"""
    #################################################################################################################################################################################################
    # 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available(): from .tokenization_mbart import MBartTokenizer
else: MBartTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV",
"my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class MBartTokenizerFast(PreTrainedTokenizerFast):
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    slow_tokenizer_class = MBartTokenizer
    prefix_tokens: List[int] = []
    suffix_tokens: List[int] = []
    def __init__(self, vocab_file=None, tokenizer_file=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>",
    mask_token="<mask>", src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs):
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
        _additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
        if additional_special_tokens is not None: _additional_special_tokens.extend([t for t in additional_special_tokens if t not in _additional_special_tokens])
        super().__init__(vocab_file=vocab_file, tokenizer_file=tokenizer_file, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token,
        unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=_additional_special_tokens, **kwargs)
        self.vocab_file = vocab_file
        self.lang_code_to_id = {lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES}
        self._src_lang = src_lang if src_lang is not None else "en_XX"
        self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
        self.tgt_lang = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)
    @property
    def can_save_slow_tokenizer(self) -> bool: return os.path.isfile(self.vocab_file) if self.vocab_file else False
    @property
    def src_lang(self) -> str: return self._src_lang
    @src_lang.setter
    def src_lang(self, new_src_lang: str) -> None:
        self._src_lang = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)
    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 self.prefix_tokens + token_ids_0 + self.suffix_tokens
        return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
    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]
        cls = [self.cls_token_id]
        if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
    def _build_translation_inputs(self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
        if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
        self.src_lang = src_lang
        inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs
    def prepare_seq2seq_batch(self, src_texts: List[str], src_lang: str = "en_XX", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro_RO", **kwargs) -> BatchEncoding:
        self.src_lang = src_lang
        self.tgt_lang = tgt_lang
        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
    def _switch_to_input_mode(self): return self.set_src_lang_special_tokens(self.src_lang)
    def _switch_to_target_mode(self): return self.set_tgt_lang_special_tokens(self.tgt_lang)
    def set_src_lang_special_tokens(self, src_lang) -> None:
        self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
        self._tokenizer.post_processor = processors.TemplateProcessing(single=prefix_tokens_str + ["$A"] + suffix_tokens_str, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
        special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)))
    def set_tgt_lang_special_tokens(self, lang: str) -> None:
        self.cur_lang_code = self.convert_tokens_to_ids(lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
        self._tokenizer.post_processor = processors.TemplateProcessing(single=prefix_tokens_str + ["$A"] + suffix_tokens_str, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
        special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)))
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not self.can_save_slow_tokenizer: raise ValueError("Your fast tokenizer does not have the necessary information to save the vocabulary for a slow tokenizer.")
        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): copyfile(self.vocab_file, out_vocab_file)
        return (out_vocab_file,)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
