"""
    #################################################################################################################################################################################################
    # 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 json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    def __init__(self, vocab_file=None, merges_file=None, tokenizer_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, **kwargs):
        super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token,
        add_bos_token=add_bos_token, add_eos_token=add_eos_token, add_prefix_space=add_prefix_space, **kwargs)
        self._add_bos_token = add_bos_token
        self._add_eos_token = add_eos_token
        self.update_post_processor()
        pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
            pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
            pre_tok_state["add_prefix_space"] = add_prefix_space
            self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
        self.add_prefix_space = add_prefix_space
    @property
    def add_eos_token(self): return self._add_eos_token
    @property
    def add_bos_token(self): return self._add_bos_token
    @add_eos_token.setter
    def add_eos_token(self, value):
        self._add_eos_token = value
        self.update_post_processor()
    @add_bos_token.setter
    def add_bos_token(self, value):
        self._add_bos_token = value
        self.update_post_processor()
    def update_post_processor(self):
        bos = self.bos_token
        bos_token_id = self.bos_token_id
        if bos is None and self.add_bos_token: raise ValueError("add_bos_token = True but bos_token = None")
        eos = self.eos_token
        eos_token_id = self.eos_token_id
        if eos is None and self.add_eos_token: raise ValueError("add_eos_token = True but eos_token = None")
        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
        special_tokens = []
        if self.add_bos_token: special_tokens.append((bos, bos_token_id))
        if self.add_eos_token: special_tokens.append((eos, eos_token_id))
        self._tokenizer.post_processor = processors.TemplateProcessing(single=single, pair=pair, special_tokens=special_tokens)
    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)
        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []
        if token_ids_1 is None: return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id)
    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
        output = bos_token_id + token_ids_0 + eos_token_id
        if token_ids_1 is not None: output = output + bos_token_id + token_ids_1 + eos_token_id
        return output
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
        return tuple(files)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
