# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The Open AI Team Authors and 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 json
import os
import shutil
from functools import lru_cache

from paddle.utils import try_import

from .. import AddedToken, PretrainedTokenizer

__all__ = ["MambaTokenizer"]


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a significant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    _chr = chr
    bs = (
        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [_chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.

    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class MambaTokenizer(PretrainedTokenizer):

    resource_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
    pretrained_resource_files_map = {"vocab_file": {}, "merges_file": {}}
    pretrained_init_configuration = {}
    model_input_names = ["input_ids"]

    def __init__(
        self,
        vocab_file=None,
        merges_file=None,
        unk_token="<|endoftext|>",
        bos_token="<|endoftext|>",
        eos_token="<|endoftext|>",
        pad_token=None,
        add_bos_token=False,
        add_eos_token=False,
        add_prefix_space=False,
        max_length=None,
        errors="replace",
        **kwargs,
    ):
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token

        self._build_special_tokens_map_extended(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
        )

        # NOTE: add special tokens to the vocab
        value = kwargs.pop("added_tokens_decoder", {})
        additional_special_tokens = []
        for _, token_kwargs in value.items():
            if isinstance(token_kwargs, AddedToken):
                content = token_kwargs
            else:
                content = AddedToken(**token_kwargs)
            additional_special_tokens.append(content)
        if len(additional_special_tokens) > 0:
            self._build_special_tokens_map_extended(
                additional_special_tokens=additional_special_tokens,
            )

        self._vocab_file = vocab_file
        self._merges_file = merges_file
        self.max_length = max_length if max_length is not None else int(1e12)
        self.num_command_tokens = 2
        self.num_type_tokens = 2

        with open(vocab_file, "r", encoding="utf-8") as f:
            self.encoder = json.load(f)

        self.decoder = {v: k for k, v in self.encoder.items()}

        self.num_tokens = len(self.encoder)
        self.num_text_tokens = self.num_tokens - 1
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}

        with open(merges_file, encoding="utf-8") as f:
            bpe_data = f.read().split("\n")[1:-1]

        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        self.cache = {}
        self.add_prefix_space = add_prefix_space
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token

        re = try_import("regex")
        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")

        super().__init__(**kwargs)

    @property
    def vocab_size(self):
        """
        Returns the size of vocabulary.

        Returns:
            int: The sum of size of vocabulary and the size of special tokens.

        """
        return len(self.encoder)

    def __len__(self):
        """
        Size of the full vocabulary with the added tokens.
        """
        return len(dict(self.encoder, **self.added_tokens_encoder))

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        while True:
            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = " ".join(word)
        self.cache[token] = word
        return word

    def _tokenize(self, text):
        """Tokenize a string."""
        bpe_tokens = []
        re = try_import("regex")
        for token in re.findall(self.pat, text):
            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    def _convert_token_to_id(self, token):
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index):
        return self.decoder[index]

    def convert_ids_to_string(self, ids):
        """
        Converts a single index or a sequence of indices to texts.

        Args:
            ids (int|List[int]):
                The token id (or token ids) to be converted to text.

        Returns:
            str: The decoded text.

        Example:
            .. code-block::

                from paddlenlp.transformers import MambaTokenizer
                tokenizer = MambaTokenizer.from_pretrained('state-spaces/mamba-2.8b-hf')
                print(tokenizer.convert_ids_to_string([21096, 281, 897, 367, 17014, 49, 17014, 285, 367, 17014, 47, 13010]))
                # 'Welcome to use PaddlePaddle and PaddleNLP'

        """

        text = "".join([self.decoder[id] for id in ids])
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)

        return text

    def save_resources(self, save_directory):
        """
        Saves `SentencePiece <https://github.com/google/sentencepiece>`__ file
        (ends with '.spm') under `save_directory`.

        Args:
            save_directory (str): Directory to save files into.
        """
        for name, file_name in self.resource_files_names.items():
            source_path = getattr(self, "_%s" % name)

            save_path = os.path.join(save_directory, file_name)
            if os.path.abspath(source_path) != os.path.abspath(save_path):
                shutil.copyfile(source_path, save_path)

    def convert_tokens_to_string(self, tokens):
        """
        Converts a sequence of tokens (string) in a single string.
        """
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text

    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
        if is_split_into_words or add_prefix_space:
            text = " " + text
        return (text, kwargs)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if self.add_bos_token:
            bos_token_ids = [self.bos_token_id]
        else:
            bos_token_ids = []

        if self.add_eos_token:
            eos_token_ids = [self.eos_token_id]
        else:
            eos_token_ids = []

        output = bos_token_ids + token_ids_0

        if token_ids_1 is None:
            return output + eos_token_ids

        return output + bos_token_ids + token_ids_1 + eos_token_ids

    def decode(
        self,
        token_ids,
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        spaces_between_special_tokens: bool = False,
        **kwargs,
    ) -> str:
        return super().decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            spaces_between_special_tokens=spaces_between_special_tokens,
            **kwargs,
        )
