

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      DebertaTokenizer
   lowerCamelCase__				=      True
   lowerCamelCase__				=      DebertaTokenizerFast





   def      _snake_case		(       self					:			int			):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE										=   [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "[UNK]",
       ]
       SCREAMING_SNAKE_CASE										=   dict(zip(__lowerCamelCase     , range(len(__lowerCamelCase			)			)			)			)
       SCREAMING_SNAKE_CASE										=   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       SCREAMING_SNAKE_CASE										=   {"unk_token": "[UNK]"}

       SCREAMING_SNAKE_CASE										=   os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["merges_file"]			)
       with open(self.vocab_file     , "w"     , encoding="utf-8"			) as fp:
           fp.write(json.dumps(__lowerCamelCase			) + "\n"			)
       with open(self.merges_file     , "w"     , encoding="utf-8"			) as fp:
           fp.write("\n".join(__lowerCamelCase			)			)





   def      _snake_case		(       self					:			int     , **__lowerCamelCase					:			Optional[Any]			):
       kwargs.update(self.special_tokens_map			)
       return self.tokenizer_class.from_pretrained(self.tmpdirname     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   "lower newer"
       SCREAMING_SNAKE_CASE										=   "lower newer"
       return input_text, output_text





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.get_tokenizer()
       SCREAMING_SNAKE_CASE										=   "lower newer"
       SCREAMING_SNAKE_CASE										=   ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       SCREAMING_SNAKE_CASE										=   tokenizer.tokenize(__lowerCamelCase			)
       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokens + [tokenizer.unk_token]
       SCREAMING_SNAKE_CASE										=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase			)     , __lowerCamelCase			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   self.get_tokenizer()
       SCREAMING_SNAKE_CASE										=   tokenizer("Hello"     , "World"			)
       SCREAMING_SNAKE_CASE										=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["token_type_ids"]     , __lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.tokenizer_class.from_pretrained("microsoft/deberta-base"			)

       SCREAMING_SNAKE_CASE										=   tokenizer.encode("sequence builders"     , add_special_tokens=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   tokenizer.encode("multi-sequence build"     , add_special_tokens=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer.encode(
           "sequence builders"     , add_special_tokens=__lowerCamelCase     , add_prefix_space=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   tokenizer.encode(
           "sequence builders"     , "multi-sequence build"     , add_special_tokens=__lowerCamelCase     , add_prefix_space=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer.build_inputs_with_special_tokens(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   tokenizer.build_inputs_with_special_tokens(__lowerCamelCase     , __lowerCamelCase			)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





   @slow
   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   [self.tokenizer_class]
       if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class			)

       for tokenizer_class in tokenizer_classes:
           SCREAMING_SNAKE_CASE										=   tokenizer_class.from_pretrained("microsoft/deberta-base"			)

           SCREAMING_SNAKE_CASE										=   [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           SCREAMING_SNAKE_CASE										=   tokenizer(__lowerCamelCase     , padding=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   [tokenizer.decode(__lowerCamelCase     , skip_special_tokens=__lowerCamelCase			) for seq in encoding["input_ids"]]

           # fmt: off
           SCREAMING_SNAKE_CASE										=   {
               "input_ids": [
                   [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           SCREAMING_SNAKE_CASE										=   [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data     , __lowerCamelCase			)

           for expected, decoded in zip(__lowerCamelCase     , __lowerCamelCase			):
               self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)




'''simple docstring'''





__a  			=						"Alexander Joslin"

import operator as op

from .stack import Stack





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

      snake_case__    :       Stack[int]       		= Stack()
      snake_case__    :       Stack[str]       		= Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_lowerCAmelCase						)						)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_lowerCAmelCase						)
            elif i == ")":
                  # RULE 4
                  snake_case__    :       Union[str, Any]       		= operator_stack.peek()
                  operator_stack.pop()
                  snake_case__    :       Any       		= operand_stack.peek()
                  operand_stack.pop()
                  snake_case__    :       str       		= operand_stack.peek()
                  operand_stack.pop()

                  snake_case__    :       Optional[Any]       		= operators[opr](_lowerCAmelCase	,  _lowerCAmelCase						)
                  operand_stack.push(_lowerCAmelCase						)

    # RULE 5
      return operand_stack.peek()


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