# coding=utf-8
# Copyright 2022 Tsimur Hadeliya. 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.
""" Testing suite for the Longformer tokenizer. """


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


# Copied from transformers.tests.roberta.test_modeling_roberta.py with Roberta->Longformer
@require_tokenizers
class LongformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = LongformerTokenizer
    test_slow_tokenizer = True
    rust_tokenizer_class = LongformerTokenizerFast
    test_rust_tokenizer = True

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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        vocab = [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

    def get_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        input_text = "lower newer"
        output_text = "lower newer"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = self.tokenizer_class(self.vocab_file, self.merges_file, **self.special_tokens_map)
        text = "lower newer"
        bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        tokens = tokenizer.tokenize(text)  # , add_prefix_space=True)
        self.assertListEqual(tokens, bpe_tokens)

        input_tokens = tokens + [tokenizer.unk_token]
        input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)

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

        self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [0, 31414, 232, 328, 2])
        self.assertListEqual(
            tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False),
            [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2],
        )

    @slow
    def test_sequence_builders(self):
        tokenizer = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096")

        text = tokenizer.encode("sequence builders", add_special_tokens=False)
        text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)

        encoded_text_from_decode = tokenizer.encode(
            "sequence builders", add_special_tokens=True, add_prefix_space=False
        )
        encoded_pair_from_decode = tokenizer.encode(
            "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False
        )

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

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode

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

        sequence = "Encode this sequence."
        space_encoding = tokenizer.byte_encoder[" ".encode("utf-8")[0]]

        # Testing encoder arguments
        encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=False)
        first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
        self.assertNotEqual(first_char, space_encoding)

        encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
        first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
        self.assertEqual(first_char, space_encoding)

        tokenizer.add_special_tokens({"bos_token": "<s>"})
        encoded = tokenizer.encode(sequence, add_special_tokens=True)
        first_char = tokenizer.convert_ids_to_tokens(encoded[1])[0]
        self.assertNotEqual(first_char, space_encoding)

        # Testing spaces after special tokens
        mask = "<mask>"
        tokenizer.add_special_tokens(
            {"mask_token": AddedToken(mask, lstrip=True, rstrip=False)}
        )  # mask token has a left space
        mask_ind = tokenizer.convert_tokens_to_ids(mask)

        sequence = "Encode <mask> sequence"
        sequence_nospace = "Encode <mask>sequence"

        encoded = tokenizer.encode(sequence)
        mask_loc = encoded.index(mask_ind)
        first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
        self.assertEqual(first_char, space_encoding)

        encoded = tokenizer.encode(sequence_nospace)
        mask_loc = encoded.index(mask_ind)
        first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
        self.assertNotEqual(first_char, space_encoding)

    def test_pretokenized_inputs(self):
        pass

    def test_embeded_special_tokens(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                sentence = "A, <mask> AllenNLP sentence."
                tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
                tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
                    sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
                )

                tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
                self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

                self.assertSequenceEqual(
                    tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )
                self.assertSequenceEqual(
                    tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )

    def test_change_add_prefix_space_and_trim_offsets_args(self):
        for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2):
            tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                self.tmpdirname, use_fast=True, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets
            )

            pre_tokenizer_state = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
            post_processor_state = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

            self.assertEqual(pre_tokenizer_state["add_prefix_space"], add_prefix_space)

            self.assertEqual(post_processor_state["add_prefix_space"], add_prefix_space)
            self.assertEqual(post_processor_state["trim_offsets"], trim_offsets)

    def test_offsets_mapping_with_different_add_prefix_space_and_trim_space_arguments(self):
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
        # `trim_offsets`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                text_of_1_token = "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                text = f"{text_of_1_token} {text_of_1_token}"

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                text = f" {text}"

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                )

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False
                )
                encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token)))
                self.assertEqual(
                    encoding.offset_mapping[1],
                    (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                )
