"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ....tokenization_utils import PreTrainedTokenizer
from ....utils import logging
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "prophetnet.tokenizer"}
def load_vocab(vocab_file):
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader: tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab
class XLMProphetNetTokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    def __init__(self, vocab_file, bos_token="[SEP]", eos_token="[SEP]", sep_token="[SEP]", unk_token="[UNK]", pad_token="[PAD]", cls_token="[CLS]",
    mask_token="[MASK]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs) -> None:
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        try: import sentencepiece as spm
        except ImportError:
            logger.warning("You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece pip install sentencepiece")
            raise
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(vocab_file))
        self.vocab_file = vocab_file
        self.fairseq_tokens_to_ids = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}
        for i in range(10):
            tok = f"[unused{i}]"
            self.fairseq_tokens_to_ids[tok] = 5 + i
        self.fairseq_offset = 12
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        super().__init__(bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
    @property
    def can_save_slow_tokenizer(self) -> bool: return os.path.isfile(self.vocab_file) if self.vocab_file else False
    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state
    def __setstate__(self, d):
        self.__dict__ = d
        try: import sentencepiece as spm
        except ImportError:
            logger.warning("You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece pip install sentencepiece")
            raise
        if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {}
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)
    def get_special_tokens_mask(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) -> List[int]:
        if already_has_special_tokens: return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
        if token_ids_1 is None: return ([0] * len(token_ids_0)) + [1]
        return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
    def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]:
        sep = [self.sep_token_id]
        if token_ids_1 is None: return len(token_ids_0 + sep) * [0]
        return len(token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
    @property
    def vocab_size(self): return len(self.sp_model) + self.fairseq_offset
    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab
    def _tokenize(self, text: str) -> str: return self.sp_model.encode(text, out_type=str)
    def _convert_token_to_id(self, token):
        if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token]
        spm_id = self.sp_model.PieceToId(token)
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
    def _convert_id_to_token(self, index):
        if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset)
    def convert_tokens_to_string(self, tokens):
        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
        return out_string
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)
        return (out_vocab_file,)
    def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]:
        if token_ids_1 is None: return token_ids_0 + [self.sep_token_id]
        sep = [self.sep_token_id]
        return token_ids_0 + sep + token_ids_1 + sep
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
