


"""simple docstring"""



__snake_case   			=    """Alexander Joslin"""

import operator as op

from .stack import Stack







def   __lowerCAmelCase						(						lowercase  :  str )	->						int:


    """simple docstring"""





    snake_case    :      List[Any]      					=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    snake_case    :      List[Any]      					=    Stack()
    snake_case    :      Tuple      					=    Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE_ ) )
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE_ )
        elif i == ")":
            # RULE 4
            snake_case    :      int      					=    operator_stack.peek()
            operator_stack.pop()
            snake_case    :      Dict      					=    operand_stack.peek()
            operand_stack.pop()
            snake_case    :      List[Any]      					=    operand_stack.peek()
            operand_stack.pop()

            snake_case    :      Optional[Any]      					=    operators[opr](SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_ )
            operand_stack.push(SCREAMING_SNAKE_CASE_ )

    # RULE 5
    return operand_stack.peek()


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











import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

UpperCamelCase__    			=			"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    int					=    CamembertTokenizer
						_a							:    Dict					=    CamembertTokenizerFast
						_a							:    Tuple					=    True
						_a							:    List[Any]					=    True






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "<pad>"
											__lowerCAmelCase				   =       1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A			)		,			_A			)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A			)		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       list(self.get_tokenizer().get_vocab().keys()			)

											self.assertEqual(vocab_keys[0]		,			"<s>NOTUSED"			)
											self.assertEqual(vocab_keys[1]		,			"<pad>"			)
											self.assertEqual(vocab_keys[-1]		,			"<mask>"			)
											self.assertEqual(len(_A			)		,			1_0_0_4			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.assertEqual(self.get_tokenizer().vocab_size		,			1_0_0_5			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)
											__lowerCAmelCase				   =       CamembertTokenizerFast.from_pretrained(self.tmpdirname			)

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											# <unk> tokens are not the same for `rust` than for `slow`.
											# Because spm gives back raw token instead of `unk` in EncodeAsPieces
											# tokens = tokenizer.tokenize(sequence)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.test_rust_tokenizer:
																return

											__lowerCAmelCase				   =       self.get_tokenizer()
											__lowerCAmelCase				   =       self.get_rust_tokenizer()

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.tokenize(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       self.get_rust_tokenizer()
											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[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, 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, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# camembert is a french model. So we also use french texts.
											__lowerCAmelCase				   =       [
											    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
											    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
											    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
											    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
											    "telles que la traduction et la synthèse de texte.",
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=_A		,			model_name="camembert-base"		,			revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"		,			sequences=_A		,			)




