


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__   	=							{
    "vocab_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
        ),
        "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
        "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-openqa": (
            "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-reader": (
            "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-openqa": (
            "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-reader": (
            "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__   	=							{
    "google/realm-cc-news-pretrained-embedder": 512,
    "google/realm-cc-news-pretrained-encoder": 512,
    "google/realm-cc-news-pretrained-scorer": 512,
    "google/realm-cc-news-pretrained-openqa": 512,
    "google/realm-orqa-nq-openqa": 512,
    "google/realm-orqa-nq-reader": 512,
    "google/realm-orqa-wq-openqa": 512,
    "google/realm-orqa-wq-reader": 512,
}

UpperCAmelCase__   	=							{
    "google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-reader": {"do_lower_case": True},
    "google/realm-orqa-wq-openqa": {"do_lower_case": True},
    "google/realm-orqa-wq-reader": {"do_lower_case": True},
}







class       lowerCamelCase__   (       A__):
     SCREAMING_SNAKE_CASE__      			=			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__      			=			RealmTokenizer





     def __init__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=True		,     UpperCAmelCase="[UNK]"		,     UpperCAmelCase="[SEP]"		,     UpperCAmelCase="[PAD]"		,     UpperCAmelCase="[CLS]"		,     UpperCAmelCase="[MASK]"		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   str:
      super().__init__(
          _a		,     tokenizer_file=_a		,     do_lower_case=_a		,     unk_token=_a		,     sep_token=_a		,     pad_token=_a		,     cls_token=_a		,     mask_token=_a		,     tokenize_chinese_chars=_a		,     strip_accents=_a		,     **_a		,     )

      _lowercase							      =json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
      if (
          normalizer_state.get('''lowercase'''		,     _a       ) != do_lower_case
          or normalizer_state.get('''strip_accents'''		,     _a       ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''		,     _a       ) != tokenize_chinese_chars
      ):
       _lowercase							      =getattr(_a		,     normalizer_state.pop('''type'''       )       )
       _lowercase							      =do_lower_case
       _lowercase							      =strip_accents
       _lowercase							      =tokenize_chinese_chars
       _lowercase							      =normalizer_class(**_a       )

      _lowercase							      =do_lower_case





     def      __A					(self		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Any:
      _lowercase							      =PaddingStrategy.MAX_LENGTH

      _lowercase							      =text
      _lowercase							      =kwargs.pop('''text_pair'''		,     _a       )
      _lowercase							      =kwargs.pop('''return_tensors'''		,     _a       )

      _lowercase							      ={
          '''input_ids''': [],
          '''attention_mask''': [],
          '''token_type_ids''': [],
      }

      for idx, candidate_text in enumerate(_a       ):
       if batch_text_pair is not None:
        _lowercase							      =batch_text_pair[idx]
       else:
        _lowercase							      =None

       _lowercase							      =super().__call__(_a		,     _a		,     return_tensors=_a		,     **_a       )

       _lowercase							      =encoded_candidates.get('''input_ids'''       )
       _lowercase							      =encoded_candidates.get('''attention_mask'''       )
       _lowercase							      =encoded_candidates.get('''token_type_ids'''       )

       if encoded_input_ids is not None:
        output_data["input_ids"].append(_a       )
       if encoded_attention_mask is not None:
        output_data["attention_mask"].append(_a       )
       if encoded_token_type_ids is not None:
        output_data["token_type_ids"].append(_a       )

      _lowercase							      ={key: item for key, item in output_data.items() if len(_a       ) != 0}

      return BatchEncoding(_a		,     tensor_type=_a       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      _lowercase							      =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:
      _lowercase							      =[self.sep_token_id]
      _lowercase							      =[self.cls_token_id]
      if token_ids_a is None:
       return len(cls + token_ids_a + sep       ) * [0]
      return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   Tuple[str]:
      _lowercase							      =self._tokenizer.model.save(_a		,     name=_a       )
      return tuple(_a       )




'''simple docstring'''
from __future__ import annotations

import math


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5							, int(math.sqrt(_UpperCamelCase				) + 1				)							, 6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =str(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =[n]
     for i in range(1							, len(_UpperCamelCase				)				):
          list_nums.append(int(str_num[i:]				)				)
          list_nums.append(int(str_num[:-i]				)				)
     return list_nums


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if len(str(_UpperCamelCase				)				) > 3:
          if not is_prime(int(str(_UpperCamelCase				)[-3:]				)				) or not is_prime(int(str(_UpperCamelCase				)[:3]				)				):
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int = 11				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =13
     while len(_UpperCamelCase				) != count:
          if validate(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =list_truncated_nums(_UpperCamelCase				)
               if all(is_prime(_UpperCamelCase				) for i in list_nums				):
                    list_truncated_primes.append(_UpperCamelCase				)
          num += 2
     return list_truncated_primes


def        _lowerCAmelCase					(						)      ->			int:
     """simple docstring"""




     return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(f'''{sum(compute_truncated_primes(1_1)) = }''')
