




"""simple docstring"""



import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	"▁"

SCREAMING_SNAKE_CASE    =	{"vocab_file": "prophetnet.tokenizer"}

SCREAMING_SNAKE_CASE    =	{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

SCREAMING_SNAKE_CASE    =	{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

SCREAMING_SNAKE_CASE    =	{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[Any]:
 A__	=    collections.OrderedDict()
 with open(lowercase_      ,			"r"      ,			encoding="utf-8"						) as reader:
  A__	=    reader.readlines()
 for index, token in enumerate(lowercase_						):
  A__	=    token.rstrip("\n"						)
  A__	=    index
 return vocab






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				['''input_ids''', '''attention_mask''']




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




     '''simple docstring'''

     A__	=    {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=snake_case_     ,			eos_token=snake_case_     ,			sep_token=snake_case_     ,			unk_token=snake_case_     ,			pad_token=snake_case_     ,			cls_token=snake_case_     ,			mask_token=snake_case_     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**snake_case_     ,			)

     try:
      import sentencepiece as spm
     except ImportError:
      logger.warning(
          "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
          " pip install sentencepiece"				)
      raise

     A__	=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(str(snake_case_				)				)
     A__	=    vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # put special tokens and [unused] tokens into the vocab
     A__	=    {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

     for i in range(10				):
      A__	=    F"""[unused{i}]"""
      A__	=    5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     A__	=    12
     A__	=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
      self.unique_no_split_tokens.append(snake_case_				)




    def __getstate__(   self						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.__dict__.copy()
     A__	=    None
     return state




    def __setstate__(   self						:	Any     ,			snake_case_						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    d
     try:
      import sentencepiece as spm
     except ImportError:
      logger.warning(
          "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
          " pip install sentencepiece"				)
      raise

     # for backward compatibility
     if not hasattr(self     ,			"sp_model_kwargs"				):
      A__	=    {}

     A__	=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(self.vocab_file				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	bool = False				)				->							List[int]:




     '''simple docstring'''

     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=snake_case_     ,			token_ids_a=snake_case_     ,			already_has_special_tokens=snake_case_				)

     if token_ids_a is None:
      return ([0] * len(snake_case_				)) + [1]
     return ([0] * len(snake_case_				)) + [1] + ([0] * len(snake_case_				)) + [1]




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




     '''simple docstring'''

     A__	=    [self.sep_token_id]

     if token_ids_a is None:
      return len(token_ids_a + sep				) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep				) * [0]




    @property
    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     return len(self.sp_model				) + self.fairseq_offset




    def 							__magic_name__							(   self						:	str				)				->							int:




     '''simple docstring'''

     A__	=    {self.convert_ids_to_tokens(snake_case_				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab




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




     '''simple docstring'''

     return self.sp_model.encode(snake_case_     ,			out_type=snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	List[Any]				)				->							str:




     '''simple docstring'''

     if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
     A__	=    self.sp_model.PieceToId(snake_case_				)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




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




     '''simple docstring'''

     if index in self.fairseq_ids_to_tokens:
      return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset				)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	int				)				->							List[Any]:




     '''simple docstring'''

     A__	=    "".join(snake_case_				).replace(snake_case_     ,			" "				).strip()
     return out_string




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




     '''simple docstring'''

     if not os.path.isdir(snake_case_				):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
      return
     A__	=    os.path.join(
         snake_case_     ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case_				) and os.path.isfile(self.vocab_file				):
      copyfile(self.vocab_file     ,			snake_case_				)
     elif not os.path.isfile(self.vocab_file				):
      with open(snake_case_     ,			"wb"				) as fi:
       A__	=    self.sp_model.serialized_model_proto()
       fi.write(snake_case_				)

     return (out_vocab_file,)




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




     '''simple docstring'''

     if token_ids_a is None:
      return token_ids_a + [self.sep_token_id]
     A__	=    [self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep











"""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)}')





