do_resize,
        size=size,
        resample=resample,
    )

    # All transformations expect numpy arrays.
    images = [to_numpy_array(image) for image in images]

    if input_data_format is None:
        # We assume that all images have the same channel dimension format.
        input_data_format = infer_channel_dimension_format(images[0])

    if apply_ocr:
        requires_backends(self, "pytesseract")
        words_batch = []
        boxes_batch = []
        for image in images:
            words, boxes = apply_tesseract(image, ocr_lang, tesseract_config, input_data_format=input_data_format)
            words_batch.append(words)
            boxes_batch.append(boxes)

    if do_resize:
        images = [
            self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
            for image in images
        ]

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    images = [flip_channel_order(image, input_data_format=input_data_format) for image in images]
    images = [
        to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
    ]

    data = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)

    if apply_ocr:
        data["words"] = words_batch
        data["boxes"] = boxes_batch
    return data

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.LayoutLMv2ImageProcessor.resize(image, size, resample=PILImageResampling.BILINEAR, data_format=None, input_data_format=None, **kwargs)

Resize an image to (size["height"], size["width"]).

PARAMETER DESCRIPTION
image

Image to resize.

TYPE: `np.ndarray`

size

Dictionary in the format {"height": int, "width": int} specifying the size of the output image.

TYPE: `Dict[str, int]`

resample

PILImageResampling filter to use when resizing the image e.g. PILImageResampling.BILINEAR.

TYPE: `PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR` DEFAULT: BILINEAR

data_format

The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
  • "none" or ChannelDimension.NONE: image in (height, width) format.

TYPE: `ChannelDimension` or `str`, *optional* DEFAULT: None

input_data_format

The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
  • "none" or ChannelDimension.NONE: image in (height, width) format.

TYPE: `ChannelDimension` or `str`, *optional* DEFAULT: None

RETURNS DESCRIPTION
ndarray

np.ndarray: The resized image.
Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
) -> np.ndarray:
    """
    Resize an image to `(size["height"], size["width"])`.

    Args:
        image (`np.ndarray`):
            Image to resize.
        size (`Dict[str, int]`):
            Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
        data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the output image. If unset, the channel dimension format of the input
            image is used. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image. If unset, the channel dimension format is inferred
            from the input image. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

    Returns:
        `np.ndarray`: The resized image.
    """
    size = get_size_dict(size)
    if "height" not in size or "width" not in size:
        raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
    output_size = (size["height"], size["width"])
    return resize(
        image,
        size=output_size,
        resample=resample,
        data_format=data_format,
        input_data_format=input_data_format,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.apply_tesseract(image, lang, tesseract_config=None, input_data_format=None)

Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.

Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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def apply_tesseract(
        image: np.ndarray,
        lang: Optional[str],
        tesseract_config: Optional[str] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
    """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
    tesseract_config = tesseract_config if tesseract_config is not None else ""

    # apply OCR
    pil_image = to_pil_image(image, input_data_format=input_data_format)
    image_width, image_height = pil_image.size
    data = pytesseract.image_to_data(pil_image, lang=lang, output_type="dict", config=tesseract_config)
    words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]

    # filter empty words and corresponding coordinates
    irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
    words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
    left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
    top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
    width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
    height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    actual_boxes = []
    for x, y, w, h in zip(left, top, width, height):
        actual_box = [x, y, x + w, y + h]
        actual_boxes.append(actual_box)

    # finally, normalize the bounding boxes
    normalized_boxes = []
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(box, image_width, image_height))

    assert len(words) == len(normalized_boxes), "Not as many words as there are bounding boxes"

    return words, normalized_boxes

mindnlp.transformers.models.layoutlmv2.image_processing_layoutlmv2.normalize_box(box, width, height)

PARAMETER DESCRIPTION
box

width

height

Source code in mindnlp\transformers\models\layoutlmv2\image_processing_layoutlmv2.py 
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def normalize_box(box, width, height):
    """
    Args:
        box:
        width:
        height:

    Returns: list
    """
    return [
        int(1000 * (box[0] / width)),
        int(1000 * (box[1] / height)),
        int(1000 * (box[2] / width)),
        int(1000 * (box[3] / height)),
    ]

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2

MindSpore LayoutLMv2 model.

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Embeddings

Bases: Module

Construct the embeddings from word, position and token_type embeddings.

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2Embeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super(LayoutLMv2Embeddings, self).__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)

        self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
        self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
        self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
        self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        self.register_buffer(
            "position_ids", ops.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def _calc_spatial_position_embeddings(self, bbox):
        try:
            left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
            upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
            right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
            lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
        except IndexError as e:
            raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e

        h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
        w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])

        spatial_position_embeddings = ops.cat(
            [
                left_position_embeddings,
                upper_position_embeddings,
                right_position_embeddings,
                lower_position_embeddings,
                h_position_embeddings,
                w_position_embeddings,
            ],
            dim=-1,
        )
        return spatial_position_embeddings

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForQuestionAnswering

Bases: LayoutLMv2PreTrainedModel

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2ForQuestionAnswering(LayoutLMv2PreTrainedModel):
    def __init__(self, config, has_visual_segment_embedding=True):
        super().__init__(config)
        self.num_labels = config.num_labels
        config.has_visual_segment_embedding = has_visual_segment_embedding
        self.layoutlmv2 = LayoutLMv2Model(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.layoutlmv2.embeddings.word_embeddings

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        bbox: Optional[mindspore.Tensor] = None,
        image: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        start_positions: Optional[mindspore.Tensor] = None,
        end_positions: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
        r"""
        start_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.

        Returns:

        Example:

        In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us
        a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
        >>> import torch
        >>> from PIL import Image
        >>> from datasets import load_dataset

        >>> set_seed(0)
        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
        >>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")

        >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
        >>> image_path = dataset["test"][0]["file"]
        >>> image = Image.open(image_path).convert("RGB")
        >>> question = "When is coffee break?"
        >>> encoding = processor(image, question, return_tensors="ms")

        >>> outputs = model(**encoding)
        >>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
        >>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
        >>> predicted_start_idx, predicted_end_idx
        (30, 191)

        >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
        >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
        >>> predicted_answer  # results are not good without further fine-tuning
        ```

        ```python
        >>> target_start_index = mindspore.tensor([7])
        >>> target_end_index = mindspore.tensor([14])
        >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
        >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
        >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
        >>> predicted_answer_span_start, predicted_answer_span_end
        (30, 191)
        ```
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.layoutlmv2(
            input_ids=input_ids,
            bbox=bbox,
            image=image,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]
        # only take the text part of the output representations
        sequence_output = outputs[0][:, :seq_length]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = ops.split(logits, 1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.shape) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.shape) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.shape[1]
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForQuestionAnswering.forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, output_attentions=None, output_hidden_states=None, return_dict=None)

start_positions (mindspore.Tensor of shape (batch_size,), optional): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. end_positions (mindspore.Tensor of shape (batch_size,), optional): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns:

Example:

In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).

>>> from transformers import AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
>>> import torch
>>> from PIL import Image
>>> from datasets import load_dataset

>>> set_seed(0)
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
>>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")

>>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
>>> image_path = dataset["test"][0]["file"]
>>> image = Image.open(image_path).convert("RGB")
>>> question = "When is coffee break?"
>>> encoding = processor(image, question, return_tensors="ms")

>>> outputs = model(**encoding)
>>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
>>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
>>> predicted_start_idx, predicted_end_idx
(30, 191)

>>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
>>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
>>> predicted_answer  # results are not good without further fine-tuning

>>> target_start_index = mindspore.tensor([7])
>>> target_end_index = mindspore.tensor([14])
>>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
>>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
>>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
>>> predicted_answer_span_start, predicted_answer_span_end
(30, 191)
Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    bbox: Optional[mindspore.Tensor] = None,
    image: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    start_positions: Optional[mindspore.Tensor] = None,
    end_positions: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
    r"""
    start_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for position (index) of the start of the labelled span for computing the token classification loss.
        Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
        are not taken into account for computing the loss.
    end_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for position (index) of the end of the labelled span for computing the token classification loss.
        Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
        are not taken into account for computing the loss.

    Returns:

    Example:

    In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us
    a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).

    ```python
    >>> from transformers import AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
    >>> import torch
    >>> from PIL import Image
    >>> from datasets import load_dataset

    >>> set_seed(0)
    >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
    >>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")

    >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
    >>> image_path = dataset["test"][0]["file"]
    >>> image = Image.open(image_path).convert("RGB")
    >>> question = "When is coffee break?"
    >>> encoding = processor(image, question, return_tensors="ms")

    >>> outputs = model(**encoding)
    >>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
    >>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
    >>> predicted_start_idx, predicted_end_idx
    (30, 191)

    >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
    >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
    >>> predicted_answer  # results are not good without further fine-tuning
    ```

    ```python
    >>> target_start_index = mindspore.tensor([7])
    >>> target_end_index = mindspore.tensor([14])
    >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
    >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
    >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
    >>> predicted_answer_span_start, predicted_answer_span_end
    (30, 191)
    ```
    """

    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs = self.layoutlmv2(
        input_ids=input_ids,
        bbox=bbox,
        image=image,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    if input_ids is not None:
        input_shape = input_ids.shape
    else:
        input_shape = inputs_embeds.shape[:-1]

    seq_length = input_shape[1]
    # only take the text part of the output representations
    sequence_output = outputs[0][:, :seq_length]

    logits = self.qa_outputs(sequence_output)
    start_logits, end_logits = ops.split(logits, 1, dim=-1)
    start_logits = start_logits.squeeze(-1)
    end_logits = end_logits.squeeze(-1)

    total_loss = None
    if start_positions is not None and end_positions is not None:
        if len(start_positions.shape) > 1:
            start_positions = start_positions.squeeze(-1)
        if len(end_positions.shape) > 1:
            end_positions = end_positions.squeeze(-1)
        # sometimes the start/end positions are outside our model inputs, we ignore these terms
        ignored_index = start_logits.shape[1]
        start_positions = start_positions.clamp(0, ignored_index)
        end_positions = end_positions.clamp(0, ignored_index)

        loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
        start_loss = loss_fct(start_logits, start_positions)
        end_loss = loss_fct(end_logits, end_positions)
        total_loss = (start_loss + end_loss) / 2

    if not return_dict:
        output = (start_logits, end_logits) + outputs[2:]
        return ((total_loss,) + output) if total_loss is not None else output

    return QuestionAnsweringModelOutput(
        loss=total_loss,
        start_logits=start_logits,
        end_logits=end_logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForSequenceClassification

Bases: LayoutLMv2PreTrainedModel

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2ForSequenceClassification(LayoutLMv2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.layoutlmv2 = LayoutLMv2Model(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.layoutlmv2.embeddings.word_embeddings

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        bbox: Optional[mindspore.Tensor] = None,
        image: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Example:

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
        >>> from PIL import Image
        >>> import torch
        >>> from datasets import load_dataset

        >>> set_seed(0)

        >>> dataset = load_dataset("aharley/rvl_cdip", split="train", streaming=True, trust_remote_code=True)
        >>> data = next(iter(dataset))
        >>> image = data["image"].convert("RGB")

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
        >>> model = LayoutLMv2ForSequenceClassification.from_pretrained(
        ...     "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
        ... )

        >>> encoding = processor(image, return_tensors="ms")
        >>> sequence_label = mindspore.tensor([data["label"]])

        >>> outputs = model(**encoding, labels=sequence_label)

        >>> loss, logits = outputs.loss, outputs.logits
        >>> predicted_idx = logits.argmax(dim=-1).item()
        >>> predicted_answer = dataset.info.features["label"].names[4]
        >>> predicted_idx, predicted_answer  # results are not good without further fine-tuning
        (7, 'advertisement')
        ```
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.shape
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.shape[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        visual_shape = list(input_shape)
        visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
        final_shape = list(input_shape)
        final_shape[1] += visual_shape[1]

        visual_bbox = self.layoutlmv2._calc_visual_bbox(
            self.config.image_feature_pool_shape, bbox, final_shape
        )

        visual_position_ids = ops.arange(0, visual_shape[1], dtype=mindspore.int64).tile(
            (input_shape[0], 1)
        )

        initial_image_embeddings = self.layoutlmv2._calc_img_embeddings(
            image=image,
            bbox=visual_bbox,
            position_ids=visual_position_ids,
        )

        outputs = self.layoutlmv2(
            input_ids=input_ids,
            bbox=bbox,
            image=image,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]
        sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:]

        cls_final_output = sequence_output[:, 0, :]

        # average-pool the visual embeddings
        pooled_initial_image_embeddings = ops.mean(initial_image_embeddings, dim=1)
        pooled_final_image_embeddings = ops.mean(final_image_embeddings, dim=1)
        # concatenate with cls_final_output
        sequence_output = ops.cat(
            [cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1
        )
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForSequenceClassification.forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size,), optional): Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns:

Example:

>>> from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
>>> from PIL import Image
>>> import torch
>>> from datasets import load_dataset

>>> set_seed(0)

>>> dataset = load_dataset("aharley/rvl_cdip", split="train", streaming=True, trust_remote_code=True)
>>> data = next(iter(dataset))
>>> image = data["image"].convert("RGB")

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
>>> model = LayoutLMv2ForSequenceClassification.from_pretrained(
...     "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
... )

>>> encoding = processor(image, return_tensors="ms")
>>> sequence_label = mindspore.tensor([data["label"]])

>>> outputs = model(**encoding, labels=sequence_label)

>>> loss, logits = outputs.loss, outputs.logits
>>> predicted_idx = logits.argmax(dim=-1).item()
>>> predicted_answer = dataset.info.features["label"].names[4]
>>> predicted_idx, predicted_answer  # results are not good without further fine-tuning
(7, 'advertisement')
Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    bbox: Optional[mindspore.Tensor] = None,
    image: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
        config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
        `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

    Returns:

    Example:

    ```python
    >>> from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
    >>> from PIL import Image
    >>> import torch
    >>> from datasets import load_dataset

    >>> set_seed(0)

    >>> dataset = load_dataset("aharley/rvl_cdip", split="train", streaming=True, trust_remote_code=True)
    >>> data = next(iter(dataset))
    >>> image = data["image"].convert("RGB")

    >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
    >>> model = LayoutLMv2ForSequenceClassification.from_pretrained(
    ...     "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
    ... )

    >>> encoding = processor(image, return_tensors="ms")
    >>> sequence_label = mindspore.tensor([data["label"]])

    >>> outputs = model(**encoding, labels=sequence_label)

    >>> loss, logits = outputs.loss, outputs.logits
    >>> predicted_idx = logits.argmax(dim=-1).item()
    >>> predicted_answer = dataset.info.features["label"].names[4]
    >>> predicted_idx, predicted_answer  # results are not good without further fine-tuning
    (7, 'advertisement')
    ```
    """

    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if input_ids is not None and inputs_embeds is not None:
        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
    elif input_ids is not None:
        self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
        input_shape = input_ids.shape
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.shape[:-1]
    else:
        raise ValueError("You have to specify either input_ids or inputs_embeds")

    visual_shape = list(input_shape)
    visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
    final_shape = list(input_shape)
    final_shape[1] += visual_shape[1]

    visual_bbox = self.layoutlmv2._calc_visual_bbox(
        self.config.image_feature_pool_shape, bbox, final_shape
    )

    visual_position_ids = ops.arange(0, visual_shape[1], dtype=mindspore.int64).tile(
        (input_shape[0], 1)
    )

    initial_image_embeddings = self.layoutlmv2._calc_img_embeddings(
        image=image,
        bbox=visual_bbox,
        position_ids=visual_position_ids,
    )

    outputs = self.layoutlmv2(
        input_ids=input_ids,
        bbox=bbox,
        image=image,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    if input_ids is not None:
        input_shape = input_ids.shape
    else:
        input_shape = inputs_embeds.shape[:-1]

    seq_length = input_shape[1]
    sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:]

    cls_final_output = sequence_output[:, 0, :]

    # average-pool the visual embeddings
    pooled_initial_image_embeddings = ops.mean(initial_image_embeddings, dim=1)
    pooled_final_image_embeddings = ops.mean(final_image_embeddings, dim=1)
    # concatenate with cls_final_output
    sequence_output = ops.cat(
        [cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1
    )
    sequence_output = self.dropout(sequence_output)
    logits = self.classifier(sequence_output)

    loss = None
    if labels is not None:
        if self.config.problem_type is None:
            if self.num_labels == 1:
                self.config.problem_type = "regression"
            elif self.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                self.config.problem_type = "single_label_classification"
            else:
                self.config.problem_type = "multi_label_classification"

        if self.config.problem_type == "regression":
            loss_fct = MSELoss()
            if self.num_labels == 1:
                loss = loss_fct(logits.squeeze(), labels.squeeze())
            else:
                loss = loss_fct(logits, labels)
        elif self.config.problem_type == "single_label_classification":
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        elif self.config.problem_type == "multi_label_classification":
            loss_fct = BCEWithLogitsLoss()
            loss = loss_fct(logits, labels)
    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return SequenceClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForTokenClassification

Bases: LayoutLMv2PreTrainedModel

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.layoutlmv2 = LayoutLMv2Model(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.layoutlmv2.embeddings.word_embeddings

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        bbox: Optional[mindspore.Tensor] = None,
        image: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, TokenClassifierOutput]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
        >>> from PIL import Image
        >>> from datasets import load_dataset

        >>> set_seed(0)

        >>> datasets = load_dataset("nielsr/funsd", split="test", trust_remote_code=True)
        >>> labels = datasets.features["ner_tags"].feature.names
        >>> id2label = {v: k for v, k in enumerate(labels)}

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
        >>> model = LayoutLMv2ForTokenClassification.from_pretrained(
        ...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
        ... )

        >>> data = datasets[0]
        >>> image = Image.open(data["image_path"]).convert("RGB")
        >>> words = data["words"]
        >>> boxes = data["bboxes"]  # make sure to normalize your bounding boxes
        >>> word_labels = data["ner_tags"]
        >>> encoding = processor(
        ...     image,
        ...     words,
        ...     boxes=boxes,
        ...     word_labels=word_labels,
        ...     padding="max_length",
        ...     truncation=True,
        ...     return_tensors="ms",
        ... )

        >>> outputs = model(**encoding)
        >>> logits, loss = outputs.logits, outputs.loss

        >>> predicted_token_class_ids = logits.argmax(-1)
        >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
        >>> predicted_tokens_classes[:5]  # results are not good without further fine-tuning
        ['I-HEADER', 'I-HEADER', 'I-QUESTION', 'I-HEADER', 'I-QUESTION']
        ```
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.layoutlmv2(
            input_ids=input_ids,
            bbox=bbox,
            image=image,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]
        # only take the text part of the output representations
        sequence_output = outputs[0][:, :seq_length]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2ForTokenClassification.forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size, sequence_length), optional): Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

Returns:

Example:

>>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
>>> from PIL import Image
>>> from datasets import load_dataset

>>> set_seed(0)

>>> datasets = load_dataset("nielsr/funsd", split="test", trust_remote_code=True)
>>> labels = datasets.features["ner_tags"].feature.names
>>> id2label = {v: k for v, k in enumerate(labels)}

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
>>> model = LayoutLMv2ForTokenClassification.from_pretrained(
...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
... )

>>> data = datasets[0]
>>> image = Image.open(data["image_path"]).convert("RGB")
>>> words = data["words"]
>>> boxes = data["bboxes"]  # make sure to normalize your bounding boxes
>>> word_labels = data["ner_tags"]
>>> encoding = processor(
...     image,
...     words,
...     boxes=boxes,
...     word_labels=word_labels,
...     padding="max_length",
...     truncation=True,
...     return_tensors="ms",
... )

>>> outputs = model(**encoding)
>>> logits, loss = outputs.logits, outputs.loss

>>> predicted_token_class_ids = logits.argmax(-1)
>>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes[:5]  # results are not good without further fine-tuning
['I-HEADER', 'I-HEADER', 'I-QUESTION', 'I-HEADER', 'I-QUESTION']
Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    bbox: Optional[mindspore.Tensor] = None,
    image: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
        Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

    Returns:

    Example:

    ```python
    >>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
    >>> from PIL import Image
    >>> from datasets import load_dataset

    >>> set_seed(0)

    >>> datasets = load_dataset("nielsr/funsd", split="test", trust_remote_code=True)
    >>> labels = datasets.features["ner_tags"].feature.names
    >>> id2label = {v: k for v, k in enumerate(labels)}

    >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
    >>> model = LayoutLMv2ForTokenClassification.from_pretrained(
    ...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
    ... )

    >>> data = datasets[0]
    >>> image = Image.open(data["image_path"]).convert("RGB")
    >>> words = data["words"]
    >>> boxes = data["bboxes"]  # make sure to normalize your bounding boxes
    >>> word_labels = data["ner_tags"]
    >>> encoding = processor(
    ...     image,
    ...     words,
    ...     boxes=boxes,
    ...     word_labels=word_labels,
    ...     padding="max_length",
    ...     truncation=True,
    ...     return_tensors="ms",
    ... )

    >>> outputs = model(**encoding)
    >>> logits, loss = outputs.logits, outputs.loss

    >>> predicted_token_class_ids = logits.argmax(-1)
    >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
    >>> predicted_tokens_classes[:5]  # results are not good without further fine-tuning
    ['I-HEADER', 'I-HEADER', 'I-QUESTION', 'I-HEADER', 'I-QUESTION']
    ```
    """

    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs = self.layoutlmv2(
        input_ids=input_ids,
        bbox=bbox,
        image=image,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    if input_ids is not None:
        input_shape = input_ids.shape
    else:
        input_shape = inputs_embeds.shape[:-1]

    seq_length = input_shape[1]
    # only take the text part of the output representations
    sequence_output = outputs[0][:, :seq_length]
    sequence_output = self.dropout(sequence_output)
    logits = self.classifier(sequence_output)

    loss = None
    if labels is not None:
        loss_fct = CrossEntropyLoss()
        loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return TokenClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Model

Bases: LayoutLMv2PreTrainedModel

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.has_visual_segment_embedding = config.has_visual_segment_embedding
        self.embeddings = LayoutLMv2Embeddings(config)

        self.visual = LayoutLMv2VisualBackbone(config)
        self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size)
        if self.has_visual_segment_embedding:
            self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0])
        self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.visual_dropout = nn.Dropout(config.hidden_dropout_prob)

        self.encoder = LayoutLMv2Encoder(config)
        self.pooler = LayoutLMv2Pooler(config)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids, inputs_embeds=None):
        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = ops.arange(seq_length, dtype=mindspore.int64)
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        if token_type_ids is None:
            token_type_ids = ops.zeros_like(input_ids)

        if inputs_embeds is None:
            inputs_embeds = self.embeddings.word_embeddings(input_ids)
        position_embeddings = self.embeddings.position_embeddings(position_ids)
        spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
        token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + position_embeddings + spatial_position_embeddings + token_type_embeddings
        embeddings = self.embeddings.LayerNorm(embeddings)
        embeddings = self.embeddings.dropout(embeddings)
        return embeddings

    def _calc_img_embeddings(self, image, bbox, position_ids):
        visual_embeddings = self.visual_proj(self.visual(image))
        position_embeddings = self.embeddings.position_embeddings(position_ids)
        spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
        embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings
        if self.has_visual_segment_embedding:
            embeddings += self.visual_segment_embedding
        embeddings = self.visual_LayerNorm(embeddings)
        embeddings = self.visual_dropout(embeddings)
        return embeddings

    def _calc_visual_bbox(self, image_feature_pool_shape, bbox, final_shape):
        visual_bbox_x = ops.div(
            ops.arange(
                0,
                1000 * (image_feature_pool_shape[1] + 1),
                1000,
                dtype=bbox.dtype,
            ),
            self.config.image_feature_pool_shape[1],
            rounding_mode="floor",
        )
        visual_bbox_y = ops.div(
            ops.arange(
                0,
                1000 * (self.config.image_feature_pool_shape[0] + 1),
                1000,
                dtype=bbox.dtype,
            ),
            self.config.image_feature_pool_shape[0],
            rounding_mode="floor",
        )
        visual_bbox = ops.stack(
            [
                visual_bbox_x[:-1].tile((image_feature_pool_shape[0], 1)),
                ops.transpose(visual_bbox_y[:-1].tile((image_feature_pool_shape[1], 1)), 0, 1),
                visual_bbox_x[1:].tile((image_feature_pool_shape[0], 1)),
                ops.transpose(visual_bbox_y[1:].tile((image_feature_pool_shape[1], 1)), 0, 1),
            ],
            dim=-1,
        ).view(-1, bbox.shape[-1])

        visual_bbox = visual_bbox.tile((final_shape[0], 1, 1))

        return visual_bbox

    def _get_input_shape(self, input_ids=None, inputs_embeds=None):
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            return input_ids.shape
        elif inputs_embeds is not None:
            return inputs_embeds.shape[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        bbox: Optional[mindspore.Tensor] = None,
        image: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        r"""
        Return:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2Model, set_seed
        >>> from PIL import Image
        >>> import torch
        >>> from datasets import load_dataset

        >>> set_seed(0)

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
        >>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")


        >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
        >>> image_path = dataset["test"][0]["file"]
        >>> image = Image.open(image_path).convert("RGB")

        >>> encoding = processor(image, return_tensors="ms")

        >>> outputs = model(**encoding)
        >>> last_hidden_states = outputs.last_hidden_state

        >>> last_hidden_states.shape
        [1, 342, 768])
        ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        input_shape = self._get_input_shape(input_ids, inputs_embeds)

        visual_shape = list(input_shape)
        visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
        # needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur
        final_shape = list(self._get_input_shape(input_ids, inputs_embeds))
        final_shape[1] += visual_shape[1]

        visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, final_shape)
        final_bbox = ops.cat([bbox, visual_bbox], dim=1)

        if attention_mask is None:
            attention_mask = ops.ones(input_shape)

        visual_attention_mask = ops.ones(visual_shape, dtype=attention_mask.dtype)
        final_attention_mask = ops.cat([attention_mask, visual_attention_mask], dim=1)

        if token_type_ids is None:
            token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

        if position_ids is None:
            seq_length = input_shape[1]
            position_ids = self.embeddings.position_ids[:, :seq_length]
            position_ids = position_ids.expand(input_shape)

        visual_position_ids = ops.arange(0, visual_shape[1], dtype=mindspore.int64).tile(
            (input_shape[0], 1)
        )
        final_position_ids = ops.cat([position_ids, visual_position_ids], dim=1)

        if bbox is None:
            bbox = ops.zeros(tuple(list(input_shape) + [4]), dtype=mindspore.int64)

        text_layout_emb = self._calc_text_embeddings(
            input_ids=input_ids,
            bbox=bbox,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
        )

        visual_emb = self._calc_img_embeddings(
            image=image,
            bbox=visual_bbox,
            position_ids=visual_position_ids,
        )
        final_emb = ops.cat([text_layout_emb, visual_emb], dim=1)

        extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)

        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
        extended_attention_mask = (1.0 - extended_attention_mask) * float(ops.finfo(self.dtype).min)

        if head_mask is not None:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
            elif head_mask.dim() == 2:
                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
            head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
        else:
            head_mask = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            final_emb,
            extended_attention_mask,
            bbox=final_bbox,
            position_ids=final_position_ids,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Model.forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None)

Examples:

>>> from transformers import AutoProcessor, LayoutLMv2Model, set_seed
>>> from PIL import Image
>>> import torch
>>> from datasets import load_dataset

>>> set_seed(0)

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
>>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")


>>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
>>> image_path = dataset["test"][0]["file"]
>>> image = Image.open(image_path).convert("RGB")

>>> encoding = processor(image, return_tensors="ms")

>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_state

>>> last_hidden_states.shape
[1, 342, 768])
Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    bbox: Optional[mindspore.Tensor] = None,
    image: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
    r"""
    Return:

    Examples:

    ```python
    >>> from transformers import AutoProcessor, LayoutLMv2Model, set_seed
    >>> from PIL import Image
    >>> import torch
    >>> from datasets import load_dataset

    >>> set_seed(0)

    >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
    >>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")


    >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa", trust_remote_code=True)
    >>> image_path = dataset["test"][0]["file"]
    >>> image = Image.open(image_path).convert("RGB")

    >>> encoding = processor(image, return_tensors="ms")

    >>> outputs = model(**encoding)
    >>> last_hidden_states = outputs.last_hidden_state

    >>> last_hidden_states.shape
    [1, 342, 768])
    ```
    """
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    input_shape = self._get_input_shape(input_ids, inputs_embeds)

    visual_shape = list(input_shape)
    visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
    # needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur
    final_shape = list(self._get_input_shape(input_ids, inputs_embeds))
    final_shape[1] += visual_shape[1]

    visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, final_shape)
    final_bbox = ops.cat([bbox, visual_bbox], dim=1)

    if attention_mask is None:
        attention_mask = ops.ones(input_shape)

    visual_attention_mask = ops.ones(visual_shape, dtype=attention_mask.dtype)
    final_attention_mask = ops.cat([attention_mask, visual_attention_mask], dim=1)

    if token_type_ids is None:
        token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

    if position_ids is None:
        seq_length = input_shape[1]
        position_ids = self.embeddings.position_ids[:, :seq_length]
        position_ids = position_ids.expand(input_shape)

    visual_position_ids = ops.arange(0, visual_shape[1], dtype=mindspore.int64).tile(
        (input_shape[0], 1)
    )
    final_position_ids = ops.cat([position_ids, visual_position_ids], dim=1)

    if bbox is None:
        bbox = ops.zeros(tuple(list(input_shape) + [4]), dtype=mindspore.int64)

    text_layout_emb = self._calc_text_embeddings(
        input_ids=input_ids,
        bbox=bbox,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        inputs_embeds=inputs_embeds,
    )

    visual_emb = self._calc_img_embeddings(
        image=image,
        bbox=visual_bbox,
        position_ids=visual_position_ids,
    )
    final_emb = ops.cat([text_layout_emb, visual_emb], dim=1)

    extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)

    extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
    extended_attention_mask = (1.0 - extended_attention_mask) * float(ops.finfo(self.dtype).min)

    if head_mask is not None:
        if head_mask.dim() == 1:
            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
            head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
        elif head_mask.dim() == 2:
            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
        head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
    else:
        head_mask = [None] * self.config.num_hidden_layers

    encoder_outputs = self.encoder(
        final_emb,
        extended_attention_mask,
        bbox=final_bbox,
        position_ids=final_position_ids,
        head_mask=head_mask,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = encoder_outputs[0]
    pooled_output = self.pooler(sequence_output)

    if not return_dict:
        return (sequence_output, pooled_output) + encoder_outputs[1:]

    return BaseModelOutputWithPooling(
        last_hidden_state=sequence_output,
        pooler_output=pooled_output,
        hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions,
    )

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2PreTrainedModel

Bases: PreTrainedModel

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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class LayoutLMv2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = LayoutLMv2Config
    base_model_prefix = "layoutlmv2"

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight[module.padding_idx] = 0
        elif isinstance(module, nn.LayerNorm):
            nn.init.zeros_(module.bias)
            nn.init.ones_(module.weight)
        elif isinstance(module, LayoutLMv2Model):
            if hasattr(module, "visual_segment_embedding"):
                nn.init.normal_(module.visual_segment_embedding, mean=0.0, std=self.config.initializer_range)

mindnlp.transformers.models.layoutlmv2.modeling_layoutlmv2.relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128)

Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions

=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on.

PARAMETER DESCRIPTION
relative_position

an int32 Tensor
bidirectional

a boolean - whether the attention is bidirectional

DEFAULT: True

num_buckets

an integer

DEFAULT: 32

max_distance

an integer

DEFAULT: 128

RETURNS DESCRIPTION

a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
Source code in mindnlp\transformers\models\layoutlmv2\modeling_layoutlmv2.py 
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def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
    """
    Adapted from Mesh Tensorflow:
    https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
    Translate relative position to a bucket number for relative attention. The relative position is defined as
    memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
    position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small
    absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions
    >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should
    allow for more graceful generalization to longer sequences than the model has been trained on.

    Args:
        relative_position: an int32 Tensor
        bidirectional: a boolean - whether the attention is bidirectional
        num_buckets: an integer
        max_distance: an integer

    Returns:
        a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
    """

    ret = 0
    if bidirectional:
        num_buckets //= 2
        ret += (relative_position > 0).long() * num_buckets
        n = ops.abs(relative_position)
    else:
        n = ops.max(-relative_position, ops.zeros_like(relative_position))
    # now n is in the range [0, inf)

    # half of the buckets are for exact increments in positions
    max_exact = num_buckets // 2
    is_small = n < max_exact

    # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
    val_if_large = max_exact + (
        ops.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
    ).to(mindspore.int64)
    val_if_large = ops.minimum(val_if_large, ops.full_like(val_if_large, num_buckets - 1))

    ret += ops.where(is_small, n, val_if_large)
    return ret

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2

Processor class for LayoutLMv2.

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor

Bases: ProcessorMixin

Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a single processor.

[LayoutLMv2Processor] offers all the functionalities you need to prepare data for the model.

It first uses [LayoutLMv2ImageProcessor] to resize document images to a fixed size, and optionally applies OCR to get words and normalized bounding boxes. These are then provided to [LayoutLMv2Tokenizer] or [LayoutLMv2TokenizerFast], which turns the words and bounding boxes into token-level input_ids, attention_mask, token_type_ids, bbox. Optionally, one can provide integer word_labels, which are turned into token-level labels for token classification tasks (such as FUNSD, CORD).

PARAMETER DESCRIPTION
image_processor

An instance of [LayoutLMv2ImageProcessor]. The image processor is a required input.

TYPE: `LayoutLMv2ImageProcessor`, *optional* DEFAULT: None

tokenizer

An instance of [LayoutLMv2Tokenizer] or [LayoutLMv2TokenizerFast]. The tokenizer is a required input.

TYPE: `LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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class LayoutLMv2Processor(ProcessorMixin):
    r"""
    Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a
    single processor.

    [`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.

    It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
    get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
    [`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
    `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
    into token-level `labels` for token classification tasks (such as FUNSD, CORD).

    Args:
        image_processor (`LayoutLMv2ImageProcessor`, *optional*):
            An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
        tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`, *optional*):
            An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "LayoutLMv2ImageProcessor"
    tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
        """
        Initialize the LayoutLMv2Processor class.

        Args:
            self (object): The instance of the class.
            image_processor (object): An object representing the image processor.
                It can be an instance of a specific image processing class or None.
                If None, it will default to the value of 'feature_extractor'.
            tokenizer (object): An object representing the tokenizer to be used.
                This should be a valid tokenizer object required for processing the input data.

        Returns:
            None.

        Raises:
            ValueError: If either 'image_processor' is not provided or if 'tokenizer' is not specified.
            FutureWarning: If the 'feature_extractor' argument is used (deprecated) in place of 'image_processor'.
        """
        feature_extractor = None
        if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead.",
                FutureWarning,
            )
            feature_extractor = kwargs.pop("feature_extractor")

        image_processor = image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")

        super().__init__(image_processor, tokenizer)

    def __call__(
            self,
            images,
            text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
            text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
            boxes: Union[List[List[int]], List[List[List[int]]]] = None,
            word_labels: Optional[Union[List[int], List[List[int]]]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = False,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            return_tensors: Optional[Union[str, TensorType]] = None,
            **kwargs,
    ) -> BatchEncoding:
        """
        This method first forwards the `images` argument to [`~LayoutLMv2ImageProcessor.__call__`]. In case
        [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
        bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
        together with resized `images`. In case [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to
        `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
        arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.

        Please refer to the docstring of the above two methods for more information.
        """
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
            )

        if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
            )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")

        # first, apply the image processor
        features = self.image_processor(images=images, return_tensors=return_tensors)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(text, str):
                text = [text]  # add batch dimension (as the image processor always adds a batch dimension)
            text_pair = features["words"]

        encoded_inputs = self.tokenizer(
            text=text if text is not None else features["words"],
            text_pair=text_pair if text_pair is not None else None,
            boxes=boxes if boxes is not None else features["boxes"],
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            return_tensors=return_tensors,
            **kwargs,
        )

        # add pixel values
        images = features.pop("pixel_values")
        if return_overflowing_tokens is True:
            images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
        encoded_inputs["image"] = images

        return encoded_inputs

    def get_overflowing_images(self, images, overflow_to_sample_mapping):
        """

        Args:
            images: List of images
            overflow_to_sample_mapping: List of indices of samples that have overflowing tokens

        Returns:
            List of images that correspond to samples with overflowing tokens
        """
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        images_with_overflow = []
        for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx])

        if len(images_with_overflow) != len(overflow_to_sample_mapping):
            raise ValueError(
                "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
            )

        return images_with_overflow

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        """
        This method returns a list of input names used by the LayoutLMv2Processor.

        Args:
            self (LayoutLMv2Processor): The instance of the LayoutLMv2Processor.

        Returns:
            list: A list containing the input names, including 'input_ids', 'bbox', 'token_type_ids',
                'attention_mask', and 'image'.

        Raises:
            None.
        """
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

    @property
    def feature_extractor_class(self):
        """
        Deprecated property, will be removed in v5. Use `image_processor_class` instead.
        """
        warnings.warn(
            "`feature_extractor_class` is deprecated. Use `image_processor_class` instead.",
            FutureWarning,
        )
        return self.image_processor_class

    @property
    def feature_extractor(self):
        """
        Deprecated property, will be removed in v5. Use `image_processor` instead.
        """
        warnings.warn(
            "`feature_extractor` is deprecated. Use `image_processor` instead.",
            FutureWarning,
        )
        return self.image_processor

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.feature_extractor property

Deprecated property, will be removed in v5. Use image_processor instead.

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.feature_extractor_class property

Deprecated property, will be removed in v5. Use image_processor_class instead.

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.model_input_names property

This method returns a list of input names used by the LayoutLMv2Processor.

PARAMETER DESCRIPTION
self

The instance of the LayoutLMv2Processor.

TYPE: LayoutLMv2Processor

RETURNS DESCRIPTION
list

A list containing the input names, including 'input_ids', 'bbox', 'token_type_ids', 'attention_mask', and 'image'.

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.__call__(images, text=None, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=False, max_length=None, stride=0, pad_to_multiple_of=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, return_tensors=None, **kwargs)

This method first forwards the images argument to [~LayoutLMv2ImageProcessor.__call__]. In case [LayoutLMv2ImageProcessor] was initialized with apply_ocr set to True, it passes the obtained words and bounding boxes along with the additional arguments to [~LayoutLMv2Tokenizer.__call__] and returns the output, together with resized images. In case [LayoutLMv2ImageProcessor] was initialized with apply_ocr set to False, it passes the words (text/text_pair`) and `boxes` specified by the user along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images.

Please refer to the docstring of the above two methods for more information.

Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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def __call__(
        self,
        images,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
) -> BatchEncoding:
    """
    This method first forwards the `images` argument to [`~LayoutLMv2ImageProcessor.__call__`]. In case
    [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
    bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
    together with resized `images`. In case [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to
    `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
    arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.

    Please refer to the docstring of the above two methods for more information.
    """
    # verify input
    if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
        )

    if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
        )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
        raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")

    # first, apply the image processor
    features = self.image_processor(images=images, return_tensors=return_tensors)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(text, str):
            text = [text]  # add batch dimension (as the image processor always adds a batch dimension)
        text_pair = features["words"]

    encoded_inputs = self.tokenizer(
        text=text if text is not None else features["words"],
        text_pair=text_pair if text_pair is not None else None,
        boxes=boxes if boxes is not None else features["boxes"],
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        return_tensors=return_tensors,
        **kwargs,
    )

    # add pixel values
    images = features.pop("pixel_values")
    if return_overflowing_tokens is True:
        images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
    encoded_inputs["image"] = images

    return encoded_inputs

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.__init__(image_processor=None, tokenizer=None, **kwargs)

Initialize the LayoutLMv2Processor class.

PARAMETER DESCRIPTION
self

The instance of the class.

TYPE: object

image_processor

An object representing the image processor. It can be an instance of a specific image processing class or None. If None, it will default to the value of 'feature_extractor'.

TYPE: object DEFAULT: None

tokenizer

An object representing the tokenizer to be used. This should be a valid tokenizer object required for processing the input data.

TYPE: object DEFAULT: None

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
ValueError

If either 'image_processor' is not provided or if 'tokenizer' is not specified.
FutureWarning

If the 'feature_extractor' argument is used (deprecated) in place of 'image_processor'.
Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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def __init__(self, image_processor=None, tokenizer=None, **kwargs):
    """
    Initialize the LayoutLMv2Processor class.

    Args:
        self (object): The instance of the class.
        image_processor (object): An object representing the image processor.
            It can be an instance of a specific image processing class or None.
            If None, it will default to the value of 'feature_extractor'.
        tokenizer (object): An object representing the tokenizer to be used.
            This should be a valid tokenizer object required for processing the input data.

    Returns:
        None.

    Raises:
        ValueError: If either 'image_processor' is not provided or if 'tokenizer' is not specified.
        FutureWarning: If the 'feature_extractor' argument is used (deprecated) in place of 'image_processor'.
    """
    feature_extractor = None
    if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead.",
            FutureWarning,
        )
        feature_extractor = kwargs.pop("feature_extractor")

    image_processor = image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
        raise ValueError("You need to specify an `image_processor`.")
    if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`.")

    super().__init__(image_processor, tokenizer)

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.batch_decode(*args, **kwargs)

This method forwards all its arguments to PreTrainedTokenizer's [~PreTrainedTokenizer.batch_decode]. Please refer to the docstring of this method for more information.

Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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def batch_decode(self, *args, **kwargs):
    """
    This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
    refer to the docstring of this method for more information.
    """
    return self.tokenizer.batch_decode(*args, **kwargs)

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.decode(*args, **kwargs)

This method forwards all its arguments to PreTrainedTokenizer's [~PreTrainedTokenizer.decode]. Please refer to the docstring of this method for more information.

Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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def decode(self, *args, **kwargs):
    """
    This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
    to the docstring of this method for more information.
    """
    return self.tokenizer.decode(*args, **kwargs)

mindnlp.transformers.models.layoutlmv2.processing_layoutlmv2.LayoutLMv2Processor.get_overflowing_images(images, overflow_to_sample_mapping)

PARAMETER DESCRIPTION
images

List of images
overflow_to_sample_mapping

List of indices of samples that have overflowing tokens
RETURNS DESCRIPTION

List of images that correspond to samples with overflowing tokens
Source code in mindnlp\transformers\models\layoutlmv2\processing_layoutlmv2.py 
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def get_overflowing_images(self, images, overflow_to_sample_mapping):
    """

    Args:
        images: List of images
        overflow_to_sample_mapping: List of indices of samples that have overflowing tokens

    Returns:
        List of images that correspond to samples with overflowing tokens
    """
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    images_with_overflow = []
    for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx])

    if len(images_with_overflow) != len(overflow_to_sample_mapping):
        raise ValueError(
            "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
            f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
        )

    return images_with_overflow

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2

Tokenization class for LayoutLMv2.

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.BasicTokenizer

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).

PARAMETER DESCRIPTION
do_lower_case

Whether or not to lowercase the input when tokenizing.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

never_split

Collection of tokens which will never be split during tokenization. Only has an effect when do_basic_tokenize=True

TYPE: `Iterable`, *optional* DEFAULT: None

tokenize_chinese_chars

Whether or not to tokenize Chinese characters.

This should likely be deactivated for Japanese (see this issue).

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

strip_accents

Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

TYPE: `bool`, *optional* DEFAULT: None

do_split_on_punc

In some instances we want to skip the basic punctuation splitting so that later tokenization can capture the full context of the words, such as contractions.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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class BasicTokenizer:
    """
    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).

    Args:
        do_lower_case (`bool`, *optional*, defaults to `True`):
            Whether or not to lowercase the input when tokenizing.
        never_split (`Iterable`, *optional*):
            Collection of tokens which will never be split during tokenization. Only has an effect when
            `do_basic_tokenize=True`
        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
            Whether or not to tokenize Chinese characters.

            This should likely be deactivated for Japanese (see this
            [issue](https://github.com/huggingface/transformers/issues/328)).
        strip_accents (`bool`, *optional*):
            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
            value for `lowercase` (as in the original BERT).
        do_split_on_punc (`bool`, *optional*, defaults to `True`):
            In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
            the full context of the words, such as contractions.
    """
    def __init__(
            self,
            do_lower_case=True,
            never_split=None,
            tokenize_chinese_chars=True,
            strip_accents=None,
            do_split_on_punc=True,
    ):
        """
        Initializes a new instance of the BasicTokenizer class.

        Args:
            self: The object itself.
            do_lower_case (bool): A boolean indicating whether to convert the text to lowercase. Defaults to True.
            never_split (list): A list of tokens that should never be split during tokenization. Defaults to None.
            tokenize_chinese_chars (bool): A boolean indicating whether to tokenize Chinese characters. Defaults to True.
            strip_accents (str or None): A string indicating whether to strip accents from the text. Defaults to None.
            do_split_on_punc (bool): A boolean indicating whether to split tokens on punctuation. Defaults to True.

        Returns:
            None.

        Raises:
            None.
        """
        if never_split is None:
            never_split = []
        self.do_lower_case = do_lower_case
        self.never_split = set(never_split)
        self.tokenize_chinese_chars = tokenize_chinese_chars
        self.strip_accents = strip_accents
        self.do_split_on_punc = do_split_on_punc

    def tokenize(self, text, never_split=None):
        """
        Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.

        Args:
            never_split (`List[str]`, *optional*)
                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
        """
        # union() returns a new set by concatenating the two sets.
        never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
        text = self._clean_text(text)

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        if self.tokenize_chinese_chars:
            text = self._tokenize_chinese_chars(text)
        # prevents treating the same character with different unicode codepoints as different characters
        unicode_normalized_text = unicodedata.normalize("NFC", text)
        orig_tokens = whitespace_tokenize(unicode_normalized_text)
        split_tokens = []
        for token in orig_tokens:
            if token not in never_split:
                if self.do_lower_case:
                    token = token.lower()
                    if self.strip_accents is not False:
                        token = self._run_strip_accents(token)
                elif self.strip_accents:
                    token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token, never_split))

        output_tokens = whitespace_tokenize(" ".join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """Strips accents from a piece of text."""
        text = unicodedata.normalize("NFD", text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == "Mn":
                continue
            output.append(char)
        return "".join(output)

    def _run_split_on_punc(self, text, never_split=None):
        """Splits punctuation on a piece of text."""
        if not self.do_split_on_punc or (never_split is not None and text in never_split):
            return [text]
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return ["".join(x) for x in output]

    def _tokenize_chinese_chars(self, text):
        """Adds whitespace around any CJK character."""
        output = []
        for char in text:
            cp = ord(char)
            if self._is_chinese_char(cp):
                output.append(" ")
                output.append(char)
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

    def _is_chinese_char(self, cp):
        """Checks whether CP is the codepoint of a CJK character."""
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if (
                (0x4E00 <= cp <= 0x9FFF)
                or (0x3400 <= cp <= 0x4DBF)  #
                or (0x20000 <= cp <= 0x2A6DF)  #
                or (0x2A700 <= cp <= 0x2B73F)  #
                or (0x2B740 <= cp <= 0x2B81F)  #
                or (0x2B820 <= cp <= 0x2CEAF)  #
                or (0xF900 <= cp <= 0xFAFF)
                or (0x2F800 <= cp <= 0x2FA1F)  #
        ):  #
            return True

        return False

    def _clean_text(self, text):
        """Performs invalid character removal and whitespace cleanup on text."""
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.BasicTokenizer.__init__(do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None, do_split_on_punc=True)

Initializes a new instance of the BasicTokenizer class.

PARAMETER DESCRIPTION
self

The object itself.
do_lower_case

A boolean indicating whether to convert the text to lowercase. Defaults to True.

TYPE: bool DEFAULT: True

never_split

A list of tokens that should never be split during tokenization. Defaults to None.

TYPE: list DEFAULT: None

tokenize_chinese_chars

A boolean indicating whether to tokenize Chinese characters. Defaults to True.

TYPE: bool DEFAULT: True

strip_accents

A string indicating whether to strip accents from the text. Defaults to None.

TYPE: str or None DEFAULT: None

do_split_on_punc

A boolean indicating whether to split tokens on punctuation. Defaults to True.

TYPE: bool DEFAULT: True

RETURNS DESCRIPTION

None.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def __init__(
        self,
        do_lower_case=True,
        never_split=None,
        tokenize_chinese_chars=True,
        strip_accents=None,
        do_split_on_punc=True,
):
    """
    Initializes a new instance of the BasicTokenizer class.

    Args:
        self: The object itself.
        do_lower_case (bool): A boolean indicating whether to convert the text to lowercase. Defaults to True.
        never_split (list): A list of tokens that should never be split during tokenization. Defaults to None.
        tokenize_chinese_chars (bool): A boolean indicating whether to tokenize Chinese characters. Defaults to True.
        strip_accents (str or None): A string indicating whether to strip accents from the text. Defaults to None.
        do_split_on_punc (bool): A boolean indicating whether to split tokens on punctuation. Defaults to True.

    Returns:
        None.

    Raises:
        None.
    """
    if never_split is None:
        never_split = []
    self.do_lower_case = do_lower_case
    self.never_split = set(never_split)
    self.tokenize_chinese_chars = tokenize_chinese_chars
    self.strip_accents = strip_accents
    self.do_split_on_punc = do_split_on_punc

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.BasicTokenizer.tokenize(text, never_split=None)

Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def tokenize(self, text, never_split=None):
    """
    Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.

    Args:
        never_split (`List[str]`, *optional*)
            Kept for backward compatibility purposes. Now implemented directly at the base class level (see
            [`PreTrainedTokenizer.tokenize`]) List of token not to split.
    """
    # union() returns a new set by concatenating the two sets.
    never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
    text = self._clean_text(text)

    # This was added on November 1st, 2018 for the multilingual and Chinese
    # models. This is also applied to the English models now, but it doesn't
    # matter since the English models were not trained on any Chinese data
    # and generally don't have any Chinese data in them (there are Chinese
    # characters in the vocabulary because Wikipedia does have some Chinese
    # words in the English Wikipedia.).
    if self.tokenize_chinese_chars:
        text = self._tokenize_chinese_chars(text)
    # prevents treating the same character with different unicode codepoints as different characters
    unicode_normalized_text = unicodedata.normalize("NFC", text)
    orig_tokens = whitespace_tokenize(unicode_normalized_text)
    split_tokens = []
    for token in orig_tokens:
        if token not in never_split:
            if self.do_lower_case:
                token = token.lower()
                if self.strip_accents is not False:
                    token = self._run_strip_accents(token)
            elif self.strip_accents:
                token = self._run_strip_accents(token)
        split_tokens.extend(self._run_split_on_punc(token, never_split))

    output_tokens = whitespace_tokenize(" ".join(split_tokens))
    return output_tokens

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer

Bases: PreTrainedTokenizer

Construct a LayoutLMv2 tokenizer. Based on WordPiece. [LayoutLMv2Tokenizer] can be used to turn words, word-level bounding boxes and optional word labels to token-level input_ids, attention_mask, token_type_ids, bbox, and optional labels (for token classification).

This tokenizer inherits from [PreTrainedTokenizer] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

[LayoutLMv2Tokenizer] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the word-level bounding boxes into token-level bounding boxes.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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class LayoutLMv2Tokenizer(PreTrainedTokenizer):
    r"""
    Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] can be used to turn words, word-level
    bounding boxes and optional word labels to token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and
    optional `labels` (for token classification).

    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
    this superclass for more information regarding those methods.

    [`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
    word-level bounding boxes into token-level bounding boxes.

    """
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION

    def __init__(
            self,
            vocab_file,
            do_lower_case=True,
            do_basic_tokenize=True,
            never_split=None,
            unk_token="[UNK]",
            sep_token="[SEP]",
            pad_token="[PAD]",
            cls_token="[CLS]",
            mask_token="[MASK]",
            cls_token_box=[0, 0, 0, 0],
            sep_token_box=[1000, 1000, 1000, 1000],
            pad_token_box=[0, 0, 0, 0],
            pad_token_label=-100,
            only_label_first_subword=True,
            tokenize_chinese_chars=True,
            strip_accents=None,
            model_max_length: int = 512,
            additional_special_tokens: Optional[List[str]] = None,
            **kwargs,
    ):
        """
        Initializes a LayoutLMv2Tokenizer object.

        Args:
            self: The instance of the class.
            vocab_file (str): The path to the vocabulary file.
            do_lower_case (bool, optional): Whether to lowercase the input text. Defaults to True.
            do_basic_tokenize (bool, optional): Whether to perform basic tokenization. Defaults to True.
            never_split (list, optional): List of tokens that should not be split. Defaults to None.
            unk_token (str, optional): The unknown token. Defaults to '[UNK]'.
            sep_token (str, optional): The separator token. Defaults to '[SEP]'.
            pad_token (str, optional): The padding token. Defaults to '[PAD]'.
            cls_token (str, optional): The classification token. Defaults to '[CLS]'.
            mask_token (str, optional): The masking token. Defaults to '[MASK]'.
            cls_token_box (list, optional): The bounding box coordinates for the classification token. Defaults to [0, 0, 0, 0].
            sep_token_box (list, optional): The bounding box coordinates for the separator token. Defaults to [1000, 1000, 1000, 1000].
            pad_token_box (list, optional): The bounding box coordinates for the padding token. Defaults to [0, 0, 0, 0].
            pad_token_label (int, optional): The label for the padding token. Defaults to -100.
            only_label_first_subword (bool, optional): Whether to only label the first subword. Defaults to True.
            tokenize_chinese_chars (bool, optional): Whether to tokenize Chinese characters. Defaults to True.
            strip_accents (str, optional): The accents to strip. Defaults to None.
            model_max_length (int, optional): The maximum length of the model. Defaults to 512.
            additional_special_tokens (list, optional): Additional special tokens. Defaults to None.

        Returns:
            None

        Raises:
            ValueError: If the vocabulary file cannot be found at the specified path.
        """
        sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
        unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
        cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
        mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token

        if not os.path.isfile(vocab_file):
            raise ValueError(
                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
                " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
            )
        self.vocab = load_vocab(vocab_file)
        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
        self.do_basic_tokenize = do_basic_tokenize
        if do_basic_tokenize:
            self.basic_tokenizer = BasicTokenizer(
                do_lower_case=do_lower_case,
                never_split=never_split,
                tokenize_chinese_chars=tokenize_chinese_chars,
                strip_accents=strip_accents,
            )
        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))

        # additional properties
        self.cls_token_box = cls_token_box
        self.sep_token_box = sep_token_box
        self.pad_token_box = pad_token_box
        self.pad_token_label = pad_token_label
        self.only_label_first_subword = only_label_first_subword
        super().__init__(
            do_lower_case=do_lower_case,
            do_basic_tokenize=do_basic_tokenize,
            never_split=never_split,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            cls_token_box=cls_token_box,
            sep_token_box=sep_token_box,
            pad_token_box=pad_token_box,
            pad_token_label=pad_token_label,
            only_label_first_subword=only_label_first_subword,
            tokenize_chinese_chars=tokenize_chinese_chars,
            strip_accents=strip_accents,
            model_max_length=model_max_length,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

    @property
    def do_lower_case(self):
        """
        Whether or not to lowercase the input when tokenizing.
        """
        return self.basic_tokenizer.do_lower_case

    @property
    def vocab_size(self):
        """Return the size of the vocabulary used by the LayoutLMv2Tokenizer.

        Args:
            self (LayoutLMv2Tokenizer): An instance of the LayoutLMv2Tokenizer class.

        Returns:
            None.

        Raises:
            None.
        """
        return len(self.vocab)

    def get_vocab(self):
        """
        Returns the combined vocabulary of the LayoutLMv2Tokenizer instance and any additional tokens that have been added.

        Args:
            self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.

        Returns:
            dict: A dictionary representing the combined vocabulary of the LayoutLMv2Tokenizer instance
                and any additional tokens that have been added.

        Raises:
            None.

        """
        return dict(self.vocab, **self.added_tokens_encoder)

    def _tokenize(self, text):
        """
        This method '_tokenize' is defined within the 'LayoutLMv2Tokenizer' class and is responsible
        for tokenizing the input text.

        Args:
            self: The instance of the 'LayoutLMv2Tokenizer' class.
            text (str): The input text to be tokenized.

        Returns:
            list: A list of tokens resulting from the tokenization process.

        Raises:
            None.
        """
        split_tokens = []
        if self.do_basic_tokenize:
            for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
                # If the token is part of the never_split set
                if token in self.basic_tokenizer.never_split:
                    split_tokens.append(token)
                else:
                    split_tokens += self.wordpiece_tokenizer.tokenize(token)
        else:
            split_tokens = self.wordpiece_tokenizer.tokenize(text)
        return split_tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.vocab.get(token, self.vocab.get(self.unk_token))

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.ids_to_tokens.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        out_string = " ".join(tokens).replace(" ##", "").strip()
        return out_string

    def build_inputs_with_special_tokens(
            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens. A BERT sequence has the following format:

        - single sequence: `[CLS] X [SEP]`
        - pair of sequences: `[CLS] A [SEP] B [SEP]`

        Args:
            token_ids_0 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]
        return cls + token_ids_0 + sep + token_ids_1 + sep

    def get_special_tokens_mask(
            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None,
            already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        if token_ids_1 is not None:
            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1]

    def create_token_type_ids_from_sequences(
            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
        pair mask has the following format:

        ```:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ```

        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]
        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary to a file in the specified directory.

        Args:
            self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.
            save_directory (str): The directory where the vocabulary file will be saved.
            filename_prefix (Optional[str]): A prefix to be added to the filename. Defaults to None.

        Returns:
            Tuple[str]: A tuple containing the file path of the saved vocabulary.

        Raises:
            IOError: If an I/O error occurs while writing the vocabulary file.
            ValueError: If the provided save_directory is invalid or if the vocabulary indices are not consecutive.

        """
        index = 0
        if os.path.isdir(save_directory):
            vocab_file = os.path.join(
                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
            )
        else:
            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
        with open(vocab_file, "w", encoding="utf-8") as writer:
            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                        " Please check that the vocabulary is not corrupted!"
                    )
                    index = token_index
                writer.write(token + "\n")
                index += 1
        return (vocab_file,)

    def __call__(
            self,
            text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
            text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
            boxes: Union[List[List[int]], List[List[List[int]]]] = None,
            word_labels: Optional[Union[List[int], List[List[int]]]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
        sequences with word-level normalized bounding boxes and optional labels.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
                (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
                words).
            text_pair (`List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
                (pretokenized string).
            boxes (`List[List[int]]`, `List[List[List[int]]]`):
                Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
            word_labels (`List[int]`, `List[List[int]]`, *optional*):
                Word-level integer labels (for token classification tasks such as FUNSD, CORD).
        """
        # Input type checking for clearer error
        def _is_valid_text_input(t):
            if isinstance(t, str):
                # Strings are fine
                return True
            if isinstance(t, (list, tuple)):
                # List are fine as long as they are...
                if len(t) == 0:
                    # ... empty
                    return True
                if isinstance(t[0], str):
                    # ... list of strings
                    return True
                if isinstance(t[0], (list, tuple)):
                    # ... list with an empty list or with a list of strings
                    return len(t[0]) == 0 or isinstance(t[0][0], str)
            return False

        if text_pair is not None:
            # in case text + text_pair are provided, text = questions, text_pair = words
            if not _is_valid_text_input(text):
                raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
            if not isinstance(text_pair, (list, tuple)):
                raise ValueError(
                    "Words must be of type `List[str]` (single pretokenized example), "
                    "or `List[List[str]]` (batch of pretokenized examples)."
                )
        else:
            # in case only text is provided => must be words
            if not isinstance(text, (list, tuple)):
                raise ValueError(
                    "Words must be of type `List[str]` (single pretokenized example), "
                    "or `List[List[str]]` (batch of pretokenized examples)."
                )

        if text_pair is not None:
            is_batched = isinstance(text, (list, tuple))
        else:
            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))

        words = text if text_pair is None else text_pair
        if boxes is None:
            raise ValueError("You must provide corresponding bounding boxes")
        if is_batched:
            if len(words) != len(boxes):
                raise ValueError("You must provide words and boxes for an equal amount of examples")
            for words_example, boxes_example in zip(words, boxes):
                if len(words_example) != len(boxes_example):
                    raise ValueError("You must provide as many words as there are bounding boxes")
        else:
            if len(words) != len(boxes):
                raise ValueError("You must provide as many words as there are bounding boxes")

        if is_batched:
            if text_pair is not None and len(text) != len(text_pair):
                raise ValueError(
                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
                    f" {len(text_pair)}."
                )
            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
            is_pair = bool(text_pair is not None)
            return self.batch_encode_plus(
                batch_text_or_text_pairs=batch_text_or_text_pairs,
                is_pair=is_pair,
                boxes=boxes,
                word_labels=word_labels,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_tensors=return_tensors,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_length=return_length,
                verbose=verbose,
                **kwargs,
            )

        return self.encode_plus(
            text=text,
            text_pair=text_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def batch_encode_plus(
            self,
            batch_text_or_text_pairs: Union[
                List[TextInput],
                List[TextInputPair],
                List[PreTokenizedInput],
            ],
            is_pair: bool = None,
            boxes: Optional[List[List[List[int]]]] = None,
            word_labels: Optional[Union[List[int], List[List[int]]]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Encodes a batch of text or text pairs using the LayoutLMv2 model.

        Args:
            self (LayoutLMv2Tokenizer): An instance of the LayoutLMv2Tokenizer class.
            batch_text_or_text_pairs (Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput]]):
                A list of input texts or text pairs to be encoded. The input can be either a single text, a text
                pair, or a pre-tokenized input.
            is_pair (bool, optional): Indicates whether the input is a text pair. Defaults to None.
            boxes (Optional[List[List[List[int]]]], optional): A list of bounding boxes for each token in the input.
                Defaults to None.
            word_labels (Optional[Union[List[int], List[List[int]]]], optional): A list of word labels for each token
                in the input. Defaults to None.
            add_special_tokens (bool, optional): Indicates whether to add special tokens to the input. Defaults to True.
            padding (Union[bool, str, PaddingStrategy], optional): Specifies the padding strategy to use.
                Defaults to False.
            truncation (Union[bool, str, TruncationStrategy], optional): Specifies the truncation strategy to use.
                Defaults to None.
            max_length (Optional[int], optional): The maximum sequence length after tokenization. Defaults to None.
            stride (int, optional): The stride for splitting the input into multiple chunks. Defaults to 0.
            pad_to_multiple_of (Optional[int], optional): Pad the sequence length to a multiple of this value.
                Defaults to None.
            return_tensors (Optional[Union[str, TensorType]], optional): Specifies the type of tensors to return.
                Defaults to None.
            return_token_type_ids (Optional[bool], optional): Indicates whether to return token type IDs.
                Defaults to None.
            return_attention_mask (Optional[bool], optional): Indicates whether to return attention masks.
                Defaults to None.
            return_overflowing_tokens (bool, optional): Indicates whether to return overflowing tokens.
                Defaults to False.
            return_special_tokens_mask (bool, optional): Indicates whether to return a mask indicating the special tokens.
                Defaults to False.
            return_offsets_mapping (bool, optional): Indicates whether to return the offsets mapping of tokens to
                original text. Defaults to False.
            return_length (bool, optional): Indicates whether to return the lengths of encoded sequences.
                Defaults to False.
            verbose (bool, optional): Indicates whether to print informative messages. Defaults to True.
            **kwargs: Additional keyword arguments for customizing the encoding process.

        Returns:
            BatchEncoding:
                A dictionary-like object containing the encoded batch, with the following keys:

                - 'input_ids': The input token IDs.
                - 'attention_mask': The attention mask indicating which tokens to attend to.
                - 'token_type_ids': The token type IDs indicating the segment type of each token.
                - 'overflowing_tokens': The list of overflowing tokens if return_overflowing_tokens=True.
                - 'special_tokens_mask': The mask indicating the special tokens if return_special_tokens_mask=True.
                - 'offset_mapping': The mapping of tokens to their corresponding positions in the original text
                if return_offsets_mapping=True.
                - 'length': The length of each encoded sequence if return_length=True.

        Raises:
            None.
        """
        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._batch_encode_plus(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            is_pair=is_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def _batch_encode_plus(
            self,
            batch_text_or_text_pairs: Union[
                List[TextInput],
                List[TextInputPair],
                List[PreTokenizedInput],
            ],
            is_pair: bool = None,
            boxes: Optional[List[List[List[int]]]] = None,
            word_labels: Optional[List[List[int]]] = None,
            add_special_tokens: bool = True,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Batch encodes a batch of text or text pairs using the LayoutLMv2Tokenizer.

        Args:
            self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.
            batch_text_or_text_pairs (Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput]]):
                A list of input text or text pairs to be encoded.
            is_pair (bool, optional): Specifies whether the input is a text pair. Defaults to None.
            boxes (Optional[List[List[List[int]]]], optional): The bounding boxes for each word in the input text.
                Defaults to None.
            word_labels (Optional[List[List[int]]], optional): The labels for each word in the input text.
                Defaults to None.
            add_special_tokens (bool, optional): Specifies whether to add special tokens. Defaults to True.
            padding_strategy (PaddingStrategy, optional): The strategy for padding the input.
                Defaults to PaddingStrategy.DO_NOT_PAD.
            truncation_strategy (TruncationStrategy, optional): The strategy for truncating the input.
                Defaults to TruncationStrategy.DO_NOT_TRUNCATE.
            max_length (Optional[int], optional): The maximum length of the encoded output. Defaults to None.
            stride (int, optional): The stride for splitting the input into overlapping chunks. Defaults to 0.
            pad_to_multiple_of (Optional[int], optional): The value to which the input length will be padded.
                Defaults to None.
            return_tensors (Optional[Union[str, TensorType]], optional): Specifies the type of tensors to return.
                Defaults to None.
            return_token_type_ids (Optional[bool], optional): Specifies whether to return token type IDs.
                Defaults to None.
            return_attention_mask (Optional[bool], optional): Specifies whether to return attention masks.
                Defaults to None.
            return_overflowing_tokens (bool, optional): Specifies whether to return overflowing tokens.
            qDefaults to False.
            return_special_tokens_mask (bool, optional): Specifies whether to return special tokens masks.
                Defaults to False.
            return_offsets_mapping (bool, optional): Specifies whether to return offsets mapping. Defaults to False.
            return_length (bool, optional): Specifies whether to return the length of each encoded input.
                Defaults to False.
            verbose (bool, optional): Specifies whether to print detailed information during encoding. Defaults to True.

        Returns:
            BatchEncoding: A dictionary-like object containing the encoded inputs.

        Raises:
            NotImplementedError: Raised when the 'return_offsets_mapping' parameter is set to True.
                This feature is not available when using Python tokenizers.
                To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast.
        """
        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers. "
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast."
            )

        batch_outputs = self._batch_prepare_for_model(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            is_pair=is_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            return_tensors=return_tensors,
            verbose=verbose,
        )

        return BatchEncoding(batch_outputs)

    def _batch_prepare_for_model(
            self,
            batch_text_or_text_pairs,
            is_pair: bool = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[List[int]]] = None,
            add_special_tokens: bool = True,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[str] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_length: bool = False,
            verbose: bool = True,
    ) -> BatchEncoding:
        """
        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
        manages a moving window (with user defined stride) for overflowing tokens.

        Args:
            batch_ids_pairs: list of tokenized input ids or input ids pairs
        """
        batch_outputs = {}
        for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
            batch_text_or_text_pair, boxes_example = example
            outputs = self.prepare_for_model(
                batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
                batch_text_or_text_pair[1] if is_pair else None,
                boxes_example,
                word_labels=word_labels[idx] if word_labels is not None else None,
                add_special_tokens=add_special_tokens,
                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
                truncation=truncation_strategy.value,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=None,  # we pad in batch afterward
                return_attention_mask=False,  # we pad in batch afterward
                return_token_type_ids=return_token_type_ids,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_length=return_length,
                return_tensors=None,  # We convert the whole batch to tensors at the end
                prepend_batch_axis=False,
                verbose=verbose,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                batch_outputs[key].append(value)

        batch_outputs = self.pad(
            batch_outputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)

        return batch_outputs

    def encode(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> List[int]:
        """
        This method encodes the input text and returns a list of integer input ids.

        Args:
            self: The LayoutLMv2Tokenizer instance.
            text (Union[TextInput, PreTokenizedInput]): The input text to encode. It can be either a TextInput object
                or a PreTokenizedInput object.
            text_pair (Optional[PreTokenizedInput]): The optional second input text to be encoded.
                It should be a PreTokenizedInput object.
            boxes (Optional[List[List[int]]]): The optional bounding boxes for each token in the input text.
                Each box is represented as a list of four integers [x_min, y_min, x_max, y_max].
            word_labels (Optional[List[int]]): The optional word labels associated with each token in the input text.
                It should be a list of integers.
            add_special_tokens (bool): Whether to add special tokens like [CLS], [SEP], etc. Default is True.
            padding (Union[bool, str, PaddingStrategy]): The padding strategy to apply.
                It can be a boolean value, a string, or a PaddingStrategy object. Default is False.
            truncation (Union[bool, str, TruncationStrategy]): The truncation strategy to apply.
                It can be a boolean value, a string, or a TruncationStrategy object. Default is None.
            max_length (Optional[int]): The maximum length of the encoded sequence.
                If provided, the sequence is truncated or padded to this length.
            stride (int): The stride used for tokenization. Default is 0.
            pad_to_multiple_of (Optional[int]): If specified, the sequence is padded to a multiple of this value.
            return_tensors (Optional[Union[str, TensorType]]): The type of tensor to return.
                It can be a string or a TensorType object.
            return_token_type_ids (Optional[bool]): Whether to return token type ids.
            return_attention_mask (Optional[bool]): Whether to return attention mask.
            return_overflowing_tokens (bool): Whether to return overflowing tokens.
            return_special_tokens_mask (bool): Whether to return special tokens mask.
            return_offsets_mapping (bool): Whether to return the mapping from tokens to character offsets.
            return_length (bool): Whether to return the length of the encoded inputs.
            verbose (bool): Whether to print verbose logs. Default is True.
            **kwargs: Additional keyword arguments.

        Returns:
            List[int]: A list of integer input ids representing the encoded input text.

        Raises:
            None.
        """
        encoded_inputs = self.encode_plus(
            text=text,
            text_pair=text_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

        return encoded_inputs["input_ids"]

    def encode_plus(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Tokenize and prepare for the model a sequence or a pair of sequences.
        .. warning:: This method is deprecated, `__call__` should be used instead.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
            text_pair (`List[str]` or `List[int]`, *optional*):
                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
                list of list of strings (words of a batch of examples).
        """
        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._encode_plus(
            text=text,
            boxes=boxes,
            text_pair=text_pair,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def _encode_plus(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """Encodes the given inputs into a batch of tensors with additional special tokens for LayoutLMv2 model.

        Args:
            self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.
            text (Union[TextInput, PreTokenizedInput]): The input text to be encoded.
                It can be either a raw string or a list of tokens.
            text_pair (Optional[PreTokenizedInput]): The second input text to be encoded in case of sequence pairs.
                It can be either a raw string or a list of tokens. Default is None.
            boxes (Optional[List[List[int]]]): The bounding boxes of the tokens in the text. Default is None.
            word_labels (Optional[List[int]]): The labels for each word token in the text. Default is None.
            add_special_tokens (bool): Whether to add special tokens to the encoded inputs. Default is True.
            padding_strategy (PaddingStrategy): The strategy to use for padding. Default is PaddingStrategy.DO_NOT_PAD.
            truncation_strategy (TruncationStrategy): The strategy to use for truncation.
                Default is TruncationStrategy.DO_NOT_TRUNCATE.
            max_length (Optional[int]): The maximum length of the encoded inputs. Default is None.
            stride (int): The stride to use when overflowing tokens. Default is 0.
            pad_to_multiple_of (Optional[int]): The value to pad the sequence length to a multiple of. Default is None.
            return_tensors (Optional[Union[str, TensorType]]): The type of tensors to return. Default is None.
            return_token_type_ids (Optional[bool]): Whether to return token type IDs. Default is None.
            return_attention_mask (Optional[bool]): Whether to return attention masks. Default is None.
            return_overflowing_tokens (bool): Whether to return overflowing tokens. Default is False.
            return_special_tokens_mask (bool): Whether to return a mask indicating the special tokens. Default is False.
            return_offsets_mapping (bool): Whether to return the offsets mapping. Default is False.
            return_length (bool): Whether to return the length of the encoded inputs. Default is False.
            verbose (bool): Whether to print verbose logs during encoding. Default is True.
            **kwargs: Additional keyword arguments.

        Returns:
            BatchEncoding: A batch of encoded inputs in the form of tensors.

        Raises:
            NotImplementedError: If return_offsets_mapping is set to True.
                This feature is not available when using Python tokenizers. To use this feature, change your tokenizer
                to one deriving from transformers.PreTrainedTokenizerFast.
                More information on available tokenizers can be found at
                https://github.com/huggingface/transformers/pull/2674.
        """
        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers. "
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast. "
                "More information on available tokenizers at "
                "https://github.com/huggingface/transformers/pull/2674"
            )

        return self.prepare_for_model(
            text=text,
            text_pair=text_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding_strategy.value,
            truncation=truncation_strategy.value,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            prepend_batch_axis=True,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            verbose=verbose,
        )

    def prepare_for_model(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            prepend_batch_axis: bool = False,
            **kwargs,
    ) -> BatchEncoding:
        """
        Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
        truncates sequences if overflowing while taking into account the special tokens and manages a moving window
        (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
        *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
        combination of arguments will raise an error.

        Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
        token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
        labeled with -100, such that they will be ignored by the loss function.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
            text_pair (`List[str]` or `List[int]`, *optional*):
                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
                list of list of strings (words of a batch of examples).
        """
        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        tokens = []
        pair_tokens = []
        token_boxes = []
        pair_token_boxes = []
        labels = []

        if text_pair is None:
            if word_labels is None:
                # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
                for word, box in zip(text, boxes):
                    if len(word) < 1:  # skip empty words
                        continue
                    word_tokens = self.tokenize(word)
                    tokens.extend(word_tokens)
                    token_boxes.extend([box] * len(word_tokens))
            else:
                # CASE 2: token classification (training)
                for word, box, label in zip(text, boxes, word_labels):
                    if len(word) < 1:  # skip empty words
                        continue
                    word_tokens = self.tokenize(word)
                    tokens.extend(word_tokens)
                    token_boxes.extend([box] * len(word_tokens))
                    if self.only_label_first_subword:
                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                        labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
                    else:
                        labels.extend([label] * len(word_tokens))
        else:
            # CASE 3: document visual question answering (inference)
            # text = question
            # text_pair = words
            tokens = self.tokenize(text)
            token_boxes = [self.pad_token_box for _ in range(len(tokens))]

            for word, box in zip(text_pair, boxes):
                if len(word) < 1:  # skip empty words
                    continue
                word_tokens = self.tokenize(word)
                pair_tokens.extend(word_tokens)
                pair_token_boxes.extend([box] * len(word_tokens))

        # Create ids + pair_ids
        ids = self.convert_tokens_to_ids(tokens)
        pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None

        if (
                return_overflowing_tokens
                and truncation_strategy == TruncationStrategy.LONGEST_FIRST
                and pair_ids is not None
        ):
            raise ValueError(
                "Not possible to return overflowing tokens for pair of sequences with the "
                "`longest_first`. Please select another truncation strategy than `longest_first`, "
                "for instance `only_second` or `only_first`."
            )

        # Compute the total size of the returned encodings
        pair = bool(pair_ids is not None)
        len_ids = len(ids)
        len_pair_ids = len(pair_ids) if pair else 0
        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)

        # Truncation: Handle max sequence length
        overflowing_tokens = []
        overflowing_token_boxes = []
        overflowing_labels = []
        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
            (
                ids,
                token_boxes,
                pair_ids,
                pair_token_boxes,
                labels,
                overflowing_tokens,
                overflowing_token_boxes,
                overflowing_labels,
            ) = self.truncate_sequences(
                ids,
                token_boxes,
                pair_ids=pair_ids,
                pair_token_boxes=pair_token_boxes,
                labels=labels,
                num_tokens_to_remove=total_len - max_length,
                truncation_strategy=truncation_strategy,
                stride=stride,
            )

        if return_token_type_ids and not add_special_tokens:
            raise ValueError(
                "Asking to return token_type_ids while setting add_special_tokens to False "
                "results in an undefined behavior. Please set add_special_tokens to True or "
                "set return_token_type_ids to None."
            )

        # Load from model defaults
        if return_token_type_ids is None:
            return_token_type_ids = "token_type_ids" in self.model_input_names
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        encoded_inputs = {}

        if return_overflowing_tokens:
            encoded_inputs["overflowing_tokens"] = overflowing_tokens
            encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
            encoded_inputs["overflowing_labels"] = overflowing_labels
            encoded_inputs["num_truncated_tokens"] = total_len - max_length

        # Add special tokens
        if add_special_tokens:
            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
            token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
            if pair_token_boxes:
                pair_token_boxes = pair_token_boxes + [self.sep_token_box]
            if labels:
                labels = [self.pad_token_label] + labels + [self.pad_token_label]
        else:
            sequence = ids + pair_ids if pair else ids
            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])

        # Build output dictionary
        encoded_inputs["input_ids"] = sequence
        encoded_inputs["bbox"] = token_boxes + pair_token_boxes
        if return_token_type_ids:
            encoded_inputs["token_type_ids"] = token_type_ids
        if return_special_tokens_mask:
            if add_special_tokens:
                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
            else:
                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)

        if labels:
            encoded_inputs["labels"] = labels

        # Check lengths
        self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)

        # Padding
        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
            encoded_inputs = self.pad(
                encoded_inputs,
                max_length=max_length,
                padding=padding_strategy.value,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )

        if return_length:
            encoded_inputs["length"] = len(encoded_inputs["input_ids"])

        batch_outputs = BatchEncoding(
            encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
        )

        return batch_outputs

    def truncate_sequences(
            self,
            ids: List[int],
            token_boxes: List[List[int]],
            pair_ids: Optional[List[int]] = None,
            pair_token_boxes: Optional[List[List[int]]] = None,
            labels: Optional[List[int]] = None,
            num_tokens_to_remove: int = 0,
            truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
            stride: int = 0,
    ) -> Tuple[List[int], List[int], List[int]]:
        """
        Truncates a sequence pair in-place following the strategy.

        Args:
            ids (`List[int]`):
                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
                `convert_tokens_to_ids` methods.
            token_boxes (`List[List[int]]`):
                Bounding boxes of the first sequence.
            pair_ids (`List[int]`, *optional*):
                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
                and `convert_tokens_to_ids` methods.
            pair_token_boxes (`List[List[int]]`, *optional*):
                Bounding boxes of the second sequence.
            labels (`List[int]`, *optional*):
                Labels of the first sequence (for token classification tasks).
            num_tokens_to_remove (`int`, *optional*, defaults to 0):
                Number of tokens to remove using the truncation strategy.
            truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
                The strategy to follow for truncation. Can be:

                - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
                maximum acceptable input length for the model if that argument is not provided. This will truncate
                token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
                batch of pairs) is provided.
                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
                maximum acceptable input length for the model if that argument is not provided. This will only
                truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
                maximum acceptable input length for the model if that argument is not provided. This will only
                truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
                than the model maximum admissible input size).
            stride (`int`, *optional*, defaults to 0):
                If set to a positive number, the overflowing tokens returned will contain some tokens from the main
                sequence returned. The value of this argument defines the number of additional tokens.

        Returns:
            `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
                overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
                of sequences (or a batch of pairs) is provided.
        """
        if num_tokens_to_remove <= 0:
            return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []

        if not isinstance(truncation_strategy, TruncationStrategy):
            truncation_strategy = TruncationStrategy(truncation_strategy)

        overflowing_tokens = []
        overflowing_token_boxes = []
        overflowing_labels = []
        if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
                truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
        ):
            if len(ids) > num_tokens_to_remove:
                window_len = min(len(ids), stride + num_tokens_to_remove)
                overflowing_tokens = ids[-window_len:]
                overflowing_token_boxes = token_boxes[-window_len:]
                overflowing_labels = labels[-window_len:]
                ids = ids[:-num_tokens_to_remove]
                token_boxes = token_boxes[:-num_tokens_to_remove]
                labels = labels[:-num_tokens_to_remove]
            else:
                error_msg = (
                    f"We need to remove {num_tokens_to_remove} to truncate the input "
                    f"but the first sequence has a length {len(ids)}. "
                )
                if truncation_strategy == TruncationStrategy.ONLY_FIRST:
                    error_msg = (
                            error_msg + "Please select another truncation strategy than "
                                        f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
                    )
                logger.error(error_msg)
        elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
            logger.warning(
                "Be aware, overflowing tokens are not returned for the setting you have chosen,"
                f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
                "truncation strategy. So the returned list will always be empty even if some "
                "tokens have been removed."
            )
            for _ in range(num_tokens_to_remove):
                if pair_ids is None or len(ids) > len(pair_ids):
                    ids = ids[:-1]
                    token_boxes = token_boxes[:-1]
                    labels = labels[:-1]
                else:
                    pair_ids = pair_ids[:-1]
                    pair_token_boxes = pair_token_boxes[:-1]
        elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
            if len(pair_ids) > num_tokens_to_remove:
                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
                overflowing_tokens = pair_ids[-window_len:]
                overflowing_token_boxes = pair_token_boxes[-window_len:]
                pair_ids = pair_ids[:-num_tokens_to_remove]
                pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
            else:
                logger.error(
                    f"We need to remove {num_tokens_to_remove} to truncate the input "
                    f"but the second sequence has a length {len(pair_ids)}. "
                    f"Please select another truncation strategy than {truncation_strategy}, "
                    "for instance 'longest_first' or 'only_first'."
                )

        return (
            ids,
            token_boxes,
            pair_ids,
            pair_token_boxes,
            labels,
            overflowing_tokens,
            overflowing_token_boxes,
            overflowing_labels,
        )

    def _pad(
            self,
            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
            max_length: Optional[int] = None,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            pad_to_multiple_of: Optional[int] = None,
            return_attention_mask: Optional[bool] = None,
    ) -> dict:
        """
        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

        Args:
            encoded_inputs:
                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
            max_length: maximum length of the returned list and optionally padding length (see below).
                Will truncate by taking into account the special tokens.
            padding_strategy:
                PaddingStrategy to use for padding.

                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
                - PaddingStrategy.DO_NOT_PAD: Do not pad
                The tokenizer padding sides are defined in self.padding_side:

                    - 'left': pads on the left of the sequences
                    - 'right': pads on the right of the sequences
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                `>= 7.5` (Volta).
            return_attention_mask:
                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
        """
        # Load from model defaults
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        required_input = encoded_inputs[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length

        # Initialize attention mask if not present.
        if return_attention_mask and "attention_mask" not in encoded_inputs:
            encoded_inputs["attention_mask"] = [1] * len(required_input)

        if needs_to_be_padded:
            difference = max_length - len(required_input)
            if self.padding_side == "right":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = (
                            encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
                    )
                if "bbox" in encoded_inputs:
                    encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
                if "labels" in encoded_inputs:
                    encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
            elif self.padding_side == "left":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
                        "token_type_ids"
                    ]
                if "bbox" in encoded_inputs:
                    encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
                if "labels" in encoded_inputs:
                    encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
            else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side))

        return encoded_inputs

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.do_lower_case property

Whether or not to lowercase the input when tokenizing.

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.vocab_size property

Return the size of the vocabulary used by the LayoutLMv2Tokenizer.

PARAMETER DESCRIPTION
self

An instance of the LayoutLMv2Tokenizer class.

TYPE: LayoutLMv2Tokenizer

RETURNS DESCRIPTION

None.

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.__call__(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.

PARAMETER DESCRIPTION
text

The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words).

TYPE: `str`, `List[str]`, `List[List[str]]`

text_pair

The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string).

TYPE: `List[str]`, `List[List[str]]` DEFAULT: None

boxes

Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.

TYPE: `List[List[int]]`, `List[List[List[int]]]` DEFAULT: None

word_labels

Word-level integer labels (for token classification tasks such as FUNSD, CORD).

TYPE: `List[int]`, `List[List[int]]`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
    sequences with word-level normalized bounding boxes and optional labels.

    Args:
        text (`str`, `List[str]`, `List[List[str]]`):
            The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
            (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
            words).
        text_pair (`List[str]`, `List[List[str]]`):
            The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
            (pretokenized string).
        boxes (`List[List[int]]`, `List[List[List[int]]]`):
            Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
        word_labels (`List[int]`, `List[List[int]]`, *optional*):
            Word-level integer labels (for token classification tasks such as FUNSD, CORD).
    """
    # Input type checking for clearer error
    def _is_valid_text_input(t):
        if isinstance(t, str):
            # Strings are fine
            return True
        if isinstance(t, (list, tuple)):
            # List are fine as long as they are...
            if len(t) == 0:
                # ... empty
                return True
            if isinstance(t[0], str):
                # ... list of strings
                return True
            if isinstance(t[0], (list, tuple)):
                # ... list with an empty list or with a list of strings
                return len(t[0]) == 0 or isinstance(t[0][0], str)
        return False

    if text_pair is not None:
        # in case text + text_pair are provided, text = questions, text_pair = words
        if not _is_valid_text_input(text):
            raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
        if not isinstance(text_pair, (list, tuple)):
            raise ValueError(
                "Words must be of type `List[str]` (single pretokenized example), "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )
    else:
        # in case only text is provided => must be words
        if not isinstance(text, (list, tuple)):
            raise ValueError(
                "Words must be of type `List[str]` (single pretokenized example), "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )

    if text_pair is not None:
        is_batched = isinstance(text, (list, tuple))
    else:
        is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))

    words = text if text_pair is None else text_pair
    if boxes is None:
        raise ValueError("You must provide corresponding bounding boxes")
    if is_batched:
        if len(words) != len(boxes):
            raise ValueError("You must provide words and boxes for an equal amount of examples")
        for words_example, boxes_example in zip(words, boxes):
            if len(words_example) != len(boxes_example):
                raise ValueError("You must provide as many words as there are bounding boxes")
    else:
        if len(words) != len(boxes):
            raise ValueError("You must provide as many words as there are bounding boxes")

    if is_batched:
        if text_pair is not None and len(text) != len(text_pair):
            raise ValueError(
                f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
                f" {len(text_pair)}."
            )
        batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
        is_pair = bool(text_pair is not None)
        return self.batch_encode_plus(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            is_pair=is_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    return self.encode_plus(
        text=text,
        text_pair=text_pair,
        boxes=boxes,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.__init__(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', cls_token_box=[0, 0, 0, 0], sep_token_box=[1000, 1000, 1000, 1000], pad_token_box=[0, 0, 0, 0], pad_token_label=-100, only_label_first_subword=True, tokenize_chinese_chars=True, strip_accents=None, model_max_length=512, additional_special_tokens=None, **kwargs)

Initializes a LayoutLMv2Tokenizer object.

PARAMETER DESCRIPTION
self

The instance of the class.
vocab_file

The path to the vocabulary file.

TYPE: str

do_lower_case

Whether to lowercase the input text. Defaults to True.

TYPE: bool DEFAULT: True

do_basic_tokenize

Whether to perform basic tokenization. Defaults to True.

TYPE: bool DEFAULT: True

never_split

List of tokens that should not be split. Defaults to None.

TYPE: list DEFAULT: None

unk_token

The unknown token. Defaults to '[UNK]'.

TYPE: str DEFAULT: '[UNK]'

sep_token

The separator token. Defaults to '[SEP]'.

TYPE: str DEFAULT: '[SEP]'

pad_token

The padding token. Defaults to '[PAD]'.

TYPE: str DEFAULT: '[PAD]'

cls_token

The classification token. Defaults to '[CLS]'.

TYPE: str DEFAULT: '[CLS]'

mask_token

The masking token. Defaults to '[MASK]'.

TYPE: str DEFAULT: '[MASK]'

cls_token_box

The bounding box coordinates for the classification token. Defaults to [0, 0, 0, 0].

TYPE: list DEFAULT: [0, 0, 0, 0]

sep_token_box

The bounding box coordinates for the separator token. Defaults to [1000, 1000, 1000, 1000].

TYPE: list DEFAULT: [1000, 1000, 1000, 1000]

pad_token_box

The bounding box coordinates for the padding token. Defaults to [0, 0, 0, 0].

TYPE: list DEFAULT: [0, 0, 0, 0]

pad_token_label

The label for the padding token. Defaults to -100.

TYPE: int DEFAULT: -100

only_label_first_subword

Whether to only label the first subword. Defaults to True.

TYPE: bool DEFAULT: True

tokenize_chinese_chars

Whether to tokenize Chinese characters. Defaults to True.

TYPE: bool DEFAULT: True

strip_accents

The accents to strip. Defaults to None.

TYPE: str DEFAULT: None

model_max_length

The maximum length of the model. Defaults to 512.

TYPE: int DEFAULT: 512

additional_special_tokens

Additional special tokens. Defaults to None.

TYPE: list DEFAULT: None

RETURNS DESCRIPTION

None
RAISES DESCRIPTION
ValueError

If the vocabulary file cannot be found at the specified path.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def __init__(
        self,
        vocab_file,
        do_lower_case=True,
        do_basic_tokenize=True,
        never_split=None,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        cls_token_box=[0, 0, 0, 0],
        sep_token_box=[1000, 1000, 1000, 1000],
        pad_token_box=[0, 0, 0, 0],
        pad_token_label=-100,
        only_label_first_subword=True,
        tokenize_chinese_chars=True,
        strip_accents=None,
        model_max_length: int = 512,
        additional_special_tokens: Optional[List[str]] = None,
        **kwargs,
):
    """
    Initializes a LayoutLMv2Tokenizer object.

    Args:
        self: The instance of the class.
        vocab_file (str): The path to the vocabulary file.
        do_lower_case (bool, optional): Whether to lowercase the input text. Defaults to True.
        do_basic_tokenize (bool, optional): Whether to perform basic tokenization. Defaults to True.
        never_split (list, optional): List of tokens that should not be split. Defaults to None.
        unk_token (str, optional): The unknown token. Defaults to '[UNK]'.
        sep_token (str, optional): The separator token. Defaults to '[SEP]'.
        pad_token (str, optional): The padding token. Defaults to '[PAD]'.
        cls_token (str, optional): The classification token. Defaults to '[CLS]'.
        mask_token (str, optional): The masking token. Defaults to '[MASK]'.
        cls_token_box (list, optional): The bounding box coordinates for the classification token. Defaults to [0, 0, 0, 0].
        sep_token_box (list, optional): The bounding box coordinates for the separator token. Defaults to [1000, 1000, 1000, 1000].
        pad_token_box (list, optional): The bounding box coordinates for the padding token. Defaults to [0, 0, 0, 0].
        pad_token_label (int, optional): The label for the padding token. Defaults to -100.
        only_label_first_subword (bool, optional): Whether to only label the first subword. Defaults to True.
        tokenize_chinese_chars (bool, optional): Whether to tokenize Chinese characters. Defaults to True.
        strip_accents (str, optional): The accents to strip. Defaults to None.
        model_max_length (int, optional): The maximum length of the model. Defaults to 512.
        additional_special_tokens (list, optional): Additional special tokens. Defaults to None.

    Returns:
        None

    Raises:
        ValueError: If the vocabulary file cannot be found at the specified path.
    """
    sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
    unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
    pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
    cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
    mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token

    if not os.path.isfile(vocab_file):
        raise ValueError(
            f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
            " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
        )
    self.vocab = load_vocab(vocab_file)
    self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
    self.do_basic_tokenize = do_basic_tokenize
    if do_basic_tokenize:
        self.basic_tokenizer = BasicTokenizer(
            do_lower_case=do_lower_case,
            never_split=never_split,
            tokenize_chinese_chars=tokenize_chinese_chars,
            strip_accents=strip_accents,
        )
    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))

    # additional properties
    self.cls_token_box = cls_token_box
    self.sep_token_box = sep_token_box
    self.pad_token_box = pad_token_box
    self.pad_token_label = pad_token_label
    self.only_label_first_subword = only_label_first_subword
    super().__init__(
        do_lower_case=do_lower_case,
        do_basic_tokenize=do_basic_tokenize,
        never_split=never_split,
        unk_token=unk_token,
        sep_token=sep_token,
        pad_token=pad_token,
        cls_token=cls_token,
        mask_token=mask_token,
        cls_token_box=cls_token_box,
        sep_token_box=sep_token_box,
        pad_token_box=pad_token_box,
        pad_token_label=pad_token_label,
        only_label_first_subword=only_label_first_subword,
        tokenize_chinese_chars=tokenize_chinese_chars,
        strip_accents=strip_accents,
        model_max_length=model_max_length,
        additional_special_tokens=additional_special_tokens,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.batch_encode_plus(batch_text_or_text_pairs, is_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

Encodes a batch of text or text pairs using the LayoutLMv2 model.

PARAMETER DESCRIPTION
self

An instance of the LayoutLMv2Tokenizer class.

TYPE: LayoutLMv2Tokenizer

batch_text_or_text_pairs

A list of input texts or text pairs to be encoded. The input can be either a single text, a text pair, or a pre-tokenized input.

TYPE: Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput]]

is_pair

Indicates whether the input is a text pair. Defaults to None.

TYPE: bool DEFAULT: None

boxes

A list of bounding boxes for each token in the input. Defaults to None.

TYPE: Optional[List[List[List[int]]]] DEFAULT: None

word_labels

A list of word labels for each token in the input. Defaults to None.

TYPE: Optional[Union[List[int], List[List[int]]]] DEFAULT: None

add_special_tokens

Indicates whether to add special tokens to the input. Defaults to True.

TYPE: bool DEFAULT: True

padding

Specifies the padding strategy to use. Defaults to False.

TYPE: Union[bool, str, PaddingStrategy] DEFAULT: False

truncation

Specifies the truncation strategy to use. Defaults to None.

TYPE: Union[bool, str, TruncationStrategy] DEFAULT: None

max_length

The maximum sequence length after tokenization. Defaults to None.

TYPE: Optional[int] DEFAULT: None

stride

The stride for splitting the input into multiple chunks. Defaults to 0.

TYPE: int DEFAULT: 0

pad_to_multiple_of

Pad the sequence length to a multiple of this value. Defaults to None.

TYPE: Optional[int] DEFAULT: None

return_tensors

Specifies the type of tensors to return. Defaults to None.

TYPE: Optional[Union[str, TensorType]] DEFAULT: None

return_token_type_ids

Indicates whether to return token type IDs. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

return_attention_mask

Indicates whether to return attention masks. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

return_overflowing_tokens

Indicates whether to return overflowing tokens. Defaults to False.

TYPE: bool DEFAULT: False

return_special_tokens_mask

Indicates whether to return a mask indicating the special tokens. Defaults to False.

TYPE: bool DEFAULT: False

return_offsets_mapping

Indicates whether to return the offsets mapping of tokens to original text. Defaults to False.

TYPE: bool DEFAULT: False

return_length

Indicates whether to return the lengths of encoded sequences. Defaults to False.

TYPE: bool DEFAULT: False

verbose

Indicates whether to print informative messages. Defaults to True.

TYPE: bool DEFAULT: True

**kwargs

Additional keyword arguments for customizing the encoding process.

DEFAULT: {}

RETURNS DESCRIPTION
BatchEncoding

A dictionary-like object containing the encoded batch, with the following keys:

  • 'input_ids': The input token IDs.
  • 'attention_mask': The attention mask indicating which tokens to attend to.
  • 'token_type_ids': The token type IDs indicating the segment type of each token.
  • 'overflowing_tokens': The list of overflowing tokens if return_overflowing_tokens=True.
  • 'special_tokens_mask': The mask indicating the special tokens if return_special_tokens_mask=True.
  • 'offset_mapping': The mapping of tokens to their corresponding positions in the original text if return_offsets_mapping=True.
  • 'length': The length of each encoded sequence if return_length=True.

TYPE: BatchEncoding

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput],
            List[TextInputPair],
            List[PreTokenizedInput],
        ],
        is_pair: bool = None,
        boxes: Optional[List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    Encodes a batch of text or text pairs using the LayoutLMv2 model.

    Args:
        self (LayoutLMv2Tokenizer): An instance of the LayoutLMv2Tokenizer class.
        batch_text_or_text_pairs (Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput]]):
            A list of input texts or text pairs to be encoded. The input can be either a single text, a text
            pair, or a pre-tokenized input.
        is_pair (bool, optional): Indicates whether the input is a text pair. Defaults to None.
        boxes (Optional[List[List[List[int]]]], optional): A list of bounding boxes for each token in the input.
            Defaults to None.
        word_labels (Optional[Union[List[int], List[List[int]]]], optional): A list of word labels for each token
            in the input. Defaults to None.
        add_special_tokens (bool, optional): Indicates whether to add special tokens to the input. Defaults to True.
        padding (Union[bool, str, PaddingStrategy], optional): Specifies the padding strategy to use.
            Defaults to False.
        truncation (Union[bool, str, TruncationStrategy], optional): Specifies the truncation strategy to use.
            Defaults to None.
        max_length (Optional[int], optional): The maximum sequence length after tokenization. Defaults to None.
        stride (int, optional): The stride for splitting the input into multiple chunks. Defaults to 0.
        pad_to_multiple_of (Optional[int], optional): Pad the sequence length to a multiple of this value.
            Defaults to None.
        return_tensors (Optional[Union[str, TensorType]], optional): Specifies the type of tensors to return.
            Defaults to None.
        return_token_type_ids (Optional[bool], optional): Indicates whether to return token type IDs.
            Defaults to None.
        return_attention_mask (Optional[bool], optional): Indicates whether to return attention masks.
            Defaults to None.
        return_overflowing_tokens (bool, optional): Indicates whether to return overflowing tokens.
            Defaults to False.
        return_special_tokens_mask (bool, optional): Indicates whether to return a mask indicating the special tokens.
            Defaults to False.
        return_offsets_mapping (bool, optional): Indicates whether to return the offsets mapping of tokens to
            original text. Defaults to False.
        return_length (bool, optional): Indicates whether to return the lengths of encoded sequences.
            Defaults to False.
        verbose (bool, optional): Indicates whether to print informative messages. Defaults to True.
        **kwargs: Additional keyword arguments for customizing the encoding process.

    Returns:
        BatchEncoding:
            A dictionary-like object containing the encoded batch, with the following keys:

            - 'input_ids': The input token IDs.
            - 'attention_mask': The attention mask indicating which tokens to attend to.
            - 'token_type_ids': The token type IDs indicating the segment type of each token.
            - 'overflowing_tokens': The list of overflowing tokens if return_overflowing_tokens=True.
            - 'special_tokens_mask': The mask indicating the special tokens if return_special_tokens_mask=True.
            - 'offset_mapping': The mapping of tokens to their corresponding positions in the original text
            if return_offsets_mapping=True.
            - 'length': The length of each encoded sequence if return_length=True.

    Raises:
        None.
    """
    # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        pad_to_multiple_of=pad_to_multiple_of,
        verbose=verbose,
        **kwargs,
    )

    return self._batch_encode_plus(
        batch_text_or_text_pairs=batch_text_or_text_pairs,
        is_pair=is_pair,
        boxes=boxes,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding_strategy=padding_strategy,
        truncation_strategy=truncation_strategy,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format:

  • single sequence: [CLS] X [SEP]
  • pair of sequences: [CLS] A [SEP] B [SEP]
PARAMETER DESCRIPTION
token_ids_0

List of IDs to which the special tokens will be added.

TYPE: `List[int]`

token_ids_1

Optional second list of IDs for sequence pairs.

TYPE: `List[int]`, *optional* DEFAULT: None

RETURNS DESCRIPTION
List[int]

List[int]: List of input IDs with the appropriate special tokens.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
    """
    Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
    adding special tokens. A BERT sequence has the following format:

    - single sequence: `[CLS] X [SEP]`
    - pair of sequences: `[CLS] A [SEP] B [SEP]`

    Args:
        token_ids_0 (`List[int]`):
            List of IDs to which the special tokens will be added.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.

    Returns:
        `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
    """
    if token_ids_1 is None:
        return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
    cls = [self.cls_token_id]
    sep = [self.sep_token_id]
    return cls + token_ids_0 + sep + token_ids_1 + sep

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.convert_tokens_to_string(tokens)

Converts a sequence of tokens (string) in a single string.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def convert_tokens_to_string(self, tokens):
    """Converts a sequence of tokens (string) in a single string."""
    out_string = " ".join(tokens).replace(" ##", "").strip()
    return out_string

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format:

:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence |

If token_ids_1 is None, this method only returns the first portion of the mask (0s).

Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs.

Returns: List[int]: List of token type IDs according to the given sequence(s).

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
    """
    Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
    pair mask has the following format:

    ```:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ```

    If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

    Args:
        token_ids_0 (`List[int]`):
            List of IDs.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.

    Returns:
        `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
    """
    sep = [self.sep_token_id]
    cls = [self.cls_token_id]
    if token_ids_1 is None:
        return len(cls + token_ids_0 + sep) * [0]
    return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

This method encodes the input text and returns a list of integer input ids.

PARAMETER DESCRIPTION
self

The LayoutLMv2Tokenizer instance.
text

The input text to encode. It can be either a TextInput object or a PreTokenizedInput object.

TYPE: Union[TextInput, PreTokenizedInput]

text_pair

The optional second input text to be encoded. It should be a PreTokenizedInput object.

TYPE: Optional[PreTokenizedInput] DEFAULT: None

boxes

The optional bounding boxes for each token in the input text. Each box is represented as a list of four integers [x_min, y_min, x_max, y_max].

TYPE: Optional[List[List[int]]] DEFAULT: None

word_labels

The optional word labels associated with each token in the input text. It should be a list of integers.

TYPE: Optional[List[int]] DEFAULT: None

add_special_tokens

Whether to add special tokens like [CLS], [SEP], etc. Default is True.

TYPE: bool DEFAULT: True

padding

The padding strategy to apply. It can be a boolean value, a string, or a PaddingStrategy object. Default is False.

TYPE: Union[bool, str, PaddingStrategy] DEFAULT: False

truncation

The truncation strategy to apply. It can be a boolean value, a string, or a TruncationStrategy object. Default is None.

TYPE: Union[bool, str, TruncationStrategy] DEFAULT: None

max_length

The maximum length of the encoded sequence. If provided, the sequence is truncated or padded to this length.

TYPE: Optional[int] DEFAULT: None

stride

The stride used for tokenization. Default is 0.

TYPE: int DEFAULT: 0

pad_to_multiple_of

If specified, the sequence is padded to a multiple of this value.

TYPE: Optional[int] DEFAULT: None

return_tensors

The type of tensor to return. It can be a string or a TensorType object.

TYPE: Optional[Union[str, TensorType]] DEFAULT: None

return_token_type_ids

Whether to return token type ids.

TYPE: Optional[bool] DEFAULT: None

return_attention_mask

Whether to return attention mask.

TYPE: Optional[bool] DEFAULT: None

return_overflowing_tokens

Whether to return overflowing tokens.

TYPE: bool DEFAULT: False

return_special_tokens_mask

Whether to return special tokens mask.

TYPE: bool DEFAULT: False

return_offsets_mapping

Whether to return the mapping from tokens to character offsets.

TYPE: bool DEFAULT: False

return_length

Whether to return the length of the encoded inputs.

TYPE: bool DEFAULT: False

verbose

Whether to print verbose logs. Default is True.

TYPE: bool DEFAULT: True

**kwargs

Additional keyword arguments.

DEFAULT: {}

RETURNS DESCRIPTION
List[int]

List[int]: A list of integer input ids representing the encoded input text.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def encode(
        self,
        text: Union[TextInput, PreTokenizedInput],
        text_pair: Optional[PreTokenizedInput] = None,
        boxes: Optional[List[List[int]]] = None,
        word_labels: Optional[List[int]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> List[int]:
    """
    This method encodes the input text and returns a list of integer input ids.

    Args:
        self: The LayoutLMv2Tokenizer instance.
        text (Union[TextInput, PreTokenizedInput]): The input text to encode. It can be either a TextInput object
            or a PreTokenizedInput object.
        text_pair (Optional[PreTokenizedInput]): The optional second input text to be encoded.
            It should be a PreTokenizedInput object.
        boxes (Optional[List[List[int]]]): The optional bounding boxes for each token in the input text.
            Each box is represented as a list of four integers [x_min, y_min, x_max, y_max].
        word_labels (Optional[List[int]]): The optional word labels associated with each token in the input text.
            It should be a list of integers.
        add_special_tokens (bool): Whether to add special tokens like [CLS], [SEP], etc. Default is True.
        padding (Union[bool, str, PaddingStrategy]): The padding strategy to apply.
            It can be a boolean value, a string, or a PaddingStrategy object. Default is False.
        truncation (Union[bool, str, TruncationStrategy]): The truncation strategy to apply.
            It can be a boolean value, a string, or a TruncationStrategy object. Default is None.
        max_length (Optional[int]): The maximum length of the encoded sequence.
            If provided, the sequence is truncated or padded to this length.
        stride (int): The stride used for tokenization. Default is 0.
        pad_to_multiple_of (Optional[int]): If specified, the sequence is padded to a multiple of this value.
        return_tensors (Optional[Union[str, TensorType]]): The type of tensor to return.
            It can be a string or a TensorType object.
        return_token_type_ids (Optional[bool]): Whether to return token type ids.
        return_attention_mask (Optional[bool]): Whether to return attention mask.
        return_overflowing_tokens (bool): Whether to return overflowing tokens.
        return_special_tokens_mask (bool): Whether to return special tokens mask.
        return_offsets_mapping (bool): Whether to return the mapping from tokens to character offsets.
        return_length (bool): Whether to return the length of the encoded inputs.
        verbose (bool): Whether to print verbose logs. Default is True.
        **kwargs: Additional keyword arguments.

    Returns:
        List[int]: A list of integer input ids representing the encoded input text.

    Raises:
        None.
    """
    encoded_inputs = self.encode_plus(
        text=text,
        text_pair=text_pair,
        boxes=boxes,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

    return encoded_inputs["input_ids"]

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode_plus(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, __call__ should be used instead.

PARAMETER DESCRIPTION
text

The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.

TYPE: `str`, `List[str]`, `List[List[str]]`

text_pair

Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples).

TYPE: `List[str]` or `List[int]`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput],
        text_pair: Optional[PreTokenizedInput] = None,
        boxes: Optional[List[List[int]]] = None,
        word_labels: Optional[List[int]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    Tokenize and prepare for the model a sequence or a pair of sequences.
    .. warning:: This method is deprecated, `__call__` should be used instead.

    Args:
        text (`str`, `List[str]`, `List[List[str]]`):
            The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
        text_pair (`List[str]` or `List[int]`, *optional*):
            Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
            list of list of strings (words of a batch of examples).
    """
    # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        pad_to_multiple_of=pad_to_multiple_of,
        verbose=verbose,
        **kwargs,
    )

    return self._encode_plus(
        text=text,
        boxes=boxes,
        text_pair=text_pair,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding_strategy=padding_strategy,
        truncation_strategy=truncation_strategy,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

PARAMETER DESCRIPTION
token_ids_0

List of IDs.

TYPE: `List[int]`

token_ids_1

Optional second list of IDs for sequence pairs.

TYPE: `List[int]`, *optional* DEFAULT: None

already_has_special_tokens

Whether or not the token list is already formatted with special tokens for the model.

TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

RETURNS DESCRIPTION
List[int]

List[int]: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False
) -> List[int]:
    """
    Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
    special tokens using the tokenizer `prepare_for_model` method.

    Args:
        token_ids_0 (`List[int]`):
            List of IDs.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.
        already_has_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not the token list is already formatted with special tokens for the model.

    Returns:
        `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
    """
    if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
        )

    if token_ids_1 is not None:
        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
    return [1] + ([0] * len(token_ids_0)) + [1]

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.get_vocab()

Returns the combined vocabulary of the LayoutLMv2Tokenizer instance and any additional tokens that have been added.

PARAMETER DESCRIPTION
self

The instance of the LayoutLMv2Tokenizer class.

TYPE: LayoutLMv2Tokenizer

RETURNS DESCRIPTION
dict

A dictionary representing the combined vocabulary of the LayoutLMv2Tokenizer instance and any additional tokens that have been added.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def get_vocab(self):
    """
    Returns the combined vocabulary of the LayoutLMv2Tokenizer instance and any additional tokens that have been added.

    Args:
        self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.

    Returns:
        dict: A dictionary representing the combined vocabulary of the LayoutLMv2Tokenizer instance
            and any additional tokens that have been added.

    Raises:
        None.

    """
    return dict(self.vocab, **self.added_tokens_encoder)

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.prepare_for_model(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, prepend_batch_axis=False, **kwargs)

Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for text_pair different than None and truncation_strategy = longest_first or True, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error.

Word-level boxes are turned into token-level bbox. If provided, word-level word_labels are turned into token-level labels. The word label is used for the first token of the word, while remaining tokens are labeled with -100, such that they will be ignored by the loss function.

PARAMETER DESCRIPTION
text

The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.

TYPE: `str`, `List[str]`, `List[List[str]]`

text_pair

Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples).

TYPE: `List[str]` or `List[int]`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def prepare_for_model(
        self,
        text: Union[TextInput, PreTokenizedInput],
        text_pair: Optional[PreTokenizedInput] = None,
        boxes: Optional[List[List[int]]] = None,
        word_labels: Optional[List[int]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        prepend_batch_axis: bool = False,
        **kwargs,
) -> BatchEncoding:
    """
    Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
    truncates sequences if overflowing while taking into account the special tokens and manages a moving window
    (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
    *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
    combination of arguments will raise an error.

    Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
    token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
    labeled with -100, such that they will be ignored by the loss function.

    Args:
        text (`str`, `List[str]`, `List[List[str]]`):
            The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
        text_pair (`List[str]` or `List[int]`, *optional*):
            Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
            list of list of strings (words of a batch of examples).
    """
    # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        pad_to_multiple_of=pad_to_multiple_of,
        verbose=verbose,
        **kwargs,
    )

    tokens = []
    pair_tokens = []
    token_boxes = []
    pair_token_boxes = []
    labels = []

    if text_pair is None:
        if word_labels is None:
            # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
            for word, box in zip(text, boxes):
                if len(word) < 1:  # skip empty words
                    continue
                word_tokens = self.tokenize(word)
                tokens.extend(word_tokens)
                token_boxes.extend([box] * len(word_tokens))
        else:
            # CASE 2: token classification (training)
            for word, box, label in zip(text, boxes, word_labels):
                if len(word) < 1:  # skip empty words
                    continue
                word_tokens = self.tokenize(word)
                tokens.extend(word_tokens)
                token_boxes.extend([box] * len(word_tokens))
                if self.only_label_first_subword:
                    # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                    labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
                else:
                    labels.extend([label] * len(word_tokens))
    else:
        # CASE 3: document visual question answering (inference)
        # text = question
        # text_pair = words
        tokens = self.tokenize(text)
        token_boxes = [self.pad_token_box for _ in range(len(tokens))]

        for word, box in zip(text_pair, boxes):
            if len(word) < 1:  # skip empty words
                continue
            word_tokens = self.tokenize(word)
            pair_tokens.extend(word_tokens)
            pair_token_boxes.extend([box] * len(word_tokens))

    # Create ids + pair_ids
    ids = self.convert_tokens_to_ids(tokens)
    pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None

    if (
            return_overflowing_tokens
            and truncation_strategy == TruncationStrategy.LONGEST_FIRST
            and pair_ids is not None
    ):
        raise ValueError(
            "Not possible to return overflowing tokens for pair of sequences with the "
            "`longest_first`. Please select another truncation strategy than `longest_first`, "
            "for instance `only_second` or `only_first`."
        )

    # Compute the total size of the returned encodings
    pair = bool(pair_ids is not None)
    len_ids = len(ids)
    len_pair_ids = len(pair_ids) if pair else 0
    total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)

    # Truncation: Handle max sequence length
    overflowing_tokens = []
    overflowing_token_boxes = []
    overflowing_labels = []
    if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
        (
            ids,
            token_boxes,
            pair_ids,
            pair_token_boxes,
            labels,
            overflowing_tokens,
            overflowing_token_boxes,
            overflowing_labels,
        ) = self.truncate_sequences(
            ids,
            token_boxes,
            pair_ids=pair_ids,
            pair_token_boxes=pair_token_boxes,
            labels=labels,
            num_tokens_to_remove=total_len - max_length,
            truncation_strategy=truncation_strategy,
            stride=stride,
        )

    if return_token_type_ids and not add_special_tokens:
        raise ValueError(
            "Asking to return token_type_ids while setting add_special_tokens to False "
            "results in an undefined behavior. Please set add_special_tokens to True or "
            "set return_token_type_ids to None."
        )

    # Load from model defaults
    if return_token_type_ids is None:
        return_token_type_ids = "token_type_ids" in self.model_input_names
    if return_attention_mask is None:
        return_attention_mask = "attention_mask" in self.model_input_names

    encoded_inputs = {}

    if return_overflowing_tokens:
        encoded_inputs["overflowing_tokens"] = overflowing_tokens
        encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
        encoded_inputs["overflowing_labels"] = overflowing_labels
        encoded_inputs["num_truncated_tokens"] = total_len - max_length

    # Add special tokens
    if add_special_tokens:
        sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
        token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
        token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
        if pair_token_boxes:
            pair_token_boxes = pair_token_boxes + [self.sep_token_box]
        if labels:
            labels = [self.pad_token_label] + labels + [self.pad_token_label]
    else:
        sequence = ids + pair_ids if pair else ids
        token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])

    # Build output dictionary
    encoded_inputs["input_ids"] = sequence
    encoded_inputs["bbox"] = token_boxes + pair_token_boxes
    if return_token_type_ids:
        encoded_inputs["token_type_ids"] = token_type_ids
    if return_special_tokens_mask:
        if add_special_tokens:
            encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
        else:
            encoded_inputs["special_tokens_mask"] = [0] * len(sequence)

    if labels:
        encoded_inputs["labels"] = labels

    # Check lengths
    self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)

    # Padding
    if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
        encoded_inputs = self.pad(
            encoded_inputs,
            max_length=max_length,
            padding=padding_strategy.value,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

    if return_length:
        encoded_inputs["length"] = len(encoded_inputs["input_ids"])

    batch_outputs = BatchEncoding(
        encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
    )

    return batch_outputs

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.save_vocabulary(save_directory, filename_prefix=None)

Save the vocabulary to a file in the specified directory.

PARAMETER DESCRIPTION
self

The instance of the LayoutLMv2Tokenizer class.

TYPE: LayoutLMv2Tokenizer

save_directory

The directory where the vocabulary file will be saved.

TYPE: str

filename_prefix

A prefix to be added to the filename. Defaults to None.

TYPE: Optional[str] DEFAULT: None

RETURNS DESCRIPTION
Tuple[str]

Tuple[str]: A tuple containing the file path of the saved vocabulary.
RAISES DESCRIPTION
IOError

If an I/O error occurs while writing the vocabulary file.
ValueError

If the provided save_directory is invalid or if the vocabulary indices are not consecutive.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
    """
    Save the vocabulary to a file in the specified directory.

    Args:
        self (LayoutLMv2Tokenizer): The instance of the LayoutLMv2Tokenizer class.
        save_directory (str): The directory where the vocabulary file will be saved.
        filename_prefix (Optional[str]): A prefix to be added to the filename. Defaults to None.

    Returns:
        Tuple[str]: A tuple containing the file path of the saved vocabulary.

    Raises:
        IOError: If an I/O error occurs while writing the vocabulary file.
        ValueError: If the provided save_directory is invalid or if the vocabulary indices are not consecutive.

    """
    index = 0
    if os.path.isdir(save_directory):
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )
    else:
        vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
    with open(vocab_file, "w", encoding="utf-8") as writer:
        for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
            if index != token_index:
                logger.warning(
                    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                    " Please check that the vocabulary is not corrupted!"
                )
                index = token_index
            writer.write(token + "\n")
            index += 1
    return (vocab_file,)

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.truncate_sequences(ids, token_boxes, pair_ids=None, pair_token_boxes=None, labels=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0)

Truncates a sequence pair in-place following the strategy.

PARAMETER DESCRIPTION
ids

Tokenized input ids of the first sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.

TYPE: `List[int]`

token_boxes

Bounding boxes of the first sequence.

TYPE: `List[List[int]]`

pair_ids

Tokenized input ids of the second sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.

TYPE: `List[int]`, *optional* DEFAULT: None

pair_token_boxes

Bounding boxes of the second sequence.

TYPE: `List[List[int]]`, *optional* DEFAULT: None

labels

Labels of the first sequence (for token classification tasks).

TYPE: `List[int]`, *optional* DEFAULT: None

num_tokens_to_remove

Number of tokens to remove using the truncation strategy.

TYPE: `int`, *optional*, defaults to 0 DEFAULT: 0

truncation_strategy

The strategy to follow for truncation. Can be:

  • 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

TYPE: `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False` DEFAULT: 'longest_first'

stride

If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens.

TYPE: `int`, *optional*, defaults to 0 DEFAULT: 0

RETURNS DESCRIPTION
Tuple[List[int], List[int], List[int]]

Tuple[List[int], List[int], List[int]]: The truncated ids, the truncated pair_ids and the list of overflowing tokens. Note: The longest_first strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def truncate_sequences(
        self,
        ids: List[int],
        token_boxes: List[List[int]],
        pair_ids: Optional[List[int]] = None,
        pair_token_boxes: Optional[List[List[int]]] = None,
        labels: Optional[List[int]] = None,
        num_tokens_to_remove: int = 0,
        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
        stride: int = 0,
) -> Tuple[List[int], List[int], List[int]]:
    """
    Truncates a sequence pair in-place following the strategy.

    Args:
        ids (`List[int]`):
            Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
            `convert_tokens_to_ids` methods.
        token_boxes (`List[List[int]]`):
            Bounding boxes of the first sequence.
        pair_ids (`List[int]`, *optional*):
            Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
            and `convert_tokens_to_ids` methods.
        pair_token_boxes (`List[List[int]]`, *optional*):
            Bounding boxes of the second sequence.
        labels (`List[int]`, *optional*):
            Labels of the first sequence (for token classification tasks).
        num_tokens_to_remove (`int`, *optional*, defaults to 0):
            Number of tokens to remove using the truncation strategy.
        truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            The strategy to follow for truncation. Can be:

            - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
            maximum acceptable input length for the model if that argument is not provided. This will truncate
            token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
            batch of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
            maximum acceptable input length for the model if that argument is not provided. This will only
            truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
            maximum acceptable input length for the model if that argument is not provided. This will only
            truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
            than the model maximum admissible input size).
        stride (`int`, *optional*, defaults to 0):
            If set to a positive number, the overflowing tokens returned will contain some tokens from the main
            sequence returned. The value of this argument defines the number of additional tokens.

    Returns:
        `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
            overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
            of sequences (or a batch of pairs) is provided.
    """
    if num_tokens_to_remove <= 0:
        return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []

    if not isinstance(truncation_strategy, TruncationStrategy):
        truncation_strategy = TruncationStrategy(truncation_strategy)

    overflowing_tokens = []
    overflowing_token_boxes = []
    overflowing_labels = []
    if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
            truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
    ):
        if len(ids) > num_tokens_to_remove:
            window_len = min(len(ids), stride + num_tokens_to_remove)
            overflowing_tokens = ids[-window_len:]
            overflowing_token_boxes = token_boxes[-window_len:]
            overflowing_labels = labels[-window_len:]
            ids = ids[:-num_tokens_to_remove]
            token_boxes = token_boxes[:-num_tokens_to_remove]
            labels = labels[:-num_tokens_to_remove]
        else:
            error_msg = (
                f"We need to remove {num_tokens_to_remove} to truncate the input "
                f"but the first sequence has a length {len(ids)}. "
            )
            if truncation_strategy == TruncationStrategy.ONLY_FIRST:
                error_msg = (
                        error_msg + "Please select another truncation strategy than "
                                    f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
                )
            logger.error(error_msg)
    elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
        logger.warning(
            "Be aware, overflowing tokens are not returned for the setting you have chosen,"
            f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
            "truncation strategy. So the returned list will always be empty even if some "
            "tokens have been removed."
        )
        for _ in range(num_tokens_to_remove):
            if pair_ids is None or len(ids) > len(pair_ids):
                ids = ids[:-1]
                token_boxes = token_boxes[:-1]
                labels = labels[:-1]
            else:
                pair_ids = pair_ids[:-1]
                pair_token_boxes = pair_token_boxes[:-1]
    elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
        if len(pair_ids) > num_tokens_to_remove:
            window_len = min(len(pair_ids), stride + num_tokens_to_remove)
            overflowing_tokens = pair_ids[-window_len:]
            overflowing_token_boxes = pair_token_boxes[-window_len:]
            pair_ids = pair_ids[:-num_tokens_to_remove]
            pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
        else:
            logger.error(
                f"We need to remove {num_tokens_to_remove} to truncate the input "
                f"but the second sequence has a length {len(pair_ids)}. "
                f"Please select another truncation strategy than {truncation_strategy}, "
                "for instance 'longest_first' or 'only_first'."
            )

    return (
        ids,
        token_boxes,
        pair_ids,
        pair_token_boxes,
        labels,
        overflowing_tokens,
        overflowing_token_boxes,
        overflowing_labels,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.WordpieceTokenizer

Runs WordPiece tokenization.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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class WordpieceTokenizer:
    """Runs WordPiece tokenization."""
    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
        """
        Initializes a new instance of the WordpieceTokenizer class.

        Args:
            self (WordpieceTokenizer): The instance of the WordpieceTokenizer class.
            vocab (list): A list of strings representing the vocabulary.
            unk_token (str): The unknown token to be used for out-of-vocabulary words.
            max_input_chars_per_word (int, optional): The maximum number of characters per word. Defaults to 100.

        Returns:
            None.

        Raises:
            None.

        """
        self.vocab = vocab
        self.unk_token = unk_token
        self.max_input_chars_per_word = max_input_chars_per_word

    def tokenize(self, text):
        """
        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
        tokenization using the given vocabulary.

        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.

        Args:
            text: A single token or whitespace separated tokens. This should have
                already been passed through *BasicTokenizer*.

        Returns:
            A list of wordpiece tokens.
        """
        output_tokens = []
        for token in whitespace_tokenize(text):
            chars = list(token)
            if len(chars) > self.max_input_chars_per_word:
                output_tokens.append(self.unk_token)
                continue

            is_bad = False
            start = 0
            sub_tokens = []
            while start < len(chars):
                end = len(chars)
                cur_substr = None
                while start < end:
                    substr = "".join(chars[start:end])
                    if start > 0:
                        substr = "##" + substr
                    if substr in self.vocab:
                        cur_substr = substr
                        break
                    end -= 1
                if cur_substr is None:
                    is_bad = True
                    break
                sub_tokens.append(cur_substr)
                start = end

            if is_bad:
                output_tokens.append(self.unk_token)
            else:
                output_tokens.extend(sub_tokens)
        return output_tokens

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.WordpieceTokenizer.__init__(vocab, unk_token, max_input_chars_per_word=100)

Initializes a new instance of the WordpieceTokenizer class.

PARAMETER DESCRIPTION
self

The instance of the WordpieceTokenizer class.

TYPE: WordpieceTokenizer

vocab

A list of strings representing the vocabulary.

TYPE: list

unk_token

The unknown token to be used for out-of-vocabulary words.

TYPE: str

max_input_chars_per_word

The maximum number of characters per word. Defaults to 100.

TYPE: int DEFAULT: 100

RETURNS DESCRIPTION

None.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
    """
    Initializes a new instance of the WordpieceTokenizer class.

    Args:
        self (WordpieceTokenizer): The instance of the WordpieceTokenizer class.
        vocab (list): A list of strings representing the vocabulary.
        unk_token (str): The unknown token to be used for out-of-vocabulary words.
        max_input_chars_per_word (int, optional): The maximum number of characters per word. Defaults to 100.

    Returns:
        None.

    Raises:
        None.

    """
    self.vocab = vocab
    self.unk_token = unk_token
    self.max_input_chars_per_word = max_input_chars_per_word

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.WordpieceTokenizer.tokenize(text)

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary.

For example, input = "unaffable" wil return as output ["un", "##aff", "##able"].

PARAMETER DESCRIPTION
text

A single token or whitespace separated tokens. This should have already been passed through BasicTokenizer.
RETURNS DESCRIPTION

A list of wordpiece tokens.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def tokenize(self, text):
    """
    Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
    tokenization using the given vocabulary.

    For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.

    Args:
        text: A single token or whitespace separated tokens. This should have
            already been passed through *BasicTokenizer*.

    Returns:
        A list of wordpiece tokens.
    """
    output_tokens = []
    for token in whitespace_tokenize(text):
        chars = list(token)
        if len(chars) > self.max_input_chars_per_word:
            output_tokens.append(self.unk_token)
            continue

        is_bad = False
        start = 0
        sub_tokens = []
        while start < len(chars):
            end = len(chars)
            cur_substr = None
            while start < end:
                substr = "".join(chars[start:end])
                if start > 0:
                    substr = "##" + substr
                if substr in self.vocab:
                    cur_substr = substr
                    break
                end -= 1
            if cur_substr is None:
                is_bad = True
                break
            sub_tokens.append(cur_substr)
            start = end

        if is_bad:
            output_tokens.append(self.unk_token)
        else:
            output_tokens.extend(sub_tokens)
    return output_tokens

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.load_vocab(vocab_file)

Loads a vocabulary file into a dictionary.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader:
        tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.subfinder(mylist, pattern)

PARAMETER DESCRIPTION
mylist

A list in which to search for the pattern.
pattern

A list that we are trying to find in mylist.
RETURNS DESCRIPTION

Conditional return: The first matching pattern found in mylist and its starting index. If no match is found, returns None and 0.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def subfinder(mylist, pattern):
    """
    Args:
        mylist: A list in which to search for the pattern.
        pattern: A list that we are trying to find in mylist.

    Returns:
        Conditional return:
            The first matching pattern found in mylist and its starting index.
            If no match is found, returns None and 0.
    """
    matches = []
    indices = []
    for idx, i in enumerate(range(len(mylist))):
        if mylist[i] == pattern[0] and mylist[i: i + len(pattern)] == pattern:
            matches.append(pattern)
            indices.append(idx)
    if matches:
        return matches[0], indices[0]
    return None, 0

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2.whitespace_tokenize(text)

Runs basic whitespace cleaning and splitting on a piece of text.

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2.py 
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def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast

Fast tokenization class for LayoutLMv2. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus and _encode_plus, in which the Rust tokenizer is used.

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast

Bases: PreTrainedTokenizerFast

Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's tokenizers library). Based on WordPiece.

This tokenizer inherits from [PreTrainedTokenizerFast] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

PARAMETER DESCRIPTION
vocab_file

File containing the vocabulary.

TYPE: `str` DEFAULT: None

do_lower_case

Whether or not to lowercase the input when tokenizing.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

unk_token

The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

TYPE: `str`, *optional*, defaults to `"[UNK]"` DEFAULT: '[UNK]'

sep_token

The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

TYPE: `str`, *optional*, defaults to `"[SEP]"` DEFAULT: '[SEP]'

pad_token

The token used for padding, for example when batching sequences of different lengths.

TYPE: `str`, *optional*, defaults to `"[PAD]"` DEFAULT: '[PAD]'

cls_token

The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

TYPE: `str`, *optional*, defaults to `"[CLS]"` DEFAULT: '[CLS]'

mask_token

The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

TYPE: `str`, *optional*, defaults to `"[MASK]"` DEFAULT: '[MASK]'

cls_token_box

The bounding box to use for the special [CLS] token.

TYPE: `List[int]`, *optional*, defaults to `[0, 0, 0, 0]` DEFAULT: [0, 0, 0, 0]

sep_token_box

The bounding box to use for the special [SEP] token.

TYPE: `List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]` DEFAULT: [1000, 1000, 1000, 1000]

pad_token_box

The bounding box to use for the special [PAD] token.

TYPE: `List[int]`, *optional*, defaults to `[0, 0, 0, 0]` DEFAULT: [0, 0, 0, 0]

pad_token_label

The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch's CrossEntropyLoss.

TYPE: `int`, *optional*, defaults to -100 DEFAULT: -100

only_label_first_subword

Whether or not to only label the first subword, in case word labels are provided.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

tokenize_chinese_chars

Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this issue).

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

strip_accents

Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original LayoutLMv2).

TYPE: `bool`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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class LayoutLMv2TokenizerFast(PreTrainedTokenizerFast):
    r"""
    Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.

    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
    refer to this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`):
            File containing the vocabulary.
        do_lower_case (`bool`, *optional*, defaults to `True`):
            Whether or not to lowercase the input when tokenizing.
        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
            sequence classification or for a text and a question for question answering. It is also used as the last
            token of a sequence built with special tokens.
        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
            The token used for padding, for example when batching sequences of different lengths.
        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
            The classifier token which is used when doing sequence classification (classification of the whole sequence
            instead of per-token classification). It is the first token of the sequence when built with special tokens.
        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
            The token used for masking values. This is the token used when training this model with masked language
            modeling. This is the token which the model will try to predict.
        cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
            The bounding box to use for the special [CLS] token.
        sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
            The bounding box to use for the special [SEP] token.
        pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
            The bounding box to use for the special [PAD] token.
        pad_token_label (`int`, *optional*, defaults to -100):
            The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
            CrossEntropyLoss.
        only_label_first_subword (`bool`, *optional*, defaults to `True`):
            Whether or not to only label the first subword, in case word labels are provided.
        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
            Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
            issue](https://github.com/huggingface/transformers/issues/328)).
        strip_accents (`bool`, *optional*):
            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
            value for `lowercase` (as in the original LayoutLMv2).
    """
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    slow_tokenizer_class = LayoutLMv2Tokenizer

    def __init__(
            self,
            vocab_file=None,
            tokenizer_file=None,
            do_lower_case=True,
            unk_token="[UNK]",
            sep_token="[SEP]",
            pad_token="[PAD]",
            cls_token="[CLS]",
            mask_token="[MASK]",
            cls_token_box=[0, 0, 0, 0],
            sep_token_box=[1000, 1000, 1000, 1000],
            pad_token_box=[0, 0, 0, 0],
            pad_token_label=-100,
            only_label_first_subword=True,
            tokenize_chinese_chars=True,
            strip_accents=None,
            **kwargs,
    ):
        """
        This method initializes an instance of the LayoutLMv2TokenizerFast class.

        Args:
            self: The instance of the class.
            vocab_file (str): Path to the vocabulary file. Defaults to None.
            tokenizer_file (str): Path to the tokenizer file. Defaults to None.
            do_lower_case (bool): Flag indicating whether to convert tokens to lowercase. Defaults to True.
            unk_token (str): The token representing unknown words. Defaults to '[UNK]'.
            sep_token (str): The separator token. Defaults to '[SEP]'.
            pad_token (str): The padding token. Defaults to '[PAD]'.
            cls_token (str): The classification token. Defaults to '[CLS]'.
            mask_token (str): The masking token. Defaults to '[MASK]'.
            cls_token_box (list): A list of four integer values representing the bounding box for
                the classification token. Defaults to [0, 0, 0, 0].
            sep_token_box (list): A list of four integer values representing the bounding box for
                the separator token. Defaults to [1000, 1000, 1000, 1000].
            pad_token_box (list): A list of four integer values representing the bounding box for
                the padding token. Defaults to [0, 0, 0, 0].
            pad_token_label (int): The label for padding tokens. Defaults to -100.
            only_label_first_subword (bool): Flag indicating whether to only label the first subword. Defaults to True.
            tokenize_chinese_chars (bool): Flag indicating whether to tokenize Chinese characters. Defaults to True.
            strip_accents (str): Method for stripping accents. Defaults to None.

        Returns:
            None.

        Raises:
            ValueError: If an invalid argument is provided.
            TypeError: If input types are incorrect.
            FileNotFoundError: If the specified vocab_file or tokenizer_file is not found.
            JSONDecodeError: If there is an issue decoding the pre_tok_state JSON.
            AttributeError: If there is an issue with setting the backend_tokenizer normalizer.
            KeyError: If required keys are missing in the pre_tok_state.
        """
        super().__init__(
            vocab_file,
            tokenizer_file=tokenizer_file,
            do_lower_case=do_lower_case,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            cls_token_box=cls_token_box,
            sep_token_box=sep_token_box,
            pad_token_box=pad_token_box,
            pad_token_label=pad_token_label,
            only_label_first_subword=only_label_first_subword,
            tokenize_chinese_chars=tokenize_chinese_chars,
            strip_accents=strip_accents,
            **kwargs,
        )

        pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
                pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
                or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
        ):
            pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
            pre_tok_state["lowercase"] = do_lower_case
            pre_tok_state["strip_accents"] = strip_accents
            self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)

        self.do_lower_case = do_lower_case

        # additional properties
        self.cls_token_box = cls_token_box
        self.sep_token_box = sep_token_box
        self.pad_token_box = pad_token_box
        self.pad_token_label = pad_token_label
        self.only_label_first_subword = only_label_first_subword

    def __call__(
            self,
            text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
            text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
            boxes: Union[List[List[int]], List[List[List[int]]]] = None,
            word_labels: Optional[Union[List[int], List[List[int]]]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
        sequences with word-level normalized bounding boxes and optional labels.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
                (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
                words).
            text_pair (`List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
                (pretokenized string).
            boxes (`List[List[int]]`, `List[List[List[int]]]`):
                Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
            word_labels (`List[int]`, `List[List[int]]`, *optional*):
                Word-level integer labels (for token classification tasks such as FUNSD, CORD).
        """
        # Input type checking for clearer error
        def _is_valid_text_input(t):
            if isinstance(t, str):
                # Strings are fine
                return True
            if isinstance(t, (list, tuple)):
                # List are fine as long as they are...
                if len(t) == 0:
                    # ... empty
                    return True
                if isinstance(t[0], str):
                    # ... list of strings
                    return True
                if isinstance(t[0], (list, tuple)):
                    # ... list with an empty list or with a list of strings
                    return len(t[0]) == 0 or isinstance(t[0][0], str)
            return False

        if text_pair is not None:
            # in case text + text_pair are provided, text = questions, text_pair = words
            if not _is_valid_text_input(text):
                raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
            if not isinstance(text_pair, (list, tuple)):
                raise ValueError(
                    "Words must be of type `List[str]` (single pretokenized example), "
                    "or `List[List[str]]` (batch of pretokenized examples)."
                )
        else:
            # in case only text is provided => must be words
            if not isinstance(text, (list, tuple)):
                raise ValueError(
                    "Words must be of type `List[str]` (single pretokenized example), "
                    "or `List[List[str]]` (batch of pretokenized examples)."
                )

        if text_pair is not None:
            is_batched = isinstance(text, (list, tuple))
        else:
            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))

        words = text if text_pair is None else text_pair
        if boxes is None:
            raise ValueError("You must provide corresponding bounding boxes")
        if is_batched:
            if len(words) != len(boxes):
                raise ValueError("You must provide words and boxes for an equal amount of examples")
            for words_example, boxes_example in zip(words, boxes):
                if len(words_example) != len(boxes_example):
                    raise ValueError("You must provide as many words as there are bounding boxes")
        else:
            if len(words) != len(boxes):
                raise ValueError("You must provide as many words as there are bounding boxes")

        if is_batched:
            if text_pair is not None and len(text) != len(text_pair):
                raise ValueError(
                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
                    f" {len(text_pair)}."
                )
            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
            is_pair = bool(text_pair is not None)
            return self.batch_encode_plus(
                batch_text_or_text_pairs=batch_text_or_text_pairs,
                is_pair=is_pair,
                boxes=boxes,
                word_labels=word_labels,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_tensors=return_tensors,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_length=return_length,
                verbose=verbose,
                **kwargs,
            )

        return self.encode_plus(
            text=text,
            text_pair=text_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def batch_encode_plus(
            self,
            batch_text_or_text_pairs: Union[
                List[TextInput],
                List[TextInputPair],
                List[PreTokenizedInput],
            ],
            is_pair: bool = None,
            boxes: Optional[List[List[List[int]]]] = None,
            word_labels: Optional[Union[List[int], List[List[int]]]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        This method encodes a batch of text or text pairs using LayoutLMv2TokenizerFast.

        Args:
            self: The instance of the LayoutLMv2TokenizerFast class.
            batch_text_or_text_pairs (List[TextInput] or List[TextInputPair] or List[PreTokenizedInput]):
                A list of text inputs or text pairs to be encoded.
            is_pair (bool, optional): Specifies whether the input is a text pair. Default is None.
            boxes (List[List[List[int]]], optional): Optional bounding boxes for text elements in the input text.
                Default is None.
            word_labels (List[int] or List[List[int]], optional): Optional word labels for the input text.
                Default is None.
            add_special_tokens (bool): Whether to add special tokens to the encoded inputs. Default is True.
            padding (bool or str or PaddingStrategy): Padding strategy to apply. Default is False.
            truncation (bool or str or TruncationStrategy, optional): Truncation strategy to apply. Default is None.
            max_length (int, optional): Maximum length of the encoded inputs. Default is None.
            stride (int): The stride to use for overflowing tokens. Default is 0.
            pad_to_multiple_of (int, optional): Pad the sequence length to a multiple of this value. Default is None.
            return_tensors (str or TensorType, optional): Specifies the tensor type to return. Default is None.
            return_token_type_ids (bool, optional): Whether to return token type IDs. Default is None.
            return_attention_mask (bool, optional): Whether to return attention masks. Default is None.
            return_overflowing_tokens (bool): Whether to return overflowing tokens. Default is False.
            return_special_tokens_mask (bool): Whether to return a special tokens mask. Default is False.
            return_offsets_mapping (bool): Whether to return offsets mapping. Default is False.
            return_length (bool): Whether to return the lengths of the encoded inputs. Default is False.
            verbose (bool): Verbosity flag. Default is True.
            **kwargs: Additional keyword arguments for customization.

        Returns:
            BatchEncoding: A dictionary-like object containing the encoded inputs with various attributes.

        Raises:
            None
        """
        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._batch_encode_plus(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            is_pair=is_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
        """
        Tokenizes a given text using the LayoutLMv2TokenizerFast.

        Args:
            self (LayoutLMv2TokenizerFast): An instance of the LayoutLMv2TokenizerFast class.
            text (str): The input text to be tokenized.
            pair (str, optional): The second input text if tokenizing a pair of texts. Defaults to None.
            add_special_tokens (bool, optional): Whether to add special tokens to the input sequence. Defaults to False.
            **kwargs: Additional keyword arguments to be passed to the underlying tokenizer.

        Returns:
            List[str]: A list of tokens representing the tokenized input text.

        Raises:
            None.

        """
        batched_input = [(text, pair)] if pair else [text]
        encodings = self._tokenizer.encode_batch(
            batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
        )

        return encodings[0].tokens

    def encode_plus(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding: Union[bool, str, PaddingStrategy] = False,
            truncation: Union[bool, str, TruncationStrategy] = None,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[Union[str, TensorType]] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
        `__call__` should be used instead.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
            text_pair (`List[str]` or `List[int]`, *optional*):
                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
                list of list of strings (words of a batch of examples).
        """
        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._encode_plus(
            text=text,
            boxes=boxes,
            text_pair=text_pair,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def _batch_encode_plus(
            self,
            batch_text_or_text_pairs: Union[
                List[TextInput],
                List[TextInputPair],
                List[PreTokenizedInput],
            ],
            is_pair: bool = None,
            boxes: Optional[List[List[List[int]]]] = None,
            word_labels: Optional[List[List[int]]] = None,
            add_special_tokens: bool = True,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[str] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
    ) -> BatchEncoding:
        """
        This method performs batch encoding for the LayoutLMv2TokenizerFast class.

        Args:
            self: The instance of the LayoutLMv2TokenizerFast class.
            batch_text_or_text_pairs (Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput]]):
                A list of input text or text pairs to be encoded.
            is_pair (bool): A flag indicating whether the input consists of text pairs.
            boxes (Optional[List[List[List[int]]]): Optional bounding boxes for the input text or text pairs.
            word_labels (Optional[List[List[int]]]): Optional word labels for the input text or text pairs.
            add_special_tokens (bool): Flag to indicate whether to add special tokens during encoding.
            padding_strategy (PaddingStrategy): The strategy for padding the sequences.
            truncation_strategy (TruncationStrategy): The strategy for truncating the sequences.
            max_length (Optional[int]): The maximum length of the encoded sequences.
            stride (int): The stride for truncation.
            pad_to_multiple_of (Optional[int]): Value to pad the sequence length to a multiple of this value.
            return_tensors (Optional[str]): Optional flag to indicate the type of tensor to return.
            return_token_type_ids (Optional[bool]): Optional flag to indicate whether to return token type IDs.
            return_attention_mask (Optional[bool]): Optional flag to indicate whether to return attention masks.
            return_overflowing_tokens (bool): Flag to indicate whether to return overflowing tokens.
            return_special_tokens_mask (bool): Flag to indicate whether to return special tokens masks.
            return_offsets_mapping (bool): Flag to indicate whether to return offset mappings.
            return_length (bool): Flag to indicate whether to return the length of the encoded sequences.
            verbose (bool): Flag to indicate whether to display verbose output.

        Returns:
            BatchEncoding: A dictionary containing sanitized tokens and encodings.

        Raises:
            TypeError: If batch_text_or_text_pairs is not a list.
            ValueError: If the ID of a token is not recognized.
        """
        if not isinstance(batch_text_or_text_pairs, list):
            raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")

        # Set the truncation and padding strategy and restore the initial configuration
        self.set_truncation_and_padding(
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
        )

        if is_pair:
            batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]

        encodings = self._tokenizer.encode_batch(
            batch_text_or_text_pairs,
            add_special_tokens=add_special_tokens,
            is_pretokenized=True,  # we set this to True as LayoutLMv2 always expects pretokenized inputs
        )

        # Convert encoding to dict
        # `Tokens` has type: Tuple[
        #                       List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
        #                       List[EncodingFast]
        #                    ]
        # with nested dimensions corresponding to batch, overflows, sequence length
        tokens_and_encodings = [
            self._convert_encoding(
                encoding=encoding,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=True
                if word_labels is not None
                else return_offsets_mapping,  # we use offsets to create the labels
                return_length=return_length,
                verbose=verbose,
            )
            for encoding in encodings
        ]

        # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
        # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
        # (we say ~ because the number of overflow varies with the example in the batch)
        #
        # To match each overflowing sample with the original sample in the batch
        # we add an overflow_to_sample_mapping array (see below)
        sanitized_tokens = {}
        for key in tokens_and_encodings[0][0].keys():
            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
            sanitized_tokens[key] = stack
        sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]

        # If returning overflowing tokens, we need to return a mapping
        # from the batch idx to the original sample
        if return_overflowing_tokens:
            overflow_to_sample_mapping = []
            for i, (toks, _) in enumerate(tokens_and_encodings):
                overflow_to_sample_mapping += [i] * len(toks["input_ids"])
            sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping

        for input_ids in sanitized_tokens["input_ids"]:
            self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)

        # create the token boxes
        token_boxes = []
        for batch_index in range(len(sanitized_tokens["input_ids"])):
            if return_overflowing_tokens:
                original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
            else:
                original_index = batch_index
            token_boxes_example = []
            for token_id, sequence_id, word_id in zip(
                    sanitized_tokens["input_ids"][batch_index],
                    sanitized_encodings[batch_index].sequence_ids,
                    sanitized_encodings[batch_index].word_ids,
            ):
                if word_id is not None:
                    if is_pair and sequence_id == 0:
                        token_boxes_example.append(self.pad_token_box)
                    else:
                        token_boxes_example.append(boxes[original_index][word_id])
                else:
                    if token_id == self.cls_token_id:
                        token_boxes_example.append(self.cls_token_box)
                    elif token_id == self.sep_token_id:
                        token_boxes_example.append(self.sep_token_box)
                    elif token_id == self.pad_token_id:
                        token_boxes_example.append(self.pad_token_box)
                    else:
                        raise ValueError("Id not recognized")
            token_boxes.append(token_boxes_example)

        sanitized_tokens["bbox"] = token_boxes

        # optionally, create the labels
        if word_labels is not None:
            labels = []
            for batch_index in range(len(sanitized_tokens["input_ids"])):
                if return_overflowing_tokens:
                    original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
                else:
                    original_index = batch_index
                labels_example = []
                for token_id, offset, word_id in zip(
                        sanitized_tokens["input_ids"][batch_index],
                        sanitized_tokens["offset_mapping"][batch_index],
                        sanitized_encodings[batch_index].word_ids,
                ):
                    if word_id is not None:
                        if self.only_label_first_subword:
                            if offset[0] == 0:
                                # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                                labels_example.append(word_labels[original_index][word_id])
                            else:
                                labels_example.append(self.pad_token_label)
                        else:
                            labels_example.append(word_labels[original_index][word_id])
                    else:
                        labels_example.append(self.pad_token_label)
                labels.append(labels_example)

            sanitized_tokens["labels"] = labels
            # finally, remove offsets if the user didn't want them
            if not return_offsets_mapping:
                del sanitized_tokens["offset_mapping"]

        return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)

    def _encode_plus(
            self,
            text: Union[TextInput, PreTokenizedInput],
            text_pair: Optional[PreTokenizedInput] = None,
            boxes: Optional[List[List[int]]] = None,
            word_labels: Optional[List[int]] = None,
            add_special_tokens: bool = True,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
            max_length: Optional[int] = None,
            stride: int = 0,
            pad_to_multiple_of: Optional[int] = None,
            return_tensors: Optional[bool] = None,
            return_token_type_ids: Optional[bool] = None,
            return_attention_mask: Optional[bool] = None,
            return_overflowing_tokens: bool = False,
            return_special_tokens_mask: bool = False,
            return_offsets_mapping: bool = False,
            return_length: bool = False,
            verbose: bool = True,
            **kwargs,
    ) -> BatchEncoding:
        """
        This method encodes the input text and optional text pair into a batch of tokenized and encoded outputs.
        It provides various options for special tokens, padding and truncation strategies, and return types.

        Args:
            self: The instance of the LayoutLMv2TokenizerFast class.
            text (Union[TextInput, PreTokenizedInput]): The input text to be encoded.
                It can be either a plain TextInput or a PreTokenizedInput.
            text_pair (Optional[PreTokenizedInput]): Optional input text pair to be encoded. Defaults to None.
            boxes (Optional[List[List[int]]]): Optional bounding boxes for each token in the input text.
                Defaults to None.
            word_labels (Optional[List[int]]): Optional word labels for each token in the input text. Defaults to None.
            add_special_tokens (bool): Whether to add special tokens (e.g., [CLS], [SEP]) to the encoded output.
                Defaults to True.
            padding_strategy (PaddingStrategy): The padding strategy to use. Defaults to PaddingStrategy.DO_NOT_PAD.
            truncation_strategy (TruncationStrategy): The truncation strategy to use.
                Defaults to TruncationStrategy.DO_NOT_TRUNCATE.
            max_length (Optional[int]): The maximum length of the encoded output. Defaults to None.
            stride (int): The stride to use for overflowing tokens. Defaults to 0.
            pad_to_multiple_of (Optional[int]): The padding length will be a multiple of this value. Defaults to None.
            return_tensors (Optional[bool]): Whether to return the encoded output as PyTorch/TensorFlow tensors.
                Defaults to None.
            return_token_type_ids (Optional[bool]): Whether to return the token type IDs. Defaults to None.
            return_attention_mask (Optional[bool]): Whether to return the attention mask. Defaults to None.
            return_overflowing_tokens (bool): Whether to return overflowing tokens if the input length exceeds max_length.
                Defaults to False.
            return_special_tokens_mask (bool): Whether to return a mask indicating the position of special tokens.
                Defaults to False.
            return_offsets_mapping (bool): Whether to return the mapping from token indices to character offsets.
                Defaults to False.
            return_length (bool): Whether to return the length of each encoded sequence. Defaults to False.
            verbose (bool): Whether to enable verbose logging. Defaults to True.
            **kwargs: Additional keyword arguments for future extensibility.

        Returns:
            BatchEncoding: A dictionary-like object containing the encoded inputs with optional additional
                information such as token type IDs, attention mask, and more.

        Raises:
            None.
        """
        # make it a batched input
        # 2 options:
        # 1) only text, in case text must be a list of str
        # 2) text + text_pair, in which case text = str and text_pair a list of str
        batched_input = [(text, text_pair)] if text_pair else [text]
        batched_boxes = [boxes]
        batched_word_labels = [word_labels] if word_labels is not None else None
        batched_output = self._batch_encode_plus(
            batched_input,
            is_pair=bool(text_pair is not None),
            boxes=batched_boxes,
            word_labels=batched_word_labels,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

        # Return tensor is None, then we can remove the leading batch axis
        # Overflowing tokens are returned as a batch of output so we keep them in this case
        if return_tensors is None and not return_overflowing_tokens:
            batched_output = BatchEncoding(
                {
                    key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
                    for key, value in batched_output.items()
                },
                batched_output.encodings,
            )

        self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)

        return batched_output

    def _pad(
            self,
            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
            max_length: Optional[int] = None,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            pad_to_multiple_of: Optional[int] = None,
            return_attention_mask: Optional[bool] = None,
    ) -> dict:
        """
        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

        Args:
            encoded_inputs:
                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
            max_length: maximum length of the returned list and optionally padding length (see below).
                Will truncate by taking into account the special tokens.
            padding_strategy:
                PaddingStrategy to use for padding.

                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
                - PaddingStrategy.DO_NOT_PAD: Do not pad

                The tokenizer padding sides are defined in self.padding_side:

                - 'left': pads on the left of the sequences
                - 'right': pads on the right of the sequences
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                `>= 7.5` (Volta).
            return_attention_mask:
                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
        """
        # Load from model defaults
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        required_input = encoded_inputs[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length

        # Initialize attention mask if not present.
        if return_attention_mask and "attention_mask" not in encoded_inputs:
            encoded_inputs["attention_mask"] = [1] * len(required_input)

        if needs_to_be_padded:
            difference = max_length - len(required_input)
            if self.padding_side == "right":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = (
                            encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
                    )
                if "bbox" in encoded_inputs:
                    encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
                if "labels" in encoded_inputs:
                    encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
            elif self.padding_side == "left":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
                        "token_type_ids"
                    ]
                if "bbox" in encoded_inputs:
                    encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
                if "labels" in encoded_inputs:
                    encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
            else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side))

        return encoded_inputs

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens. A BERT sequence has the following format:

        - single sequence: `[CLS] X [SEP]`
        - pair of sequences: `[CLS] A [SEP] B [SEP]`

        Args:
            token_ids_0 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
        """
        output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]

        if token_ids_1:
            output += token_ids_1 + [self.sep_token_id]

        return output

    def create_token_type_ids_from_sequences(
            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
        pair mask has the following format:

        ```:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second```

        sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]
        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary files of the LayoutLMv2TokenizerFast model.

        Args:
            self: Instance of the LayoutLMv2TokenizerFast class.
            save_directory (str): The directory where the vocabulary files will be saved.
            filename_prefix (Optional[str], optional): Prefix to be added to the filename of the vocabulary files.
                Defaults to None.

        Returns:
            Tuple[str]: A tuple containing the paths to the saved vocabulary files.

        Raises:
            This method does not raise any exceptions.
        """
        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
        return tuple(files)

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.__call__(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.

PARAMETER DESCRIPTION
text

The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words).

TYPE: `str`, `List[str]`, `List[List[str]]`

text_pair

The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string).

TYPE: `List[str]`, `List[List[str]]` DEFAULT: None

boxes

Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.

TYPE: `List[List[int]]`, `List[List[List[int]]]` DEFAULT: None

word_labels

Word-level integer labels (for token classification tasks such as FUNSD, CORD).

TYPE: `List[int]`, `List[List[int]]`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
    sequences with word-level normalized bounding boxes and optional labels.

    Args:
        text (`str`, `List[str]`, `List[List[str]]`):
            The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
            (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
            words).
        text_pair (`List[str]`, `List[List[str]]`):
            The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
            (pretokenized string).
        boxes (`List[List[int]]`, `List[List[List[int]]]`):
            Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
        word_labels (`List[int]`, `List[List[int]]`, *optional*):
            Word-level integer labels (for token classification tasks such as FUNSD, CORD).
    """
    # Input type checking for clearer error
    def _is_valid_text_input(t):
        if isinstance(t, str):
            # Strings are fine
            return True
        if isinstance(t, (list, tuple)):
            # List are fine as long as they are...
            if len(t) == 0:
                # ... empty
                return True
            if isinstance(t[0], str):
                # ... list of strings
                return True
            if isinstance(t[0], (list, tuple)):
                # ... list with an empty list or with a list of strings
                return len(t[0]) == 0 or isinstance(t[0][0], str)
        return False

    if text_pair is not None:
        # in case text + text_pair are provided, text = questions, text_pair = words
        if not _is_valid_text_input(text):
            raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
        if not isinstance(text_pair, (list, tuple)):
            raise ValueError(
                "Words must be of type `List[str]` (single pretokenized example), "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )
    else:
        # in case only text is provided => must be words
        if not isinstance(text, (list, tuple)):
            raise ValueError(
                "Words must be of type `List[str]` (single pretokenized example), "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )

    if text_pair is not None:
        is_batched = isinstance(text, (list, tuple))
    else:
        is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))

    words = text if text_pair is None else text_pair
    if boxes is None:
        raise ValueError("You must provide corresponding bounding boxes")
    if is_batched:
        if len(words) != len(boxes):
            raise ValueError("You must provide words and boxes for an equal amount of examples")
        for words_example, boxes_example in zip(words, boxes):
            if len(words_example) != len(boxes_example):
                raise ValueError("You must provide as many words as there are bounding boxes")
    else:
        if len(words) != len(boxes):
            raise ValueError("You must provide as many words as there are bounding boxes")

    if is_batched:
        if text_pair is not None and len(text) != len(text_pair):
            raise ValueError(
                f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
                f" {len(text_pair)}."
            )
        batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
        is_pair = bool(text_pair is not None)
        return self.batch_encode_plus(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            is_pair=is_pair,
            boxes=boxes,
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    return self.encode_plus(
        text=text,
        text_pair=text_pair,
        boxes=boxes,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.__init__(vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', cls_token_box=[0, 0, 0, 0], sep_token_box=[1000, 1000, 1000, 1000], pad_token_box=[0, 0, 0, 0], pad_token_label=-100, only_label_first_subword=True, tokenize_chinese_chars=True, strip_accents=None, **kwargs)

This method initializes an instance of the LayoutLMv2TokenizerFast class.

PARAMETER DESCRIPTION
self

The instance of the class.
vocab_file

Path to the vocabulary file. Defaults to None.

TYPE: str DEFAULT: None

tokenizer_file

Path to the tokenizer file. Defaults to None.

TYPE: str DEFAULT: None

do_lower_case

Flag indicating whether to convert tokens to lowercase. Defaults to True.

TYPE: bool DEFAULT: True

unk_token

The token representing unknown words. Defaults to '[UNK]'.

TYPE: str DEFAULT: '[UNK]'

sep_token

The separator token. Defaults to '[SEP]'.

TYPE: str DEFAULT: '[SEP]'

pad_token

The padding token. Defaults to '[PAD]'.

TYPE: str DEFAULT: '[PAD]'

cls_token

The classification token. Defaults to '[CLS]'.

TYPE: str DEFAULT: '[CLS]'

mask_token

The masking token. Defaults to '[MASK]'.

TYPE: str DEFAULT: '[MASK]'

cls_token_box

A list of four integer values representing the bounding box for the classification token. Defaults to [0, 0, 0, 0].

TYPE: list DEFAULT: [0, 0, 0, 0]

sep_token_box

A list of four integer values representing the bounding box for the separator token. Defaults to [1000, 1000, 1000, 1000].

TYPE: list DEFAULT: [1000, 1000, 1000, 1000]

pad_token_box

A list of four integer values representing the bounding box for the padding token. Defaults to [0, 0, 0, 0].

TYPE: list DEFAULT: [0, 0, 0, 0]

pad_token_label

The label for padding tokens. Defaults to -100.

TYPE: int DEFAULT: -100

only_label_first_subword

Flag indicating whether to only label the first subword. Defaults to True.

TYPE: bool DEFAULT: True

tokenize_chinese_chars

Flag indicating whether to tokenize Chinese characters. Defaults to True.

TYPE: bool DEFAULT: True

strip_accents

Method for stripping accents. Defaults to None.

TYPE: str DEFAULT: None

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
ValueError

If an invalid argument is provided.
TypeError

If input types are incorrect.
FileNotFoundError

If the specified vocab_file or tokenizer_file is not found.
JSONDecodeError

If there is an issue decoding the pre_tok_state JSON.
AttributeError

If there is an issue with setting the backend_tokenizer normalizer.
KeyError

If required keys are missing in the pre_tok_state.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def __init__(
        self,
        vocab_file=None,
        tokenizer_file=None,
        do_lower_case=True,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        cls_token_box=[0, 0, 0, 0],
        sep_token_box=[1000, 1000, 1000, 1000],
        pad_token_box=[0, 0, 0, 0],
        pad_token_label=-100,
        only_label_first_subword=True,
        tokenize_chinese_chars=True,
        strip_accents=None,
        **kwargs,
):
    """
    This method initializes an instance of the LayoutLMv2TokenizerFast class.

    Args:
        self: The instance of the class.
        vocab_file (str): Path to the vocabulary file. Defaults to None.
        tokenizer_file (str): Path to the tokenizer file. Defaults to None.
        do_lower_case (bool): Flag indicating whether to convert tokens to lowercase. Defaults to True.
        unk_token (str): The token representing unknown words. Defaults to '[UNK]'.
        sep_token (str): The separator token. Defaults to '[SEP]'.
        pad_token (str): The padding token. Defaults to '[PAD]'.
        cls_token (str): The classification token. Defaults to '[CLS]'.
        mask_token (str): The masking token. Defaults to '[MASK]'.
        cls_token_box (list): A list of four integer values representing the bounding box for
            the classification token. Defaults to [0, 0, 0, 0].
        sep_token_box (list): A list of four integer values representing the bounding box for
            the separator token. Defaults to [1000, 1000, 1000, 1000].
        pad_token_box (list): A list of four integer values representing the bounding box for
            the padding token. Defaults to [0, 0, 0, 0].
        pad_token_label (int): The label for padding tokens. Defaults to -100.
        only_label_first_subword (bool): Flag indicating whether to only label the first subword. Defaults to True.
        tokenize_chinese_chars (bool): Flag indicating whether to tokenize Chinese characters. Defaults to True.
        strip_accents (str): Method for stripping accents. Defaults to None.

    Returns:
        None.

    Raises:
        ValueError: If an invalid argument is provided.
        TypeError: If input types are incorrect.
        FileNotFoundError: If the specified vocab_file or tokenizer_file is not found.
        JSONDecodeError: If there is an issue decoding the pre_tok_state JSON.
        AttributeError: If there is an issue with setting the backend_tokenizer normalizer.
        KeyError: If required keys are missing in the pre_tok_state.
    """
    super().__init__(
        vocab_file,
        tokenizer_file=tokenizer_file,
        do_lower_case=do_lower_case,
        unk_token=unk_token,
        sep_token=sep_token,
        pad_token=pad_token,
        cls_token=cls_token,
        mask_token=mask_token,
        cls_token_box=cls_token_box,
        sep_token_box=sep_token_box,
        pad_token_box=pad_token_box,
        pad_token_label=pad_token_label,
        only_label_first_subword=only_label_first_subword,
        tokenize_chinese_chars=tokenize_chinese_chars,
        strip_accents=strip_accents,
        **kwargs,
    )

    pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
    if (
            pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
            or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
    ):
        pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
        pre_tok_state["lowercase"] = do_lower_case
        pre_tok_state["strip_accents"] = strip_accents
        self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)

    self.do_lower_case = do_lower_case

    # additional properties
    self.cls_token_box = cls_token_box
    self.sep_token_box = sep_token_box
    self.pad_token_box = pad_token_box
    self.pad_token_label = pad_token_label
    self.only_label_first_subword = only_label_first_subword

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.batch_encode_plus(batch_text_or_text_pairs, is_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

This method encodes a batch of text or text pairs using LayoutLMv2TokenizerFast.

PARAMETER DESCRIPTION
self

The instance of the LayoutLMv2TokenizerFast class.
batch_text_or_text_pairs

A list of text inputs or text pairs to be encoded.

TYPE: List[TextInput] or List[TextInputPair] or List[PreTokenizedInput]

is_pair

Specifies whether the input is a text pair. Default is None.

TYPE: bool DEFAULT: None

boxes

Optional bounding boxes for text elements in the input text. Default is None.

TYPE: List[List[List[int]]] DEFAULT: None

word_labels

Optional word labels for the input text. Default is None.

TYPE: List[int] or List[List[int]] DEFAULT: None

add_special_tokens

Whether to add special tokens to the encoded inputs. Default is True.

TYPE: bool DEFAULT: True

padding

Padding strategy to apply. Default is False.

TYPE: bool or str or PaddingStrategy DEFAULT: False

truncation

Truncation strategy to apply. Default is None.

TYPE: bool or str or TruncationStrategy DEFAULT: None

max_length

Maximum length of the encoded inputs. Default is None.

TYPE: int DEFAULT: None

stride

The stride to use for overflowing tokens. Default is 0.

TYPE: int DEFAULT: 0

pad_to_multiple_of

Pad the sequence length to a multiple of this value. Default is None.

TYPE: int DEFAULT: None

return_tensors

Specifies the tensor type to return. Default is None.

TYPE: str or TensorType DEFAULT: None

return_token_type_ids

Whether to return token type IDs. Default is None.

TYPE: bool DEFAULT: None

return_attention_mask

Whether to return attention masks. Default is None.

TYPE: bool DEFAULT: None

return_overflowing_tokens

Whether to return overflowing tokens. Default is False.

TYPE: bool DEFAULT: False

return_special_tokens_mask

Whether to return a special tokens mask. Default is False.

TYPE: bool DEFAULT: False

return_offsets_mapping

Whether to return offsets mapping. Default is False.

TYPE: bool DEFAULT: False

return_length

Whether to return the lengths of the encoded inputs. Default is False.

TYPE: bool DEFAULT: False

verbose

Verbosity flag. Default is True.

TYPE: bool DEFAULT: True

**kwargs

Additional keyword arguments for customization.

DEFAULT: {}

RETURNS DESCRIPTION
BatchEncoding

A dictionary-like object containing the encoded inputs with various attributes.

TYPE: BatchEncoding

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput],
            List[TextInputPair],
            List[PreTokenizedInput],
        ],
        is_pair: bool = None,
        boxes: Optional[List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    This method encodes a batch of text or text pairs using LayoutLMv2TokenizerFast.

    Args:
        self: The instance of the LayoutLMv2TokenizerFast class.
        batch_text_or_text_pairs (List[TextInput] or List[TextInputPair] or List[PreTokenizedInput]):
            A list of text inputs or text pairs to be encoded.
        is_pair (bool, optional): Specifies whether the input is a text pair. Default is None.
        boxes (List[List[List[int]]], optional): Optional bounding boxes for text elements in the input text.
            Default is None.
        word_labels (List[int] or List[List[int]], optional): Optional word labels for the input text.
            Default is None.
        add_special_tokens (bool): Whether to add special tokens to the encoded inputs. Default is True.
        padding (bool or str or PaddingStrategy): Padding strategy to apply. Default is False.
        truncation (bool or str or TruncationStrategy, optional): Truncation strategy to apply. Default is None.
        max_length (int, optional): Maximum length of the encoded inputs. Default is None.
        stride (int): The stride to use for overflowing tokens. Default is 0.
        pad_to_multiple_of (int, optional): Pad the sequence length to a multiple of this value. Default is None.
        return_tensors (str or TensorType, optional): Specifies the tensor type to return. Default is None.
        return_token_type_ids (bool, optional): Whether to return token type IDs. Default is None.
        return_attention_mask (bool, optional): Whether to return attention masks. Default is None.
        return_overflowing_tokens (bool): Whether to return overflowing tokens. Default is False.
        return_special_tokens_mask (bool): Whether to return a special tokens mask. Default is False.
        return_offsets_mapping (bool): Whether to return offsets mapping. Default is False.
        return_length (bool): Whether to return the lengths of the encoded inputs. Default is False.
        verbose (bool): Verbosity flag. Default is True.
        **kwargs: Additional keyword arguments for customization.

    Returns:
        BatchEncoding: A dictionary-like object containing the encoded inputs with various attributes.

    Raises:
        None
    """
    # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        pad_to_multiple_of=pad_to_multiple_of,
        verbose=verbose,
        **kwargs,
    )

    return self._batch_encode_plus(
        batch_text_or_text_pairs=batch_text_or_text_pairs,
        is_pair=is_pair,
        boxes=boxes,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding_strategy=padding_strategy,
        truncation_strategy=truncation_strategy,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format:

  • single sequence: [CLS] X [SEP]
  • pair of sequences: [CLS] A [SEP] B [SEP]
PARAMETER DESCRIPTION
token_ids_0

List of IDs to which the special tokens will be added.

TYPE: `List[int]`

token_ids_1

Optional second list of IDs for sequence pairs.

TYPE: `List[int]`, *optional* DEFAULT: None

RETURNS DESCRIPTION

List[int]: List of input IDs with the appropriate special tokens.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
    """
    Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
    adding special tokens. A BERT sequence has the following format:

    - single sequence: `[CLS] X [SEP]`
    - pair of sequences: `[CLS] A [SEP] B [SEP]`

    Args:
        token_ids_0 (`List[int]`):
            List of IDs to which the special tokens will be added.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.

    Returns:
        `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
    """
    output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]

    if token_ids_1:
        output += token_ids_1 + [self.sep_token_id]

    return output

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format:

:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second

sequence | If token_ids_1 is None, this method only returns the first portion of the mask (0s).

Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs.

Returns: List[int]: List of token type IDs according to the given sequence(s).

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
    """
    Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
    pair mask has the following format:

    ```:: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second```

    sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

    Args:
        token_ids_0 (`List[int]`):
            List of IDs.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.

    Returns:
        `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
    """
    sep = [self.sep_token_id]
    cls = [self.cls_token_id]
    if token_ids_1 is None:
        return len(cls + token_ids_0 + sep) * [0]
    return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.encode_plus(text, text_pair=None, boxes=None, word_labels=None, add_special_tokens=True, padding=False, truncation=None, max_length=None, stride=0, pad_to_multiple_of=None, return_tensors=None, return_token_type_ids=None, return_attention_mask=None, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, return_length=False, verbose=True, **kwargs)

Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, __call__ should be used instead.

PARAMETER DESCRIPTION
text

The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.

TYPE: `str`, `List[str]`, `List[List[str]]`

text_pair

Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples).

TYPE: `List[str]` or `List[int]`, *optional* DEFAULT: None

Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput],
        text_pair: Optional[PreTokenizedInput] = None,
        boxes: Optional[List[List[int]]] = None,
        word_labels: Optional[List[int]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs,
) -> BatchEncoding:
    """
    Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
    `__call__` should be used instead.

    Args:
        text (`str`, `List[str]`, `List[List[str]]`):
            The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
        text_pair (`List[str]` or `List[int]`, *optional*):
            Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
            list of list of strings (words of a batch of examples).
    """
    # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
        padding=padding,
        truncation=truncation,
        max_length=max_length,
        pad_to_multiple_of=pad_to_multiple_of,
        verbose=verbose,
        **kwargs,
    )

    return self._encode_plus(
        text=text,
        boxes=boxes,
        text_pair=text_pair,
        word_labels=word_labels,
        add_special_tokens=add_special_tokens,
        padding_strategy=padding_strategy,
        truncation_strategy=truncation_strategy,
        max_length=max_length,
        stride=stride,
        pad_to_multiple_of=pad_to_multiple_of,
        return_tensors=return_tensors,
        return_token_type_ids=return_token_type_ids,
        return_attention_mask=return_attention_mask,
        return_overflowing_tokens=return_overflowing_tokens,
        return_special_tokens_mask=return_special_tokens_mask,
        return_offsets_mapping=return_offsets_mapping,
        return_length=return_length,
        verbose=verbose,
        **kwargs,
    )

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.save_vocabulary(save_directory, filename_prefix=None)

Save the vocabulary files of the LayoutLMv2TokenizerFast model.

PARAMETER DESCRIPTION
self

Instance of the LayoutLMv2TokenizerFast class.
save_directory

The directory where the vocabulary files will be saved.

TYPE: str

filename_prefix

Prefix to be added to the filename of the vocabulary files. Defaults to None.

TYPE: Optional[str] DEFAULT: None

RETURNS DESCRIPTION
Tuple[str]

Tuple[str]: A tuple containing the paths to the saved vocabulary files.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
    """
    Save the vocabulary files of the LayoutLMv2TokenizerFast model.

    Args:
        self: Instance of the LayoutLMv2TokenizerFast class.
        save_directory (str): The directory where the vocabulary files will be saved.
        filename_prefix (Optional[str], optional): Prefix to be added to the filename of the vocabulary files.
            Defaults to None.

    Returns:
        Tuple[str]: A tuple containing the paths to the saved vocabulary files.

    Raises:
        This method does not raise any exceptions.
    """
    files = self._tokenizer.model.save(save_directory, name=filename_prefix)
    return tuple(files)

mindnlp.transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.tokenize(text, pair=None, add_special_tokens=False, **kwargs)

Tokenizes a given text using the LayoutLMv2TokenizerFast.

PARAMETER DESCRIPTION
self

An instance of the LayoutLMv2TokenizerFast class.

TYPE: LayoutLMv2TokenizerFast

text

The input text to be tokenized.

TYPE: str

pair

The second input text if tokenizing a pair of texts. Defaults to None.

TYPE: str DEFAULT: None

add_special_tokens

Whether to add special tokens to the input sequence. Defaults to False.

TYPE: bool DEFAULT: False

**kwargs

Additional keyword arguments to be passed to the underlying tokenizer.

DEFAULT: {}

RETURNS DESCRIPTION
List[str]

List[str]: A list of tokens representing the tokenized input text.
Source code in mindnlp\transformers\models\layoutlmv2\tokenization_layoutlmv2_fast.py 
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def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
    """
    Tokenizes a given text using the LayoutLMv2TokenizerFast.

    Args:
        self (LayoutLMv2TokenizerFast): An instance of the LayoutLMv2TokenizerFast class.
        text (str): The input text to be tokenized.
        pair (str, optional): The second input text if tokenizing a pair of texts. Defaults to None.
        add_special_tokens (bool, optional): Whether to add special tokens to the input sequence. Defaults to False.
        **kwargs: Additional keyword arguments to be passed to the underlying tokenizer.

    Returns:
        List[str]: A list of tokens representing the tokenized input text.

    Raises:
        None.

    """
    batched_input = [(text, pair)] if pair else [text]
    encodings = self._tokenizer.encode_batch(
        batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
    )

    return encodings[0].tokens