




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': 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,
}

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      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				PRETRAINED_INIT_CONFIGURATION
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				RealmTokenizer




    def __init__(   self						:	List[Any]     ,			snake_case_						:	int=None     ,			snake_case_						:	Optional[Any]=None     ,			snake_case_						:	int=True     ,			snake_case_						:	Union[str, Any]="[UNK]"     ,			snake_case_						:	Optional[Any]="[SEP]"     ,			snake_case_						:	Tuple="[PAD]"     ,			snake_case_						:	Tuple="[CLS]"     ,			snake_case_						:	Optional[Any]="[MASK]"     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	List[str]=None     ,			**snake_case_						:	List[Any]     ,			)				->							int:




     '''simple docstring'''

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

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

     A__	=    do_lower_case




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]     ,			**snake_case_						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     A__	=    PaddingStrategy.MAX_LENGTH

     A__	=    text
     A__	=    kwargs.pop("text_pair"     ,			_snake_case				)
     A__	=    kwargs.pop("return_tensors"     ,			_snake_case				)

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

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

      A__	=    super().__call__(_snake_case     ,			_snake_case     ,			return_tensors=_snake_case     ,			**_snake_case				)

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

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

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

     return BatchEncoding(_snake_case     ,			tensor_type=_snake_case				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]=None				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    [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 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int = None				)				->							List[int]:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [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 							__magic_name__							(   self						:	str     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any] = None				)				->							Tuple[str]:




     '''simple docstring'''

     A__	=    self._tokenizer.model.save(_snake_case     ,			name=_snake_case				)
     return tuple(_snake_case				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  elif i == ")":
   # RULE 4
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





