

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_									=   {
    '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'
        ),
    },
}

lowercase_									=   {
    'google/realm-cc-news-pretrained-embedder': 5_1_2,
    'google/realm-cc-news-pretrained-encoder': 5_1_2,
    'google/realm-cc-news-pretrained-scorer': 5_1_2,
    'google/realm-cc-news-pretrained-openqa': 5_1_2,
    'google/realm-orqa-nq-openqa': 5_1_2,
    'google/realm-orqa-nq-reader': 5_1_2,
    'google/realm-orqa-wq-openqa': 5_1_2,
    'google/realm-orqa-wq-reader': 5_1_2,
}

lowercase_									=   {
    '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     (					SCREAMING_SNAKE_CASE							):
  _a									=  VOCAB_FILES_NAMES
  _a									=  PRETRAINED_VOCAB_FILES_MAP
  _a									=  PRETRAINED_INIT_CONFIGURATION
  _a									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a									=  RealmTokenizer




  def __init__(				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   lowerCAmelCase="[UNK]"							,   lowerCAmelCase="[SEP]"							,   lowerCAmelCase="[PAD]"							,   lowerCAmelCase="[CLS]"							,   lowerCAmelCase="[MASK]"							,   lowerCAmelCase=True							,   lowerCAmelCase=None							,   **lowerCAmelCase							,   )  -> Optional[int]:
        '''simple docstring'''






        super().__init__(
            lowerCAmelCase							,   tokenizer_file=lowerCAmelCase							,   do_lower_case=lowerCAmelCase							,   unk_token=lowerCAmelCase							,   sep_token=lowerCAmelCase							,   pad_token=lowerCAmelCase							,   cls_token=lowerCAmelCase							,   mask_token=lowerCAmelCase							,   tokenize_chinese_chars=lowerCAmelCase							,   strip_accents=lowerCAmelCase							,   **lowerCAmelCase							,   )

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

        _lowercase	       =do_lower_case




  def   A__     (				self							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =PaddingStrategy.MAX_LENGTH

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

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

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

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

              _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(lowerCAmelCase					)
              if encoded_attention_mask is not None:
                    output_data["attention_mask"].append(lowerCAmelCase					)
              if encoded_token_type_ids is not None:
                    output_data["token_type_ids"].append(lowerCAmelCase					)

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

        return BatchEncoding(lowerCAmelCase							,   tensor_type=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> str:
        '''simple docstring'''






        _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							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> List[int]:
        '''simple docstring'''






        _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							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> Tuple[str]:
        '''simple docstring'''






        _lowercase	       =self._tokenizer.model.save(lowerCAmelCase							,   name=lowerCAmelCase					)
        return tuple(lowerCAmelCase					)





import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


