# Copyright 2024 The HuggingFace Inc. team.
#
# 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 unittest
from functools import lru_cache

from tests.test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible
from transformers import SplinterTokenizerFast, is_tf_available, is_torch_available
from transformers.models.splinter import SplinterTokenizer
from transformers.testing_utils import get_tests_dir, slow


SAMPLE_VOCAB = get_tests_dir("fixtures/vocab.txt")


if is_torch_available():
    FRAMEWORK = "pt"
elif is_tf_available():
    FRAMEWORK = "tf"
else:
    FRAMEWORK = "jax"


class SplinterTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = SplinterTokenizer
    rust_tokenizer_class = SplinterTokenizerFast
    space_between_special_tokens = False
    test_rust_tokenizer = False
    test_sentencepiece_ignore_case = False
    pre_trained_model_path = "tau/splinter-base"

    # Copied from transformers.models.siglip.SiglipTokenizationTest.setUp
    @classmethod
    def setUpClass(cls):
        super().setUpClass()
        tokenizer = SplinterTokenizer(SAMPLE_VOCAB)
        tokenizer.vocab["[UNK]"] = len(tokenizer.vocab)
        tokenizer.vocab["[QUESTION]"] = len(tokenizer.vocab)
        tokenizer.vocab["."] = len(tokenizer.vocab)
        tokenizer.add_tokens("this is a test thou shall not determine rigor truly".split())
        tokenizer.save_pretrained(cls.tmpdirname)

    @classmethod
    @use_cache_if_possible
    @lru_cache(maxsize=64)
    def get_tokenizer(cls, pretrained_name=None, **kwargs) -> SplinterTokenizer:
        pretrained_name = pretrained_name or cls.tmpdirname
        return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

    @classmethod
    @use_cache_if_possible
    @lru_cache(maxsize=64)
    def get_rust_tokenizer(cls, pretrained_name=None, **kwargs) -> SplinterTokenizerFast:
        pretrained_name = pretrained_name or cls.tmpdirname
        return cls.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_get_vocab
    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], "[SEP]")
        self.assertEqual(vocab_keys[2], "[MASK]")

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_convert_token_and_id
    def test_convert_token_and_id(self):
        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_question_token_id(self):
        tokenizer = self.get_tokenizer()
        self.assertEqual(tokenizer.question_token_id, tokenizer.convert_tokens_to_ids(tokenizer.question_token))

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_full_tokenizer
    def test_full_tokenizer(self):
        tokenizer = self.get_tokenizer()
        test_str = "This is a test"

        unk_token = tokenizer.unk_token
        unk_token_id = tokenizer._convert_token_to_id_with_added_voc(unk_token)

        expected_tokens = test_str.lower().split()
        tokenizer.add_tokens(expected_tokens)
        tokens = tokenizer.tokenize(test_str)
        self.assertListEqual(tokens, expected_tokens)

        # test with out of vocabulary string
        tokens = tokenizer.tokenize(test_str + " oov")
        self.assertListEqual(tokens, expected_tokens + [unk_token])

        expected_token_ids = [13, 14, 15, 16, unk_token_id]
        token_ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(token_ids, expected_token_ids)

        tokenizer = self.get_tokenizer(basic_tokenize=False)
        expected_token_ids = [13, 14, 15, 16, unk_token_id]
        token_ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(token_ids, expected_token_ids)

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_rust_and_python_full_tokenizers
    def test_rust_and_python_full_tokenizers(self):
        tokenizer = self.get_tokenizer()
        rust_tokenizer = self.get_rust_tokenizer()

        sequence = "I need to test this rigor"
        tokens = tokenizer.tokenize(sequence, add_special_tokens=False)
        rust_tokens = rust_tokenizer.tokenize(sequence, add_special_tokens=False)
        self.assertListEqual(tokens, rust_tokens)

        ids = tokenizer.encode(sequence)
        rust_ids = rust_tokenizer.encode(sequence)
        self.assertListEqual(ids, rust_ids)

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_max_length
    def test_max_length(self):
        max_length = 20
        tokenizer = self.get_tokenizer()
        texts = ["this is a test", "I have pizza for lunch"]
        tokenized = tokenizer(
            text_target=texts,
            max_length=max_length,
            padding="max_length",
            truncation=True,
            return_tensors=FRAMEWORK,
        )
        self.assertEqual(len(tokenized["input_ids"]), len(texts))
        self.assertEqual(len(tokenized["input_ids"][0]), max_length)
        self.assertEqual(len(tokenized["input_ids"][1]), max_length)
        self.assertEqual(len(tokenized["attention_mask"][0]), max_length)
        self.assertEqual(len(tokenized["attention_mask"][1]), max_length)
        self.assertEqual(len(tokenized["token_type_ids"][0]), max_length)
        self.assertEqual(len(tokenized["token_type_ids"][1]), max_length)

    # Copied from transformers.models.siglip.SiglipTokenizationTest.test_tokenizer_integration
    # fmt:skip
    @slow
    def test_tokenizer_integration(self):
        tokenizer = SplinterTokenizer.from_pretrained("tau/splinter-base", max_length=10)
        texts = [
            "The cat sat on the windowsill, watching birds in the garden.",
            "She baked a delicious cake for her sister's birthday party.",
            "The sun set over the horizon, painting the sky with vibrant colors.",
        ]
        # fmt:off
        expected_token_id_list = [
            [101, 1109, 5855, 2068, 1113, 1103, 3751, 7956, 117, 2903, 4939, 1107, 1103, 4605, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1153, 19983, 170, 13108, 10851, 1111, 1123, 2104, 112, 188, 5913, 1710, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1109, 3336, 1383, 1166, 1103, 11385, 117, 3504, 1103, 3901, 1114, 18652, 5769, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        ]
        # fmt:on
        for text, expected_token_ids in zip(texts, expected_token_id_list):
            input_ids = tokenizer(text, padding="max_length").input_ids
            self.assertListEqual(input_ids, expected_token_ids)

    def test_special_tokens_mask_input_pairs(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence_0 = "Encode this."
                sequence_1 = "This one too please."
                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
                encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    sequence_0,
                    sequence_1,
                    add_special_tokens=True,
                    return_special_tokens_mask=True,
                )
                encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
                special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
                # splinter tokenizer always add cls, question_suffix, and 2 separators
                # while in special_token_mask it does not seems to do that
                self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special) - 2)
