# coding=utf-8
# Copyright 2020 The Google AI Team, Stanford University and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "unc-nlp/lxmert-base-uncased": 512,
}

PRETRAINED_INIT_CONFIGURATION = {
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}


class LxmertTokenizerFast(BertTokenizerFast):
    r"""
    Construct a "fast" LXMERT tokenizer (backed by HuggingFace's `tokenizers` library).

    :class:`~transformers.LxmertTokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
    end-to-end tokenization: punctuation splitting and wordpiece.

    Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
    parameters.
    """
    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
    slow_tokenizer_class = LxmertTokenizer
