# coding=utf-8
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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 tempfile
import unittest

from datasets import load_dataset

from transformers import (
    AddedToken,
    GemmaTokenizer,
    GemmaTokenizerFast,
    is_torch_available,
)
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_jinja,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
    pass


@require_sentencepiece
@require_tokenizers
class GemmaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "google/gemma-7b"
    tokenizer_class = GemmaTokenizer
    rust_tokenizer_class = GemmaTokenizerFast

    test_rust_tokenizer = False
    test_sentencepiece = True
    from_pretrained_kwargs = {}

    def setUp(self):
        super().setUp()
        # We have a SentencePiece fixture for testing
        tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True)
        tokenizer.pad_token = tokenizer.eos_token
        tokenizer.save_pretrained(self.tmpdirname)

    @require_torch
    def test_batch_tokenization(self):
        if not self.test_seq2seq:
            return

        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Longer text that will definitely require truncation.
                text = [
                    " UN Chief Says There Is No Military Solution in Syria",
                    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                    " will only worsen the violence and misery for millions of people.",
                ]
                try:
                    batch = tokenizer(
                        text=text,
                        max_length=3,
                        max_target_length=10,
                        return_tensors="pt",
                    )
                except NotImplementedError:
                    return
                self.assertEqual(batch.input_ids.shape[1], 3)
                # max_target_length will default to max_length if not specified
                batch = tokenizer(text, max_length=3, return_tensors="pt")
                self.assertEqual(batch.input_ids.shape[1], 3)

                batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
                self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
                self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
                self.assertNotIn("decoder_input_ids", batch_encoder_only)

    @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
    def test_save_slow_from_fast_and_reload_fast(self):
        pass

    def test_special_tokens_initialization(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                added_tokens = [AddedToken("<special>", lstrip=True)]

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
                )
                r_output = tokenizer_r.encode("Hey this is a <special> token")

                special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]

                self.assertTrue(special_token_id in r_output)

                if self.test_slow_tokenizer:
                    tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name,
                        additional_special_tokens=added_tokens,
                        **kwargs,  # , from_slow=True <- unfortunately too slow to convert
                    )
                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )

                    p_output = tokenizer_p.encode("Hey this is a <special> token")

                    cr_output = tokenizer_cr.encode("Hey this is a <special> token")

                    self.assertEqual(p_output, r_output)
                    self.assertEqual(cr_output, r_output)
                    self.assertTrue(special_token_id in p_output)
                    self.assertTrue(special_token_id in cr_output)

    @slow
    def test_tokenizer_integration(self):
        expected_encoding =  {'input_ids': [[2, 158434, 591, 84193, 3836, 685, 6599, 31223, 235290, 140247, 578, 6599, 31223, 235290, 145139, 235290, 3491, 235275, 6572, 3311, 235290, 38197, 109959, 591, 25894, 235269, 162174, 235290, 235284, 235269, 1791, 6362, 12481, 235269, 1576, 18622, 235269, 2900, 1136, 86684, 235269, 29092, 4632, 16994, 604, 13146, 14944, 40371, 591, 19700, 235327, 235275, 578, 13146, 14944, 25511, 591, 235300, 12474, 235275, 675, 1163, 235248, 235304, 235284, 235340, 229903, 5377, 575, 235248, 235274, 235276, 235276, 235340, 17044, 578, 5271, 1061, 118345, 1865, 125247, 235269, 8745, 111226, 578, 176888, 235265], [2, 25894, 603, 6869, 577, 953, 235290, 8297, 5271, 209099, 41642, 774, 748, 78253, 2793, 731, 51506, 34346, 611, 2145, 2731, 578, 1833, 4807, 575, 832, 16630, 235265], [2, 651, 4320, 8426, 25341, 36271, 1163, 573, 27894, 5929, 235265]], '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]]}  # fmt: skip
        self.tokenizer_integration_test_util(
            expected_encoding=expected_encoding,
            model_name="hf-internal-testing/dummy-gemma",
            revision="",
            padding=False,
        )

    @unittest.skip("worker 'gw4' crashed on CI, passing locally.")
    def test_pickle_subword_regularization_tokenizer(self):
        pass

    @unittest.skip("worker 'gw4' crashed on CI, passing locally.")
    def test_subword_regularization_tokenizer(self):
        pass

    @unittest.skip("Skipping")
    def test_torch_encode_plus_sent_to_model(self):
        pass


@require_torch
@require_sentencepiece
@require_tokenizers
class GemmaIntegrationTest(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        checkpoint_name = "hf-internal-testing/dummy-gemma"
        cls.tokenizer: GemmaTokenizer = GemmaTokenizer.from_pretrained(
            checkpoint_name, eos_token="<s>"
        )  # add this token
        cls.rust_tokenizer = GemmaTokenizerFast.from_pretrained(
            checkpoint_name, eos_token="<s>", from_slow=True
        )  # add this token
        return cls

    @require_torch
    def integration_tests(self):
        inputs = self.tokenizer(
            ["The following string should be properly encoded: Hello.", "But ird and ปี   ird   ด"],
            return_tensors="pt",
        )

        self.assertEqual(
            nested_simplify(inputs),
            {
                "input_ids": [
                    [2, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
                    [2, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
                ],
                "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]],
            },
        )

    def test_fast_special_tokens(self):
        slow_tokenizer = self.tokenizer
        fast_tokenizer = self.rust_tokenizer
        slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
        assert slow == [2, 235280, 6453, 2121]

        fast_tokenizer.add_eos_token = False
        fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
        assert fast == [2, 235280, 6453, 2121]

        fast_tokenizer.add_eos_token = True
        fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
        assert fast == [2, 235280, 6453, 2121, 204]

        slow_tokenizer.add_eos_token = True
        slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
        assert slow == [2, 235280, 6453, 2121, 204]

        self.tokenizer.add_eos_token = False
        self.rust_tokenizer.add_eos_token = False

    @unittest.skip("Not super important and always failing. Let's skip it")
    @slow
    def test_conversion(self):
        # This is excruciatingly slow since it has to recreate the entire merge
        # list from the original vocabulary in spm
        self.rust_tokenizer.save_pretrained("./out")
        with tempfile.TemporaryDirectory() as dirname:
            self.rust_tokenizer.save_pretrained(dirname)

            with open(os.path.join(dirname, "tokenizer.json"), "r") as f:
                old_serialized = f.read()

        new_tokenizer = convert_slow_tokenizer(self.tokenizer)
        with tempfile.NamedTemporaryFile() as f:
            new_tokenizer.save(f.name)
            # Re-opening since `f` is in bytes.
            new_serialized = open(f.name, "r").read()
            with open("out_tokenizer.json", "w") as g:
                g.write(new_serialized)

            self.assertEqual(old_serialized, new_serialized)

    def test_simple_encode_decode(self):
        pyth_tokenizer = self.tokenizer
        rust_tokenizer = self.rust_tokenizer

        self.tokenizer.add_eos_token = False
        self.rust_tokenizer.add_eos_token = False

        self.assertEqual(pyth_tokenizer.encode("This is a test"), [2, 1596, 603, 476, 2121])
        self.assertEqual(rust_tokenizer.encode("This is a test"), [2, 1596, 603, 476, 2121])
        self.assertEqual(pyth_tokenizer.decode([2, 1596, 603, 476, 2121], skip_special_tokens=True), "This is a test")
        self.assertEqual(rust_tokenizer.decode([2, 1596, 603, 476, 2121], skip_special_tokens=True), "This is a test")

        # bytefallback showcase
        self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [2, 122182, 235710, 245467, 235427] )  # fmt: skip
        self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [2, 122182, 235710, 245467, 235427] )  # fmt: skip
        self.assertEqual(
            pyth_tokenizer.decode([2, 122182, 235710, 245467, 235427], skip_special_tokens=True),
            "生活的真谛是",
        )
        self.assertEqual(
            rust_tokenizer.decode([2, 122182, 235710, 245467, 235427], skip_special_tokens=True),
            "生活的真谛是",
        )

        # Inner spaces showcase
        self.assertEqual(pyth_tokenizer.encode("Hi  Hello"), [2, 2151, 139, 4521])
        self.assertEqual(rust_tokenizer.encode("Hi  Hello"), [2, 2151, 139, 4521])
        self.assertEqual(pyth_tokenizer.decode([2, 2151, 139, 4521], skip_special_tokens=True), "Hi  Hello")
        self.assertEqual(rust_tokenizer.decode([2, 2151, 139, 4521], skip_special_tokens=True), "Hi  Hello")

        self.assertEqual(pyth_tokenizer.encode("Hi   Hello"), [2, 2151, 140, 4521])
        self.assertEqual(rust_tokenizer.encode("Hi   Hello"), [2, 2151, 140, 4521])
        self.assertEqual(pyth_tokenizer.decode([2, 2151, 140, 4521], skip_special_tokens=True), "Hi   Hello")
        self.assertEqual(rust_tokenizer.decode([2, 2151, 140, 4521], skip_special_tokens=True), "Hi   Hello")

        self.assertEqual(pyth_tokenizer.encode(""), [2])
        self.assertEqual(rust_tokenizer.encode(""), [2])

        self.assertEqual(pyth_tokenizer.encode(" "), [2, 235248])
        self.assertEqual(rust_tokenizer.encode(" "), [2, 235248])

        self.assertEqual(pyth_tokenizer.encode("  "), [2, 139])
        self.assertEqual(rust_tokenizer.encode("  "), [2, 139])

        self.assertEqual(pyth_tokenizer.encode(" Hello"), [2, 25957])
        self.assertEqual(rust_tokenizer.encode(" Hello"), [2, 25957])

    def test_no_differences_decode(self):
        self.tokenizer.add_eos_token = False
        self.rust_tokenizer.add_eos_token = False
        pyth_tokenizer = self.tokenizer
        rust_tokenizer = self.rust_tokenizer

        self.assertEqual(pyth_tokenizer.decode([869]), "og")
        self.assertEqual(rust_tokenizer.decode([869]), "og")

        self.assertEqual(pyth_tokenizer.decode([30112, 869]), " expenditureog")
        self.assertEqual(rust_tokenizer.decode([30112, 869]), " expenditureog")

    def test_no_differences_special_tokens(self):
        pyth_tokenizer = self.tokenizer
        rust_tokenizer = self.rust_tokenizer
        self.assertEqual(pyth_tokenizer.encode(""), [2])
        self.assertEqual(rust_tokenizer.encode(""), [2])

        self.assertEqual(pyth_tokenizer.encode("<s>"), [2, 204])
        self.assertEqual(rust_tokenizer.encode("<s>"), [2, 204])

    @unittest.skipIf(
        os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
        "RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
    )
    def test_integration_test_xnli(self):
        import tqdm

        pyth_tokenizer = self.tokenizer
        rust_tokenizer = self.rust_tokenizer

        dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
        for item in tqdm.tqdm(dataset["validation"]):
            string = item["code"]
            encoded1 = pyth_tokenizer.encode(string)
            encoded2 = rust_tokenizer.encode(string)

            self.assertEqual(encoded1, encoded2)

            decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
            decoded2 = rust_tokenizer.decode(encoded1, skip_special_tokens=True)

            self.assertEqual(decoded1, decoded2)

        dataset = load_dataset("xnli", "all_languages")

        for item in tqdm.tqdm(dataset["train"]):
            for string in item["premise"].values():
                encoded1 = pyth_tokenizer.encode(string)
                encoded2 = rust_tokenizer.encode(string)

                self.assertEqual(encoded1, encoded2)

                decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
                decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)

                self.assertEqual(decoded1, decoded2)

    def test_special_token_special_word(self):
        # the word inform should be split as ['in', 'form']
        tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")
        tokenizer.add_tokens([AddedToken("<REPR_END>", rstrip=True, lstrip=True)], special_tokens=False)
        out1 = tokenizer.decode(
            tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
        )
        self.assertEqual(out1, "<REPR_END>inform")
        out2 = tokenizer.decode(
            tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=True
        )
        # decoding strips the added prefix space.
        self.assertEqual(out2, "<REPR_END> inform")
        input_ids = tokenizer.encode("<REPR_END>inform", add_special_tokens=False)
        self.assertEqual(input_ids, [256000, 43910])

        out2 = tokenizer.decode(
            tokenizer.encode(" <REPR_END> inform", add_special_tokens=False), spaces_between_special_tokens=False
        )
        # TODO @ArthurZ currently we strip left and right, so this will not keep the spaces
        self.assertEqual(out2, "<REPR_END>inform")

        ### Let's make sure decoding does not add extra spaces here and there
        # TODO @ArthurZ this should be affected by the lstrip/rstrip/single word /normalize refactoring
        # Since currently we always strip left and right of the token, results are as such
        input_ids = tokenizer.encode("<s> Hello<s>how", add_special_tokens=False)
        self.assertEqual(input_ids, [204, 25957, 204, 1139])
        tokens = tokenizer.tokenize("<s> Hello<s>how", add_special_tokens=False)
        self.assertEqual(tokens, ["<s>", "▁Hello", "<s>", "how"])
        decoded_tokens = tokenizer.decode(input_ids)
        self.assertEqual(decoded_tokens, "<s> Hello<s>how")

        # Let's make sure that if there are any spaces, we don't remove them!
        input_ids = tokenizer.encode(" <s> Hello<s> how", add_special_tokens=False)
        self.assertEqual(input_ids, [235248, 204, 25957, 204, 1368])
        tokens = tokenizer.tokenize(" <s> Hello<s> how", add_special_tokens=False)
        self.assertEqual(tokens, ["▁", "<s>", "▁Hello", "<s>", "▁how"])
        decoded_tokens = tokenizer.decode(input_ids)
        self.assertEqual(decoded_tokens, " <s> Hello<s> how")

    def test_some_edge_cases(self):
        tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")

        sp_tokens = tokenizer.sp_model.encode("<s>>", out_type=str)
        self.assertEqual(sp_tokens, ["<s>", ">"])
        tokens = tokenizer.tokenize("<s>>")
        self.assertEqual(sp_tokens, tokens)
        self.assertEqual(tokens, ["<s>", ">"])

        tokens = tokenizer.tokenize("")
        self.assertEqual(tokens, [])
        self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))

        tokens = tokenizer.tokenize(" ")
        self.assertEqual(tokens, ["▁"])
        # a dummy prefix space is not added by the sp_model as it was de-activated
        self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))

        tokens = tokenizer.tokenize("▁")
        self.assertEqual(tokens, ["▁"])
        # a dummy prefix space is not added by the sp_model as it was de-activated
        self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))

        tokens = tokenizer.tokenize(" ▁")
        self.assertEqual(tokens, ["▁▁"])
        # a dummy prefix space is not added by the sp_model as it was de-activated
        self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁", out_type=str))

    @require_jinja
    def test_tokenization_for_chat(self):
        tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")

        test_chats = [
            [{"role": "user", "content": "Hello!"}],
            [
                {"role": "user", "content": "Hello!"},
                {"role": "assistant", "content": "Nice to meet you."},
            ],
            [{"role": "user", "content": "Hello!"}],
        ]
        # Matt: The third test case tests the default system message, but if this is ever changed in the
        #       class/repo code then that test will fail, and the case will need to be updated.
        tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
        expected_tokens = [[235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108], [235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108, 235322, 235371, 571, 235298, 2997, 73786, 105776, 108, 7731, 577, 4664, 692, 35606, 235371, 571, 235298, 615, 73786, 108], [235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108]]  # fmt: skip
        for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
            self.assertListEqual(tokenized_chat, expected_tokens)


@require_sentencepiece
@require_tokenizers
class CommonSpmIntegrationTests(unittest.TestCase):
    """
    A class that regroups important test to make sure that we properly handle the special tokens.
    """

    def test_edge_case_tabulation(self):
        fast_tokenizer = GemmaTokenizerFast.from_pretrained("hf-internal-testing/dummy-gemma")
        slow_tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")
        input_text = "Hey<eos>. \t\t \n\nyou  é  @#😈  🤗!       , 1234 15 5,61"
        EXPECTED_IDS = [ 2, 6750, 1, 235265, 235248, 255969, 235248, 109, 4747, 139, 235335, 139, 216311, 241316, 139, 239880, 235341, 144, 235269, 235248, 235274, 235284, 235304, 235310, 235248, 235274, 235308, 235248, 235308, 235269, 235318, 235274]  # fmt: skip
        EXPECTED_TOKENS = [ "Hey", "<eos>", ".", "▁", "\t\t", "▁", "\n\n", "you", "▁▁", "é", "▁▁", "@#", "😈", "▁▁", "🤗", "!", "▁▁▁▁▁▁▁", ",", "▁", "1", "2", "3", "4", "▁", "1", "5", "▁", "5", ",", "6", "1"]  # fmt: skip

        tokens = fast_tokenizer.tokenize(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(tokens, EXPECTED_TOKENS)

        tokens = slow_tokenizer.tokenize(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(tokens, EXPECTED_TOKENS)

        input_ids = fast_tokenizer.encode(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(input_ids, EXPECTED_IDS)

        input_ids = slow_tokenizer.encode(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(input_ids, EXPECTED_IDS)

        text = fast_tokenizer.decode(EXPECTED_IDS)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(text, "<bos>Hey<eos>. \t\t \n\nyou  é  @#😈  🤗!       , 1234 15 5,61")

        text = slow_tokenizer.decode(EXPECTED_IDS)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(text, "<bos>Hey<eos>. \t\t \n\nyou  é  @#😈  🤗!       , 1234 15 5,61")

        input_text = "\t\t\t\t \n\n61"
        EXPECTED_IDS = [2, 255971, 235248, 109, 235318, 235274]
        EXPECTED_TOKENS = ["\t\t\t\t", "▁", "\n\n", "6", "1"]

        tokens = fast_tokenizer.tokenize(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(tokens, EXPECTED_TOKENS)

        tokens = slow_tokenizer.tokenize(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(tokens, EXPECTED_TOKENS)

        input_ids = fast_tokenizer.encode(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(input_ids, EXPECTED_IDS)

        input_ids = slow_tokenizer.encode(input_text)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(input_ids, EXPECTED_IDS)

        text = fast_tokenizer.decode(EXPECTED_IDS)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(text, "<bos>\t\t\t\t \n\n61")

        text = slow_tokenizer.decode(EXPECTED_IDS)
        with self.subTest("test fast edge case fast"):
            self.assertEqual(text, "<bos>\t\t\t\t \n\n61")
