# 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 unittest
from functools import lru_cache

from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES
from transformers.testing_utils import require_jinja, require_tiktoken, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible


@require_tokenizers
class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "openai-community/gpt2"
    tokenizer_class = GPT2Tokenizer
    rust_tokenizer_class = GPT2TokenizerFast
    test_rust_tokenizer = True
    from_pretrained_kwargs = {"add_prefix_space": True}
    test_seq2seq = False

    @classmethod
    def setUpClass(cls):
        super().setUpClass()

        # 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>",
            "<|endoftext|>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        cls.special_tokens_map = {"unk_token": "<unk>"}

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

    @classmethod
    @use_cache_if_possible
    @lru_cache(maxsize=64)
    def get_tokenizer(cls, pretrained_name=None, **kwargs):
        kwargs.update(cls.special_tokens_map)
        pretrained_name = pretrained_name or cls.tmpdirname
        return GPT2Tokenizer.from_pretrained(pretrained_name, **kwargs)

    @classmethod
    @use_cache_if_possible
    @lru_cache(maxsize=64)
    def get_rust_tokenizer(cls, pretrained_name=None, **kwargs):
        kwargs.update(cls.special_tokens_map)
        pretrained_name = pretrained_name or cls.tmpdirname
        return GPT2TokenizerFast.from_pretrained(pretrained_name, **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 = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
        text = "lower newer"
        bpe_tokens = ["\u0120low", "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 = [14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)

    def test_rust_and_python_full_tokenizers(self):
        if not self.test_rust_tokenizer:
            self.skipTest(reason="test_rust_tokenizer is set to False")

        tokenizer = self.get_tokenizer()
        rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)

        sequence = "lower newer"

        # Testing tokenization
        tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
        rust_tokens = rust_tokenizer.tokenize(sequence)
        self.assertListEqual(tokens, rust_tokens)

        # Testing conversion to ids without special tokens
        ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
        rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
        self.assertListEqual(ids, rust_ids)

        # Testing conversion to ids with special tokens
        rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
        ids = tokenizer.encode(sequence, add_prefix_space=True)
        rust_ids = rust_tokenizer.encode(sequence)
        self.assertListEqual(ids, rust_ids)

        # Testing the unknown token
        input_tokens = tokens + [rust_tokenizer.unk_token]
        input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)

    @unittest.skip
    def test_pretokenized_inputs(self, *args, **kwargs):
        # It's very difficult to mix/test pretokenization with byte-level
        # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
        pass

    def test_padding(self, max_length=15):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)

                # Simple input
                s = "This is a simple input"
                s2 = ["This is a simple input 1", "This is a simple input 2"]
                p = ("This is a simple input", "This is a pair")
                p2 = [
                    ("This is a simple input 1", "This is a simple input 2"),
                    ("This is a simple pair 1", "This is a simple pair 2"),
                ]

                # Simple input tests
                self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")

                # Simple input
                self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")

                # Simple input
                self.assertRaises(
                    ValueError,
                    tokenizer_r.batch_encode_plus,
                    s2,
                    max_length=max_length,
                    padding="max_length",
                )

                # Pair input
                self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")

                # Pair input
                self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")

                # Pair input
                self.assertRaises(
                    ValueError,
                    tokenizer_r.batch_encode_plus,
                    p2,
                    max_length=max_length,
                    padding="max_length",
                )

    def test_padding_if_pad_token_set_slow(self):
        tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>")

        # Simple input
        s = "This is a simple input"
        s2 = ["This is a simple input looooooooong", "This is a simple input"]
        p = ("This is a simple input", "This is a pair")
        p2 = [
            ("This is a simple input loooooong", "This is a simple input"),
            ("This is a simple pair loooooong", "This is a simple pair"),
        ]

        pad_token_id = tokenizer.pad_token_id

        out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np")
        out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np")
        out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np")
        out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np")

        # s
        # test single string max_length padding
        self.assertEqual(out_s["input_ids"].shape[-1], 30)
        self.assertTrue(pad_token_id in out_s["input_ids"])
        self.assertTrue(0 in out_s["attention_mask"])

        # s2
        # test automatic padding
        self.assertEqual(out_s2["input_ids"].shape[-1], 33)
        # long slice doesn't have padding
        self.assertFalse(pad_token_id in out_s2["input_ids"][0])
        self.assertFalse(0 in out_s2["attention_mask"][0])
        # short slice does have padding
        self.assertTrue(pad_token_id in out_s2["input_ids"][1])
        self.assertTrue(0 in out_s2["attention_mask"][1])

        # p
        # test single pair max_length padding
        self.assertEqual(out_p["input_ids"].shape[-1], 60)
        self.assertTrue(pad_token_id in out_p["input_ids"])
        self.assertTrue(0 in out_p["attention_mask"])

        # p2
        # test automatic padding pair
        self.assertEqual(out_p2["input_ids"].shape[-1], 52)
        # long slice pair doesn't have padding
        self.assertFalse(pad_token_id in out_p2["input_ids"][0])
        self.assertFalse(0 in out_p2["attention_mask"][0])
        # short slice pair does have padding
        self.assertTrue(pad_token_id in out_p2["input_ids"][1])
        self.assertTrue(0 in out_p2["attention_mask"][1])

    def test_add_bos_token_slow(self):
        bos_token = "$$$"
        tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True)

        s = "This is a simple input"
        s2 = ["This is a simple input 1", "This is a simple input 2"]

        bos_token_id = tokenizer.bos_token_id

        out_s = tokenizer(s)
        out_s2 = tokenizer(s2)

        self.assertEqual(out_s.input_ids[0], bos_token_id)
        self.assertTrue(all(o[0] == bos_token_id for o in out_s2.input_ids))

        decode_s = tokenizer.decode(out_s.input_ids)
        decode_s2 = tokenizer.batch_decode(out_s2.input_ids)

        self.assertTrue(decode_s.startswith(bos_token))
        self.assertTrue(all(d.startswith(bos_token) for d in decode_s2))

    @unittest.skip(reason="tokenizer has no padding token")
    def test_padding_different_model_input_name(self):
        pass

    def test_special_tokens_mask_input_pairs_and_bos_token(self):
        # TODO: change to self.get_tokenizers() when the fast version is implemented
        tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)]
        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"]
                self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))

                filtered_sequence = [
                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
                ]
                filtered_sequence = [x for x in filtered_sequence if x is not None]
                self.assertEqual(encoded_sequence, filtered_sequence)

    @require_jinja
    def test_tokenization_for_chat(self):
        tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname)
        tokenizer.chat_template = "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}"
        test_chats = [
            [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
            [
                {"role": "system", "content": "You are a helpful chatbot."},
                {"role": "user", "content": "Hello!"},
                {"role": "assistant", "content": "Nice to meet you."},
            ],
            [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}],
        ]
        tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
        # fmt: off
        expected_tokens = [[20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20],
                          [20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20, 20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20],
                          [20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20, 20, 3, 0, 0, 1, 20, 20]]
        # fmt: on
        for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
            self.assertListEqual(tokenized_chat, expected_tokens)

    @require_tiktoken
    def test_tokenization_tiktoken(self):
        from tiktoken import encoding_name_for_model

        from transformers.integrations.tiktoken import convert_tiktoken_to_fast

        encoding = encoding_name_for_model("gpt2")
        convert_tiktoken_to_fast(encoding, self.tmpdirname)

        tiktoken_fast_tokenizer = GPT2TokenizerFast.from_pretrained(self.tmpdirname)
        rust_tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
        sequence = "lower newer"
        self.assertEqual(
            rust_tokenizer.decode(rust_tokenizer.encode(sequence)),
            tiktoken_fast_tokenizer.decode(rust_tokenizer.encode(sequence)),
        )


@require_tokenizers
class OPTTokenizationTest(unittest.TestCase):
    def test_serialize_deserialize_fast_opt(self):
        # More context:
        # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
        # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
        # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True)
        text = "A photo of a cat"

        tokens_ids = tokenizer.encode(
            text,
        )
        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])
        tokenizer.save_pretrained("test_opt")

        tokenizer = AutoTokenizer.from_pretrained("./test_opt")
        tokens_ids = tokenizer.encode(
            text,
        )
        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])

    def test_fast_slow_equivalence(self):
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", use_slow=True)
        text = "A photo of a cat"

        tokens_ids = tokenizer.encode(
            text,
        )
        # Same as above
        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])

    @unittest.skip(reason="This test is failing because of a bug in the fast tokenizer")
    def test_users_can_modify_bos(self):
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True)

        tokenizer.bos_token = "bos"
        tokenizer.bos_token_id = tokenizer.get_vocab()["bos"]

        text = "A photo of a cat"
        tokens_ids = tokenizer.encode(
            text,
        )
        # We changed the bos token
        self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])
        tokenizer.save_pretrained("./tok")
        tokenizer = AutoTokenizer.from_pretrained("./tok")
        self.assertTrue(tokenizer.is_fast)
        tokens_ids = tokenizer.encode(
            text,
        )
        self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])
