






'''simple docstring'''

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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__											=     {
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE__											=     {
    '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,
}

SCREAMING_SNAKE_CASE__											=     {
    '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        a_		(    lowerCamelCase       ):
  lowercase  	=   VOCAB_FILES_NAMES
  lowercase  	=   PRETRAINED_VOCAB_FILES_MAP
  lowercase  	=   PRETRAINED_INIT_CONFIGURATION
  lowercase  	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase  	=   RealmTokenizer

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="[UNK]"    ,	_SCREAMING_SNAKE_CASE="[SEP]"    ,	_SCREAMING_SNAKE_CASE="[PAD]"    ,	_SCREAMING_SNAKE_CASE="[CLS]"    ,	_SCREAMING_SNAKE_CASE="[MASK]"    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Optional[Any]:



        """simple docstring"""






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

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

        UpperCamelCase       						=      do_lower_case

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      PaddingStrategy.MAX_LENGTH

        UpperCamelCase       						=      text
        UpperCamelCase       						=      kwargs.pop("""text_pair"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""return_tensors"""    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      {
            """input_ids""": [],
            """attention_mask""": [],
            """token_type_ids""": [],
        }

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

              UpperCamelCase       						=      super().__call__(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_tensors=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      encoded_candidates.get("""input_ids"""   )
              UpperCamelCase       						=      encoded_candidates.get("""attention_mask"""   )
              UpperCamelCase       						=      encoded_candidates.get("""token_type_ids"""   )

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

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

        return BatchEncoding(_SCREAMING_SNAKE_CASE    ,	tensor_type=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=None   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      [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    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   List[int]:



        """simple docstring"""






        UpperCamelCase       						=      [self.sep_token_id]
        UpperCamelCase       						=      [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    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   Tuple[str]:



        """simple docstring"""






        UpperCamelCase       						=      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE    ,	name=_SCREAMING_SNAKE_CASE   )
        return tuple(_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
