# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm import LayoutLMTokenizer
from transformers.utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt",
        "microsoft/layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/vocab.txt",
    }
}


PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "microsoft/layoutlmv2-base-uncased": 512,
    "microsoft/layoutlmv2-large-uncased": 512,
}


PRETRAINED_INIT_CONFIGURATION = {
    "microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlmv2-large-uncased": {"do_lower_case": True},
}


class LayoutLMv2Tokenizer(LayoutLMTokenizer):
    r"""
    Constructs a LayoutLMv2 tokenizer.

    :class:`~transformers.LayoutLMv2Tokenizer is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
    tokenization: punctuation splitting + wordpiece.

    Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
    parameters.
    """

    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

    def __init__(self, model_max_length=512, **kwargs):
        super().__init__(model_max_length=model_max_length, **kwargs)