# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Testing BertTokenizer op in DE
"""
import numpy as np
import mindspore.dataset as ds
from mindspore import log as logger
import mindspore.dataset.text as nlp

BERT_TOKENIZER_FILE = "../data/dataset/testTokenizerData/bert_tokenizer.txt"

vocab_bert = [
    "床", "前", "明", "月", "光", "疑", "是", "地", "上", "霜", "举", "头", "望", "低", "思", "故", "乡",
    "繁", "體", "字", "嘿", "哈", "大", "笑", "嘻",
    "i", "am", "mak", "make", "small", "mistake", "##s", "during", "work", "##ing", "hour",
    "😀", "😃", "😄", "😁", "+", "/", "-", "=", "12", "28", "40", "16", " ", "I",
    "[CLS]", "[SEP]", "[UNK]", "[PAD]", "[MASK]"
]
pad = '<pad>'
test_paras = [
    # test chinese text
    dict(
        first=1,
        last=4,
        expect_str=[[['床'], ['前'], ['明'], ['月'], ['光']],
                    [['疑'], ['是'], ['地'], ['上'], ['霜']],
                    [['举'], ['头'], ['望'], ['明'], ['月']],
                    [['低'], ['头'], ['思'], ['故'], ['乡']]],
        vocab_list=vocab_bert
    ),
    # test english text
    dict(
        first=5,
        last=5,
        expect_str=[[['i', pad],
                     ["am", pad],
                     ['mak', '##ing'],
                     ['small', pad],
                     ['mistake', '##s'],
                     ['during', pad],
                     ['work', '##ing'],
                     ['hour', '##s']]],
        lower_case=True,
        vocab_list=vocab_bert
    ),
    dict(
        first=5,
        last=5,
        expect_str=[[['I', pad],
                     ["am", pad],
                     ['mak', '##ing'],
                     ['small', pad],
                     ['mistake', '##s'],
                     ['during', pad],
                     ['work', '##ing'],
                     ['hour', '##s']]],
        lower_case=False,
        vocab_list=vocab_bert
    ),
    # test emoji tokens
    dict(
        first=6,
        last=7,
        expect_str=[
            [['😀'], ['嘿'], ['嘿'], ['😃'], ['哈'], ['哈'], ['😄'], ['大'], ['笑'], ['😁'], ['嘻'], ['嘻']],
            [['繁'], ['體'], ['字']]],
        normalization_form=nlp.utils.NormalizeForm.NFKC,
        vocab_list=vocab_bert
    ),
    # test preserved tokens
    dict(
        first=8,
        last=12,
        expect_str=[
            [['[UNK]'], ['[CLS]']],
            [['[UNK]'], ['[SEP]']],
            [['[UNK]'], ['[UNK]']],
            [['[UNK]'], ['[PAD]']],
            [['[UNK]'], ['[MASK]']],
        ],
        lower_case=False,
        vocab_list=vocab_bert,
        preserve_unused_token=True,
    ),
    # test special symbol
    dict(
        first=13,
        last=13,
        expect_str=[[['12'], ['+'], ['/'], ['-'], ['28'], ['='], ['40'], ['/'], ['-'], ['16']]],
        preserve_unused_token=True,
        vocab_list=vocab_bert
    ),
    # test non-default parms
    dict(
        first=8,
        last=8,
        expect_str=[
            [['[UNK]'], [' '], ['[CLS]']],
        ],
        lower_case=False,
        vocab_list=vocab_bert,
        preserve_unused_token=True,
        keep_whitespace=True
    ),
    dict(
        first=8,
        last=8,
        expect_str=[
            [['unused'], [' '], ['[CLS]']],
        ],
        lower_case=False,
        vocab_list=vocab_bert,
        preserve_unused_token=True,
        keep_whitespace=True,
        unknown_token=''
    ),
    dict(
        first=8,
        last=8,
        expect_str=[
            [['unused'], [' '], ['['], ['CLS'], [']']],
        ],
        lower_case=False,
        vocab_list=vocab_bert,
        preserve_unused_token=False,
        keep_whitespace=True,
        unknown_token=''
    ),
]


def check_bert_tokenizer(first, last, expect_str,
                         vocab_list,
                         suffix_indicator='##',
                         max_bytes_per_token=100, unknown_token='[UNK]',
                         lower_case=False, keep_whitespace=False,
                         normalization_form=nlp.utils.NormalizeForm.NONE,
                         preserve_unused_token=False):
    dataset = ds.TextFileDataset(BERT_TOKENIZER_FILE, shuffle=False)
    if first > 1:
        dataset = dataset.skip(first - 1)
    if last >= first:
        dataset = dataset.take(last - first + 1)
    vocab = nlp.Vocab.from_list(vocab_list)
    tokenizer_op = nlp.BertTokenizer(
        vocab=vocab, suffix_indicator=suffix_indicator,
        max_bytes_per_token=max_bytes_per_token, unknown_token=unknown_token,
        lower_case=lower_case, keep_whitespace=keep_whitespace,
        normalization_form=normalization_form,
        preserve_unused_token=preserve_unused_token)
    dataset = dataset.map(operations=tokenizer_op)
    count = 0
    for i in dataset.create_dict_iterator():
        text = nlp.to_str(i['text'])
        logger.info("Out:", text)
        logger.info("Exp:", expect_str[count])
        np.testing.assert_array_equal(text, expect_str[count])
        count = count + 1


def test_bert_tokenizer():
    """
    Test WordpieceTokenizer
    """
    for paras in test_paras:
        check_bert_tokenizer(**paras)


if __name__ == '__main__':
    test_bert_tokenizer()