# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2020 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 json
import os
import tempfile
import unittest

from paddlenlp.transformers import SPIECE_UNDERLINE, AddedToken, ReformerTokenizer
from paddlenlp.transformers.tokenizer_utils_base import BatchEncoding
from tests.testing_utils import get_tests_dir

from ..test_tokenizer_common import TokenizerTesterMixin

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


class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):

    tokenizer_class = ReformerTokenizer
    test_sentencepiece = True
    from_pretrained_vocab_key = "sentencepiece_model_file"

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

        # We have a SentencePiece fixture for testing
        tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
        tokenizer.save_pretrained(self.tmpdirname)

    def test_convert_token_and_id(self):
        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
        token = "<s>"
        token_id = 1

        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], "<unk>")
        self.assertEqual(vocab_keys[1], "<s>")
        self.assertEqual(vocab_keys[-1], "<pad>")
        self.assertEqual(len(vocab_keys), 1_101)

    def test_vocab_size(self):
        self.assertEqual(self.get_tokenizer().vocab_size, 1_100)

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

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

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(tokens),
            [285, 46, 10, 170, 382],
        )

        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
        self.assertListEqual(
            tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ],
        )
        ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(
            ids,
            [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
        )

        back_tokens = tokenizer.convert_ids_to_tokens(ids)
        self.assertListEqual(
            back_tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ],
        )

    def reformer_base_tokenizer(self):
        return ReformerTokenizer.from_pretrained("reformer-crime-and-punishment")

    def get_tokenizer(self, **kwargs) -> ReformerTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)

    def test_eos_treatment(self):
        tokenizer = self.reformer_base_tokenizer()
        batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
        batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
        self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])

    def test_prepare_batch(self):
        tokenizer = self.reformer_base_tokenizer()
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        expected_src_tokens = [
            99,
            35,
            28,
            275,
            40,
            52,
            261,
            275,
            209,
            279,
            265,
            88,
            117,
            271,
            271,
            52,
            264,
            299,
            248,
            278,
            2,
        ]
        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
        self.assertIsInstance(batch, BatchEncoding)

        result = list(batch["input_ids"].tolist()[0])

        self.assertListEqual(expected_src_tokens, result)

        self.assertEqual([2, 21], batch["input_ids"].shape)
        self.assertEqual([2, 21], batch.attention_mask.shape)

    def test_empty_target_text(self):
        tokenizer = self.reformer_base_tokenizer()
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("input_ids", batch)
        self.assertIn("attention_mask", batch)
        self.assertNotIn("decoder_input_ids", batch)
        self.assertNotIn("decoder_attention_mask", batch)

    def test_max_length(self):
        tokenizer = self.reformer_base_tokenizer()
        tgt_text = [
            "Summary of the text.",
            "Another summary.",
        ]
        targets = tokenizer(
            text=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
        )
        self.assertEqual(32, targets["input_ids"].shape[1])

    def test_outputs_not_longer_than_maxlen(self):
        tokenizer = self.reformer_base_tokenizer()

        batch = tokenizer(
            ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
        )
        self.assertIsInstance(batch, BatchEncoding)
        # Since Reformer does NOT have a max input length,
        # this test should be changed to the following in Transformers v5:
        # self.assertEqual(batch["input_ids"].shape, (2, 8001))
        self.assertEqual(batch["input_ids"].shape, [2, 9001])

    def test_eos_in_input(self):
        tokenizer = self.reformer_base_tokenizer()
        src_text = ["A long paragraph for summarization. </s>"]
        tgt_text = ["Summary of the text. </s>"]
        expected_src_tokens = [
            99,
            35,
            28,
            275,
            40,
            52,
            261,
            275,
            209,
            279,
            265,
            88,
            117,
            271,
            271,
            52,
            264,
            299,
            248,
            278,
            2,
        ]

        # TODO(wj-Mcat): to enable `expected_tgt_tokens`
        # expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]

        batch = tokenizer(src_text, text_target=tgt_text)

        self.assertEqual(expected_src_tokens, batch["input_ids"][0])
        # self.assertEqual(expected_tgt_tokens, batch["labels"][0])

    def test_token_type_ids(self):
        src_text_1 = ["A first paragraph for summarization."]
        src_text_2 = ["A second paragraph for summarization."]

        tokenizer = self.reformer_base_tokenizer()

        slow_token_type_ids = tokenizer(src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True)[
            "token_type_ids"
        ]
        self.assertEqual(len(slow_token_type_ids[0]), 44)

    def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
        tokenizer_list = []
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

        for tokenizer_class, tokenizer_utils in tokenizer_list:

            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(tmp_dir)

                with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
                    special_tokens_map = json.load(json_file)

                with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
                    tokenizer_config = json.load(json_file)

                added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]

                special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
                    "an_additional_special_token"
                ]
                tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
                    "an_additional_special_token"
                ]

                with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
                    json.dump(special_tokens_map, outfile)
                with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
                    json.dump(tokenizer_config, outfile)

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
                    tmp_dir,
                )
                self.assertIn(
                    "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
                )
                # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByReformerTokenization no vocab
                self.assertEqual(
                    ["an_additional_special_token"],
                    tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
                    ),
                )

                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
                tokenizer = tokenizer_class.from_pretrained(
                    tmp_dir,
                    additional_special_tokens=new_added_tokens,
                )

                self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
                self.assertEqual(
                    ["a_new_additional_special_token"],
                    tokenizer.convert_ids_to_tokens(
                        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
                    ),
                )

    # overwritten from `test_tokenization_common` since Reformer has no max length
    def test_pretrained_model_lists(self):
        # We should have at least one default checkpoint for each tokenizer
        # We should specify the max input length as well (used in some part to list the pretrained checkpoints)
        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1)
        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), 1)

    def test_offsets_mapping(self):
        pass

    def test_consecutive_unk_string(self):
        tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
        for tokenizer in tokenizers:
            tokens = [tokenizer.unk_token for _ in range(2)]
            string = tokenizer.convert_tokens_to_string(tokens)
            encoding = tokenizer(
                text=string,
                truncation=True,
                return_offsets_mapping=True,
            )
            self.assertEqual(len(encoding["input_ids"]), 3)
            self.assertEqual(len(encoding["offset_mapping"]), 3)
