# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 Hugging Face inc.
#
# 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.

import os
import unittest

from paddlenlp.transformers.albert.tokenizer import (
    AlbertChineseTokenizer,
    AlbertEnglishTokenizer,
)
from paddlenlp.transformers.bert.tokenizer import BasicTokenizer, WordpieceTokenizer

from ...testing_utils import get_tests_dir, slow
from ..test_tokenizer_common import TokenizerTesterMixin, filter_non_english

SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model")


class AlbertEnglishTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = AlbertEnglishTokenizer
    from_pretrained_vocab_key = "sentencepiece_model_file"
    test_sentencepiece = True
    test_sentencepiece_ignore_case = True

    def setUp(self):
        super().setUp()

        # We have a SentencePiece fixture for testing
        tokenizer = AlbertEnglishTokenizer(SAMPLE_VOCAB)
        tokenizer.save_pretrained(self.tmpdirname)

    def get_input_output_texts(self, tokenizer):
        input_text = "this is a test"
        output_text = "this is a test"
        return input_text, output_text

    def test_convert_token_and_id(self):
        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
        token = "<pad>"
        token_id = 0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)

    def test_get_vocab(self):
        vocab_keys = list(self.get_tokenizer().get_vocab().keys())
        self.assertEqual(vocab_keys[0], "<pad>")
        self.assertEqual(vocab_keys[1], "<unk>")
        self.assertEqual(vocab_keys[-1], "▁eloquent")
        self.assertEqual(len(vocab_keys), 30_000)

    def test_vocab_size(self):
        self.assertEqual(self.get_tokenizer().vocab_size, 30_000)

    def test_full_tokenizer(self):
        tokenizer = AlbertEnglishTokenizer(SAMPLE_VOCAB, keep_accents=True)

        tokens = tokenizer.tokenize("This is a test")
        self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁test"])

        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289])

        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
        self.assertListEqual(
            tokens, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]
        )
        ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(ids, [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

        back_tokens = tokenizer.convert_ids_to_tokens(ids)
        self.assertListEqual(
            back_tokens,
            ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."],
        )

    def test_sequence_builders(self):
        tokenizer = AlbertEnglishTokenizer(SAMPLE_VOCAB)

        text = tokenizer.encode("sequence builders")["input_ids"]
        text_2 = tokenizer.encode("multi-sequence build")["input_ids"]

        encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
        encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
            tokenizer.sep_token_id
        ]

    @slow
    def test_tokenizer_integration(self):
        # fmt: off
        expected_encoding = {
            'attention_mask': [
                [
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
                ],
                [
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
                ],
                [
                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
                ]],
            'input_ids': [
                [
                    2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051,
                    18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297,
                    8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822,
                    58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9,
                    9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112,
                    816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684,
                    2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128,
                    11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3
                ],
                [
                    2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732,
                    19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860,
                    19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124,
                    9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0
                ],
                [
                    2, 14, 2231, 886, 2385, 17659, 84, 14, 16792,
                    1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0
                ]],
            'token_type_ids': [
                [
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
                ],
                [
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
                ],
                [
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
                ]]
        }  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=expected_encoding,
            model_name="albert-base-v2",
        )


class AlbertChineseTokenizerTest(TokenizerTesterMixin, unittest.TestCase):

    tokenizer_class = AlbertChineseTokenizer
    space_between_special_tokens = True
    from_pretrained_filter = filter_non_english
    test_seq2seq = True

    def setUp(self):
        super().setUp()

        vocab_tokens = [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]

        self.vocab_file = os.path.join(self.tmpdirname, AlbertChineseTokenizer.resource_files_names["vocab_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

    def get_input_output_texts(self, tokenizer):
        input_text = "UNwant\u00E9d,running"
        output_text = "unwanted, running"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = self.tokenizer_class(self.vocab_file)

        tokens = tokenizer.tokenize("UNwant\u00E9d,running")
        self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])

    def test_chinese(self):
        tokenizer = BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"])

    def test_basic_tokenizer_lower(self):
        tokenizer = BasicTokenizer(do_lower_case=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "), ["hello", "!", "how", "are", "you", "?"]
        )
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])

    def test_basic_tokenizer_lower_strip_accents_false(self):
        tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "), ["hällo", "!", "how", "are", "you", "?"]
        )
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"])

    def test_basic_tokenizer_lower_strip_accents_true(self):
        tokenizer = BasicTokenizer(do_lower_case=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "), ["hallo", "!", "how", "are", "you", "?"]
        )
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])

    def test_basic_tokenizer_lower_strip_accents_default(self):
        tokenizer = BasicTokenizer(do_lower_case=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "), ["hallo", "!", "how", "are", "you", "?"]
        )
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])

    def test_basic_tokenizer_no_lower(self):
        tokenizer = BasicTokenizer(do_lower_case=False)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
        )

    def test_basic_tokenizer_no_lower_strip_accents_false(self):
        tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "), ["HäLLo", "!", "how", "Are", "yoU", "?"]
        )

    def test_basic_tokenizer_no_lower_strip_accents_true(self):
        tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "), ["HaLLo", "!", "how", "Are", "yoU", "?"]
        )

    def test_basic_tokenizer_respects_never_split_tokens(self):
        tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"])

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]
        )

    def test_wordpiece_tokenizer(self):
        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

        vocab = {}
        for (i, token) in enumerate(vocab_tokens):
            vocab[token] = i
        tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")

        self.assertListEqual(tokenizer.tokenize(""), [])

        self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"])

        self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])

    def test_clean_text(self):
        tokenizer = self.get_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]])

    @slow
    def test_sequence_builders(self):
        tokenizer = self.tokenizer_class.from_pretrained("albert-chinese-base")

        text = tokenizer.encode("sequence builders", return_token_type_ids=None, add_special_tokens=False)["input_ids"]
        text_2 = tokenizer.encode("multi-sequence build", return_token_type_ids=None, add_special_tokens=False)[
            "input_ids"
        ]

        encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
        encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)

        assert encoded_sentence == [101] + text + [102]
        assert encoded_pair == [101] + text + [102] + text_2 + [102]

    def test_offsets_with_special_characters(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                sentence = f"A, naïve {tokenizer.mask_token} AllenNLP sentence."
                tokens = tokenizer.encode(
                    sentence,
                    return_attention_mask=False,
                    return_token_type_ids=False,
                    return_offsets_mapping=True,
                    add_special_tokens=True,
                )

                do_lower_case = tokenizer.do_lower_case if hasattr(tokenizer, "do_lower_case") else False
                expected_results = (
                    [
                        ((0, 0), tokenizer.cls_token),
                        ((0, 1), "A"),
                        ((1, 2), ","),
                        ((3, 5), "na"),
                        ((5, 6), "##ï"),
                        ((6, 8), "##ve"),
                        ((9, 15), tokenizer.mask_token),
                        ((16, 21), "Allen"),
                        ((21, 23), "##NL"),
                        ((23, 24), "##P"),
                        ((25, 33), "sentence"),
                        ((33, 34), "."),
                        ((0, 0), tokenizer.sep_token),
                    ]
                    if not do_lower_case
                    else [
                        ((0, 0), tokenizer.cls_token),
                        ((0, 1), "a"),
                        ((1, 2), ","),
                        ((3, 8), "naive"),
                        ((9, 15), tokenizer.mask_token),
                        ((16, 21), "allen"),
                        ((21, 23), "##nl"),
                        ((23, 24), "##p"),
                        ((25, 33), "sentence"),
                        ((33, 34), "."),
                        ((0, 0), tokenizer.sep_token),
                    ]
                )

                self.assertEqual(
                    [e[1] for e in expected_results], tokenizer.convert_ids_to_tokens(tokens["input_ids"])
                )
                self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])

    def test_change_tokenize_chinese_chars(self):
        list_of_commun_chinese_char = ["的", "人", "有"]
        text_with_chinese_char = "".join(list_of_commun_chinese_char)
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):

                kwargs["tokenize_chinese_chars"] = True
                tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                ids_without_spe_char_p = tokenizer.encode(
                    text_with_chinese_char, return_token_type_ids=None, add_special_tokens=False
                )["input_ids"]

                tokens_without_spe_char_p = tokenizer.convert_ids_to_tokens(ids_without_spe_char_p)

                # it is expected that each Chinese character is not preceded by "##"
                self.assertListEqual(tokens_without_spe_char_p, list_of_commun_chinese_char)